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TECHNOLOGY                                           : JAVA

 

DOMAIN                                                       : IEEE TRANSACTIONS ON NETWORKING

 

 

S.NO TITLES ABSTRACT YEAR
  1. 1.       

 

Balancing the Trade-Offs between Query Delay and Data Availability in MANETs In This Project, we propose schemes to balance the trade-offs between data availability and query delay under different system settings and requirements. Mobile nodes in one partition are not able to access data hosted by nodes in other partitions, and hence significantly degrade the performance of data access. To deal with this problem, We apply data replication techniques. 2012
  1. 2.       
MeasuRouting: A Framework for Routing Assisted Traffic Monitoring In this paper we present a theoretical framework for MeasuRouting. Furthermore, as proofs-of-concept, we present synthetic and practical monitoring applications to showcase the utility enhancement achieved with MeasuRouting. 2012
  1. 3.       
Cooperative Profit Sharing in Coalition-Based Resource Allocation in Wireless Networks We model Optimal cooperation using the theory of transferable payoff coalitional games. We show that the optimum cooperation strategy, which involves the acquisition, deployment, and allocation of the channels and base stations (to customers), can be computed as the solution of a concave or an integer optimization. We next show that the grand coalition is stable in many different settings. 2012
  1. 4.       
Bloom Cast: Efficient Full-Text Retrieval over Unstructured P2Ps with Guaranteed Recall In this paper we propose Bloom Cast, an efficient and effective full-text retrieval scheme, in unstructured P2P networks. Bloom Cast is effective because it guarantees perfect recall rate with high probability. 2012
  1. 5.       
On Optimizing Overlay Topologies for Search in Unstructured Peer-to-Peer Networks We propose a novel overlay formation algorithm for unstructured P2P networks. Based on the file sharing pattern exhibiting the power-law property, our proposal is unique in that it poses rigorous performance guarantees. 2012
  1. 6.       
An MDP-Based Dynamic Optimization Methodology for Wireless Sensor Networks In this paper, we propose an automated Markov Decision Process (MDP)-based methodology to prescribe optimal sensor node operation to meet application requirements and adapt to changing environmental stimuli. Numerical results confirm the optimality of our proposed methodology and reveal that our methodology more closely meets application requirements compared to other feasible policies. 2012
  1. 7.       
Obtaining Provably Legitimate Internet Topologies In this paper, we address The Internet Topology Problems by providing a framework to generate small, realistic, and policy-aware topologies. We propose HBR, a novel sampling method, which exploits the inherent hierarchy of the policy-aware Internet topology. We formally prove that our approach generates connected and legitimate topologies, which are compatible with the policy-based routing conventions and rules. 2012
  1. 8.       
Extrema Propagation: Fast Distributed Estimation of Sums and Network Sizes This paper introduces Extrema Propagation, a probabilistic technique for distributed estimation of the sum of positive real numbers. The technique relies on the exchange of duplicate insensitive messages and can be applied in flood and/or epidemic settings, where multipath routing occurs; it is tolerant of message loss; it is fast, as the number of message exchange steps can be made just slightly above the theoretical minimum; and it is fully distributed, with no single point of failure and the result produced at every node. 2012
  1. 9.       
Latency Equalization as a New Network Service Primitive We propose a Latency Equalization (LEQ) service, which equalizes the perceived latency for all clients. 2012
  1. 10.   
Grouping-Enhanced Resilient Probabilistic En-Route Filtering of Injected False Data in WSNs This paper proposes a scheme, referred to as Grouping-enhanced Resilient Probabilistic En-route Filtering (GRPEF). In GRPEF, an efficient distributed algorithm is proposed to group nodes without incurring extra groups, and a multi axis division based approach for deriving location-aware keys is used to overcome the threshold problem and remove the dependence on the sink immobility and routing protocols. 2012
  1. 11.   
On Achieving Group-Strategy proof Multicast A multicast scheme is stragegyproof if no receiver has incentive to lie about her true valuation. It is further group strategyproof if no group of colluding receivers has incentive to lie. We study multicast schemes that target group strategyproofness, in both directed and undirected networks. 2012
  1. 12.   
Distributed -Optimal User Association and Cell Load Balancing in Wireless Networks In this paper, we develop a framework for user association in infrastructure-based wireless networks, specifically focused on flow-level cell load balancing under spatially in homogeneous traffic distributions. Our work encompasses several different user association policies: rate-optimal, throughput-optimal, delay-optimal, and load-equalizing, which we collectively denote ?-optimal user association. 2012
  1. 13.   
Opportunistic Flow-Level Latency Estimation Using Consistent Net Flow In this paper, we propose Consistent Net Flow (CNF) architecture for measuring per-flow delay measurements within routers. CNF utilizes the existing Net Flow architecture that already reports the first and last timestamps per flow, and it proposes hash-based sampling to ensure that two adjacent routers record the same flows. 2012
  1. 14.   
Leveraging a Compound Graph-Based DHT for Multi-Attribute Range Queries with Performance Analysis Resource discovery is critical to the usability and accessibility of grid computing systems. Distributed Hash Table (DHT) has been applied to grid systems as a distributed mechanism for providing scalable range-query and multi-attribute resource discovery. Multi-DHT-based approaches depend on multiple DHT networks with each network responsible for a single attribute. Single-DHT-based approaches keep the resource information of all attributes in a single node. Both classes of approaches lead to high overhead. In this paper, we propose a Low-Overhead Range-query Multi-attribute (LORM) DHT-based resource discovery approach. Unlike other DHT-based approaches, LORM relies on a single compound graph-based DHT network and distributes resource information among nodes in balance by taking advantage of the compound graph structure. Moreover, it has high capability to handle the large-scale and dynamic characteristics of resources in grids. Experimental results demonstrate the efficiency of LORM in comparison with other resource discovery approaches. LORM dramatically reduces maintenance and resource discovery overhead. In addition, it yields significant improvements in resource location efficiency. We also analyze the performance of the LORM approach rigorously by comparing it with other multi-DHT-based and single-DHT-based approaches with respect to their overhead and efficiency. The analytical results are consistent with experimental results, and prove the superiority of the LORM approach in theory 2012
  1. 15.   
Exploiting Excess Capacity to Improve Robustness of WDM Mesh Networks  Excess capacity (EC) is the unused capacity in a network. We propose EC management techniques to improve network performance. Our techniques exploit the EC in two ways. First, a connection preprovisioning algorithm is used to reduce the connection setup time. Second, whenever possible, we use protection schemes that have higher availability and shorter protection switching time. Specifically, depending on the amount of EC available in the network, our proposed EC management techniques dynamically migrate connections between high-availability, high-backup-capacity protection schemes and low-availability, low-backup-capacity protection schemes. Thus, multiple protection schemes can coexist in the network. The four EC management techniques studied in this paper differ in two respects: when the connections are migrated from one protection scheme to another, and which connections are migrated. Specifically, Lazy techniques migrate connections only when necessary, whereas Proactive techniques migrate connections to free up capacity in advance. Partial Backup Reprovisioning (PBR) techniques try to migrate a minimal set of connections, whereas Global Backup Reprovisioning (GBR) techniques migrate all connections. We develop integer linear program (ILP) formulations and heuristic algorithms for the EC management techniques. We then present numerical examples to illustrate how the EC management techniques improve network performance by exploiting the EC in wavelength-division-multiplexing (WDM) mesh networks 2012
  1. 16.   
Revisiting Dynamic Query Protocols in Unstructured Peer-to-Peer Networks In unstructured peer-to-peer networks, the average response latency and traffic cost of a query are two main performance metrics. Controlled-flooding resource query algorithms are widely used in unstructured networks such as peer-to-peer networks. In this paper, we propose a novel algorithm named Selective Dynamic Query (SDQ). Based on mathematical programming, SDQ calculates the optimal combination of an integer TTL value and a set of neighbors to control the scope of the next query. Our results demonstrate that SDQ provides finer grained control than other algorithms: its response latency is close to the well-known minimum one via Expanding Ring; in the mean time, its traffic cost is also close to the minimum. To our best knowledge, this is the first work capable of achieving a best trade-off between response latency and traffic cost. 2012
  1. 17.   
Adaptive Opportunistic Routing for Wireless Ad Hoc Networks A distributed adaptive opportunistic routing scheme for multihop wireless ad hoc networks is proposed. The proposed scheme utilizes a reinforcement learning framework to opportunistically route the packets even in the absence of reliable knowledge about channel statistics and network model. This scheme is shown to be optimal with respect to an expected average per-packet reward criterion. The proposed routing scheme jointly addresses the issues of learning and routing in an opportunistic context, where the network structure is characterized by the transmission success probabilities. In particular, this learning framework leads to a stochastic routing scheme that optimally “explores” and “exploits” the opportunities in the network. 2012
  1. 18.   
Design, Implementation, and Performance of A Load Balancer for SIP Server Clusters – projects 2012 This load balancer improves both throughput and response time versus a single node, while exposing a single interface to external clients. The algorithm achieves  Transaction Least-Work-Left (TLWL), achieves its performance by integrating several features: knowledge of the SIP protocol; dynamic estimates of back-end server load; distinguishing transactions from calls; recognizing variability in call length; and exploiting differences in processing costs for different SIP transactions. 2012
  1. 19.   
Router Support for Fine-Grained Latency Measurements – projects 2012 An increasing number of datacenter network applications, including automated trading and high-performance computing, have stringent end-to-end latency requirements where even microsecond variations may be intolerable. The resulting fine-grained measurement demands cannot be met effectively by existing technologies, such as SNMP, NetFlow, or active probing. Instrumenting routers with a hash-based primitive has been proposed that called as Lossy Difference Aggregator (LDA) to measure latencies down to tens of microseconds even in the presence of packet loss. Because LDA does not modify or encapsulate the packet, it can be deployed incrementally without changes along the forwarding path. When compared to Poisson-spaced active probing with similar overheads, LDA mechanism delivers orders of magnitude smaller relative error. Although ubiquitous deployment is ultimately desired, it may be hard to achieve in the shorter term 2012
  1. 20.   
A Framework for Routing Assisted Traffic Monitoring – projects 2012 Monitoring transit traffic at one or more points in a network is of interest to network operators for reasons of traffic accounting, debugging or troubleshooting, forensics, and traffic engineering. Previous research in the area has focused on deriving a placement of monitors across the network towards the end of maximizing the monitoring utility of the network operator for a given traffic routing. However, both traffic characteristics and measurement objectives can dynamically change over time, rendering a previously optimal placement of monitors suboptimal. It is not feasible to dynamically redeploy/reconfigure measurement infrastructure to cater to such evolving measurement requirements. This problem is addressed by strategically routing traffic sub-populations over fixed monitors. This approach is MeasuRouting. The main challenge for MeasuRouting is to work within the constraints of existing intra-domain traffic engineering operations that are geared for efficiently utilizing bandwidth resources, or meeting Quality of Service (QoS) constraints, or both. A fundamental feature of intra-domain routing, that makes MeasuRouting feasible, is that intra-domain routing is often specified for aggregate flows. MeasuRouting, can therefore, differentially route components of an aggregate flow while ensuring that the aggregate placement is compliant to original traffic engineering objectives. 2012
  1. 21.   
Independent Directed Acyclic Graphs for Resilient Multipath Routing In order to achieve resilient multipath routing we introduce the concept of Independent Directed Acyclic Graphs (IDAGs) in this study. Link-independent (Node-independent) DAGs satisfy the property that any path from a source to the root on one DAG is link-disjoint (node-disjoint) with any path from the source to the root on the other DAG. Given a network, we develop polynomial time algorithms to compute link-independent and node-independent DAGs. The algorithm developed in this paper: (1) provides multipath routing; (2) utilizes all possible edges; (3) guarantees recovery from single link failure; and (4) achieves all these with at most one bit per packet as overhead when routing is based on destination address and incoming edge. We show the effectiveness of the proposed IDAGs approach by comparing key performance indices to that of the independent trees and multiple pairs of independent trees techniques through extensive simulations 2012
  1. 22.   
A Greedy Link Scheduler for Wireless Networks With Gaussian Multiple-Access and Broadcast Channels Information-theoretic broadcast channels (BCs) and multiple-access channels (MACs) enable a single node to transmit data simultaneously to multiple nodes, and multiple nodes to transmit data simultaneously to a single node, respectively. In this paper, we address the problem of link scheduling in multihop wireless networks containing nodes with BC and MAC capabilities. We first propose an interference model that extends protocol interference models, originally designed for point-to-point channels, to include the possibility of BCs and MACs. Due to the high complexity of optimal link schedulers, we introduce the Multiuser Greedy Maximum Weight algorithm for link scheduling in multihop wireless networks containing BCs and MACs. Given a network graph, we develop new local pooling conditions and show that the performance of our algorithm can be fully characterized using the associated parameter, the multiuser local pooling factor. We provide examples of some network graphs, on which we apply local pooling conditions and derive the multiuser local pooling factor. We prove optimality of our algorithm in tree networks and show that the exploitation of BCs and MACs improve the throughput performance considerably in multihop wireless networks. 2012
  1. 23.   
A Quantization Theoretic Perspective on Simulcast and Layered Multicast Optimization We consider rate optimization in multicast systems that use several multicast trees on a communication network. The network is shared between different applications. For that reason, we model the available bandwidth for multicast as stochastic. For specific network topologies, we show that the multicast rate optimization problem is equivalent to the optimization of scalar quantization. We use results from rate-distortion theory to provide a bound on the achievable performance for the multicast rate optimization problem. A large number of receivers makes the possibility of adaptation to changing network conditions desirable in a practical system. To this end, we derive an analytical solution to the problem that is asymptotically optimal in the number of multicast trees. We derive local optimality conditions, which we use to describe a general class of iterative algorithms that give locally optimal solutions to the problem. Simulation results are provided for the multicast of an i.i.d. Gaussian process, an i.i.d. Laplacian process, and a video source. 2012
  1. 24.   
Bit Weaving A Non-Prefix Approach to Compressing Packet Classifiers in TCAMs Ternary Content Addressable Memories (TCAMs) have become the de facto standard in industry for fast packet classification. Unfortunately, TCAMs have limitations of small capacity, high power consumption, high heat generation, and high cost. The well-known range expansion problem exacerbates these limitations as each classifier rule typically has to be converted to multiple TCAM rules. One method for coping with these limitations is to use compression schemes to reduce the number of TCAM rules required to represent a classifier. Unfortunately, all existing compression schemes only produce prefix classifiers. Thus, they all miss the compression opportunities created by non-prefix ternary classifiers. 2012
  1. 25.   
Cooperative Profit Sharing in Coalition-Based Resource Allocation in Wireless Networks We consider a network in which several service providers offer wireless access service to their respective subscribed customers through potentially multi-hop routes. If providers cooperate, i.e., pool their resources, such as spectrum and base stations, and agree to serve each others’ customers, their aggregate payoffs, and individual shares, can potentially substantially increase through efficient utilization of resources and statistical multiplexing. The potential of such cooperation can however be realized only if each provider intelligently determines who it would cooperate with, when it would cooperate, and how it would share its resources during such cooperation. Also, when the providers share their aggregate revenues, developing a rational basis for such sharing is imperative for the stability of the coalitions. We model such cooperation using transferable payoff coalitional game theory. We first consider the scenario that locations of the base stations and the channels that each provider can use have already been decided apriori. We show that the optimum cooperation strategy, which involves the allocations of the channels and the base stations to mobile customers, can be obtained as solutions of convex optimizations. We next show that the grand coalition is stable in this case, i.e. if all providers cooperate, there is always an operating point that maximizes the providers’ aggregate payoff, while offering each such a share that removes any incentive to split from the coalition. Next, we show that when the providers can choose the locations of their base stations and decide which channels to acquire, the above results hold in important special cases. Finally, we examine cooperation when providers do not share their payoffs, but still share their resources so as to enhance individual payoffs. We show that the grand coalition continues to be stable. 2012
  1. 26.   
CSMACN Carrier Sense Multiple Access With Collision Notification A wireless transmitter learns of a packet loss and infers collision only after completing the entire transmission. If the transmitter could detect the collision early [such as with carrier sense multiple access with collision detection (CSMA/CD) in wired networks], it could immediately abort its transmission, freeing the channel for useful communication. There are two main hurdles to realize CSMA/CD in wireless networks. First, a wireless transmitter cannot simultaneously transmit and listen for a collision. Second, any channel activity around the transmitter may not be an indicator of collision at the receiver. This paper attempts to approximate CSMA/CD in wireless networks with a novel scheme called CSMA/CN (collision notification). Under CSMA/CN, the receiver uses PHY-layer information to detect a collision and immediately notifies the transmitter. The collision notification consists of a unique signature, sent on the same channel as the data. The transmitter employs a listener antenna and performs signature correlation to discern this notification. Once discerned, the transmitter immediately aborts the transmission. We show that the notification signature can be reliably detected at the listener antenna, even in the presence of a strong self-interference from the transmit antenna. A prototype testbed of 10 USRP/GNU Radios demonstrates the feasibility and effectiveness of CSMA/CN. 2012
  1. 27.   
Dynamic Power Allocation Under Arbitrary Varying Channels—An Online Approach A major problem in wireless networks is coping with limited resources, such as bandwidth and energy. These issues become a major algorithmic challenge in view of the dynamic nature of the wireless domain. We consider in this paper the single-transmitter power assignment problem under time-varying channels, with the objective of maximizing the data throughput. It is assumed that the transmitter has a limited power budget, to be sequentially divided during the lifetime of the battery. We deviate from the classic work in this area, which leads to explicit “water-filling” solutions, by considering a realistic scenario where the channel state quality changes arbitrarily from one transmission to the other. The problem is accordingly tackled within the framework of competitive analysis, which allows for worst case performance guarantees in setups with arbitrarily varying channel conditions. We address both a “discrete” case, where the transmitter can transmit only at a fixed power level, and a “continuous” case, where the transmitter can choose any power level out of a bounded interval. For both cases, we propose online power-allocation algorithms with proven worst-case performance bounds. In addition, we establish lower bounds on the worst-case performance of any online algorithm, and show that our proposed algorithms are optimal. 2012
  1. 28.   
Economic Issues in Shared Infrastructures In designing and managing a shared infrastructure, one must take account of the fact that its participants will make self-interested and strategic decisions about the resources that they are willing to contribute to it and/or the share of its cost that they are willing to bear. Taking proper account of the incentive issues that thereby arise, we design mechanisms that, by eliciting appropriate information from the participants, can obtain for them maximal social welfare, subject to charging payments that are sufficient to cover costs. We show that there are incentivizing roles to be played both by the payments that we ask from the participants and the specification of how resources are to be shared. New in this paper is our formulation of models for designing optimal management policies, our analysis that demonstrates the inadequacy of simple sharing policies, and our proposals for some better ones. We learn that simple policies may be far from optimal and that efficient policy design is not trivial. However, we find that optimal policies have simple forms in the limit as the number of participants becomes large. 2012
  1. 29.   
On New Approaches of Assessing Network Vulnerability Hardness and Approximation Society relies heavily on its networked physical infrastructure and information systems. Accurately assessing the vulnerability of these systems against disruptive events is vital for planning and risk management. Existing approaches to vulnerability assessments of large-scale systems mainly focus on investigating inhomogeneous properties of the underlying graph elements. These measures and the associated heuristic solutions are limited in evaluating the vulnerability of large-scale network topologies. Furthermore, these approaches often fail to provide performance guarantees of the proposed solutions. In this paper, we propose a vulnerability measure, pairwise connectivity, and use it to formulate network vulnerability assessment as a graph-theoretical optimization problem, referred to as -disruptor. The objective is to identify the minimum set of critical network elements, namely nodes and edges, whose removal results in a specific degradation of the network global pairwise connectivity. We prove the NP-completeness and inapproximability of this problem and propose an pseudo-approximation algorithm to computing the set of critical nodes and an pseudo-approximation algorithm for computing the set of critical edges. The results of an extensive simulation-based experiment show the feasibility of our proposed vulnerability assessment framework and the efficiency of the proposed approximation algorithms in comparison to other approaches. 2012
  1. 30.   
Quantifying Video-QoE Degradations of Internet Links With the proliferation of multimedia content on the Internet, there is an increasing demand for video streams with high perceptual quality. The capability of present-day Internet links in delivering high-perceptual-quality streaming services, however, is not completely understood. Link-level degradations caused by intradomain routing policies and inter-ISP peering policies are hard to obtain, as Internet service providers often consider such information proprietary. Understanding link-level degradations will enable us in designing future protocols, policies, and architectures to meet the rising multimedia demands. This paper presents a trace-driven study to understand quality-of-experience (QoE) capabilities of present-day Internet links using 51 diverse ISPs with a major presence in the US, Europe, and Asia-Pacific. We study their links from 38 vantage points in the Internet using both passive tracing and active probing for six days. We provide the first measurements of link-level degradations and case studies of intra-ISP and inter-ISP peering links from a multimedia standpoint. Our study offers surprising insights into intradomain traffic engineering, peering link loading, BGP, and the inefficiencies of using autonomous system (AS)-path lengths as a routing metric. Though our results indicate that Internet routing policies are not optimized for delivering high-perceptual-quality streaming services, we argue that alternative strategies such as overlay networks can help meet QoE demands over the Internet. Streaming services apart, our Internet measurement results can be used as an input to a variety of research problems. 2012
  1. 31.   
Order Matters Transmission Reordering in Wireless Networks Modern wireless interfaces support a physical-layer capability called Message in Message (MIM). Briefly, MIM allows a receiver to disengage from an ongoing reception and engage onto a stronger incoming signal. Links that otherwise conflict with each other can be made concurrent with MIM. However, the concurrency is not immediate and can be achieved only if conflicting links begin transmission in a specific order. The importance of link order is new in wireless research, motivating MIM-aware revisions to link-scheduling protocols. This paper identifies the opportunity in MIM-aware reordering, characterizes the optimal improvement in throughput, and designs a link-layer protocol for enterprise wireless LANs to achieve it. Testbed and simulation results confirm the performance gains of the proposed system. 2012
  1. 32.   
Static Routing and Wavelength Assignment for Multicast Advance Reservation in All-Optical Wavelength-Routed WDM Networks In this paper, we investigate the static multicast advance reservation (MCAR) problem for all-optical wavelength-routed WDM networks. Under the advanced reservation traffic model, connection requests specify their start time to be some time in the future and also specify their holding times. We investigate the static MCAR problem where the set of advance reservation requests is known ahead of time. We prove the MCAR problem is NP-complete, formulate the problem mathematically as an integer linear program (ILP), and develop three efficient heuristics, seqRWA, ISH, and SA, to solve the problem for practical size networks. We also introduce a theoretical lower bound on the number of wavelengths required. To evaluate our heuristics, we first compare their performances to the ILP for small networks, and then simulate them over real-world, large-scale networks. We find the SA heuristic provides close to optimal results compared to the ILP for our smaller networks, and up to a 33% improvement over seqRWA and up to a 22% improvement over ISH on realistic networks. SA provides, on average, solutions 1.5-1.8 times the cost given by our conservative lower bound on large networks. 2012
  1. 33.   
System-Level Optimization in Wireless Networks Managing Interference and Uncertainty via Robust Optimization We consider a robust-optimization-driven system-level approach to interference management in a cellular broadband system operating in an interference-limited and highly dynamic regime. Here, base stations in neighboring cells (partially) coordinate their transmission schedules in an attempt to avoid simultaneous max-power transmission to their mutual cell edge. Limits on communication overhead and use of the backhaul require base station coordination to occur at a slower timescale than the customer arrival process. The central challenge is to properly structure coordination decisions at the slow timescale, as these subsequently restrict the actions of each base station until the next coordination period. Moreover, because coordination occurs at the slower timescale, the statistics of the arriving customers, e.g., the load, are typically only approximately known-thus, this coordination must be done with only approximate knowledge of statistics. We show that performance of existing approaches that assume exact knowledge of these statistics can degrade rapidly as the uncertainty in the arrival process increases. We show that a two-stage robust optimization framework is a natural way to model two-timescale decision problems. We provide tractable formulations for the base-station coordination problem and show that our formulation is robust to fluctuations (uncertainties) in the arriving load. This tolerance to load fluctuation also serves to reduce the need for frequent reoptimization across base stations, thus helping minimize the communication overhead required for system-level interference reduction. Our robust optimization formulations are flexible, allowing us to control the conservatism of the solution. Our simulations show that we can build in robustness without significant degradation of nominal performance. 2012
  1. 34.   
The Case for Feed-Forward Clock Synchronization Variable latencies due to communication delays or system noise is the central challenge faced by time-keeping algorithms when synchronizing over the network. Using extensive experiments, we explore the robustness of synchronization in the face of both normal and extreme latency variability and compare the feedback approaches of ntpd and ptpd (a software implementation of IEEE-1588) to the feed-forward approach of the RADclock and advocate for the benefits of a feed-forward approach. Noting the current lack of kernel support, we present extensions to existing mechanisms in the Linux and FreeBSD kernels giving full access to all available raw counters, and then evaluate the TSC, HPET, and ACPI counters’ suitability as hardware timing sources. We demonstrate how the RADclock achieves the same microsecond accuracy with each counter. 2012

 

TECHNOLOGY                   : JAVA

 

DOMAIN                               : IEEE TRANSACTIONS ON NETWORK SECURITY

 

 

S.NO TITLES ABSTRACT YEAR
Design and Implementation of TARF: A Trust-Aware Routing Framework for WSNs We have designed and implemented TARF, a robust trust-aware routing framework for dynamic wireless sensor networks (WSN). Without tight time synchronization or known Geographic information, TARF provides trustworthy and energy-efficient route. Most importantly, TARF proves effective against those harmful attacks developed out of identity deception; the resilience of TARF is verified through extensive evaluation with both simulation and empirical experiments on large-scale WSNs under various scenarios including mobile and RF-shielding network conditions. 2012
Risk-Aware Mitigation for MANET Routing Attacks In this paper, we propose a risk-aware response mechanism to systematically Cope with the identified routing attacks. Our risk-aware approach is based on an extended Dempster-Shafer mathematical theory of Evidence introducing a notion of importance factors. 2012
Survivability Experiment and Attack Characterization for RFID In this paper, we study survivability issues for RFID. We first present an RFID survivability experiment to define a foundation to measure the degree of survivability of an RFID system under varying attacks. Then we model a series of malicious scenarios using stochastic process algebras and study the different effects of those attacks on the ability of the RFID system to provide critical services even when parts of the system have been damaged. 2012
Detecting and Resolving Firewall Policy Anomalies In this paper, we represent an innovative policy anomaly management framework for firewalls, adopting a rule-based segmentation technique to identify policy anomalies and derive effective anomaly resolutions. In particular, we articulate a grid-based representation technique, providing an intuitive cognitive sense about policy anomaly. 2012
Automatic Reconfiguration for Large-Scale Reliable Storage Systems In this paper, we present a complete solution for dynamically changing system membership in a large-scale Byzantine-fault-tolerant system. We present a service that tracks system membership and periodically notifies other system nodes of changes. 2012
Detecting Anomalous Insiders in Collaborative Information Systems In this paper, we introduce the community anomaly detection system (CADS), an unsupervised learning framework to detect insider threats based on the access logs of collaborative environments. The framework is based on the observation that typical CIS users tend to form community structures based on the subjects accessed 2012
An Extended Visual Cryptography Algorithm for General Access Structures Conventional visual secret sharing schemes generate noise-like random pixels on shares to hide secret images. It suffers a management problem. In this paper, we propose a general approach to solve the above- mentioned problems; the approach can be used for binary secret images in non computer-aided decryption environments. 2012
Mitigating Distributed Denial of Service Attacks in Multiparty Applications in the Presence of Clock Drift In this paper, we extend port-hopping to support multiparty applications, by proposing the BIGWHEEL algorithm, for each application server to communicate with multiple clients in a port-hopping manner without the need for group synchronization. Furthermore, we present an adaptive algorithm, HOPERAA, for enabling hopping in the presence of bounded asynchrony, namely, when the communicating parties have clocks with clock drifts. 2012
On the Security and Efficiency of Content Distribution via Network Coding Content distribution via network coding has received a lot of attention lately. However, direct application of network coding may be insecure. In particular, attackers can inject “bogus” data to corrupt the content distribution process so as to hinder the information dispersal or even deplete the network resource. Therefore, content verification is an important and practical issue when network coding is employed. 2012
Packet-Hiding Methods for Preventing Selective Jamming Attacks In this paper, we address the problem of selective jamming attacks in wireless networks. In these attacks, the adversary is active only for a short period of time, selectively targeting messages of high importance. 2012
Stochastic Model of Multi virus Dynamics 2012
Peering Equilibrium Multipath Routing: A Game Theory Framework for Internet Peering Settlements Our scheme relies on a game theory modeling, with a non-cooperative potential game considering both routing and congestions costs. We compare different PEMP policies to BGP Multipath schemes by emulating a realistic peering scenario. 2012
Modeling and Detection of Camouflaging Worm Our scheme uses the Power Spectral Density (PSD) distribution of the scan traffic volume and its corresponding Spectral Flatness Measure (SFM) to distinguish the C-Worm traffic from background traffic. The performance data clearly demonstrates that our scheme can effectively detect the C-Worm propagation.two heuristic algorithms for the two sub problems. 2012
Analysis of a Botnet Takeover We present the design of an advanced hybrid peer-to-peer botnet. Compared with current botnets, the proposed botnet is harder to be shut down, monitored, and hijacked. It provides individualized encryption and control traffic dispersion. 2012
Efficient Network Modification to Improve QoS Stability at Failures As real-time traffic such as video or voice increases on the Internet, ISPs are required to provide stable quality as well as connectivity at failures. For ISPs, how to effectively improve the stability of these qualities at failures with the minimum investment cost is an important issue, and they need to effectively select a limited number of locations to add link facilities. 2012
Detecting Spam Zombies by Monitoring Outgoing Messages Compromised machines are one of the key security threats on the Internet; they are often used to launch various security attacks such as spamming and spreading malware, DDoS, and identity theft. Given that spamming provides a key economic incentive for attackers to recruit the large number of compromised machines, we focus on the detection of the compromised machines in a network that are involved in the spamming activities, commonly known as spam zombies. We develop an effective spam zombie detection system named SPOT by monitoring outgoing messages of a network. SPOT is designed based on a powerful statistical tool called Sequential Probability Ratio Test, which has bounded false positive and false negative error rates. In addition, we also evaluate the performance of the developed SPOT system using a two-month e-mail trace collected in a large US campus network. Our evaluation studies show that SPOT is an effective and efficient system in automatically detecting compromised machines in a network. For example, among the 440 internal IP addresses observed in the e-mail trace, SPOT identifies 132 of them as being associated with compromised machines. Out of the 132 IP addresses identified by SPOT, 126 can be either independently confirmed (110) or highly likely (16) to be compromised. Moreover, only seven internal IP addresses associated with compromised machines in the trace are missed by SPOT. In addition, we also compare the performance of SPOT with two other spam zombie detection algorithms based on the number and percentage of spam messages originated or forwarded by internal machines, respectively, and show that SPOT outperforms these two detection algorithms. 2012
A Hybrid Approach to Private Record Matching   Network Security 2012 Java Real-world entities are not always represented by the same set of features in different data sets. Therefore, matching records of the same real-world entity distributed across these data sets is a challenging task. If the data sets contain private information, the problem becomes even more difficult. Existing solutions to this problem generally follow two approaches: sanitization techniques and cryptographic techniques. We propose a hybrid technique that combines these two approaches and enables users to trade off between privacy, accuracy, and cost. Our main contribution is the use of a blocking phase that operates over sanitized data to filter out in a privacy-preserving manner pairs of records that do not satisfy the matching condition. We also provide a formal definition of privacy and prove that the participants of our protocols learn nothing other than their share of the result and what can be inferred from their share of the result, their input and sanitized views of the input data sets (which are considered public information). Our method incurs considerably lower costs than cryptographic techniques and yields significantly more accurate matching results compared to sanitization techniques, even when privacy requirements are high. 2012
ES-MPICH2: A Message Passing Interface with Enhanced Security  Network Security 2012 Java An increasing number of commodity clusters are connected to each other by public networks, which have become a potential threat to security sensitive parallel applications running on the clusters. To address this security issue, we developed a Message Passing Interface (MPI) implementation to preserve confidentiality of messages communicated among nodes of clusters in an unsecured network. We focus on MPI rather than other protocols, because MPI is one of the most popular communication protocols for parallel computing on clusters. Our MPI implementation—called ES-MPICH2—was built based on MPICH2 developed by the Argonne National Laboratory. Like MPICH2, ES-MPICH2 aims at supporting a large variety of computation and communication platforms like commodity clusters and high-speed networks. We integrated encryption and decryption algorithms into the MPICH2 library with the standard MPI interface and; thus, data confidentiality of MPI applications can be readily preserved without a need to change the source codes of the MPI applications. MPI-application programmers can fully configure any confidentiality services in MPICHI2, because a secured configuration file in ES-MPICH2 offers the programmers flexibility in choosing any cryptographic schemes and keys seamlessly incorporated in ES-MPICH2. We used the Sandia Micro Benchmark and Intel MPI Benchmark suites to evaluate and compare the performance of ES-MPICH2 with the original MPICH2 version. Our experiments show that overhead incurred by the confidentiality services in ES-MPICH2 is marginal for small messages. The security overhead in ES-MPICH2 becomes more pronounced with larger messages. Our results also show that security overhead can be significantly reduced in ES-MPICH2 by high-performance clusters. 2012 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

  1. 19.   
Ensuring Distributed Accountability for Data Sharing in the Cloud Cloud computing enables highly scalable services to be easily consumed over the Internet on an as-needed basis. A major feature of the cloud services is that users’ data are usually processed remotely in unknown machines that users do not own or operate. While enjoying the convenience brought by this new emerging technology, users’ fears of losing control of their own data (particularly, financial and health data) can become a significant barrier to the wide adoption of cloud services. To address this problem, here, we propose a novel highly decentralized information accountability framework to keep track of the actual usage of the users’ data in the cloud. In particular, we propose an object-centered approach that enables enclosing our logging mechanism together with users’ data and policies. We leverage the JAR programmable capabilities to both create a dynamic and traveling object, and to ensure that any access to users’ data will trigger authentication and automated logging local to the JARs. To strengthen user’s control, we also provide distributed auditing mechanisms. We provide extensive experimental studies that demonstrate the efficiency and effectiveness of the proposed approaches. 2012
  1. 20.   
BECAN: A Bandwidth-Efficient Cooperative Authentication Scheme for Filtering Injected False Data in Wireless Sensor Networks – projects 2012 Injecting false data attack is a well known serious threat to wireless sensor network, for which an adversary reports bogus information to sink causing error decision at upper level and energy waste in en-route nodes. In this paper, we propose a novel bandwidth-efficient cooperative authentication (BECAN) scheme for filtering injected false data. Based on the random graph characteristics of sensor node deployment and the cooperative bit-compressed authentication technique, the proposed BECAN scheme can save energy by early detecting and filtering the majority of injected false data with minor extra overheads at the en-route nodes. In addition, only a very small fraction of injected false data needs to be checked by the sink, which thus largely reduces the burden of the sink. Both theoretical and simulation results are given to demonstrate the effectiveness of the proposed scheme in terms of high filtering probability and energy saving. 2012
A Flexible Approach to Improving System Reliability with Virtual Lockstep There is an increasing need for fault tolerance capabilities in logic devices brought about by the scaling of transistors to ever smaller geometries. This paper presents a hypervisor-based replication approach that can be applied to commodity hardware to allow for virtually lockstepped execution. It offers many of the benefits of hardware-based lockstep while being cheaper and easier to implement and more flexible in the configurations supported. A novel form of processor state fingerprinting is also presented, which can significantly reduce the fault detection latency. This further improves reliability by triggering rollback recovery before errors are recorded to a checkpoint. The mechanisms are validated using a full prototype and the benchmarks considered indicate an average performance overhead of approximately 14 percent with the possibility for significant optimization. Finally, a unique method of using virtual lockstep for fault injection testing is presented and used to show that significant detection latency reduction is achievable by comparing only a small amount of data across replicas 2012
A Learning-Based Approach to Reactive Security Despite the conventional wisdom that proactive security is superior to reactive security, we show that reactive security can be competitive with proactive security as long as the reactive defender learns from past attacks instead of myopically overreacting to the last attack. Our game-theoretic model follows common practice in the security literature by making worst case assumptions about the attacker: we grant the attacker complete knowledge of the defender’s strategy and do not require the attacker to act rationally. In this model, we bound the competitive ratio between a reactive defense algorithm (which is inspired by online learning theory) and the best fixed proactive defense. Additionally, we show that, unlike proactive defenses, this reactive strategy is robust to a lack of information about the attacker’s incentives and knowledge 2012
Automated Security Test Generation with Formal Threat Models Despite the conventional wisdom that proactive security is superior to reactive security, we show that reactive security can be competitive with proactive security as long as the reactive defender learns from past attacks instead of myopically overreacting to the last attack. Our game-theoretic model follows common practice in the security literature by making worst case assumptions about the attacker: we grant the attacker complete knowledge of the defender’s strategy and do not require the attacker to act rationally. In this model, we bound the competitive ratio between a reactive defense algorithm (which is inspired by online learning theory) and the best fixed proactive defense. Additionally, we show that, unlike proactive defenses, this reactive strategy is robust to a lack of information about the attacker’s incentives and knowledge. 2012
Automatic Reconfiguration for Large-Scale Reliable Storage Systems Byzantine-fault-tolerant replication enhances the availability and reliability of Internet services that store critical state and preserve it despite attacks or software errors. However, existing Byzantine-fault-tolerant storage systems either assume a static set of replicas, or have limitations in how they handle reconfigurations (e.g., in terms of the scalability of the solutions or the consistency levels they provide). This can be problematic in long-lived, large-scale systems where system membership is likely to change during the system lifetime. In this paper, we present a complete solution for dynamically changing system membership in a large-scale Byzantine-fault-tolerant system. We present a service that tracks system membership and periodically notifies other system nodes of membership changes. The membership service runs mostly automatically, to avoid human configuration errors; is itself Byzantine-fault-tolerant and reconfigurable; and provides applications with a sequence of consistent views of the system membership. We demonstrate the utility of this membership service by using it in a novel distributed hash table called dBQS that provides atomic semantics even across changes in replica sets. dBQS is interesting in its own right because its storage algorithms extend existing Byzantine quorum protocols to handle changes in the replica set, and because it differs from previous DHTs by providing Byzantine fault tolerance and offering strong semantics. We implemented the membership service and dBQS. Our results show that the approach works well, in practice: the membership service is able to manage a large system and the cost to change the system membership is low. 2012
JS-Reduce Defending Your Data from Sequential Background Knowledge Attacks Web queries, credit card transactions, and medical records are examples of transaction data flowing in corporate data stores, and often revealing associations between individuals and sensitive information. The serial release of these data to partner institutions or data analysis centers in a nonaggregated form is a common situation. In this paper, we show that correlations among sensitive values associated to the same individuals in different releases can be easily used to violate users’ privacy by adversaries observing multiple data releases, even if state-of-the-art privacy protection techniques are applied. We show how the above sequential background knowledge can be actually obtained by an adversary, and used to identify with high confidence the sensitive values of an individual. Our proposed defense algorithm is based on Jensen-Shannon divergence; experiments show its superiority with respect to other applicable solutions. To the best of our knowledge, this is the first work that systematically investigates the role of sequential background knowledge in serial release of transaction data. 2012
Mitigating Distributed Denial of Service Attacks in Multiparty Applications in the Presence of Clock Drifts Network-based applications commonly open some known communication port(s), making themselves easy targets for (distributed) Denial of Service (DoS) attacks. Earlier solutions for this problem are based on port-hopping between pairs of processes which are synchronous or exchange acknowledgments. However, acknowledgments, if lost, can cause a port to be open for longer time and thus be vulnerable, while time servers can become targets to DoS attack themselves. Here, we extend port-hopping to support multiparty applications, by proposing the BIGWHEEL algorithm, for each application server to communicate with multiple clients in a port-hopping manner without the need for group synchronization. Furthermore, we present an adaptive algorithm, HOPERAA, for enabling hopping in the presence of bounded asynchrony, namely, when the communicating parties have clocks with clock drifts. The solutions are simple, based on each client interacting with the server independently of the other clients, without the need of acknowledgments or time server(s). Further, they do not rely on the application having a fixed port open in the beginning, neither do they require the clients to get a “first-contact” port from a third party. We show analytically the properties of the algorithms and also study experimentally their success rates, confirm the relation with the analytical bounds. 2012
On the Security and Efficiency of Content Distribution via Network Coding Content distribution via network coding has received a lot of attention lately. However, direct application of network coding may be insecure. In particular, attackers can inject “bogus” data to corrupt the content distribution process so as to hinder the information dispersal or even deplete the network resource. Therefore, content verification is an important and practical issue when network coding is employed. When random linear network coding is used, it is infeasible for the source of the content to sign all the data, and hence, the traditional “hash-and-sign” methods are no longer applicable. Recently, a new on-the-fly verification technique has been proposed by Krohn et al. (IEEE S&P ’04), which employs a classical homomorphic hash function. However, this technique is difficult to be applied to network coding because of high computational and communication overhead. We explore this issue further by carefully analyzing different types of overhead, and propose methods to help reducing both the computational and communication cost, and provide provable security at the same time. 2012
Security of Bertino-Shang-Wagstaff Time-Bound Hierarchical Key Management Scheme for Secure Broadcasting Recently, Bertino, Shang and Wagstaff proposed a time-bound hierarchical key management scheme for secure broadcasting. Their scheme is built on elliptic curve cryptography and implemented with tamper-resistant devices. In this paper, we present two collusion attacks on Bertino-Shang-Wagstaff scheme. The first attack does not need to compromise any decryption device, while the second attack requires to compromise single decryption device only. Both attacks are feasible and effective. 2012
Survivability Experiment and Attack Characterization for RFID Radio Frequency Identification (RFID) has been developed as an important technique for many high security and high integrity settings. In this paper, we study survivability issues for RFID. We first present an RFID survivability experiment to define a foundation to measure the degree of survivability of an RFID system under varying attacks. Then we model a series of malicious scenarios using stochastic process algebras and study the different effects of those attacks on the ability of the RFID system to provide critical services even when parts of the system have been damaged. Our simulation model relates its statistic to the attack strategies and security recovery. The model helps system designers and security specialists to identify the most devastating attacks given the attacker’s capacities and the system’s recovery abilities. The goal is to improve the system survivability given possible attacks. Our model is the first of its kind to formally represent and simulate attacks on RFID systems and to quantitatively measure the degree of survivability of an RFID system under those attacks. 2012
Persuasive Cued Click-Points Design, Implementation, and Evaluation of a Knowledge- Based Authentication Mechanism This paper presents an integrated evaluation of the Persuasive Cued Click-Points graphical password scheme, including usability and security evaluations, and implementation considerations. An important usability goal for knowledge-based authentication systems is to support users in selecting passwords of higher security, in the sense of being from an expanded effective security space. We use persuasion to influence user choice in click-based graphical passwords, encouraging users to select more random, and hence more difficult to guess, click-points. 2012
Resilient Authenticated Execution of Critical Applications in Untrusted Environments Modern computer systems are built on a foundation of software components from a variety of vendors. While critical applications may undergo extensive testing and evaluation procedures, the heterogeneity of software sources threatens the integrity of the execution environment for these trusted programs. For instance, if an attacker can combine an application exploit with a privilege escalation vulnerability, the operating system (OS) can become corrupted. Alternatively, a malicious or faulty device driver running with kernel privileges could threaten the application. While the importance of ensuring application integrity has been studied in prior work, proposed solutions immediately terminate the application once corruption is detected. Although, this approach is sufficient for some cases, it is undesirable for many critical applications. In order to overcome this shortcoming, we have explored techniques for leveraging a trusted virtual machine monitor (VMM) to observe the application and potentially repair damage that occurs. In this paper, we describe our system design, which leverages efficient coding and authentication schemes, and we present the details of our prototype implementation to quantify the overhead of our approach. Our work shows that it is feasible to build a resilient execution environment, even in the presence of a corrupted OS kernel, with a reasonable amount of storage and performance overhead. 2012

 

TECHNOLOGY                               : JAVA DOMAIN                                           : IEEE TRANSACTIONS ON DATA MINING

 

S.NO TITLES ABSTRACT YEAR
A Survival Modeling Approach to Biomedical Search Result Diversification Using Wikipedia In this paper, we propose a survival modeling approach to promoting ranking diversity for biomedical information retrieval. The proposed approach concerns with finding relevant documents that can deliver more different aspects of a query. First, two probabilistic models derived from the survival analysis theory are proposed for measuring aspect novelty. 2012
A Fuzzy Approach for Multitype Relational Data Clustering In this paper, we propose a new fuzzy clustering approach for multitype relational data (FC-MR). In FC-MR, different types of objects are clustered simultaneously. An object is assigned a large membership with respect to a cluster if its related objects in this cluster have high rankings. 2012
Anonimos: An LP-Based Approach for Anonymizing Weighted Social Network Graphs We present Anonimos, a Linear Programming-based technique for anonymization of edge weights that preserves linear properties of graphs. Such properties form the foundation of many important graph-theoretic algorithms such as shortest paths problem, k-nearest neighbors, minimum cost spanning Tree and maximizing information spread. 2012
A Methodology for Direct and Indirect Discrimination Prevention in Data Mining In this paper, we tackle discrimination prevention in data mining and propose new techniques applicable for direct or indirect Discrimination prevention individually or both at the same time. We discuss how to clean training datasets and outsourced datasets in such a way that direct and/or indirect discriminatory decision rules are converted to legitimate (non-discriminatory) Classification rules. 2012
Mining Web Graphs for Recommendations In this paper, aiming at providing a general framework on mining Web graphs for recommendations, (1) we first propose a novel diffusion method which propagates similarities between different nodes and generates recommendations; (2) then we illustrate how to generalize different recommendation problems into our graph diffusion framework. 2012
Prediction of User’s Web-Browsing Behavior: Application of Markov Model Predicting user’s behavior while serving the Internet can be applied effectively in various critical applications. Such application has traditional tradeoffs between modeling complexity and prediction accuracy. In this paper, we analyze and study Markov model and all- Kth Markov model in Web prediction. We propose a new modified Markov model to alleviate the issue of scalability in the number of paths. 2012
Prototype Selection for Nearest Neighbor Classification: Taxonomy and Empirical Study This paper provides a survey of the prototype selection methods proposed in the literature from a theoretical and empirical point of view. Considering a theoretical point of view, we propose a taxonomy based on the main characteristics presented in prototype selection and we analyze their advantages and drawbacks, the nearest neighbor classifier suffers from several drawbacks such as high storage requirements, low efficiency in classification response, and low noise Tolerance. 2012
Query Planning for Continuous Aggregation Queries over a Network of Data Aggregators We present a low-cost, scalable technique to answer continuous aggregation queries using a network of aggregators of dynamic data items. In such a network of data aggregators, each data aggregator serves a set of data items at specific coherencies. 2012
Revealing Density-Based Clustering Structure from the Core-Connected Tree of a Network In this paper, we introduce a novel density-based network clustering method, called gSkeletonClu (graph-skeleton based clustering). By projecting an undirected network to its core-connected maximal spanning tree, the clustering problem can be converted to detect core connectivity components on the tree. 2012
Scalable Learning of Collective Behavior This study of collective behavior is to understand how individuals behave in a social networking environment. Oceans of data generated by social media like Facebook, Twitter, Flickr, and YouTube present opportunities and challenges to study collective behavior on a large scale. In this work, we aim to learn to predict collective behavior in social media 2012
Weakly Supervised Joint Sentiment-Topic Detection from Text This paper proposes a novel probabilistic modeling framework called joint sentiment-topic (JST) model based on latent Dirichlet allocation (LDA), which detects sentiment and topic simultaneously from text. A reparameterized version of the JST model called Reverse-JST, obtained by reversing the sequence of sentiment and topic generation in the modeling process, is also studied. 2012
A Framework for Personal Mobile Commerce Pattern Mining and Prediction Due to a wide range of potential applications, research on mobile commerce has received a lot of interests from both of the industry and academia. Among them, one of the active topic areas is the mining and prediction of users’ mobile commerce behaviors such as their movements and purchase transactions. In this paper, we propose a novel framework, called Mobile Commerce Explorer (MCE), for mining and prediction of mobile users’ movements and purchase transactions under the context of mobile commerce. The MCE framework consists of three major components: 1) Similarity Inference Model (SIM) for measuring the similarities among stores and items, which are two basic mobile commerce entities considered in this paper; 2) Personal Mobile Commerce Pattern Mine (PMCP-Mine) algorithm for efficient discovery of mobile users’ Personal Mobile Commerce Patterns (PMCPs); and 3) Mobile Commerce Behavior Predictor (MCBP) for prediction of possible mobile user behaviors. To our best knowledge, this is the first work that facilitates mining and prediction of mobile users’ commerce behaviors in order to recommend stores and items previously unknown to a user. We perform an extensive experimental evaluation by simulation and show that our proposals produce excellent results. 2012
Efficient Extended Boolean Retrieval Extended Boolean retrieval (EBR) models were proposed nearly three decades ago, but have had little practical impact, despite their significant advantages compared to either ranked keyword or pure Boolean retrieval. In particular,EBR models produce meaningful rankings; their query model allows the representation of complex concepts in an and-or format; and they are scrutable, in that the score assigned to a document depends solely on the content of that document, unaffected by any collection statistics or other external factors. These characteristics make EBR models attractive in domains typified by medical and legal searching, where the emphasis is on iterative development of  reproducible complex queries of dozens or even hundreds of terms. However, EBR is much more computationally expensive than the alternatives. We consider the implementation of the p-norm approach to EBR, and demonstrate that ideas used in the max-score and wand exact optimization techniques for ranked keyword retrieval can be adaptedto allow selective bypass of documents via a low-cost screening process for this and similar retrieval models. We also propose term independent bounds that are able to further reduce the number of score calculations for short, simple queries under the extended Boolean retrieval model. Together, these methods yield an overall saving from 50 to 80percent of the evaluation cost on test queries drawn from biomedical search. 2012
Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques Recommender systems are becoming increasingly important to individual users and businesses for providingpersonalized recommendations. However, while the majority of algorithms proposed in recommender systems literature have focused on improving recommendation accuracy (as exemplified by the recent Netflix Prize competition) , other important aspects of recommendation quality, such as the diversity of recommendations, have often been  overlooked. In this paper, we introduce and explore a number of item ranking techniques that can generate recommendations that have substantially higher aggregate diversity across all users while maintaining comparable levels of recommendation accuracy. Comprehensive empirical evaluation consistently shows the diversity gains of the proposed techniques using several real-world rating datasets and different rating prediction algorithms.
BibPro: A Citation Parser Based on Sequence Alignment Dramatic increase in the number of academic publications has led to growing demand for efficient organization of the resources to meet researchers’ needs. As a result, a number of network services have compiled databases from the public resources scattered over the Internet. However, publications by different conferences and journals adopt different citation styles. It is an interesting problem to accurately extract metadata from a citation string which is formatted in one of thousands of different styles. It has attracted a great deal of attention in research in recent years. In this paper, based on the notion of sequence alignment, we present a citation parser called BibPro that extracts components of a citation string. To demonstrate the efficacy of BibPro, we conducted experiments on three benchmark data sets. The results show that BibPro achieved over 90 percent accuracy on each benchmark. Even with citations and associated metadata retrieved from the web as training data, our experiments show that BibPro still achieves a reasonable performance 2012
Extending Attribute Information for Small Data Set Classification Data quantity is the main issue in the small data set problem, because usually insufficient data will not lead to a robust classification performance. How to extract more effective information from a small data set is thus of considerable interest. This paper proposes a new attribute construction approach which converts the original data attributes into a higher dimensional feature space to extract more attribute information by a similarity-based algorithm using the classification-oriented fuzzy membership function. Seven data sets with different attribute sizes are employed to examine the performance of the proposed method. The results show that the proposed method has a superior classification performance when compared to principal component analysis (PCA), kernel principal component analysis (KPCA), and kernel independent component analysis (KICA) with a Gaussian kernel in the support vector machine (SVM) classifier 2012
Horizontal Aggregations in SQL to Prepare Data Sets for Data Mining Analysis Preparing a data set for analysis is generally the most time consuming task in a data mining project, requiring many complex SQL queries, joining tables, and aggregating columns. Existing SQL aggregations have limitations to prepare data sets because they return one column per aggregated group. In general, a significant manual effort is required to build data sets, where a horizontal layout is required. We propose simple, yet powerful, methods to generate SQL code to return aggregated columns in a horizontal tabular layout, returning a set of numbers instead of one number per row. This new class of functions is called horizontal aggregations. Horizontal aggregations build data sets with a horizontal denormalized layout (e.g., point-dimension, observationvariable, instance-feature), which is the standard layout required by most data mining algorithms. We propose three fundamental methods to evaluate horizontal aggregations: CASE: Exploiting the programming CASE construct; SPJ: Based on standard relational algebra operators (SPJ queries); PIVOT: Using the PIVOT operator, which is offered by some DBMSs. Experiments with large tables compare the proposed query evaluation methods. Our CASE method has similar speed to the PIVOT operator and it is much faster than the SPJ method. In general, the CASE and PIVOT methods exhibit linear scalability, whereas the SPJ method does 2012
Enabling Multilevel Trust in Privacy Preserving Data Mining Privacy Preserving Data Mining (PPDM) addresses the problem of developing accurate models about aggregated data without access to precise information in individual data record. A widely studied perturbation-based PPDM approach introduces random perturbation to individual values to preserve privacy before data are published. Previous solutions of this approach are limited in their tacit assumption of single-level trust on data miners. In this work, we relax this assumption and expand the scope of perturbation-based PPDM to Multilevel Trust (MLT-PPDM). In our setting, the more trusted a data miner is, the less perturbed copy of the data it can access. Under this setting, a malicious data miner may have access to differently perturbed copies of the same data through various means, and may combine these diverse copies to jointly infer additional information about the original data that the data owner does not intend to release. Preventing such diversity attacks is the key challenge of providing MLT-PPDM services. We address this challenge by properly correlating perturbation across copies at different trust levels. We prove that our solution is robust against diversity attacks with respect to our privacy goal. That is, for data miners who have access to an arbitrary collection of the perturbed copies, our solution prevent them from jointly reconstructing the original data more accurately than the best effort using any individual copy in the collection. Our solution allows a data owner to generate perturbed copies of its data for arbitrary trust levels ondemand.This feature offers data owners maximum flexibility. 2012
Using Rule Ontology in Repeated Rule Acquisition from Similar Web Sites Inferential rules are as essential to the Semantic Web applications as ontology. Therefore, rule acquisition is also an  important issue, and the Web that implies inferential rules can be a major source of rule acquisition. We expect that it will be easier to acquire rules from a site by using similar rules of other sites in the same domain rather than starting from scratch. We proposed an automatic rule acquisition procedure using a rule ontology RuleToOnto, which represents information about the rule components and their structures. The rule acquisition procedure consists of the rule component identification step and the rule composition step. We developed A* algorithm for the rule composition and we performed experiments demonstrating that our ontology-based rule acquisition approach works in a real-world application. 2012
Efficient Processing of Uncertain Events in Rule-Based Systems There is a growing need for systems that react automatically to events. While some events are generated externally and deliver data across distributed systems, others need to be derived by the system itself based on available information. Event derivation is hampered by uncertainty attributed to causes such as unreliable data sources or the inability to determine with certainty whether an event has actually occurred, given available information. Two main challenges exist when designing a solution for event derivation under uncertainty. First, event derivation should scale under heavy loads of incoming events. Second, the associated probabilities must be correctly captured and represented. We present a solution to both problems by introducing a novel generic and formal mechanism and framework for managing event derivation under uncertainty. We also provide empirical evidence demonstrating the scalability and accuracy of our approach 2012
Feature Selection Based on Class-Dependent Densities for High-Dimensional Binary Data Data and knowledge management systems employ feature selection algorithms for removing irrelevant, redundant, and noisy information from the data. There are two well-known approaches to feature selection, feature ranking (FR) and feature subset selection (FSS). In this paper, we propose a new FR algorithm, termed as class-dependent density-based feature elimination (CDFE), for binary data sets. Our theoretical analysis shows that CDFE computes the weights, used for feature ranking, more efficiently as compared to the mutual information measure. Effectively, rankings obtained from both the two criteria approximate each other. CDFE uses a filtrapper approach to select a final subset. For data sets having hundreds of thousands of features, feature selection with FR algorithms is simple and computationally efficient but redundant information may not be removed. On the other hand, FSS algorithms analyze the data for redundancies but may become computationally impractical on high-dimensional data sets. We address these problems by combining FR and FSS methods in the form of a two-stage feature selection algorithm. When introduced as a preprocessing step to the FSS algorithms, CDFE not only presents them with a feature subset, good in terms of classification, but also relieves them from heavy computations. Two FSS algorithms are employed in the second stage to test the two-stage feature selection idea. We carry out experiments with two different classifiers (naive Bayes’ and kernel ridge regression) on three different real-life data sets (NOVA, HIVA, and GINA) of the”Agnostic Learning versus Prior Knowledge” challenge. As a stand-alone method, CDFE shows up to about 92 percent reduction in the feature set size. When combined with the FSS algorithms in two-stages, CDFE significantly improves their classification accuracy and exhibits up to 97 percent reduction in the feature set size. We also compared CDFE against the winning entries of the challenge and f- und that it outperforms the best results on NOVA and HIVA while obtaining a third position in case of GINA. 2012
  1. 22.   
Ranking Model Adaptation for Domain-Specific Search With the explosive emergence of vertical search domains, applying the broad-based ranking model directly to different domains is no longer desirable due to domain differences, while building a unique ranking model for each domain is both laborious for labeling data and time-consuming for training models. In this paper, we address these difficulties by proposing a regularization based algorithm called ranking adaptation SVM (RA-SVM), through which we can adapt an existing ranking model to a new domain, so that the amount of labeled data and the training cost is reduced while the performance is still guaranteed. Our algorithm only requires the prediction from the existing ranking models, rather than their internal representations or the data from auxiliary domains. In addition, we assume that documents similar in the domain-specific feature space should have consistent rankings, and add some constraints to control the margin and slack variables of RA-SVM adaptively. Finally, ranking adaptability measurement is proposed to quantitatively estimate if an existing ranking model can be adapted to a new domain. Experiments performed over Letor and two large scale datasets crawled from a commercial search engine demonstrate the applicabilities of the proposed ranking adaptation algorithms and the ranking adaptability measurement. 2012
Slicing: A New Approach to Privacy Preserving Data Publishing Several anonymization techniques, such as generalization and bucketization, have been designed for privacy preserving microdata publishing. Recent work has shown that general ization loses considerable amount of information, especially for high-dimensional data. Bucketization, on the other hand, does not prevent membership disclosure and does not apply for data that do not have a clear separation between quasi- identifying attributes and sensitive attributes. In this paper, we present a novel technique called slicing, which partitions the data both horizontally and vertically. We show that slicing preserves better data utility than generalization and can be used for membership disclosure protection. Another important advantage of slicing is that it can handle high-dimensional data. We show how slicing can be used for attribute disclosure protection and develop an efficient algorithm for computing the sliced data that obey the ℓ-diversity requirement. Our workload experiments confirm that slicing preserves better utility than generalization and is more effective than bucketization in workloads involving the sensitive attribute. Our experiments also demonstrate that slicing can be used to prevent membership disclosure. 2012
  1. 24.   
Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques- projects Recommender systems are becoming increasingly important to individual users and businesses for providing personalized recommendations. However, while the majority of algorithms proposed in recommender systems literature have focused on improving recommendation accuracy, other important aspects of recommendation quality, such as the diversity of recommendations, have often been overlooked. In this paper, we introduce and explore a number of item ranking techniques that can generate recommendations that have substantially higher aggregate diversity across all users while maintaining comparable levels of recommendation accuracy. Comprehensive empirical evaluation consistently shows the diversity gains of the proposed techniques using several real-world rating datasets and different rating prediction algorithms. 2012
  1. 25.   
Horizontal Aggregations in SQL to Prepare Data Sets for Data Mining Analysis Preparing a data set for analysis is generally the most time consuming task in a data mining project, requiring many complex SQL queries, joining tables and aggregating columns. Existing SQL aggregations have limitations to prepare data sets because they return one column per aggregated group. In general, a significant manual effort is required to
build data sets, where a horizontal layout is required. We propose simple, yet powerful, methods to generate SQL code to return aggregated columns in a horizontal tabular layout, returning a set of numbers instead of one number per row. This new class of functions is called horizontal aggregations.
Horizontal aggregations build data sets with a horizontal denormalized layout (e.g. point-dimension, observation-variable, instance-feature), which is the standard layout required by most data mining algorithms. We propose three fundamental methods to evaluate horizontal aggregations: CASE: Exploiting
the programming CASE construct; SPJ: Based on standard relational algebra operators (SPJ queries); PIVOT: Using the PIVOT operator, which is offered by some DBMSs. Experiments with large tables compare the proposed query evaluation methods. Our CASE method has similar speed to the PIVOT operator and it is much faster than the SPJ method. In general, the CASE and PIVOT methods exhibit linear scalability, whereasthe SPJ method does not
2012
  1. 26.   
Scalable Learning of Collective Behavior -  projects 2012 This study of collective behavior is to understand how individuals behave in a social networking environment. Oceans of data generated by social media like Face book, Twitter, Flicker, and YouTube present opportunities and challenges to study collective behavior on a large scale. In this work, we aim to learn to predict collective behavior in social media. In particular, given information about some individuals, how can we infer the behavior of unobserved individuals in the same network? A social-dimension-based approach has been shown effective in addressing the heterogeneity of connections presented in social media. However, the networks in social media are normally of colossal size, involving hundreds of thousands of actors. The scale of these networks entails scalable learning of models for collective behavior prediction. To address the scalability issue, we propose an edge-centric clustering scheme to extract sparse social dimensions. With sparse social dimensions, the proposed approach can efficiently handle networks of millions of actors while demonstrating a comparable prediction performance to other non-scalable methods 2012
  1. 27.   
Outsourced Similarity Search on Metric Data Assets – projects This paper considers a cloud computing setting in which similarity querying of metric data is outsourced to a service provider. The data is to be revealed only to trusted users, not to the service provider or anyone else. Users query the server for the most similar data objects to a query example. Outsourcing offers the data owner scalability and a low-initial investment. The need for privacy may be due to the data being sensitive (e.g., in medicine), valuable (e.g., in astronomy), or otherwise confidential. Given this setting, the paper presents techniques that transform the data prior to supplying it to the service provider for similarity queries on the transformed data. Our techniques provide interesting trade-offs between query cost and accuracy. They are then further extended to offer an intuitive privacy guarantee. Empirical studies with real data demonstrate that the techniques are capable of offering privacy while enabling efficient and accurate processing of similarity queries. 2012
A Framework for Similarity Search of Time Series Cliques with Natural Relations A Time Series Clique (TSC) consists of multiple time series which are related to each other by natural relations. The natural relations that are found between the time series depend on the application domains. For example, a TSC can consist of time series which are trajectories in video that have spatial relations. In conventional time series retrieval, such natural relations between the time series are not considered. In this paper, we formalize the problem of similarity search over a TSC database. We develop a novel framework for efficient similarity search on TSC data. The framework addresses the following issues. First, it provides a compact representation for TSC data. Second, it uses a multidimensional relation vector to capture the natural relations between the multiple time series in a TSC. Lastly, the framework defines a novel similarity measure that uses the compact representation and the relation vector. We conduct an extensive performance study, using both real-life and synthetic data sets. From the performance study, we show that our proposed framework is both effective and efficient for TSC retrieval 2012
A Genetic Programming Approach to Record Deduplication Several systems that rely on consistent data to offer high-quality services, such as digital libraries and e-commerce brokers, may be affected by the existence of duplicates, quasi replicas, or near-duplicate entries in their repositories. Because of that, there have been significant investments from private and government organizations for developing methods for removing replicas from its data repositories. This is due to the fact that clean and replica-free repositories not only allow the retrieval of higher quality information but also lead to more concise data and to potential savings in computational time and resources to process this data. In this paper, we propose a genetic programming approach to record deduplication that combines several different pieces of evidence extracted from the data content to find a deduplication function that is able to identify whether two entries in a repository are replicas or not. As shown by our experiments, our approach outperforms an existing state-of-the-art method found in the literature. Moreover, the suggested functions are computationally less demanding since they use fewer evidence. In addition, our genetic programming approach is capable of automatically adapting these functions to a given fixed replica identification boundary, freeing the user from the burden of having to choose and tune this parameter. 2012
A Probabilistic Scheme for Keyword-Based Incremental Query Construction Databases enable users to precisely express their informational needs using structured queries. However, database query construction is a laborious and error-prone process, which cannot be performed well by most end users. Keyword search alleviates the usability problem at the price of query expressiveness. As keyword search algorithms do not differentiate between the possible informational needs represented by a keyword query, users may not receive adequate results. This paper presents IQP – a novel approach to bridge the gap between usability of keyword search and expressiveness of database queries. IQP enables a user to start with an arbitrary keyword query and incrementally refine it into a structured query through an interactive interface. The enabling techniques of IQP include: 1) a probabilistic framework for incremental query construction; 2) a probabilistic model to assess the possible informational needs represented by a keyword query; 3) an algorithm to obtain the optimal query construction process. This paper presents the detailed design of IQP, and demonstrates its effectiveness and scalability through experiments over real-world data and a user study. 2012
Anónimos An LP-Based Approach for Anonymizing Weighted Social Network Graphs The increasing popularity of social networks has initiated a fertile research area in information extraction and data mining. Anonymization of these social graphs is important to facilitate publishing these data sets for analysis by external entities. Prior work has concentrated mostly on node identity anonymization and structural anonymization. But with the growing interest in analyzing social networks as a weighted network, edge weight anonymization is also gaining importance. We present Anónimos, a Linear Programming-based technique for anonymization of edge weights that preserves linear properties of graphs. Such properties form the foundation of many important graph-theoretic algorithms such as shortest paths problem, k-nearest neighbors, minimum cost spanning tree, and maximizing information spread. As a proof of concept, we apply Anónimos to the shortest paths problem and its extensions, prove the correctness, analyze complexity, and experimentally evaluate it using real social network data sets. Our experiments demonstrate that Anónimos anonymizes the weights, improves k-anonymity of the weights, and also scrambles the relative ordering of the edges sorted by weights, thereby providing robust and effective anonymization of the sensitive edge-weights. We also demonstrate the composability of different models generated using Anónimos, a property that allows a single anonymized graph to preserve multiple linear properties. 2012
Answering General Time-Sensitive Queries Time is an important dimension of relevance for a large number of searches, such as over blogs and news archives. So far, research on searching over such collections has largely focused on locating topically similar documents for a query. Unfortunately, topic similarity alone is not always sufficient for document ranking. In this paper, we observe that, for an important class of queries that we call time-sensitive queries, the publication time of the documents in a news archive is important and should be considered in conjunction with the topic similarity to derive the final document ranking. Earlier work has focused on improving retrieval for “recency” queries that target recent documents. We propose a more general framework for handling time-sensitive queries and we automatically identify the important time intervals that are likely to be of interest for a query. Then, we build scoring techniques that seamlessly integrate the temporal aspect into the overall ranking mechanism. We present an extensive experimental evaluation using a variety of news article data sets, including TREC data as well as real web data analyzed using the Amazon Mechanical Turk. We examine several techniques for detecting the important time intervals for a query over a news archive and for incorporating this information in the retrieval process. We show that our techniques are robust and significantly improve result quality for time-sensitive queries compared to state-of-the-art retrieval techniques. 2012
Clustering with Multiviewpoint-Based Similarity Measure All clustering methods have to assume some cluster relationship among the data objects that they are applied on. Similarity between a pair of objects can be defined either explicitly or implicitly. In this paper, we introduce a novel multiviewpoint-based similarity measure and two related clustering methods. The major difference between a traditional dissimilarity/similarity measure and ours is that the former uses only a single viewpoint, which is the origin, while the latter utilizes many different viewpoints, which are objects assumed to not be in the same cluster with the two objects being measured. Using multiple viewpoints, more informative assessment of similarity could be achieved. Theoretical analysis and empirical study are conducted to support this claim. Two criterion functions for document clustering are proposed based on this new measure. We compare them with several well-known clustering algorithms that use other popular similarity measures on various document collections to verify the advantages of our proposal. 2012
Cluster-Oriented Ensemble Classifier Impact of Multicluster Characterization on Ensemble Classifier Learning This paper presents a novel cluster-oriented ensemble classifier. The proposed ensemble classifier is based on original concepts such as learning of cluster boundaries by the base classifiers and mapping of cluster confidences to class decision using a fusion classifier. The categorized data set is characterized into multiple clusters and fed to a number of distinctive base classifiers. The base classifiers learn cluster boundaries and produce cluster confidence vectors. A second level fusion classifier combines the cluster confidences and maps to class decisions. The proposed ensemble classifier modifies the learning domain for the base classifiers and facilitates efficient learning. The proposed approach is evaluated on benchmark data sets from UCI machine learning repository to identify the impact of multicluster boundaries on classifier learning and classification accuracy. The experimental results and two-tailed sign test demonstrate the superiority of the proposed cluster-oriented ensemble classifier over existing ensemble classifiers published in the literature. 2012
Effective Pattern Discovery for Text Mining Many data mining techniques have been proposed for mining useful patterns in text documents. However, how to effectively use and update discovered patterns is still an open research issue, especially in the domain of text mining. Since most existing text mining methods adopted term-based approaches, they all suffer from the problems of polysemy and synonymy. Over the years, people have often held the hypothesis that pattern (or phrase)-based approaches should perform better than the term-based ones, but many experiments do not support this hypothesis. This paper presents an innovative and effective pattern discovery technique which includes the processes of pattern deploying and pattern evolving, to improve the effectiveness of using and updating discovered patterns for finding relevant and interesting information. Substantial experiments on RCV1 data collection and TREC topics demonstrate that the proposed solution achieves encouraging performance. 2012
Efficient Fuzzy Type-Ahead Search in XML Data In a traditional keyword-search system over XML data, a user composes a keyword query, submits it to the system, and retrieves relevant answers. In the case where the user has limited knowledge about the data, often the user feels “left in the dark” when issuing queries, and has to use a try-and-see approach for finding information. In this paper, we study fuzzy type-ahead search in XML data, a new information-access paradigm in which the system searches XML data on the fly as the user types in query keywords. It allows users to explore data as they type, even in the presence of minor errors of their keywords. Our proposed method has the following features: 1) Search as you type: It extends Autocomplete by supporting queries with multiple keywords in XML data. 2) Fuzzy: It can find high-quality answers that have keywords matching query keywords approximately. 3) Efficient: Our effective index structures and searching algorithms can achieve a very high interactive speed. We study research challenges in this new search framework. We propose effective index structures and top-k algorithms to achieve a high interactive speed. We examine effective ranking functions and early termination techniques to progressively identify the top-k relevant answers. We have implemented our method on real data sets, and the experimental results show that our method achieves high search efficiency and result quality. 2012
Feature Selection Based on Class-Dependent Densities for High-Dimensional Binary Data Data and knowledge management systems employ feature selection algorithms for removing irrelevant, redundant, and noisy information from the data. There are two well-known approaches to feature selection, feature ranking (FR) and feature subset selection (FSS). In this paper, we propose a new FR algorithm, termed as class-dependent density-based feature elimination (CDFE), for binary data sets. Our theoretical analysis shows that CDFE computes the weights, used for feature ranking, more efficiently as compared to the mutual information measure. Effectively, rankings obtained from both the two criteria approximate each other. CDFE uses a filtrapper approach to select a final subset. For data sets having hundreds of thousands of features, feature selection with FR algorithms is simple and computationally efficient but redundant information may not be removed. On the other hand, FSS algorithms analyze the data for redundancies but may become computationally impractical on high-dimensional data sets. We address these problems by combining FR and FSS methods in the form of a two-stage feature selection algorithm. When introduced as a preprocessing step to the FSS algorithms, CDFE not only presents them with a feature subset, good in terms of classification, but also relieves them from heavy computations. Two FSS algorithms are employed in the second stage to test the two-stage feature selection idea. We carry out experiments with two different classifiers (naive Bayes’ and kernel ridge regression) on three different real-life data sets (NOVA, HIVA, and GINA) of the”Agnostic Learning versus Prior Knowledge” challenge. As a stand-alone method, CDFE shows up to about 92 percent reduction in the feature set size. When combined with the FSS algorithms in two-stages, CDFE significantly improves their classification accuracy and exhibits up to 97 percent reduction in the feature set size. We also compared CDFE against the winning entries of the challenge and f- und that it outperforms the best results on NOVA and HIVA while obtaining a third position in case of GINA. 2012
Feedback Matching Framework for Semantic Interoperability of Product Data There is a need to promote drastically increased levels of interoperability of product data across a broad spectrum of stakeholders, while ensuring that the semantics of product knowledge are preserved, and when necessary, translated. In order to achieve this, multiple methods have been proposed to determine semantic maps across concepts from different representations. Previous research has focused on developing different individual matching methods, i.e., ones that compute mapping based on a single matching measure. These efforts assume that some weighted combination can be used to obtain the overall maps. We analyze the problem of combination of multiple individual methods to determine requirements specific to product development and propose a solution approach called FEedback Matching Framework with Implicit Training (FEMFIT). FEMFIT provides the ability to combine the different matching approaches using ranking Support Vector Machine (ranking SVM). The method accounts for nonlinear relations between the individual matchers. It overcomes the need to explicitly train the algorithm before it is used, and further reduces the decision-making load on the domain expert by implicitly capturing the expert’s decisions without requiring him to input real numbers on similarity. We apply FEMFIT to a subset of product constraints across a commercial system and the ISO standard. We observe that FEMIT demonstrates better accuracy (average correctness of the results) and stability (deviation from the average) in comparison with other existing combination methods commonly assumed to be valid in this domain. 2012
Fractal-Based Intrinsic Dimension Estimation and Its Application in Dimensionality Reduction Dimensionality reduction is an important step in knowledge discovery in databases. Intrinsic dimension indicates the number of variables necessary to describe a data set. Two methods, box-counting dimension and correlation dimension, are commonly used for intrinsic dimension estimation. However, the robustness of these two methods has not been rigorously studied. This paper demonstrates that correlation dimension is more robust with respect to data sample size. In addition, instead of using a user selected distance d, we propose a new approach to capture all log-log pairs of a data set to more precisely estimate the correlation dimension. Systematic experiments are conducted to study factors that influence the computation of correlation dimension, including sample size, the number of redundant variables, and the portion of log-log plot used for calculation. Experiments on real-world data sets confirm the effectiveness of intrinsic dimension estimation with our improved method. Furthermore, a new supervised dimensionality reduction method based on intrinsic dimension estimation was introduced and validated. 2012
Horizontal Aggregations in SQL to Prepare Data Sets for Data Mining Analysis Preparing a data set for analysis is generally the most time consuming task in a data mining project, requiring many complex SQL queries, joining tables, and aggregating columns. Existing SQL aggregations have limitations to prepare data sets because they return one column per aggregated group. In general, a significant manual effort is required to build data sets, where a horizontal layout is required. We propose simple, yet powerful, methods to generate SQL code to return aggregated columns in a horizontal tabular layout, returning a set of numbers instead of one number per row. This new class of functions is called horizontal aggregations. Horizontal aggregations build data sets with a horizontal denormalized layout (e.g., point-dimension, observation-variable, instance-feature), which is the standard layout required by most data mining algorithms. We propose three fundamental methods to evaluate horizontal aggregations: CASE: Exploiting the programming CASE construct; SPJ: Based on standard relational algebra operators (SPJ queries); PIVOT: Using the PIVOT operator, which is offered by some DBMSs. Experiments with large tables compare the proposed query evaluation methods. Our CASE method has similar speed to the PIVOT operator and it is much faster than the SPJ method. In general, the CASE and PIVOT methods exhibit linear scalability, whereas the SPJ method does not. 2012
Low-Rank Kernel Matrix Factorization for Large-Scale Evolutionary Clustering Traditional clustering techniques are inapplicable to problems where the relationships between data points evolve over time. Not only is it important for the clustering algorithm to adapt to the recent changes in the evolving data, but it also needs to take the historical relationship between the data points into consideration. In this paper, we propose ECKF, a general framework for evolutionary clustering large-scale data based on low-rank kernel matrix factorization. To the best of our knowledge, this is the first work that clusters large evolutionary data sets by the amalgamation of low-rank matrix approximation methods and matrix factorization-based clustering. Since the low-rank approximation provides a compact representation of the original matrix, and especially, the near-optimal low-rank approximation can preserve the sparsity of the original data, ECKF gains computational efficiency and hence is applicable to large evolutionary data sets. Moreover, matrix factorization-based methods have been shown to effectively cluster high-dimensional data in text mining and multimedia data analysis. From a theoretical standpoint, we mathematically prove the convergence and correctness of ECKF, and provide detailed analysis of its computational efficiency (both time and space). Through extensive experiments performed on synthetic and real data sets, we show that ECKF outperforms the existing methods in evolutionary clustering. 2012
Mining Online Reviews for Predicting Sales Performance A Case Study in the Movie Domain Posting reviews online has become an increasingly popular way for people to express opinions and sentiments toward the products bought or services received. Analyzing the large volume of online reviews available would produce useful actionable knowledge that could be of economic values to vendors and other interested parties. In this paper, we conduct a case study in the movie domain, and tackle the problem of mining reviews for predicting product sales performance. Our analysis shows that both the sentiments expressed in the reviews and the quality of the reviews have a significant impact on the future sales performance of products in question. For the sentiment factor, we propose Sentiment PLSA (S-PLSA), in which a review is considered as a document generated by a number of hidden sentiment factors, in order to capture the complex nature of sentiments. Training an S-PLSA model enables us to obtain a succinct summary of the sentiment information embedded in the reviews. Based on S-PLSFA, we propose ARSA, an Autoregressive Sentiment-Aware model for sales prediction. We then seek to further improve the accuracy of prediction by considering the quality factor, with a focus on predicting the quality of a review in the absence of user-supplied indicators, and present ARSQA, an Autoregressive Sentiment and Quality Aware model, to utilize sentiments and quality for predicting product sales performance. Extensive experiments conducted on a large movie data set confirm the effectiveness of the proposed approach. 2012
Privacy Preserving Decision Tree Learning Using Unrealized Data Sets Privacy preservation is important for machine learning and data mining, but measures designed to protect private information often result in a trade-off: reduced utility of the training samples. This paper introduces a privacy preserving approach that can be applied to decision tree learning, without concomitant loss of accuracy. It describes an approach to the preservation of the privacy of collected data samples in cases where information from the sample database has been partially lost. This approach converts the original sample data sets into a group of unreal data sets, from which the original samples cannot be reconstructed without the entire group of unreal data sets. Meanwhile, an accurate decision tree can be built directly from those unreal data sets. This novel approach can be applied directly to the data storage as soon as the first sample is collected. The approach is compatible with other privacy preserving approaches, such as cryptography, for extra protection 2012

 

TECHNOLOGY                                : JAVA

 

DOMAIN                                            : IEEE TRANSACTIONS ON MOBILE COMPUTING

 

 

S.NO TITLES ABSTRACT YEAR
The Boomerang Protocol: Tying Data to Geographic Locations in Mobile Disconnected Networks We present the boomerang protocol to efficiently retain information at a particular geographic location in a sparse network of highly mobile nodes without using infrastructure networks. To retain information around certain physical location, each mobile device passing that location will carry the information for a short while. 201c
Nature-Inspired Self-Organization, Control, and Optimization in Heterogeneous Wireless Networks In this paper, we present new models and algorithms for control and optimization of a class of next generation communication networks: Hierarchical Heterogeneous Wireless Networks (HHWNs), under real-world physical constraints. Two biology-inspired techniques, a Flocking Algorithm (FA) and a Particle Swarm Optimizer (PSO), are investigated in this context. 2012
A Cost Analysis Framework for NEMO Prefix Delegation-Based Schemes In this paper, we have developed analytical framework to measure the costs of the basic protocol for Network Mobility (NEMO), and four representative prefix delegation-based schemes. Our results show that cost of packet delivery through the partially optimized route dominates over other costs. 2012
OMAN: A Mobile Ad Hoc Network Design System In this paper, we present a high-level view of the OMAN architecture, review specific mathematical models used in the network representation, and show how OMAN is used to evaluate tradeoffs in MANET design. Specifically, we cover three case studies of optimization. 1-robust power control under uncertain channel information for a single physical layer snapshot. 2-scheduling with the availability of directional radiation patterns. 3-optimizing topology through movement planning of relay nodes. 2012
Energy-Efficient Cooperative Video Distribution with Statistical QoS Provisions over Wireless Networks In this paper, we formulate the resource allocation problem for general multihop multicast network flows and derive the optimal solution that minimizes the total energy consumption while guaranteeing a statistical end-to-end delay bound on each network path. 2012
Leveraging the Algebraic Connectivity of a Cognitive Network for Routing Design In this paper, we consider the implications of spectrum heterogeneity on connectivity and routing in a Cognitive Radio Ad Hoc Network (CRAHN). We study the Laplacian spectrum of the CRAHN graph when the activity of primary users is considered. We introduce the cognitive algebraic connectivity, i.e., the second smallest eigenvalue of the Laplacian of a graph, in a cognitive scenario. 2012
Efficient Virtual Backbone Construction with Routing Cost Constraint in Wireless Networks Using Directional Antennas In this paper, we will study a directional virtual backbone (VB) in the network where directional antennas are used. When constructing a VB, we will take routing and broadcasting into account since they are two common operations in wireless networks. Hence, we will study a VB with guaranteed routing costs, named ? Minimum rOuting Cost Directional VB (?-MOC-DVB). 2012
Stateless Multicast Protocol for Ad Hoc Networks. In this paper, we have developed a stateless receiver-based multicast (RBMulticast) protocol that simply uses a list of the multicast members’ (e.g., sinks’) addresses, embedded in packet headers, to enable receivers to decide the best way to forward the multicast traffic. 2012
Detection of Selfish Manipulation of Carrier Sensing in 802.11 Networks CCA tuning can be exploited by selfish nodes to obtain an unfair share of the available bandwidth. Specifically, a selfish entity can manipulate the CCA threshold to ignore ongoing transmissions; this increases the probability of accessing the medium and provides the entity a higher, unfair share of the bandwidth. 2012
Handling Selfishness in Replica Allocation over a Mobile Ad Hoc Network In a mobile ad hoc network, the mobility and resource constraints of mobile nodes may lead to network partitioning or performance degradation. Several data replication techniques have been proposed to minimize performance degradation. Most of them assume that all mobile nodes collaborate fully in terms of sharing their memory space. In reality, however, some nodes may selfishly decide only to cooperate partially, or not at all, with other nodes. These selfish nodes could then reduce the overall data accessibility in the network. In this paper, we examine the impact of selfish nodes in a mobile ad hoc network from the perspective of replica allocation. We term this selfish replica allocation. In particular, we develop a selfish node detection algorithm that considers partial selfishness and novel replica allocation techniques to properly cope with selfish replica allocation. The conducted simulations demonstrate the proposed approach outperforms traditional cooperative replica allocation techniques in terms of data accessibility,communication cost, and average query delay. 2012
Acknowledgment-Based Broadcast Protocol for Reliable and Efficient Data Dissemination in Vehicular Ad Hoc Networks We propose a broadcast algorithm suitable for a wide range of vehicular scenarios, which only employs local information acquired via periodic beacon messages, containing acknowledgments of the circulated broadcast messages. Each vehicle decides whether it belongs to a connected dominating set (CDS). Vehicles in the CDS use a shorter waiting period before possible retransmission. At time-out expiration, a vehicle retransmits if it is aware of at least one neighbor in need of the message. To address intermittent connectivity and appearance of new neighbors, the evaluation timer can be restarted. Our algorithm resolves propagation at road intersections without any need to even recognize intersections. It is inherently adaptable to different mobility regimes, without the need to classify network or vehicle speeds. In a thorough simulation-based performance evaluation, our algorithm is shown to provide higher reliability and message efficiency than existing approaches for nonsafety applications. 2012
Toward Reliable Data Delivery for Highly Dynamic Mobile Ad Hoc Networks This paper addresses the problem of delivering data packets for highly dynamic mobile ad hoc networks in a reliable and timely manner. Most existing ad hoc routing protocols are susceptible to node mobility, especially for large-scale networks. Driven by this issue, we propose an efficient Position-based Opportunistic Routing (POR) protocol which takes advantage of the stateless property of geographic routing and the broadcast nature of wireless medium. When a data packet is sent out, some of the neighbor nodes that have overheard the transmission will serve as forwarding candidates, and take turn to forward the packet if it is not relayed by the specific best forwarder within a certain period of time. By utilizing such in-the-air backup, communication is maintained without being interrupted. The additional latency incurred by local route recovery is greatly reduced and the duplicate relaying caused by packet reroute is also decreased. In the case of communication hole, a Virtual Destination-based Void Handling (VDVH) scheme is further proposed to work together with POR. Both theoretical analysis and simulation results show that POR achieves excellent performance even under high node mobility with acceptable overhead and the new void handling scheme also works well. 2012
Protecting Location Privacy in Sensor Networks against a Global Eavesdropper While many protocols for sensor network security provide confidentiality for the content of messages, contextual information usually remains exposed. Such contextual information can be exploited by an adversary to derive sensitive information such as the locations of monitored objects and data sinks in the field. Attacks on these components can significantly undermine any network application. Existing techniques defend the leakage of location information from a limited adversary who can only observe network traffic in a small region. However, a stronger adversary, the global eavesdropper, is realistic and can defeat these existing techniques. This paper first formalizes the location privacy issues in sensor networks under this strong adversary model and computes a lower bound on the communication overhead needed for achieving a given level of location privacy. The paper then proposes two techniques to provide location privacy to monitored objects (source-location privacy)—periodic collection and source simulation—and two techniques to provide location privacy to data sinks (sink-location privacy)—sink simulation and backbone flooding. These techniques provide trade-offs between privacy, communication cost, and latency. Through analysis and simulation, we demonstrate that the proposed techniques are efficient and effective for source and sink-location privacy in sensor networks. 2012
  1. 14.   
local broadcast algorithms in wireless ad hoc networks reducing the number of transmissions There are two main approaches, static and dynamic, to broadcast algorithms in wireless ad hoc networks. In the static approach, local algorithms determine the status (forwarding/nonforwarding) of each node proactively based on local topology information and a globally known priority function. In this paper, we first show that local broadcast algorithms based on the static approach cannot achieve a good approximation factor to the optimum solution (an NP-hard problem). However, we show that a constant approximation factor is achievable if (relative) position information is available. In the dynamic approach, local algorithms determine the status of each node “on-the-fly” based on local topology information and broadcast state information. Using the dynamic approach, it was recently shown that local broadcast algorithms can achieve a constant approximation factor to the optimum solution when (approximate) position information is available. However, using position information can simplify the problem. Also, in some applications it may not be practical to have position information. Therefore, we wish to know whether local broadcast algorithms based on the dynamic approach can achieve a constant approximation factor without using position information. We answer this question in the positive – we design a local broadcast algorithm in which the status of each node is decided “on-the-fly” and prove that the algorithm can achieve both full delivery and a constant approximation to the optimum solution 2012
  1. 15.   
Compressed-Sensing-Enabled Video Streaming for Wireless Multimedia Sensor Networks This article presents the design of a networked system for joint compression, rate control and error correction of video over resource-constrained embedded devices based on the theory of compressed sensing. The objective of this work is to design a cross-layer system that jointly controls the video encoding rate, the transmission rate, and the channel coding rate to maximize the received video quality. First, compressed sensing based video encoding for transmission over wireless multimedia sensor networks (WMSNs) is studied. It is shown that compressed sensing can overcome many of the current problems of video over WMSNs, primarily encoder complexity and low resiliency to channel errors. A rate controller is then developed with the objective of maintaining fairness among video streams while maximizing the received video quality. It is shown that the rate of compressed sensed video can be predictably controlled by varying only the compressed sensing sampling rate. It is then shown that the developed rate controller can be interpreted as the iterative solution to a convex optimization problem representing the optimization of the rate allocation across the network. The error resiliency properties of compressed sensed images and videos are then studied, and an optimal error detection and correction scheme is presented for video transmission over lossy channels. Finally, the entire system is evaluated through simulation and testbed evaluation. The rate controller is shown to outperform existing TCP-friendly rate control schemes in terms of both fairness and received video quality. Testbed results also show that the rates converge to stable values in real channels. 2012
  1. 16.   
Hop-by-Hop Routing in Wireless Mesh Networks with Bandwidth Guarantees Wireless Mesh Network (WMN) has become an important edge network to provide Internet access to remote areas and wireless connections in a metropolitan scale. In this paper, we study the problem of identifying the maximum available bandwidth path, a fundamental issue in supporting quality-of-service in WMNs. Due to interference among links, bandwidth, a well-known bottleneck metric in wired networks, is neither concave nor additive in wireless networks. We propose a new path weight which captures the available path bandwidth information. We formally prove that our hop-by-hop routing protocol based on the new path weight satisfies the consistency and loop-freeness requirements. The consistency property guarantees that each node makes a proper packet forwarding decision, so that a data packet does traverse over the intended path. Our extensive simulation experiments also show that our proposed path weight outperforms existing path metrics in identifying high-throughput paths 2012
  1. 17.   
Handling Selfishness in Replica Allocation over a Mobile Ad Hoc Network- Mobile Computing, projects 2012 In a mobile ad hoc network, the mobility and resource constraints of mobile nodes may lead to network partitioning or performance degradation. Several data replication techniques have been proposed to minimize performance degradation. Most of them assume that all mobile nodes collaborate fully in terms of sharing their memory space. In reality, however, some nodes may selfishly decide only to cooperate partially, or not at all, with other nodes. These selfish nodes could then reduce the overall data accessibility in the network. In this paper, we examine the impact of selfish nodes in a mobile ad hoc network from the perspective of replica allocation. We term this selfish replica allocation. In particular, we develop a selfish node detection algorithm that considers partial selfishness and novel replica allocation techniques to properly cope with selfish replica allocation. The conducted simulations demonstrate the proposed approach outperforms traditional cooperative replica allocation techniques in terms of data accessibility, communication cost, and average query delay. 2012
  1. 18.   
Toward Reliable Data Delivery for Highly Dynamic Mobile Ad Hoc Networks- Mobile Computing, projects 2012 This paper addresses the problem of delivering data packets for highly dynamic mobile ad hoc networks in a reliable and timely manner. Most existing ad hoc routing protocols are susceptible to node mobility, especially for large-scale networks. Driven by this issue, we propose an efficient Position-based Opportunistic Routing (POR) protocol which takes advantage of the stateless property of geographic routing and the broadcast nature of wireless medium. When a data packet is sent out, some of the neighbor nodes that have overheard the transmission will serve as forwarding candidates, and take turn to forward the packet if it is not relayed by the specific best forwarder within a certain period of time. By utilizing such in-the-air backup, communication is maintained without being interrupted. The additional latency incurred by local route recovery is greatly reduced and the duplicate relaying caused by packet reroute is also decreased. In the case of communication hole, a Virtual Destination-based Void Handling (VDVH) scheme is further proposed to work together with POR. Both theoretical analysis and simulation results show that POR achieves excellent performance even under high node mobility with acceptable overhead and the new void handling scheme also works well 2012
  1. 19.   
Fast Data Collection in Tree-Based Wireless Sensor Networks- Mobile Computing, projects 2012 We investigate the following fundamental question – how fast can information be collected from a wireless sensor network organized as tree? To address this, we explore and evaluate a number of different techniques using realistic simulation models under the many-to-one communication paradigm known as convergecast. We first consider time scheduling on a single frequency channel with the aim of minimizing the number of time slots required (schedule length) to complete a convergecast. Next, we combine scheduling with transmission power control to mitigate the effects of interference, and show that while power control helps in reducing the schedule length under a single frequency, scheduling transmissions using multiple frequencies is more efficient. We give lower bounds on the schedule length when interference is completely eliminated, and propose algorithms that achieve these bounds. We also evaluate the performance of various channel assignment methods and find empirically that for moderate size networks of about 100 nodes, the use of multi-frequency scheduling can suffice to eliminate most of the interference. Then, the data collection rate no longer remains limited by interference but by the topology of the routing tree. To this end, we construct degree-constrained spanning trees and capacitated minimal spanning trees, and show significant improvement in scheduling performance over different deployment densities. Lastly, we evaluate the impact of different interference and channel models on the schedule length. 2012
  1. 20.   
Protecting Location Privacy in Sensor Networks Against a Global EavesdropperJAVA The location privacy issue in sensor networks under this strong adversary model is considered. Proposed two techniques to provide location privacy to monitored objects (source-location privacy)-periodic collection and source simulation-and two techniques to provide location privacy to data sinks (sink-location privacy)-sink simulation and backbone flooding. 2012
  1. 21.   
Energy-Efficient Cooperative Video Distribution with Statistical QoS Provisions over Wireless Networks – projects 2012 For real-time video broadcast where multiple users are interested in the same content, mobile-to-mobile cooperation can be utilized to improve delivery efficiency and reduce network utilization. Under such cooperation, however, real-time video transmission requires end-to-end delay bounds. Due to the inherently stochastic nature of wireless fading channels, deterministic delay bounds are prohibitively difficult to guarantee. For a scalable video structure, an alternative is to provide statistical guarantees using the concept of effective capacity/bandwidth by deriving quality of service exponents for each video layer. Using this concept, we formulate the resource allocation problem for general multihop multicast network flows and derive the optimal solution that minimizes the total energy consumption while guaranteeing a statistical end-to-end delay bound on each network path. A method is described to compute the optimal resource allocation at each node in a distributed fashion. Furthermore, we propose low complexity approximation algorithms for energy-efficient flow selection from the set of directed acyclic graphs forming the candidate network flows. The flow selection and resource allocation process is adapted for each video frame according to the channel conditions on the network links. Considering different network topologies, results demonstrate that the proposed resource allocation and flow selection algorithms provide notable performance gains with small optimality gaps at a low computational cost. 2012
A Novel MAC Scheme for Multichannel Cognitive Radio Ad Hoc Networks This paper proposes a novel medium access control (MAC) scheme for multichannel cognitive radio (CR) ad hoc networks, which achieves high throughput of CR system while protecting primary users (PUs) effectively. In designing the MAC scheme, we consider that the PU signal may cover only a part of the network and the nodes can have the different sensing result for the same PU even on the same channel. By allowing the nodes to use the channel on which the PU exists as long as their transmissions do not disturb the PU, the proposed MAC scheme fully utilizes the spectrum access opportunity. To mitigate the hidden PU problem inherent to multichannel CR networks where the PU signal is detectable only to some nodes, the proposed MAC scheme adjusts the sensing priorities of channels at each node with the PU detection information of other nodes and also limits the transmission power of a CR node to the maximum allowable power for guaranteeing the quality of service requirement of PU. The performance of the proposed MAC scheme is evaluated by using simulation. The simulation results show that the CR system with the proposed MAC accomplishes good performance in throughput and packet delay, while protecting PUs properly 2012
A Statistical Mechanics-Based Framework to Analyze Ad Hoc Networks with Random Access Characterizing the performance of ad hoc networks is one of the most intricate open challenges; conventional ideas based on information-theoretic techniques and inequalities have not yet been able to successfully tackle this problem in its generality. Motivated thus, we promote the totally asymmetric simple exclusion process (TASEP), a particle flow model in statistical mechanics, as a useful analytical tool to study ad hoc networks with random access. Employing the TASEP framework, we first investigate the average end-to-end delay and throughput performance of a linear multihop flow of packets. Additionally, we analytically derive the distribution of delays incurred by packets at each node, as well as the joint distributions of the delays across adjacent hops along the flow. We then consider more complex wireless network models comprising intersecting flows, and propose the partial mean-field approximation (PMFA), a method that helps tightly approximate the throughput performance of the system. We finally demonstrate via a simple example that the PMFA procedure is quite general in that it may be used to accurately evaluate the performance of ad hoc networks with arbitrary topologies. 2012
Acknowledgment-Based Broadcast Protocol for Reliable and Efficient Data Dissemination in Vehicular Ad Hoc Networks We propose a broadcast algorithm suitable for a wide range of vehicular scenarios, which only employs local information acquired via periodic beacon messages, containing acknowledgments of the circulated broadcast messages. Each vehicle decides whether it belongs to a connected dominating set (CDS). Vehicles in the CDS use a shorter waiting period before possible retransmission. At time-out expiration, a vehicle retransmits if it is aware of at least one neighbor in need of the message. To address intermittent connectivity and appearance of new neighbors, the evaluation timer can be restarted. Our algorithm resolves propagation at road intersections without any need to even recognize intersections. It is inherently adaptable to different mobility regimes, without the need to classify network or vehicle speeds. In a thorough simulation-based performance evaluation, our algorithm is shown to provide higher reliability and message efficiency than existing approaches for nonsafety applications. 2012
Characterizing the Security Implications of Third-Party Emergency Alert Systems over Cellular Text Messaging Services Cellular text messaging services are increasingly being relied upon to disseminate critical information during emergencies. Accordingly, a wide range of organizations including colleges and universities now partner with third-party providers that promise to improve physical security by rapidly delivering such messages. Unfortunately, these products do not work as advertised due to limitations of cellular infrastructure and therefore provide a false sense of security to their users. In this paper, we perform the first extensive investigation and characterization of the limitations of an Emergency Alert System (EAS) using text messages as a security incident response mechanism. We show emergency alert systems built on text messaging not only can meet the 10 minute delivery requirement mandated by the WARN Act, but also potentially cause other voice and SMS traffic to be blocked at rates upward of 80 percent. We then show that our results are representative of reality by comparing them to a number of documented but not previously understood failures. Finally, we analyze a targeted messaging mechanism as a means of efficiently using currently deployed infrastructure and third-party EAS. In so doing, we demonstrate that this increasingly deployed security infrastructure does not achieve its stated requirements for large populations. 2012
Converge Cast On the Capacity and Delay Tradeoffs In this paper, we define an ad hoc network where multiple sources transmit packets to one destination as Converge-Cast network. We will study the capacity delay tradeoffs assuming that n wireless nodes are deployed in a unit square. For each session (the session is a dataflow from k different source nodes to 1 destination node), k nodes are randomly selected as active sources and each transmits one packet to a particular destination node, which is also randomly selected. We first consider the stationary case, where capacity is mainly discussed and delay is entirely dependent on the average number of hops. We find that the per-node capacity is Θ (1/√(n log n)) (given nonnegative functions f(n) and g(n): f(n) = O(g(n)) means there exist positive constants c and m such that f(n) ≤ cg(n) for all n ≥ m; f(n)= Ω (g(n)) means there exist positive constants c and m such that f(n) ≥ cg(n) for all n ≥ m; f(n) = Θ (g(n)) means that both f(n) = Ω (g(n)) and f(n) = O(g(n)) hold), which is the same as that of unicast, presented in (Gupta and Kumar, 2000). Then, node mobility is introduced to increase network capacity, for which our study is performed in two steps. The first step is to establish the delay in single-session transmission. We find that the delay is Θ (n log k) under 1-hop strategy, and Θ (n log k/m) under 2-hop redundant strategy, where m denotes the number of replicas for each packet. The second step is to find delay and capacity in multisession transmission. We reveal that the per-node capacity and delay for 2-hop nonredundancy strategy are Θ (1) and Θ (n log k), respectively. The optimal delay is Θ (√(n log k)+k) with redundancy, corresponding to a capacity of Θ (√((1/n log k) + (k/n log k)). Therefore, we obtain that the capacity delay tradeoff satisfies delay/rate ≥ Θ (n log k) for both strategies. 2012
Cooperative Download in Vehicular Environments We consider a complex (i.e., nonlinear) road scenario where users aboard vehicles equipped with communication interfaces are interested in downloading large files from road-side Access Points (APs). We investigate the possibility of exploiting opportunistic encounters among mobile nodes so to augment the transfer rate experienced by vehicular downloaders. To that end, we devise solutions for the selection of carriers and data chunks at the APs, and evaluate them in real-world road topologies, under different AP deployment strategies. Through extensive simulations, we show that carry&forward transfers can significantly increase the download rate of vehicular users in urban/suburban environments, and that such a result holds throughout diverse mobility scenarios, AP placements and network loads 2012
Detection of Selfish Manipulation of Carrier Sensing in 802.11 Networks Recently, tuning the clear channel assessment (CCA) threshold in conjunction with power control has been considered for improving the performance of WLANs. However, we show that, CCA tuning can be exploited by selfish nodes to obtain an unfair share of the available bandwidth. Specifically, a selfish entity can manipulate the CCA threshold to ignore ongoing transmissions; this increases the probability of accessing the medium and provides the entity a higher, unfair share of the bandwidth. We experiment on our 802.11 testbed to characterize the effects of CCA tuning on both isolated links and in 802.11 WLAN configurations. We focus on AP-client(s) configurations, proposing a novel approach to detect this misbehavior. A misbehaving client is unlikely to recognize low power receptions as legitimate packets; by intelligently sending low power probe messages, an AP can efficiently detect a misbehaving node. Our key contributions are: 1) We are the first to quantify the impact of selfish CCA tuning via extensive experimentation on various 802.11 configurations. 2) We propose a lightweight scheme for detecting selfish nodes that inappropriately increase their CCAs. 3) We extensively evaluate our system on our testbed; its accuracy is 95 percent while the false positive rate is less than 5 percent.S 2012
Distributed Throughput Maximization in Wireless Networks via Random Power Allocation We consider throughput-optimal power allocation in multi-hop wireless networks. The study of this problem has been limited due to the non-convexity of the underlying optimization problems, that prohibits an efficient solution even in a centralized setting. We take a randomization approach to deal with this difficulty. To this end, we generalize the randomization framework originally proposed for input queued switches to an SINR rate-based interference model. Further, we develop distributed power allocation and comparison algorithms that satisfy these conditions, thereby achieving (nearly) 100% throughput. We illustrate the performance of our proposed power allocation solution through numerical investigation and present several extensions for the considered problem. 2012
Efficient Rendezvous Algorithms for Mobility-Enabled Wireless Sensor Networks Recent research shows that significant energy saving can be achieved in mobility-enabled wireless sensor networks (WSNs) that visit sensor nodes and collect data from them via short-range communications. However, a major performance bottleneck of such WSNs is the significantly increased latency in data collection due to the low movement speed of mobile base stations. To address this issue, we propose a rendezvous-based data collection approach in which a subset of nodes serve as rendezvous points that buffer and aggregate data originated from sources and transfer to the base station when it arrives. This approach combines the advantages of controlled mobility and in-network data caching and can achieve a desirable balance between network energy saving and data collection delay. We propose efficient rendezvous design algorithms with provable performance bounds for mobile base stations with variable and fixed tracks, respectively. The effectiveness of our approach is validated through both theoretical analysis and extensive simulations. 2012
Efficient Virtual Backbone Construction with Routing Cost Constraint in Wireless Networks Using Directional Antennas Directional antennas can divide the transmission range into several sectors. Thus, through switching off sectors in unnecessary directions in wireless networks, we can save bandwidth and energy consumption. In this paper, we will study a directional virtual backbone (VB) in the network where directional antennas are used. When constructing a VB, we will take routing and broadcasting into account since they are two common operations in wireless networks. Hence, we will study a VB with guaranteed routing costs, named α Minimum rOuting Cost Directional VB (α-MOC-DVB). Besides the properties of regular VBs, α-MOC-DVB also has a special constraint – for any pair of nodes, there exists at least one path all intermediate directions on which must belong to α-MOC-DVB and the number of intermediate directions on the path is smaller than α times that on the shortest path. We prove that construction of a minimum α-MOC-DVB is an NP-hard problem in a general directed graph. A heuristic algorithm is proposed and theoretical analysis is also discussed in the paper. Extensive simulations demonstrate that our α-MOC-DVB is much more efficient in the sense of VB size and routing costs compared to other VBs. 2012
Energy-Efficient Strategies for Cooperative Multichannel MAC Protocols Distributed Information SHaring (DISH) is a new cooperative approach to designing multichannel MAC protocols. It aids nodes in their decision making processes by compensating for their missing information via information sharing through neighboring nodes. This approach was recently shown to significantly boost the throughput of multichannel MAC protocols. However, a critical issue for ad hoc communication devices, viz. energy efficiency, has yet to be addressed. In this paper, we address this issue by developing simple solutions that reduce the energy consumption without compromising the throughput performance and meanwhile maximize cost efficiency. We propose two energy-efficient strategies: in-situ energy conscious DISH, which uses existing nodes only, and altruistic DISH, which requires additional nodes called altruists. We compare five protocols with respect to these strategies and identify altruistic DISH to be the right choice in general: it 1) conserves 40-80 percent of energy, 2) maintains the throughput advantage, and 3) more than doubles the cost efficiency compared to protocols without this strategy. On the other hand, our study also shows that in-situ energy conscious DISH is suitable only in certain limited scenarios. 2012
Estimating Parameters of Multiple Heterogeneous Target Objects Using Composite Sensor Nodes We propose a method for estimating parameters of multiple target objects by using networked binary sensors whose locations are unknown. These target objects may have different parameters, such as size and perimeter length. Each sensors, which is incapable of monitoring the target object’s parameters, sends only binary data describing whether or not it detects target objects coming into, moving around, or leaving the sensing area at every moment. We previously developed a parameter estimation method for a single target object. However, a straight-forward extension of this method is not applicable for estimating multiple heterogeneous target objects. This is because a networked binary sensor at an unknown location cannot provide information that distinguishes individual target objects, but it can provide information on the total perimeter length and size of multiple target objects. Therefore, we propose composite sensor nodes with multiple sensors in a predetermined layout for obtaining additional information for estimating the parameter of each target object. As an example of a composite sensor node, we consider a two-sensor composite sensor node, which consists of two sensors, one at each of the two end points of a line segment of known length. For the two-sensor composite sensor node, measures are derived such as the two sensors detecting target objects. These derived measures are the basis for identifying the shape of each target object among a given set of categories (for example, disks and rectangles) and estimating parameters such as the radius and lengths of two sides of each target object. Numerical examples demonstrate that networked composite sensor nodes consisting of two binary sensors enable us to estimate the parameters of target objects. 2012
Fast Capture—Recapture Approach for Mitigating the Problem of Missing RFID Tags The technology of Radio Frequency IDentification (RFID) enables many applications that rely on passive, battery-less wireless devices. If a RFID reader needs to gather the ID from multiple tags in its range, then it needs to run an anticollision protocol. Due to errors on the wireless link, a single reader session, which contains one full execution of the anticollision protocol, may not be sufficient to retrieve the ID of all tags. This problem can be mitigated by running multiple, redundant reader sessions and use the statistical relationship between these sessions. On the other hand, each session is time consuming and therefore the number of sessions should be kept minimal. We optimize the process of running multiple reader sessions, by allowing only some of the tags already discovered to reply in subsequent reader sessions. The estimation procedure is integrated with an actual tree-based anticollision protocol, and numerical results show that the reliable tag resolution algorithm attain high speed of protocol execution, while not sacrificing the reliability of the estimators used to assess the probability of missing tags. 2012
Fast Data Collection in Tree-Based Wireless Sensor Networks We investigate the following fundamental question-how fast can information be collected from a wireless sensor network organized as tree? To address this, we explore and evaluate a number of different techniques using realistic simulation models under the many-to-one communication paradigm known as convergecast. We first consider time scheduling on a single frequency channel with the aim of minimizing the number of time slots required (schedule length) to complete a convergecast. Next, we combine scheduling with transmission power control to mitigate the effects of interference, and show that while power control helps in reducing the schedule length under a single frequency, scheduling transmissions using multiple frequencies is more efficient. We give lower bounds on the schedule length when interference is completely eliminated, and propose algorithms that achieve these bounds. We also evaluate the performance of various channel assignment methods and find empirically that for moderate size networks of about 100 nodes, the use of multifrequency scheduling can suffice to eliminate most of the interference. Then, the data collection rate no longer remains limited by interference but by the topology of the routing tree. To this end, we construct degree-constrained spanning trees and capacitated minimal spanning trees, and show significant improvement in scheduling performance over different deployment densities. Lastly, we evaluate the impact of different interference and channel models on the schedule length. 2012
Fault Localization Using Passive End-to-End Measurements and Sequential Testing for Wireless Sensor Networks Faulty components in a network need to be localized and repaired to sustain the health of the network. In this paper, we propose a novel approach that carefully combines active and passive measurements to localize faults in wireless sensor networks. More specifically, we formulate a problem of optimal sequential testing guided by end-to-end data. This problem determines an optimal testing sequence of network components based on end-to-end data in sensor networks to minimize expected testing cost. We prove that this problem is NP-hard, and propose a recursive approach to solve it. This approach leads to a polynomial-time optimal algorithm for line topologies while requiring exponential running time for general topologies. We further develop two polynomial-time heuristic schemes that are applicable to general topologies. Extensive simulation shows that our heuristic schemes only require testing a very small set of network components to localize and repair all faults in the network. Our approach is superior to using active and passive measurements in isolation. It also outperforms the state-of-the-art approaches that localize and repair all faults in a network. 2012
FESCIM Fair, Efficient, and Secure Cooperation Incentive Mechanism for Multihop Cellular Networks In multihop cellular networks, the mobile nodes usually relay others’ packets for enhancing the network performance and deployment. However, selfish nodes usually do not cooperate but make use of the cooperative nodes to relay their packets, which has a negative effect on the network fairness and performance. In this paper, we propose a fair and efficient incentive mechanism to stimulate the node cooperation. Our mechanism applies a fair charging policy by charging the source and destination nodes when both of them benefit from the communication. To implement this charging policy efficiently, hashing operations are used in the ACK packets to reduce the number of public-key-cryptography operations. Moreover, reducing the overhead of the payment checks is essential for the efficient implementation of the incentive mechanism due to the large number of payment transactions. Instead of generating a check per message, a small-size check can be generated per route, and a check submission scheme is proposed to reduce the number of submitted checks and protect against collusion attacks. Extensive analysis and simulations demonstrate that our mechanism can secure the payment and significantly reduce the checks’ overhead, and the fair charging policy can be implemented almost computationally free by using hashing operations. 2012
Geometry and Motion-Based Positioning Algorithms for Mobile Tracking in NLOS Environments This paper presents positioning algorithms for cellular network-based vehicle tracking in severe non-line-of-sight (NLOS) propagation scenarios. The aim of the algorithms is to enhance positional accuracy of network-based positioning systems when the GPS receiver does not perform well due to the complex propagation environment. A one-step position estimation method and another two-step method are proposed and developed. Constrained optimization is utilized to minimize the cost function which takes account of the NLOS error so that the NLOS effect is significantly reduced. Vehicle velocity and heading direction measurements are exploited in the algorithm development, which may be obtained using a speedometer and a heading sensor, respectively. The developed algorithms are practical so that they are suitable for implementation in practice for vehicle applications. It is observed through simulation that in severe NLOS propagation scenarios, the proposed positioning methods outperform the existing cellular network-based positioning algorithms significantly. Further, when the distance measurement error is modeled as the sum of an exponential bias variable and a Gaussian noise variable, the exact expressions of the CRLB are derived to benchmark the performance of the positioning algorithms. 2012
Handling Selfishness in Replica Allocation over a Mobile Ad Hoc Network In a mobile ad hoc network, the mobility and resource constraints of mobile nodes may lead to network partitioning or performance degradation. Several data replication techniques have been proposed to minimize performance degradation. Most of them assume that all mobile nodes collaborate fully in terms of sharing their memory space. In reality, however, some nodes may selfishly decide only to cooperate partially, or not at all, with other nodes. These selfish nodes could then reduce the overall data accessibility in the network. In this paper, we examine the impact of selfish nodes in a mobile ad hoc network from the perspective of replica allocation. We term this selfish replica allocation. In particular, we develop a selfish node detection algorithm that considers partial selfishness and novel replica allocation techniques to properly cope with selfish replica allocation. The conducted simulations demonstrate the proposed approach outperforms traditional cooperative replica allocation techniques in terms of data accessibility, communication cost, and average query delay. 2012
Heuristic Burst Construction Algorithm for Improving Downlink Capacity in IEEE 802.16 OFDMA Systems IEEE 802.16 OFDMA systems have gained much attention for their ability to support high transmission rates and broadband access services. For multiuser environments, IEEE 802.16 OFDMA systems require a resource allocation algorithm to use the limited downlink resource efficiently. The IEEE 802.16 standard defines that resource allocation should be performed with a rectangle region of slots, called a burst. However, the standard does not specify how to construct bursts. In this paper, we propose a heuristic burst construction algorithm, called HuB, to improve the downlink capacity in IEEE 802.16 OFDMA systems. To increase the downlink capacity, during burst constructions HuB reduces resource wastage by considering padded slots and unused slots and reduces resource usage by considering the power boosting possibility. For simple burst constructions, HuB makes a HuB-tree, in which a node represents an available downlink resource and edges of a node represent a burst rectangle region. Thus, making child nodes of a parent node is the same as constructing a burst in a given downlink resource. We analyzed the proposed algorithm and performed simulations to compare the performance of the proposed algorithm with existing algorithms. Our simulation study results show that HuB shows improved downlink capacity over existing algorithms. 2012
Hop-by-Hop Routing in Wireless Mesh Networks with Bandwidth Guarantees Wireless Mesh Network (WMN) has become an important edge network to provide Internet access to remote areas and wireless connections in a metropolitan scale. In this paper, we study the problem of identifying the maximum available bandwidth path, a fundamental issue in supporting quality-of-service in WMNs. Due to interference among links, bandwidth, a well-known bottleneck metric in wired networks, is neither concave nor additive in wireless networks. We propose a new path weight which captures the available path bandwidth information. We formally prove that our hop-by-hop routing protocol based on the new path weight satisfies the consistency and loop-freeness requirements. The consistency property guarantees that each node makes a proper packet forwarding decision, so that a data packet does traverse over the intended path. Our extensive simulation experiments also show that our proposed path weight outperforms existing path metrics in identifying high-throughput paths. 2012
Jointly Optimal Source-Flow, Transmit-Power, and Sending-Rate Control for Maximum- Throughput Delivery of VBR Traffic over Faded Links Emerging media overlay networks for wireless applications aim at delivering Variable Bit Rate (VBR) encoded media contents to nomadic end users by exploiting the (fading-impaired and time-varying) access capacity offered by the “last-hop” wireless channel. In this application scenario, a still open question concerns the closed-form design of control policies that maximize the average throughput sent over the wireless last hop, under constraints on the maximum connection bandwidth available at the Application (APP) layer, the queue capacity available at the Data Link (DL) layer, and the average and peak energies sustained by the Physical (PHY) layer. The approach we follow relies on the maximization on a per-slot basis of the throughput averaged over the fading statistic and conditioned on the queue state, without resorting to cumbersome iterative algorithms. The resulting optimal controller operates in a cross-layer fashion that involves the APP, DL, and PHY layers of the underlying protocol stack. Finally, we develop the operating conditions allowing the proposed controller also to maximize the unconditional average throughput (i.e., the throughput averaged over both queue and channel-state statistics). The carried out numerical tests give insight into the connection bandwidth-versus-queue delay trade-off achieved by the optimal controller. 2012
Moderated Group Authoring System for Campus-Wide Workgroups This paper describes the design and implementation of a file system-based distributed authoring system for campus-wide workgroups. We focus on documents for which changes by different group members are harder to automatically reconcile into a single version. Prior approaches relied on using group-aware editors. Others built collaborative middleware that allowed the group members to use traditional authoring tools. These approaches relied on an ability to automatically detect conflicting updates. They also operated on specific document types. Instead, our system relies on users to moderate and reconcile updates by other group members. Our file system-based approach also allows group members to modify any document type. We maintain one updateable copy of the shared content on each group member’s node. We also hoard read-only copies of each of these updateable copies in any interested group member’s node. All these copies are propagated to other group members at a rate that is solely dictated by the wireless user availability. The various copies are reconciled using the moderation operation; each group member manually incorporates updates from all the other group members into their own copy. The various document versions eventually converge into a single version through successive moderation operations. The system assists with this convergence process by using the made-with knowledge of all causal file system reads of contents from other replicas. An analysis using a long-term wireless user availability traces from a university shows the strength of our asynchronous and distributed update propagation mechanism. Our user space file system prototype exhibits acceptable file system performance. A subjective evaluation showed that the moderation operation was intuitive for students. 2012
Network Connectivity with a Family of Group Mobility Models We investigate the communication range of the nodes necessary for network connectivity, which we call bidirectional connectivity, in a simple setting. Unlike in most of existing studies, however, the locations or mobilities of the nodes may be correlated through group mobility: nodes are broken into groups, with each group comprising the same number of nodes, and lie on a unit circle. The locations of the nodes in the same group are not mutually independent, but are instead conditionally independent given the location of the group. We examine the distribution of the smallest communication range needed for bidirectional connectivity, called the critical transmission range (CTR), when both the number of groups and the number of nodes in a group are large. We first demonstrate that the CTR exhibits a parametric sensitivity with respect to the space each group occupies on the unit circle. Then, we offer an explanation for the observed sensitivity by identifying what is known as a very strong threshold and asymptotic bounds for CTR. 2012
OMAN A Mobile Ad Hoc Network Design System We present a software library that aids in the design of mobile ad hoc networks (MANET). The OMAN design engine works by taking a specification of network requirements and objectives, and allocates resources which satisfy the input constraints and maximize the communication performance objective. The tool is used to explore networking design options and challenges, including: power control, adaptive modulation, flow control, scheduling, mobility, uncertainty in channel models, and cross-layer design. The unaddressed niche which OMAN seeks to fill is the general framework for optimization of any network resource, under arbitrary constraints, and with any selection of multiple objectives. While simulation is an important part of measuring the effectiveness of implemented optimization techniques, the novelty and focus of OMAN is on proposing novel network design algorithms, aggregating existing approaches, and providing a general framework for a network designer to test out new proposed resource allocation methods. In this paper, we present a high-level view of the OMAN architecture, review specific mathematical models used in the network representation, and show how OMAN is used to evaluate tradeoffs in MANET design. Specifically, we cover three case studies of optimization. The first case is robust power control under uncertain channel information for a single physical layer snapshot. The second case is scheduling with the availability of directional radiation patterns. The third case is optimizing topology through movement planning of relay nodes. 2012
Robust Topology Engineering in Multiradio Multichannel Wireless Networks Topology engineering concerns with the problem of automatic determination of physical layer parameters to form a network with desired properties. In this paper, we investigate the joint power control, channel assignment, and radio interface selection for robust provisioning of link bandwidth in infrastructure multiradio multichannel wireless networks in presence of channel variability and external interference. To characterize the logical relationship between spatial contention constraints and transmit power, we formulate the joint power control and radio-channel assignment as a generalized disjunctive programming problem. The generalized Benders decomposition technique is applied for decomposing the radio-channel assignment (combinatorial constraints) and network resource allocation (continuous constraints) so that the problem can be solved efficiently. The proposed algorithm is guaranteed to converge to the optimal solution within a finite number of iterations. We have evaluated our scheme using traces collected from two wireless testbeds and simulation studies in Qualnet. Experiments show that the proposed algorithm is superior to existing schemes in providing larger interference margin, and reducing outage and packet loss probabilities. 2012
SenseLess A Database-Driven White Spaces Network The 2010 FCC ruling on white spaces proposes relying on a database of incumbents as the primary means of determining white space availability at any white space device (WSD). While the ruling provides broad guidelines for the database, the specifics of its design, features, implementation, and use are yet to be determined. Furthermore, architecting a network where all WSDs rely on the database raises several systems and networking challenges that have remained unexplored. Also, the ruling treats the database only as a storehouse for incumbents. We believe that the mandated use of the database has an additional opportunity: a means to dynamically manage the RF spectrum. Motivated by this opportunity, in this paper, we present SenseLess, a database-driven white spaces network. As suggested by its very name, in SenseLess, WSDs rely on a database service to determine white spaces availability as opposed to spectrum sensing. The service, using a combination of an up-to-date database of incumbents, sophisticated signal propagation modeling, and an efficient content dissemination mechanism to ensure efficient, scalable, and safe white space network operation. We build, deploy, and evaluate SenseLess and compare our results to ground truth spectrum measurements. We present the unique system design considerations that arise due to operating over the white spaces. We also evaluate its efficiency and scalability. To the best of our knowledge, this is the first paper that identifies and examines the systems and networking challenges that arise from operating a white space network, which is solely dependent on a channel occupancy database. 2012
Smooth Trade-Offs between Throughput and Delay in Mobile Ad Hoc Networks Throughput capacity in mobile ad hoc networks has been studied extensively under many different mobility models. However, most previous research assumes global mobility, and the results show that a constant per-node throughput can be achieved at the cost of very high delay. Thus, we are having a very big gap here, i.e., either low throughput and low delay in static networks or high throughput and high delay in mobile networks. In this paper, employing a practical restricted random mobility model, we try to fill this gap. Specifically, we assume that a network of unit area with n nodes is evenly divided into cells with an area of n -2α, each of which is further evenly divided into squares with an area of n-2β(0≤ α ≤ β ≤1/2). All nodes can only move inside the cell which they are initially distributed in, and at the beginning of each time slot, every node moves from its current square to a uniformly chosen point in a uniformly chosen adjacent square. By proposing a new multihop relay scheme, we present smooth trade-offs between throughput and delay by controlling nodes’ mobility. We also consider a network of area nγ (0 ≤ γ ≤ 1) and find that network size does not affect the results obtained before. 2012
Spectrum-Aware Mobility Management in Cognitive Radio Cellular Networks Cognitive radio (CR) networks have been proposed as a solution to both spectrum inefficiency and spectrum scarcity problems. However, they face several challenges based on the fluctuating nature of the available spectrum, making it more difficult to support seamless communications, especially in CR cellular networks. In this paper, a spectrum-aware mobility management scheme is proposed for CR cellular networks. First, a novel network architecture is introduced to mitigate heterogeneous spectrum availability. Based on this architecture, a unified mobility management framework is developed to support diverse mobility events in CR networks, which consists of spectrum mobility management, user mobility management, and intercell resource allocation. The spectrum mobility management scheme determines a target cell and spectrum band for CR users adaptively dependent on time-varying spectrum opportunities, leading to increase in cell capacity. In the user mobility management scheme, a mobile user selects a proper handoff mechanism so as to minimize a switching latency at the cell boundary by considering spatially heterogeneous spectrum availability. Intercell resource allocation helps to improve the performance of both mobility management schemes by efficiently sharing spectrum resources with multiple cells. Simulation results show that the proposed method can achieve better performance than conventional handoff schemes in terms of both cell capacity as well as mobility support in communications. 2012
Stateless Multicast Protocol for Ad Hoc Networks Multicast routing protocols typically rely on the a priori creation of a multicast tree (or mesh), which requires the individual nodes to maintain state information. In dynamic networks with bursty traffic, where long periods of silence are expected between the bursts of data, this multicast state maintenance adds a large amount of communication, processing, and memory overhead for no benefit to the application. Thus, we have developed a stateless receiver-based multicast (RBMulticast) protocol that simply uses a list of the multicast members’ (e.g., sinks’) addresses, embedded in packet headers, to enable receivers to decide the best way to forward the multicast traffic. This protocol, called Receiver-Based Multicast, exploits the knowledge of the geographic locations of the nodes to remove the need for costly state maintenance (e.g., tree/mesh/neighbor table maintenance), making it ideally suited for multicasting in dynamic networks. RBMulticast was implemented in the OPNET simulator and tested using a sensor network implementation. Both simulation and experimental results confirm that RBMulticast provides high success rates and low delay without the burden of state maintenance.

 

         TECHNOLOGY  : JAVA          DOMAIN              : IEEE TRANSACTIONS ON IMAGE PROCESSING

 

S.NO TITLES DESCRIPTION YEAR
Image Super-Resolution With Sparse Neighbor Embedding In this paper, we propose a sparse neighbor selection scheme for SR reconstruction.We first predetermine a larger number of neighbors as potential candidates and develop an extended Robust-SL0 algorithm tosimultaneously find the neighbors and to solve the reconstruction weights. Recognizing that the -nearest neighbor ( -NN) for reconstruction should have similar local geometric structures basedon clustering, we employ a local statistical feature, namely histograms of oriented gradients (HoG) of low-resolution (LR) image patches, to perform such clustering. 2012
Scalable Coding of Encrypted Images This paper proposes a novel scheme of scalable coding for encrypted images. In the encryption phase, the original pixel values are masked by a modulo-256 addition with pseudorandom numbers that are derived from a secret key. Then, the data of quantized sub image and coefficients are regarded as a set of bit streams. 2012
PDE-Based Enhancement of Color Images in RGB Space The proposed model is based on using the single vectors of the gradient magnitude and the second derivatives as a manner to relate different color components of the image. This model can be viewed as a generalization of the Bettahar–Stambouli filter to multivalued images. The proposed algorithm is more efficient than the mentioned filter and some previous works at color images denoising and deblurring without creating false colors 2012
Abrupt Motion Tracking Via Intensively Adaptive Markov-Chain Monte Carlo Sampling The robust tracking of abrupt motion is a challenging task in computer vision due to its large motion uncertainty. While various particle filters and conventional Markov-chain Monte Carlo (MCMC) methods have been proposed for visual tracking, these methods often suffer from the well-known local-trap problem or from poor convergence rate. In this paper, we propose a novel sampling-based tracking scheme for the abrupt motion problem in the Bayesian filtering framework. To effectively handle the local-trap problem, we first introduce the stochastic approximation Monte Carlo (SAMC) sampling method into the Bayesian filter tracking framework, in which the filtering distribution is adaptively estimated as the sampling proceeds, and thus, a good approximation to the target distribution is achieved. In addition, we propose a new MCMC sampler with intensive adaptation to further improve the sampling efficiency, which combines a density-grid-based predictive model with the SAMC sampling, to give a proposal adaptation scheme. The proposed method is effective and computationally efficient in addressing the abrupt motion problem. We compare our approach with several alternative tracking algorithms, and extensive experimental results are presented to demonstrate the effectiveness and the efficiency of the proposed method in dealing with various types of abrupt motions. 2012
Vehicle Detection in Aerial Surveillance Using Dynamic Bayesian Networks We present an automatic vehicle detection system for aerial surveillance in this paper. In this system, we escape from the stereotype and existing frameworks of vehicle detection in aerial surveillance, which are either region based or sliding window based. We design a pixelwise classification method for vehicle detection. The novelty lies in the fact that, in spite of performing pixelwise classification, relations among neighboring pixels in a region are preserved in the feature extraction process.We consider features including vehicle colors and local features. For vehicle color extraction, we utilize a color transform to separate vehicle colors and nonvehicle colors effectively. For edge detection, we apply moment preserving to adjust the thresholds of the Canny edge detector automatically, which increases the adaptability and the accuracy for detection in various aerial images. Afterward, a dynamic Bayesian network (DBN) is constructed for the classification purpose. We convert regional local features into quantitative observations that can be referenced when applying pixelwise classification via DBN. Experiments were conducted on a wide variety of aerial videos. The results demonstrate flexibility and good generalization abilities of the proposed method on a challenging data set with aerial surveillance images taken at different heights and under different camera angles. 2012
A Secret-Sharing-Based Method for Authentication of Grayscale Document Images via the Use of the PNG Image With a Data Repair Capability A new blind authentication method based on the secret sharing technique with a data repair capability for grayscale document images via the use of the Portable Network Graphics (PNG) image is proposed. An authentication signal is generated for each block of a grayscale document image, which, together with the binarized block content, is transformed into several shares using the Shamir secret sharing scheme. The involved parameters are carefully chosen so that as many shares as possible are generated and embedded into an alpha channel plane. The alpha channel plane is then combined with the original grayscale image to form a PNG image. During the embedding process, the computed share values are mapped into a range of alpha channel values near their maximum value of 255 to yield a transparent stego-image with a disguise effect. In the process of image authentication, an image block is marked as tampered if the authentication signal computed from the current block content does not match that extracted from the shares embedded in the alpha channel plane. Data repairing is then applied to each tampered block by a reverse Shamir scheme after collecting two shares from unmarked blocks. Measures for protecting the security of the data hidden in the alpha channel are also proposed. Good experimental results prove the effectiveness of the proposed method for real applications. 2012
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Learn to Personalized Image Search from the Photo Sharing Websites – projects 2012 Increasingly developed social sharing websites, like Flickr and Youtube, allow users to create, share, annotate and comment medias. The large-scale user-generated meta-data not only facilitate users in sharing and organizing multimedia content,but provide useful information to improve media retrieval and management. Personalized search serves as one of such examples where the web search experience is improved by generating the returned list according to the modified user search intents. In this paper, we exploit the social annotations and propose a novel framework simultaneously considering the user and query relevance to learn to personalized image search. The basic premise is to embed the user preference and query-related search intent into user-specific topic spaces. Since the users’ original annotation is too sparse for topic modeling, we need to enrich users’ annotation pool before user-specific topic spaces construction. The proposed framework contains two components:
A Discriminative Model of Motion and Cross Ratio for View-Invariant Action Recognition Action recognition is very important for many applications such as video surveillance, human-computer interaction, and so on; view-invariant action recognition is hot and difficult as well in this field. In this paper, a new discriminative model is proposed for video-based view-invariant action recognition. In the discriminative model, motion pattern and view invariants are perfectly fused together to make a better combination of invariance and distinctiveness. We address a series of issues, including interest point detection in image sequence, motion feature extraction and description, and view-invariant calculation. First, motion detection is used to extract motion information from videos, which is much more efficient than traditional background modeling and tracking-based methods. Second, as for feature representation, we exact variety of statistical information from motion and view-invariant feature based on cross ratio. Last, in the action modeling, we apply a discriminative probabilistic model-hidden conditional random field to model motion patterns and view invariants, by which we could fuse the statistics of motion and projective invariability of cross ratio in one framework. Experimental results demonstrate that our method can improve the ability to distinguish different categories of actions with high robustness to view change in real circumstances. 2012
A General Fast Registration Framework by Learning Deformation–Appearance Correlation In this paper, we propose a general framework for performance improvement of the current state-of-the-art registration algorithms in terms of both accuracy and computation time. The key concept involves rapid prediction of a deformation field for registration initialization, which is achieved by a statistical correlation model learned between image appearances and deformation fields. This allows us to immediately bring a template image as close as possible to a subject image that we need to register. The task of the registration algorithm is hence reduced to estimating small deformation between the subject image and the initially warped template image, i.e., the intermediate template (IT). Specifically, to obtain a good subject-specific initial deformation, support vector regression is utilized to determine the correlation between image appearances and their respective deformation fields. When registering a new subject onto the template, an initial deformation field is first predicted based on the subject’s image appearance for generating an IT. With the IT, only the residual deformation needs to be estimated, presenting much less challenge to the existing registration algorithms. Our learning-based framework affords two important advantages: 1) by requiring only the estimation of the residual deformation between the IT and the subject image, the computation time can be greatly reduced; 2) by leveraging good deformation initialization, local minima giving suboptimal solution could be avoided. Our framework has been extensively evaluated using medical images from different sources, and the results indicate that, on top of accuracy improvement, significant registration speedup can be achieved, as compared with the case where no prediction of initial deformation is performed. 2012
A Geometric Construction of Multivariate Sinc Functions We present a geometric framework for explicit derivation of multivariate sampling functions (sinc) on multidimensional lattices. The approach leads to a generalization of the link between sinc functions and the Lagrange interpolation in the multivariate setting. Our geometric approach also provides a frequency partition of the spectrum that leads to a nonseparable extension of the 1-D Shannon (sinc) wavelets to the multivariate setting. Moreover, we propose a generalization of the Lanczos window function that provides a practical and unbiased approach for signal reconstruction on sampling lattices. While this framework is general for lattices of any dimension, we specifically characterize all 2-D and 3-D lattices and show the detailed derivations for 2-D hexagonal body-centered cubic (BCC) and face-centered cubic (FCC) lattices. Both visual and numerical comparisons validate the theoretical expectations about superiority of the BCC and FCC lattices over the commonly used Cartesian lattice. 2012
A Novel Algorithm for View and Illumination Invariant Image Matching The challenges in local-feature-based image matching are variations of view and illumination. Many methods have been recently proposed to address these problems by using invariant feature detectors and distinctive descriptors. However, the matching performance is still unstable and inaccurate, particularly when large variation in view or illumination occurs. In this paper, we propose a view and illumination invariant image-matching method. We iteratively estimate the relationship of the relative view and illumination of the images, transform the view of one image to the other, and normalize their illumination for accurate matching. Our method does not aim to increase the invariance of the detector but to improve the accuracy, stability, and reliability of the matching results. The performance of matching is significantly improved and is not affected by the changes of view and illumination in a valid range. The proposed method would fail when the initial view and illumination method fails, which gives us a new sight to evaluate the traditional detectors. We propose two novel indicators for detector evaluation, namely, valid angle and valid illumination, which reflect the maximum allowable change in view and illumination, respectively. Extensive experimental results show that our method improves the traditional detector significantly, even in large variations, and the two indicators are much more distinctive. 2012
A Spectral and Spatial Measure of Local Perceived Sharpness in Natural Images This paper presents an algorithm designed to measure the local perceived sharpness in an image. Our method utilizes both spectral and spatial properties of the image: For each block, we measure the slope of the magnitude spectrum and the total spatial variation. These measures are then adjusted to account for visual perception, and then, the adjusted measures are combined via a weighted geometric mean. The resulting measure, i.e., S3 (spectral and spatial sharpness), yields a perceived sharpness map in which greater values denote perceptually sharper regions. This map can be collapsed into a single index, which quantifies the overall perceived sharpness of the whole image. We demonstrate the utility of the S3 measure for within-image and across-image sharpness prediction, no-reference image quality assessment of blurred images, and monotonic estimation of the standard deviation of the impulse response used in Gaussian blurring. We further evaluate the accuracy of S3 in local sharpness estimation by comparing S3 maps to sharpness maps generated by human subjects. We show that S3 can generate sharpness maps, which are highly correlated with the human-subject maps. 2012
A Unified Feature and Instance Selection Framework Using Optimum Experimental Design The goal of feature selection is to identify the most informative features for compact representation, whereas the goal of active learning is to select the most informative instances for prediction. Previous studies separately address these two problems, despite of the fact that selecting features and instances are dual operations over a data matrix. In this paper, we consider the novel problem of simultaneously selecting the most informative features and instances and develop a solution from the perspective of optimum experimental design. That is, by using the selected features as the new representation and the selected instances as training data, the variance of the parameter estimate of a learning function can be minimized. Specifically, we propose a novel approach, which is called Unified criterion for Feature and Instance selection (UFI), to simultaneously identify the most informative features and instances that minimize the trace of the parameter covariance matrix. A greedy algorithm is introduced to efficiently solve the optimization problem. Experimental results on two benchmark data sets demonstrate the effectiveness of our proposed method. 2012
An Algorithm for the Contextual Adaption of SURF Octave Selection With Good Matching Performance Best Octaves Speeded-Up Robust Features is a feature extraction algorithm designed for real-time execution, although this is rarely achievable on low-power hardware such as that in mobile robots. One way to reduce the computation is to discard some of the scale-space octaves, and previous research has simply discarded the higher octaves. This paper shows that this approach is not always the most sensible and presents an algorithm for choosing which octaves to discard based on the properties of the imagery. Results obtained with this best octaves algorithm show that it is able to achieve a significant reduction in computation without compromising matching performance. 2012
An Efficient Camera Calibration Technique Offering Robustness and Accuracy Over a Wide Range of Lens Distortion In the field of machine vision, camera calibration refers to the experimental determination of a set of parameters that describe the image formation process for a given analytical model of the machine vision system. Researchers working with low-cost digital cameras and off-the-shelf lenses generally favor camera calibration techniques that do not rely on specialized optical equipment, modifications to the hardware, or an a priori knowledge of the vision system. Most of the commonly used calibration techniques are based on the observation of a single 3-D target or multiple planar (2-D) targets with a large number of control points. This paper presents a novel calibration technique that offers improved accuracy, robustness, and efficiency over a wide range of lens distortion. This technique operates by minimizing the error between the reconstructed image points and their experimentally determined counterparts in “distortion free” space. This facilitates the incorporation of the exact lens distortion model. In addition, expressing spatial orientation in terms of unit quaternions greatly enhances the proposed calibration solution by formulating a minimally redundant system of equations that is free of singularities. Extensive performance benchmarking consisting of both computer simulation and experiments confirmed higher accuracy in calibration regardless of the amount of lens distortion present in the optics of the camera. This paper also experimentally confirmed that a comprehensive lens distortion model including higher order radial and tangential distortion terms improves calibration accuracy. 2012
Bayesian Estimation for Optimized Structured Illumination Microscopy Structured illumination microscopy is a recent imaging technique that aims at going beyond the classical optical resolution by reconstructing high-resolution (HR) images from low-resolution (LR) images acquired through modulation of the transfer function of the microscope. The classical implementation has a number of drawbacks, such as requiring a large number of images to be acquired and parameters to be manually set in an ad-hoc manner that have, until now, hampered its wide dissemination. Here, we present a new framework based on a Bayesian inverse problem formulation approach that enables the computation of one HR image from a reduced number of LR images and has no specific constraints on the modulation. Moreover, it permits to automatically estimate the optimal reconstruction hyperparameters and to compute an uncertainty bound on the estimated values. We demonstrate through numerical evaluations on simulated data and examples on real microscopy data that our approach represents a decisive advance for a wider use of HR microscopy through structured illumination. 2012
Binarization of Low-Quality Barcode Images Captured by Mobile Phones Using Local Window of Adaptive Location and Size It is difficult to directly apply existing binarization approaches to the barcode images captured by mobile device due to their low quality. This paper proposes a novel scheme for the binarization of such images. The barcode and background regions are differentiated by the number of edge pixels in a search window. Unlike existing approaches that center the pixel to be binarized with a window of fixed size, we propose to shift the window center to the nearest edge pixel so that the balance of the number of object and background pixels can be achieved. The window size is adaptive either to the minimum distance to edges or minimum element width in the barcode. The threshold is calculated using the statistics in the window. Our proposed method has demonstrated its capability in handling the nonuniform illumination problem and the size variation of objects. Experimental results conducted on 350 images captured by five mobile phones achieve about 100% of recognition rate in good lighting conditions, and about 95% and 83% in bad lighting conditions. Comparisons made with nine existing binarization methods demonstrate the advancement of our proposed scheme 2012
B-Spline Explicit Active Surfaces An Efficient Framework for Real-Time 3-D Region-Based Segmentation A new formulation of active contours based on explicit functions has been recently suggested. This novel framework allows real-time 3-D segmentation since it reduces the dimensionality of the segmentation problem. In this paper, we propose a B-spline formulation of this approach, which further improves the computational efficiency of the algorithm. We also show that this framework allows evolving the active contour using local region-based terms, thereby overcoming the limitations of the original method while preserving computational speed. The feasibility of real-time 3-D segmentation is demonstrated using simulated and medical data such as liver computer tomography and cardiac ultrasound images. 2012
Change Detection in Synthetic Aperture Radar Images based on Image Fusion and Fuzzy Clustering This paper presents an unsupervised distribution-free change detection approach for synthetic aperture radar (SAR) images based on an image fusion strategy and a novel fuzzy clustering algorithm. The image fusion technique is introduced to generate a difference image by using complementary information from a mean-ratio image and a log-ratio image. In order to restrain the background information and enhance the information of changed regions in the fused difference image, wavelet fusion rules based on an average operator and minimum local area energy are chosen to fuse the wavelet coefficients for a low-frequency band and a high-frequency band, respectively. A reformulated fuzzy local-information C-means clustering algorithm is proposed for classifying changed and unchanged regions in the fused difference image. It incorporates the information about spatial context in a novel fuzzy way for the purpose of enhancing the changed information and of reducing the effect of speckle noise. Experiments on real SAR images show that the image fusion strategy integrates the advantages of the log-ratio operator and the mean-ratio operator and gains a better performance. The change detection results obtained by the improved fuzzy clustering algorithm exhibited lower error than its preexistences. 2012
Color Constancy for Multiple Light Sources Color constancy algorithms are generally based on the simplifying assumption that the spectral distribution of a light source is uniform across scenes. However, in reality, this assumption is often violated due to the presence of multiple light sources. In this paper, we will address more realistic scenarios where the uniform light-source assumption is too restrictive. First, a methodology is proposed to extend existing algorithms by applying color constancy locally to image patches, rather than globally to the entire image. After local (patch-based) illuminant estimation, these estimates are combined into more robust estimations, and a local correction is applied based on a modified diagonal model. Quantitative and qualitative experiments on spectral and real images show that the proposed methodology reduces the influence of two light sources simultaneously present in one scene. If the chromatic difference between these two illuminants is more than 1° , the proposed framework outperforms algorithms based on the uniform light-source assumption (with error-reduction up to approximately 30%). Otherwise, when the chromatic difference is less than 1° and the scene can be considered to contain one (approximately) uniform light source, the performance of the proposed method framework is similar to global color constancy methods. 2012
Coupled Bias–Variance Tradeoff for Cross-Pose Face Recognition Subspace-based face representation can be looked as a regression problem. From this viewpoint, we first revisited the problem of recognizing faces across pose differences, which is a bottleneck in face recognition. Then, we propose a new approach for cross-pose face recognition using a regressor with a coupled bias-variance tradeoff. We found that striking a coupled balance between bias and variance in regression for different poses could improve the regressor-based cross-pose face representation, i.e., the regressor can be more stable against a pose difference. With the basic idea, ridge regression and lasso regression are explored. Experimental results on CMU PIE, the FERET, and the Multi-PIE face databases show that the proposed bias-variance tradeoff can achieve considerable reinforcement in recognition performance 2012
Depth From Motion and Optical Blur With an Unscented Kalman Filter Space-variantly blurred images of a scene contain valuable depth information. In this paper, our objective is to recover the 3-D structure of a scene from motion blur/optical defocus. In the proposed approach, the difference of blur between two observations is used as a cue for recovering depth, within a recursive state estimation framework. For motion blur, we use an unblurred-blurred image pair. Since the relationship between the observation and the scale factor of the point spread function associated with the depth at a point is nonlinear, we propose and develop a formulation of unscented Kalman filter for depth estimation. There are no restrictions on the shape of the blur kernel. Furthermore, within the same formulation, we address a special and challenging scenario of depth from defocus with translational jitter. The effectiveness of our approach is evaluated on synthetic as well as real data, and its performance is also compared with contemporary techniques. 2012
Design of Almost Symmetric Orthogonal Wavelet Filter Bank Via Direct Optimization It is a well-known fact that (compact-support) dyadic wavelets [based on the two channel filter banks (FBs)] cannot be simultaneously orthogonal and symmetric. Although orthogonal wavelets have the energy preservation property, biorthogonal wavelets are preferred in image processing applications because of their symmetric property. In this paper, a novel method is presented for the design of almost symmetric orthogonal wavelet FB. Orthogonality is structurally imposed by using the unnormalized lattice structure, and this leads to an objective function, which is relatively simple to optimize. The designed filters have good frequency response, flat group delay, almost symmetric filter coefficients, and symmetric wavelet function 2012
Design of Interpolation Functions for Subpixel-Accuracy Stereo-Vision Systems Traditionally, subpixel interpolation in stereo-vision systems was designed for the block-matching algorithm. During the evaluation of different interpolation strategies, a strong correlation was observed between the type of the stereo algorithm and the subpixel accuracy of the different solutions. Subpixel interpolation should be adapted to each stereo algorithm to achieve maximum accuracy. In consequence, it is more important to propose methodologies for interpolation function generation than specific function shapes. We propose two such methodologies based on data generated by the stereo algorithms. The first proposal uses a histogram to model the environment and applies histogram equalization to an existing solution adapting it to the data. The second proposal employs synthetic images of a known environment and applies function fitting to the resulted data. The resulting function matches the algorithm and the data as best as possible. An extensive evaluation set is used to validate the findings. Both real and synthetic test cases were employed in different scenarios. The test results are consistent and show significant improvements compared with traditional solutions. 2012
Entropy-Functional-Based Online Adaptive Decision Fusion Framework With Application to Wildfire Detection in Video In this paper, an entropy-functional-based online adaptive decision fusion (EADF) framework is developed for image analysis and computer vision applications. In this framework, it is assumed that the compound algorithm consists of several subalgorithms, each of which yields its own decision as a real number centered around zero, representing the confidence level of that particular subalgorithm. Decision values are linearly combined with weights that are updated online according to an active fusion method based on performing entropic projections onto convex sets describing subalgorithms. It is assumed that there is an oracle, who is usually a human operator, providing feedback to the decision fusion method. A video-based wildfire detection system was developed to evaluate the performance of the decision fusion algorithm. In this case, image data arrive sequentially, and the oracle is the security guard of the forest lookout tower, verifying the decision of the combined algorithm. The simulation results are presented. 2012
Fast Semantic Diffusion for Large-Scale Context-Based Image and Video Annotation Exploring context information for visual recognition has recently received significant research attention. This paper proposes a novel and highly efficient approach, which is named semantic diffusion, to utilize semantic context for large-scale image and video annotation. Starting from the initial annotation of a large number of semantic concepts (categories), obtained by either machine learning or manual tagging, the proposed approach refines the results using a graph diffusion technique, which recovers the consistency and smoothness of the annotations over a semantic graph. Different from the existing graph-based learning methods that model relations among data samples, the semantic graph captures context by treating the concepts as nodes and the concept affinities as the weights of edges. In particular, our approach is capable of simultaneously improving annotation accuracy and adapting the concept affinities to new test data. The adaptation provides a means to handle domain change between training and test data, which often occurs in practice. Extensive experiments are conducted to improve concept annotation results using Flickr images and TV program videos. Results show consistent and significant performance gain (10 on both image and video data sets). Source codes of the proposed algorithms are available online. 2012
Gradient-Based Image Recovery Methods From Incomplete Fourier Measurements A major problem in imaging applications such as magnetic resonance imaging and synthetic aperture radar is the task of trying to reconstruct an image with the smallest possible set of Fourier samples, every single one of which has a potential time and/or power cost. The theory of compressive sensing (CS) points to ways of exploiting inherent sparsity in such images in order to achieve accurate recovery using sub-Nyquist sampling schemes. Traditional CS approaches to this problem consist of solving total-variation (TV) minimization programs with Fourier measurement constraints or other variations thereof. This paper takes a different approach. Since the horizontal and vertical differences of a medical image are each more sparse or compressible than the corresponding TV image, CS methods will be more successful in recovering these differences individually. We develop an algorithm called GradientRec that uses a CS algorithm to recover the horizontal and vertical gradients and then estimates the original image from these gradients. We present two methods of solving the latter inverse problem, i.e., one based on least-square optimization and the other based on a generalized Poisson solver. After a thorough derivation of our complete algorithm, we present the results of various experiments that compare the effectiveness of the proposed method against other leading methods. 2012
Groupwise Registration of Multimodal Images by an Efficient Joint Entropy Minimization Scheme Groupwise registration is concerned with bringing a group of images into the best spatial alignment. If images in the group are from different modalities, then the intensity correspondences across the images can be modeled by the joint density function (JDF) of the cooccurring image intensities. We propose a so-called treecode registration method for groupwise alignment of multimodal images that uses a hierarchical intensity-space subdivision scheme through which an efficient yet sufficiently accurate estimation of the (high-dimensional) JDF based on the Parzen kernel method is computed. To simultaneously align a group of images, a gradient-based joint entropy minimization was employed that also uses the same hierarchical intensity-space subdivision scheme. If the Hilbert kernel is used for the JDF estimation, then the treecode method requires no data-dependent bandwidth selection and is thus fully automatic. The treecode method was compared with the ensemble clustering (EC) method on four different publicly available multimodal image data sets and on a synthetic monomodal image data set. The obtained results indicate that the treecode method has similar and, for two data sets, even superior performances compared to the EC method in terms of registration error and success rate. The obtained good registration performances can be mostly attributed to the sufficiently accurate estimation of the JDF, which is computed through the hierarchical intensity-space subdivision scheme, that captures all the important features needed to detect the correct intensity correspondences across a multimodal group of images undergoing registration. 2012
Higher Degree Total Variation (HDTV) Regularization for Image Recovery We introduce novel image regularization penalties to overcome the practical problems associated with the classical total variation (TV) scheme. Motivated by novel reinterpretations of the classical TV regularizer, we derive two families of functionals involving higher degree partial image derivatives; we term these families as isotropic and anisotropic higher degree TV (HDTV) penalties, respectively. The isotropic penalty is the mixed norm of the directional image derivatives, while the anisotropic penalty is the separable norm of directional derivatives. These functionals inherit the desirable properties of standard TV schemes such as invariance to rotations and translations, preservation of discontinuities, and convexity. The use of mixed norms in isotropic penalties encourages the joint sparsity of the directional derivatives at each pixel, thus encouraging isotropic smoothing. In contrast, the fully separable norm in the anisotropic penalty ensures the preservation of discontinuities, while continuing to smooth along the line like features; this scheme thus enhances the linenlike image characteristics analogous to standard TV. We also introduce efficient majorize-minimize algorithms to solve the resulting optimization problems. The numerical comparison of the proposed scheme with classical TV penalty, current second-degree methods, and wavelet algorithms clearly demonstrate the performance improvement. Specifically, the proposed algorithms minimize the staircase and ringing artifacts that are common with TV and wavelet schemes, while better preserving the singularities. We also observe that anisotropic HDTV penalty provides consistently improved reconstructions compared with the isotropic HDTV penalty. 2012
Human Identification Using Finger Images This paper presents a new approach to improve the performance of finger-vein identification systems presented in the literature. The proposed system simultaneously acquires the finger-vein and low-resolution fingerprint images and combines these two evidences using a novel score-level combination strategy. We examine the previously proposed finger-vein identification approaches and develop a new approach that illustrates it superiority over prior published efforts. The utility of low-resolution fingerprint images acquired from a webcam is examined to ascertain the matching performance from such images. We develop and investigate two new score-level combinations, i.e., holistic and nonlinear fusion, and comparatively evaluate them with more popular score-level fusion approaches to ascertain their effectiveness in the proposed system. The rigorous experimental results presented on the database of 6264 images from 156 subjects illustrate significant improvement in the performance, i.e., both from the authentication and recognition experiments. 2012
Image Fusion Using Higher Order Singular Value Decomposition A novel higher order singular value decomposition (HOSVD)-based image fusion algorithm is proposed. The key points are given as follows: 1) Since image fusion depends on local information of source images, the proposed algorithm picks out informative image patches of source images to constitute the fused image by processing the divided subtensors rather than the whole tensor; 2) the sum of absolute values of the coefficients (SAVC) from HOSVD of subtensors is employed for activity-level measurement to evaluate the quality of the related image patch; and 3) a novel sigmoid-function-like coefficient-combining scheme is applied to construct the fused result. Experimental results show that the proposed algorithm is an alternative image fusion approach. 2012
Image Segmentation Based on the Poincaré Map Method Active contour models (ACMs) integrated with various kinds of external force fields to pull the contours to the exact boundaries have shown their powerful abilities in object segmentation. However, local minimum problems still exist within these models, particularly the vector field’s “equilibrium issues.” Different from traditional ACMs, within this paper, the task of object segmentation is achieved in a novel manner by the Poincaré map method in a defined vector field in view of dynamical systems. An interpolated swirling and attracting flow (ISAF) vector field is first generated for the observed image. Then, the states on the limit cycles of the ISAF are located by the convergence of Newton-Raphson sequences on the given Poincaré sections. Meanwhile, the periods of limit cycles are determined. Consequently, the objects’ boundaries are represented by integral equations with the corresponding converged states and periods. Experiments and comparisons with some traditional external force field methods are done to exhibit the superiority of the proposed method in cases of complex concave boundary segmentation, multiple-object segmentation, and initialization flexibility. In addition, it is more computationally efficient than traditional ACMs by solving the problem in some lower dimensional subspace without using level-set methods. 2012
Implicit Polynomial Representation Through a Fast Fitting Error Estimation This paper presents a simple distance estimation for implicit polynomial fitting. It is computed as the height of a simplex built between the point and the surface (i.e., a triangle in 2-D or a tetrahedron in 3-D), which is used as a coarse but reliable estimation of the orthogonal distance. The proposed distance can be described as a function of the coefficients of the implicit polynomial. Moreover, it is differentiable and has a smooth behavior . Hence, it can be used in any gradient-based optimization. In this paper, its use in a Levenberg-Marquardt framework is shown, which is particularly devoted for nonlinear least squares problems. The proposed estimation is a generalization of the gradient-based distance estimation, which is widely used in the literature. Experimental results, both in 2-D and 3-D data sets, are provided. Comparisons with state-of-the-art techniques are presented, showing the advantages of the proposed approach. 2012
Integrating Segmentation Information for Improved MRF-Based Elastic Image Registration In this paper, we propose a method to exploit segmentation information for elastic image registration using a Markov-random-field (MRF)-based objective function. MRFs are suitable for discrete labeling problems, and the labels are defined as the joint occurrence of displacement fields (for registration) and segmentation class probability. The data penalty is a combination of the image intensity (or gradient information) and the mutual dependence of registration and segmentation information. The smoothness is a function of the interaction between the defined labels. Since both terms are a function of registration and segmentation labels, the overall objective function captures their mutual dependence. A multiscale graph-cut approach is used to achieve subpixel registration and reduce the computation time. The user defines the object to be registered in the floating image, which is rigidly registered before applying our method. We test our method on synthetic image data sets with known levels of added noise and simulated deformations, and also on natural and medical images. Compared with other registration methods not using segmentation information, our proposed method exhibits greater robustness to noise and improved registration accuracy. 2012
Iterative Narrowband-Based Graph Cuts Optimization for Geodesic Active Contours With Region Forces (GACWRF) In this paper, an iterative narrow-band-based graph cuts (INBBGC) method is proposed to optimize the geodesic active contours with region forces (GACWRF) model for interactive object segmentation. Based on cut metric on graphs proposed by Boykov and Kolmogorov, an NBBGC method is devised to compute the local minimization of GAC. An extension to an iterative manner, namely, INBBGC, is developed for less sensitivity to the initial curve. The INBBGC method is similar to graph-cuts-based active contour (GCBAC) presented by Xu , and their differences have been analyzed and discussed. We then integrate the region force into GAC. An improved INBBGC (IINBBGC) method is proposed to optimize the GACWRF model, thus can effectively deal with the concave region and complicated real-world images segmentation. Two region force models such as mean and probability models are studied. Therefore, the GCBAC method can be regarded as the special case of our proposed IINBBGC method without region force. Our proposed algorithm has been also analyzed to be similar to the Grabcut method when the Gaussian mixture model region force is adopted, and the band region is extended to the whole image. Thus, our proposed IINBBGC method can be regarded as narrow-band-based Grabcut method or GCBAC with region force method. We apply our proposed IINBBGC algorithm on synthetic and real-world images to emphasize its performance, compared with other segmentation methods, such as GCBAC and Grabcut methods. 2012
PDE-Based Enhancement of Color Images in RGB Space A novel method for color image enhancement is proposed as an extension of the scalar-diffusion-shock-filter coupling model, where noisy and blurred images are denoised and sharpened. The proposed model is based on using the single vectors of the gradient magnitude and the second derivatives as a manner to relate different color components of the image. This model can be viewed as a generalization of the Bettahar-Stambouli filter to multivalued images. The proposed algorithm is more efficient than the mentioned filter and some previous works at color images denoising and deblurring without creating false colors. 2012
Polyview Fusion A Strategy to Enhance Video-Denoising Algorithms We propose a simple but effective strategy that aims to enhance the performance of existing video denoising algorithms, i.e., polyview fusion (PVF). The idea is to denoise the noisy video as a 3-D volume using a given base 2-D denoising algorithm but applied from multiple views (front, top, and side views). A fusion algorithm is then designed to merge the resulting multiple denoised videos into one, so that the visual quality of the fused video is improved. Extensive tests using a variety of base video-denoising algorithms show that the proposed PVF method leads to surprisingly significant and consistent gain in terms of both peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) performance, particularly at high noise levels, where the improvement over state-of-the-art denoising algorithms is often more than 2 dB in PSNR. 2012
Preconditioning for Edge-Preserving Image Super Resolution We propose a simple preconditioning method for accelerating the solution of edge-preserving image super-resolution (SR) problems in which a linear shift-invariant point spread function is employed. Our technique involves reordering the high-resolution (HR) pixels in a similar manner to what is done in preconditioning methods for quadratic SR formulations. However, due to the edge preserving requirements, the Hessian matrix of the cost function varies during the minimization process. We develop an efficient update scheme for the preconditioner in order to cope with this situation. Unlike some other acceleration strategies that round the displacement values between the low-resolution (LR) images on the HR grid, the proposed method does not sacrifice the optimality of the observation model. In addition, we describe a technique for preconditioning SR problems involving rational magnification factors. The use of such factors is motivated in part by the fact that, under certain circumstances, optimal SR zooms are nonintegers. We show that, by reordering the pixels of the LR images, the structure of the problem to solve is modified in such a way that preconditioners based on circulant operators can be used. 2012
PSF Estimation via Gradient Domain Correlation This paper proposes an efficient method to estimate the point spread function (PSF) of a blurred image using image gradients spatial correlation. A patch-based image degradation model is proposed for estimating the sample covariance matrix of the gradient domain natural image. Based on the fact that the gradients of clean natural images are approximately uncorrelated to each other, we estimated the autocorrelation function of the PSF from the covariance matrix of gradient domain blurred image using the proposed patch-based image degradation model. The PSF is computed using a phase retrieval technique to remove the ambiguity introduced by the absence of the phase. Experimental results show that the proposed method significantly reduces the computational burden in PSF estimation, compared with existing methods, while giving comparable blurring kernel 2012
Rigid-Motion-Invariant Classification of 3-D Textures This paper studies the problem of 3-D rigid-motion- invariant texture discrimination for discrete 3-D textures that are spatially homogeneous by modeling them as stationary Gaussian random fields. The latter property and our formulation of a 3-D rigid motion of a texture reduce the problem to the study of 3-D rotations of discrete textures. We formally develop the concept of 3-D texture rotations in the 3-D digital domain. We use this novel concept to define a “distance” between 3-D textures that remains invariant under all 3-D rigid motions of the texture. This concept of “distance” can be used for a monoscale or a mill tiscale 3-D rigid- motion-invariant testing of the statistical similarity of the 3-D textures. To compute the “distance” between any two rotations R1 and R2 of two given 3-D textures, we use the Kullback-Leibler divergence between 3-D Gaussian Markov random fields fitted to the rotated texture data. Then, the 3-D rigid-motion-invariant texture distance is the integral average, with respect to the Haar measure of the group SO(3), of all of these divergences when rotations R1 and R2 vary throughout SO(3). We also present an algorithm enabling the computation of the proposed 3-D rigid-motion-invariant texture distance as well as rules for 3-D rigid-motion-invariant texture discrimination/classification and experimental results demonstrating the capabilities of the proposed 3-D rigid-motion texture discrimination rules when applied in a multiscale setting, even on very general 3-D texture models. 2012
Robust Image Hashing Based on Random Gabor Filtering and Dithered Lattice Vector Quantization In this paper, we propose a robust-hash function based on random Gabor filtering and dithered lattice vector quantization (LVQ). In order to enhance the robustness against rotation manipulations, the conventional Gabor filter is adapted to be rotation invariant, and the rotation-invariant filter is randomized to facilitate secure feature extraction. Particularly, a novel dithered-LVQ-based quantization scheme is proposed for robust hashing. The dithered-LVQ-based quantization scheme is well suited for robust hashing with several desirable features, including better tradeoff between robustness and discrimination, higher randomness, and secrecy, which are validated by analytical and experimental results. The performance of the proposed hashing algorithm is evaluated over a test image database under various content-preserving manipulations. The proposed hashing algorithm shows superior robustness and discrimination performance compared with other state-of-the-art algorithms, particularly in the robustness against rotations (of large degrees). 2012
Snakes With an Ellipse-Reproducing Property We present a new class of continuously defined parametric snakes using a special kind of exponential splines as basis functions. We have enforced our bases to have the shortest possible support subject to some design constraints to maximize efficiency. While the resulting snakes are versatile enough to provide a good approximation of any closed curve in the plane, their most important feature is the fact that they admit ellipses within their span. Thus, they can perfectly generate circular and elliptical shapes. These features are appropriate to delineate cross sections of cylindrical-like conduits and to outline bloblike objects. We address the implementation details and illustrate the capabilities of our snake with synthetic and real data. 2012

 

TECHNOLOGY                               : JAVA

 

DOMAIN                                            : IEEE TRANSACTIONS ON SOFTWARE ENGINEERING

 

S.NO TITLES ABSTRACT YEAR
Automated Behavioral Testing of Refactoring Engines We present a technique to test Java refactoring engines. It automates test input generation by using a Java program generator that exhaustively generates programs for a given scope of Java declarations. The refactoring under2test is applied to each generated program. The technique uses SAFEREFACTOR, a tool for detecting behavioral changes, as oracle to evaluate the correctness of these transformations. 2012
Towards Comprehensible Software Fault Prediction Models Using Bayesian Network Classifiers This study contributes to the literature by considering 15 different Bayesian Network (BN) classifiers and comparing them to other popular machine learning techniques. Furthermore, the applicability of the Markov blanket principle for feature selection, which is a natural extension to BN theory, is investigated. 2012
Using Dependency Structures for Prioritisation of Functional Test Suites In this paper, we present a family of test case prioritisation techniques that use the dependency information from a test suite to prioritise that test suite. The nature of the techniques preserves the dependencies in the test ordering. 2012
Automatically Generating Test Cases for Specification Mining Dynamic specification mining observes program executions to infer models of normal program behavior. What makes us believe that we have seen sufficiently many executions? The TAUTOKO (“Tautoko” is the Mãori word for “enhance, enrich.”) typestate miner generates test cases that cover previously unobserved behavior, systematically extending the execution space, and enriching the specification. To our knowledge, this is the first combination of systematic test case generation and typestate mining-a combination with clear benefits: On a sample of 800 defects seeded into six Java subjects, a static typestate verifier fed with enriched models would report significantly more true positives and significantly fewer false positives than the initial models 2012
Fault Localization for Dynamic Web Applications In recent years, there has been significant interest in fault-localization techniques that are based on statistical analysis of program constructs executed by passing and failing executions. This paper shows how the Tarantula, Ochiai, and Jaccard fault-localization algorithms can be enhanced to localize faults effectively in web applications written in PHP by using an extended domain for conditional and function-call statements and by using a source mapping. We also propose several novel test-generation strategies that are geared toward producing test suites that have maximal fault-localization effectiveness. We implemented various fault-localization techniques and test-generation strategies in Apollo, and evaluated them on several open-source PHP applications. Our results indicate that a variant of the Ochiai algorithm that includes all our enhancements localizes 87.8 percent of all faults to within 1 percent of all executed statements, compared to only 37.4 percent for the unenhanced Ochiai algorithm. We also found that all the test-generation strategies that we considered are capable of generating test suites with maximal fault-localization effectiveness when given an infinite time budget for test generation. However, on average, a directed strategy based on path-constraint similarity achieves this maximal effectiveness after generating only 6.5 tests, compared to 46.8 tests for an undirected test-generation strategy. 2012

 

TECHNOLOGY       : JAVA

 

DOMAIN                   : IEEE TRANSACTIONS ON GRID & CLOUD COMPUTING

 

 

S.NO TITLES ABSTRACT YEAR
Business-OWL (BOWL)—A Hierarchical Task Network Ontology for Dynamic Business Process Decomposition and Formulation This paper introduces the Business-OWL (BOWL), an ontology rooted in the Web Ontology Language (OWL), and modeled as a Hierarchical Task Network (HTN) for the dynamic formation of business processes 2012
Detecting And Resolving Firewall Policy Anomalies The advent of emerging computing technologies such as service-oriented architecture and cloud computing has enabled us to perform business services more efficiently and effectively. 2012
Online System for Grid Resource Monitoring and Machine Learning-Based Prediction In this paper, we present the design and evaluation of system architecture for grid resource monitoring and prediction. We discuss the key issues for system implementation, including machine learning-based methodologies for modeling and optimization of resource prediction models. 2012
SOAP Processing Performance and Enhancement SOAP communications produce considerable network traffic, making them unfit for distributed, loosely coupled and heterogeneous computing environments such as the open Internet. They introduce higher latency and processing delays than other technologies, like Java RMI & CORBA. WS research has recently focused on SOAP performance enhancement. 2011
Weather data sharing system: an agent-based distributed data management Intelligent agents can play an important role in helping achieve the ‘data grid’ vision. In this study, the authors present a multi-agent-based framework to implement manage, share and query weather data in a geographical distributed environment, named weather data sharing system 2011
pCloud: A Distributed System for Practical PIR In this paper we present pCloud, a distributed system that constitutes the ?rst attempt towards practical cPIR. Our approach assumes a disk-based architecture that retrieves one page with a single query. Using a striping technique, we distribute the database to a number of cooperative peers, and leverage their computational resources to process cPIR queries in parallel. We implemented pCloud on the PlanetLab network, and experimented extensively with several system parameters. Results 2012
A Gossip Protocol for Dynamic Resource Management in Large Cloud Environments. We address the problem of dynamic resource management for a large-scale cloud environment. Our contribution includes outlining a distributed middleware architecture and presenting one of its key elements: a gossip protocol that (1) ensures fair resource allocation among sites/applications, (2) dynamically adapts the allocation to load changes and (3) scales both in the number of physical machines and sites/applications. 2012
A Novel Process Network Model for Interacting Context-aware Web Services In this paper, we explore a novel approach to model dynamic behaviors of interacting context-aware web services. It aims to effectively process and take advantage of contexts and realize behavior adaptation of web services, further to facilitate the development of context-aware application of web services. 2012
Monitoring and Detecting Abnormal Behavior in MobileCloud Infrastructure  Recently, several mobile services are changing to cloud-based mobile services with richer communications and higher flexibility. We present a new mobile cloud infrastructure that combines mobile devices and cloud services. This new infrastructure provides virtual mobile instances through cloud computing. To commercialize new services with this infrastructure, service providers should be aware of security issues. Here, we first define new mobile cloud services through mobile cloud infrastructure and discuss possible security threats through the use of several service scenarios. Then, we propose a methodology and architecture for detecting abnormal behavior through the monitoring of both host and network data. To validate our methodology, we injected malicious programs into our mobile cloud test bed and used a machine learning algorithm to detect the abnormal behavior that arose from these programs. 2012
Impact of Storage Acquisition Intervals on the Cost-Efficiency of the Private vs. Public Storage. The volume of worldwide digital content has increased nine-fold within the last five years, and this immense growth is predicted to continue in foreseeable future eaching 8ZB already by 2015. Traditionally, in order to cope with the growing demand for storage capacity, organizations proactively built and managed their private storage facilities. Recently, with the proliferation of public cloud infrastructure offerings, many organizations, instead, welcomed the alternative of outsourcing their storage needs to the providers of public cloud storage services. The comparative cost-efficiency of these two alternatives depends on a number of factors, among which are e.g. the prices of the public and private storage, the charging and the storage acquisition intervals, and the predictability of the demand for storage. In this paper, we study how the cost-efficiency of the private vs. public storage depends on the acquisition interval at which the organization re-assessesits storage needs and acquires additional private storage. The analysis in the paper suggests that the shorter the acquisition interval, the more likely it is that the private storage solution is less expensive as compared with the public cloud infrastructure. This phenomenon is also illustrated in the paper numerically using the storage needs encountered by a university back-up and archiving service as an example. Since the acquisition interval is determined by the organization’s ability to foresee the growth of storage demand, by the provisioning schedules of storage equipment providers, and by internal practices of the organization, among other factors, the organization owning a private storage solution may want to control some of these factors in order to attain a shorter acquisition interval and thus make the private storage (more) cost-efficient.. 2012
Managing A Cloud for Multi-agent Systems on  Ad-hoc Networks We present a novel execution environment for multiagent systems building on concepts from cloud computing and peer-to-peer networks. The novel environment can provide the computing power of a cloud for multi-agent systems in intermittently connected networks. We present the design and implementation of a prototype operating system for managing the environment. The operating system provides the user with a consistent view of a single machine, a single file system, and a unified programming model while providing elasticity and availability. 2012
Cloud Computing Security: From Single toMulti-Clouds The use of cloud computing has increased rapidly in many organizations. Cloud computing provides many benefits in terms of low cost and accessibility of data. Ensuring the security of cloud computing is a major factor in the cloud computing environment, as users often store sensitive information with cloud storage providers but these providers may be untrusted. Dealing with “single cloud” providers is predicted to become less popular with customers due to risks of service availability failure and the possibility of malicious insiders in the single cloud. A movement towards “multi-clouds”, or in other words, “interclouds” or “cloud-ofclouds”has emerged recently. This paper surveys recent research related to single and multi-cloud security and addresses possible solutions. It is found that the research into the use of multicloud providers to maintain security has received less attention from the research community than has the use of single clouds. This work aims to promote the use of  multi-clouds due to its ability to reduce security risks that affect the cloud computing user. 2012
Optimization of Resource Provisioning Cost in Cloud Computing In cloud computing, cloud providers can offer cloud consumers two provisioning plans for computing resources, namely reservation and on-demand plans. In general, cost of utilizing computing resources provisioned by reservation plan is cheaper than that provisioned by on-demand plan, since cloud consumer has to pay to provider in advance. With the reservation plan, the consumer can reduce the total resource provisioning cost. However, the best advance reservation of resources is difficult to be achieved due to uncertainty of consumer’s future demand and providers’ resource prices. To address this problem, an optimal cloud resource provisioning (OCRP) algorithm is proposed by formulating a stochastic programming model. The OCRP algorithm can provision computing resources for being used in multiple provisioning stages as well as a long-term plan, e.g., four stages in a quarter plan and twelve stages in a yearly plan. The demand and price uncertainty is considered in OCRP. In this paper, different approaches to obtain the solution of the OCRP algorithm are considered including deterministic equivalent formulation, sample-average approximation, and Benders decomposition. Numerical studies are extensively performed in which the results clearly show that with the OCRP algorithm, cloud consumer can successfully minimize total cost of resource provisioning in cloud computing environments 2012
A Secure Erasure Code-Based Cloud Storage System with Secure Data Forwarding A cloud storage system, consisting of a collection of storage servers, provides long-term storage services over the Internet.Storing data in a third party’s cloud system causes serious concern over data confidentiality. General encryption schemes protect data confidentiality, but also limit the functionality of the storage system because a few operations are supported over encrypted data. Constructing a secure storage system that supports multiple functions is challenging when the storage system is distributed and has no central authority. We propose a threshold proxy re-encryption scheme and integrate it with a decentralized erasure code such that a secure distributed storage system is formulated. The distributed storage system not only supports secure and robust data storage and retrieval, but also lets a user forward his data in the storage servers to another user without retrieving the data back. The main technical contribution is that the proxy re-encryption scheme supports encoding operations over encrypted messages as well as forwarding operations over encoded and encrypted messages. Our method fully integrates encrypting, encoding, and forwarding. We analyze and suggest suitable parameters for the number of copies of a message dispatched to storage servers and the number of storage servers queried by a key server. These parameters allow more flexible adjustment between the number of storage servers . 2012
HASBE: A Hierarchical Attribute-Based Solution for Flexible and Scalable Access Control in Cloud Computing Cloud computing has emerged as one of the most influential paradigms in the IT industry in recent years. Since thisnew computing technology requires users to entrust their valuable data to cloud providers, there have been increasing security and privacy concerns on outsourced data. Several schemes employing attribute-based encryption (ABE) have been proposed for access control of outsourced data in cloud computing; however, most of them suffer from inflexibility in implementing complex access control policies. In order to realize scalable, flexible, and fine-grained access control of outsourced data in cloud computing, in this paper, we propose hierarchical attribute-set-based encryption (HASBE) by extending ciphertext-policy attribute-set-based encryption (ASBE) with a hierarchical structure of users. The proposed scheme not only achieves scalability due to its hierarchical structure, but also inherits flexibility and fine-grained access control in supporting compound attributes of ASBE. In addition, HASBE employs multiple value assignments for access expiration time to deal with user revocation more efficiently than existing schemes. We formally prove the security of HASBE based on security of the ciphertext-policy attribute-based encryption (CP-ABE) scheme by Bethencourt et al. and analyze its performance and computational complexity. We implement our scheme and show that it is both efficient and flexible in dealing with access control for outsourced data in cloud computing with comprehensive experiments. 2012
A Distributed Access Control Architecture for Cloud Computing The large-scale, dynamic, and heterogeneous nature of cloud computing poses numerous security challenges. But the cloud’s main challenge is to provide a robust authorization mechanism that incorporates multitenancy and virtualization aspects of resources. The authors present a distributed architecture that incorporates principles from security management and software engineering and propose key requirements and a design model for the architecture. 2012
Cloud Computing Security: From Single to Multi-clouds The use of cloud computing has increased rapidly in many organizations. Cloud computing provides many benefits in terms of low cost and accessibility of data. Ensuring the security of cloud computing is a major factor in the cloud computing environment, as users often store sensitive information with cloud storage providers but these providers may be untrusted. Dealing with “single cloud” providers is predicted to become less popular with customers due to risks of service availability failure and the possibility of malicious insiders in the single cloud. A movement towards “multi-clouds”, or in other words, “interclouds” or “cloud-of-clouds” has emerged recently. This paper surveys recent research related to single and multi-cloud security and addresses possible solutions. It is found that the research into the use of multi-cloud providers to maintain security has received less attention from the research community than has the use of single clouds. This work aims to promote the use of multi-clouds due to its ability to reduce security risks that affect the cloud computing user. 2012
  1. 18.   
Scalable and Secure Sharing of Personal Health Records in Cloud Computing using Attribute-based Encryption Personal health record (PHR) is an emerging patient-centric model of health information exchange, which is often outsourced to be stored at a third party, such as cloud providers. However, there have been wide privacy concerns as personal health information could be exposed to those third party servers and to unauthorized parties. To assure the patients’ control over access to their own PHRs, it is a promising method to encrypt the PHRs before outsourcing. Yet, issues such as risks of privacy exposure, scalability in key management, flexible access and efficient user revocation, have remained the most important challenges toward achieving fine-grained, cryptographically enforced data access control. In this paper, we propose a novel patient-centric framework and a suite of mechanisms for data access control to PHRs stored in semi-trusted servers. To achieve fine-grained and scalable data access control for PHRs, we leverage attribute based encryption (ABE) techniques to encrypt each patient’s PHR file. Different from previous works in secure data outsourcing, we focus on the multiple data owner scenario, and divide the users in the PHR system into multiple security domains that greatly reduces the key management complexity for owners and users. A high degree of patient privacy is guaranteed simultaneously by exploiting multi-authority ABE. Our scheme also enables dynamic modification of access policies or file attributes, supports efficient on-demand user/attribute revocation and break-glass access under emergency scenarios. Extensive analytical and experimental results are presented which show the security, scalability and efficiency of our proposed scheme 2012
Cloud Data Production for Masses Offering strong data protection to cloud users while enabling rich applications is a challenging task. We explore a new cloud platform architecture called Data Protection as a Service, which dramatically reduces the per-application development effort required to offer data protection, while still allowing rapid development and maintenance. 2012
  1. 20.   
Secure and Practical Outsourcing of Linear Programming in Cloud Computing Cloud Computing has great potential of providing robust computational power to the society at reduced cost. It enables customers with limited computational resources to outsource their large computation workloads to the cloud, and economically enjoy the massive computational power, bandwidth, storage, and even appropriate software that can be shared in a pay-per-use manner. Despite the tremendous benefits, security is the primary obstacle that prevents the wide adoption of this promising computing model, especially for customers when their confidential data are consumed and produced during the computation. Treating the cloud as an intrinsically insecure computing platform from the viewpoint of the cloud customers, we must design mechanisms that not only protect sensitive information by enabling computations with encrypted data, but also protect customers from malicious behaviors by enabling the validation of the computation result. Such a mechanism of general secure computation outsourcing was recently shown to be feasible in theory, but to design mechanisms that are practically efficient remains a very challenging problem. Focusing on engineering computing and optimization tasks, this paper investigates secure outsourcing of widely applicable linear programming (LP) computations. In order to achieve practical efficiency, our mechanism design explicitly decomposes the LP computation outsourcing into public LP solvers running on the cloud and private LP parameters owned by the customer. The resulting flexibility allows us to explore appropriate security/ efficiency tradeoff via higher-level abstraction of LP computations than the general circuit representation. In particular, by formulating private data owned by the customer for LP problem as a set of matrices and vectors, we are able to develop a set of efficient privacy-preserving problem transformation techniques, which allow customers to transform original LP problem into some arbitrary one while protecting sensitive input/output information. To validate the computation result, we further explore the fundamental duality theorem of LP computation and derive the necessary and sufficient conditions that correct result must satisfy. Such result verification mechanism is extremely efficient and incurs close-to-zero additional cost on both cloud server and customers. Extensive security analysis and experiment results show the immediate practicability of our mechanism design. 2012
  1. 21.   
Efficient audit service outsourcing for data integrity in clouds – projects 2012 Cloud-based outsourced storage relieves the client’s burden for storage management and maintenance by providing a comparably low-cost, scalable, location-independent platform. However, the fact that clients no longer have physical possession of data indicates that they are facing a potentially formidable risk for missing or corrupted data. To avoid the security risks, audit services are critical to ensure the integrity and availability of outsourced data and to achieve digital forensics and credibility on cloud computing. Provable data possession (PDP), which is a cryptographic technique for verifying the integrity of data without retrieving it at an untrusted server, can be used to realize audit services. In this paper, profiting from the interactive zero-knowledge proof system, we address the construction of an interactive PDP protocol to prevent the fraudulence of prover (soundness property) and the leakage of verified data (zero-knowledge property). We prove that our construction holds these properties based on the computation Diffie–Hellman assumption and the rewindable black-box knowledge extractor. We also propose an efficient mechanism with respect to probabilistic queries and periodic verification to reduce the audit costs per verification and implement abnormal detection timely. In addition, we present an efficient method for selecting an optimal parameter value to minimize computational overheads of cloud audit services. Our experimental results demonstrate the effectiveness of our approach 2012
  1. 22.   
Efficient audit service outsourcing for data integrity in clouds – projects 2012 Cloud-based outsourced storage relieves the client’s burden for storage management and maintenance by providing a comparably low-cost, scalable, location-independent platform. However, the fact that clients no longer have physical possession of data indicates that they are facing a potentially formidable risk for missing or corrupted data. To avoid the security risks, audit services are critical to ensure the integrity and availability of outsourced data and to achieve digital forensics and credibility on cloud computing. Provable data possession (PDP), which is a cryptographic technique for verifying the integrity of data without retrieving it at an untrusted server, can be used to realize audit services. In this paper, profiting from the interactive zero-knowledge proof system, we address the construction of an interactive PDP protocol to prevent the fraudulence of prover (soundness property) and the leakage of verified data (zero-knowledge property). We prove that our construction holds these properties based on the computation Diffie–Hellman assumption and the rewindable black-box knowledge extractor. We also propose an efficient mechanism with respect to probabilistic queries and periodic verification to reduce the audit costs per verification and implement abnormal detection timely. In addition, we present an efficient method for selecting an optimal parameter value to minimize computational overheads of cloud audit services. Our experimental results demonstrate the effectiveness of our approach. 2012
  1. 23.   
Secure and privacy preserving keyword searching for cloud storage services – projects 2012 Cloud storage services enable users to remotely access data in a cloud anytime and anywhere, using any device, in a pay-as-you-go manner. Moving data into a cloud offers great convenience to users since they do not have to care about the large capital investment in both the deployment and management of the hardware infrastructures. However, allowing a cloud service provider (CSP), whose purpose is mainly for making a profit, to take the custody of sensitive data, raises underlying security and privacy issues. To keep user data confidential against an untrusted CSP, a natural way is to apply cryptographic approaches, by disclosing the data decryption key only to authorized users. However, when a user wants to retrieve files containing certain keywords using a thin client, the adopted encryption system should not only support keyword searching over encrypted data, but also provide high performance. In this paper, we investigate the characteristics of cloud storage services and propose a secure and privacy preserving keyword searching (SPKS) scheme, which allows the CSP to participate in the decipherment, and to return only files containing certain keywords specified by the users, so as to reduce both the computational and communication overhead in decryption for users, on the condition of preserving user data privacy and user querying privacy. Performance analysis shows that the SPKS scheme is applicable to a cloud environment 2012
  1. 24.   
Secure and privacy preserving keyword searching for cloud storage services – projects 2012 Cloud storage services enable users to remotely access data in a cloud anytime and anywhere, using any device, in a pay-as-you-go manner. Moving data into a cloud offers great convenience to users since they do not have to care about the large capital investment in both the deployment and management of the hardware infrastructures. However, allowing a cloud service provider (CSP), whose purpose is mainly for making a profit, to take the custody of sensitive data, raises underlying security and privacy issues. To keep user data confidential against an untrusted CSP, a natural way is to apply cryptographic approaches, by disclosing the data decryption key only to authorized users. However, when a user wants to retrieve files containing certain keywords using a thin client, the adopted encryption system should not only support keyword searching over encrypted data, but also provide high performance. In this paper, we investigate the characteristics of cloud storage services and propose a secure and privacy preserving keyword searching (SPKS) scheme, which allows the CSP to participate in the decipherment, and to return only files containing certain keywords specified by the users, so as to reduce both the computational and communication overhead in decryption for users, on the condition of preserving user data privacy and user querying privacy. Performance analysis shows that the SPKS scheme is applicable to a cloud environment 2012
  1. 25.   
Cooperative Provable Data Possession for Integrity Verification in Multi-Cloud Storage Provable data possession (PDP) is a technique for ensuring the integrity of data in storage outsourcing. In this paper, we address the construction of an efficient PDP scheme for distributed cloud storage to support the scalability of service and data migration, in which we consider the existence of multiple cloud service providers to cooperatively store and maintain the clients’ data. We present a cooperative PDP (CPDP) scheme based on homomorphic verifiable response and hash index hierarchy. We prove the security of our scheme based on multi-prover zero-knowledge proof system, which can satisfy completeness, knowledge soundness, and zero-knowledge properties. In addition, we articulate performance optimization mechanisms for our scheme, and in particular present an efficient method for selecting optimal parameter values to minimize the computation costs of clients and storage service providers. Our experiments show that our solution introduces lower computation and communication overheads in comparison with non-cooperative approaches 2012
  1. 26.   
Cooperative Provable Data Possession for Integrity Verification in Multi-Cloud Storage Provable data possession (PDP) is a technique for ensuring the integrity of data in storage outsourcing. In this paper, we address the construction of an efficient PDP scheme for distributed cloud storage to support the scalability of service and data migration, in which we consider the existence of multiple cloud service providers to cooperatively store and maintain the clients’ data. We present a cooperative PDP (CPDP) scheme based on homomorphic verifiable response and hash index hierarchy. We prove the security of our scheme based on multi-prover zero-knowledge proof system, which can satisfy completeness, knowledge soundness, and zero-knowledge properties. In addition, we articulate performance optimization mechanisms for our scheme, and in particular present an efficient method for selecting optimal parameter values to minimize the computation costs of clients and storage service providers. Our experiments show that our solution introduces lower computation and communication overheads in comparison with non-cooperative approaches 2012
  1. 27.   
Bootstrapping Ontologies for Web Services – projects 2012 Ontologies have become the de-facto modeling tool of choice, employed in many applications and prominently in the semantic web. Nevertheless, ontology construction remains a daunting task. Ontological bootstrapping, which aims at automatically generating concepts and their relations in a given domain, is a promising technique for ontology construction. Bootstrapping an ontology based on a set of predefined textual sources, such as web services, must address the problem of multiple, largely unrelated concepts. In this paper, we propose an ontology bootstrapping process for web services. We exploit the advantage that web services usually consist of both WSDL and free text descriptors. The WSDL descriptor is evaluated using two methods, namely Term Frequency/Inverse Document Frequency (TF/IDF) and web context generation. Our proposed ontology bootstrapping process integrates the results of both methods and applies a third method to validate the concepts using the service free text descriptor, thereby offering a more accurate definition of ontologies. We extensively validated our bootstrapping method using a large repository of real-world web services and verified the results against existing ontologies. The experimental results indicate high precision. Furthermore, the recall versus precision comparison of the results when each method is separately implemented presents the advantage of our integrated bootstrapping approach. 2012
Data Security and Privacy Protection Issues in Cloud Computing It is well-known that cloud computing has many potential advantages and many enterprise applications and data are migrating to public or hybrid cloud. But regarding some business-critical applications, the organizations, especially large enterprises, still wouldn’t move them to cloud. The market size the cloud computing shared is still far behind the one expected. From the consumers’ perspective, cloud computing security concerns, especially data security and privacy protection issues, remain the primary inhibitor for adoption of cloud computing services. This paper provides a concise but all-round analysis on data security and privacy protection issues associated with cloud computing across all stages of data life cycle. Then this paper discusses some current solutions. Finally, this paper describes future research work about data security and privacy protection issues in cloud. 2012
Stochastic models of load balancing and scheduling in cloud computing clusters Cloud computing services are becoming ubiquitous, and are starting to serve as the primary source of computing power for both enterprises and personal computing applications. We consider a stochastic model of a cloud computing cluster, where jobs arrive according to a stochastic process and request virtual machines (VMs), which are specified in terms of resources such as CPU, memory and storage space. While there are many design issues associated with such systems, here we focus only on resource allocation problems, such as the design of algorithms for load balancing among servers, and algorithms for scheduling VM configurations. Given our model of a cloud, we first define its capacity, i.e., the maximum rates at which jobs can be processed in such a system. Then, we show that the widely-used Best-Fit scheduling algorithm is not throughput-optimal, and present alternatives which achieve any arbitrary fraction of the capacity region of the cloud. We then study the delay performance of these alternative algorithms through simulations. 2012
A comber approach to protect cloud computing against XML DDoS and HTTP DDoS attack Cloud computing is an internet based pay as use service which provides three layered services (Software as a Service, Platform as a Service and Infrastructure as a Service) to its consumers on demand. These on demand service facilities provide to its consumers in multitenant environment but as facility increases complexity and security problems also increase. Here all the resources are at one place in data centers. Cloud uses public and private APIs (Application Programming Interface) to provide services to its consumers in multitenant environment. In this environment Distributed Denial of Service attack (DDoS), especially HTTP, XML or REST based DDoS attacks may be very dangerous and may provide very harmful effects for availability of services and all consumers will get affected at the same time. One other reason is that because the cloud computing users make their request in XML then send this request using HTTP protocol and build their system interface with REST protocol such as Amazon EC2 or Microsoft Azure. So the threaten coming from distributed REST attacks are more and easy to implement by the attacker, but to security expert very difficult to resolve. So to resolve these attacks this paper introduces a comber approach for security services called filtering tree. This filtering tree has five filters to detect and resolve XML and HTTP DDoS attack 2012
Resource allocation and scheduling in cloud computing Cloud computing is a platform that hosts applications and services for businesses and users to accesses computing as a service. In this paper, we identify two scheduling and resource allocation problems in cloud computing. We describe Hadoop MapReduce and its schedulers, and present recent research efforts in this area including alternative schedulers and enhancements to existing schedulers. The second scheduling problem is the provisioning of virtual machines to resources in the cloud. We present a survey of the different approaches to solve this resource allocation problem. We also include recent research and standards for inter-connecting clouds and discuss the suitability of running scientific applications in the cloud. 2012
Application study of online education platform based on cloud computing Aimed at some problems in Network Education Resources Construction at present, we analyse the characteristics and application range of cloud computing, and present an integrated solving scheme. On that basis, some critical technologies such as the cloud storage, streaming media and cloud safety are analyzed in detail. Finally, the paper gives summarization and expectation. 2012
Towards temporal access control in cloud computing Access control is one of the most important security mechanisms in cloud computing. Attribute-based access control provides a flexible approach that allows data owners to integrate data access policies within the encrypted data. However, little work has been done to explore temporal attributes in specifying and enforcing the data owner’s policy and the data user’s privileges in cloud-based environments. In this paper, we present an efficient temporal access control encryption scheme for cloud services with the help of cryptographic integer comparisons and a proxy-based re-encryption mechanism on the current time. We also provide a dual comparative expression of integer ranges to extend the power of attribute expression for implementing various temporal constraints. We prove the security strength of the proposed scheme and our experimental results not only validate the effectiveness of our scheme, but also show that the proposed integer comparison scheme performs significantly better than previous bitwise comparison scheme. 2012
Privacy-Preserving DRM for Cloud Computing We come up with a digital rights management (DRM) concept for cloud computing and show how license management for software within the cloud can be achieved in a privacy-friendly manner. In our scenario, users who buy software from software providers stay anonymous. At the same time, our approach guarantees that software licenses are bound to users and their validity is checked before execution. We employ a software re-encryption scheme so that computing centers which execute users’ software are not able to build user profiles – not even under pseudonym – of their users. We combine secret sharing and homomorphic encryption. We make sure that malicious users are unable to relay software to others. DRM constitutes an incentive for software providers to take partin a future cloud computing scenario. We make this scenario more attractive for users by preserving their privacy. 2012
Pricing and peak aware scheduling algorithm for cloud computing The proposed cloud computing scheduling algorithms demonstrated feasibility of interactions between distributors and one of their heavy use customers in a smart grid environment. Specifically, the proposed algorithms take cues from the dynamic pricing and schedule the jobs/tasks in ways that the energy usage is what distributors are hinted. In addition, a peak threshold can be dynamically assigned such that the energy usage at any given time will not exceed the threshold. The proposed scheduling algorithm proved the feasibility of managing the energy usage of cloud computers in collaboration with the energy distributor 2012
Comparison of Network Intrusion Detection Systems in cloud computing environment Computer Networks face a constant struggle against intruders and attackers. Attacks on distributed systems grow stronger and more prevalent each and every day. Intrusion detection methods are a key to control and potentially eradicate attacks on a system. An Intrusion detection system pertains to the methods used to identify an attack on a computer or computer network. In cloud computing environment the applications are user-centric and the customers should be confident about their applications stored in the cloud server. Network Intrusion Detection System (NIDS) plays an important role in providing the network security. They provide a defence layer which monitors the network traffic for pre-defined suspicious activity or pattern. In this paper Snort, Tcpdump and Network Flight Recorder which are the most famous NIDS in cloud system are examined and contrasted. 2012
Intelligent and Active Defense Strategy of Cloud Computing Cloud’s development has entered the practical stage, but safety issues must be resolved. How to avoid the risk on the web page, how to prevent attacks from hacker, how to protect user data in the cloud. This paper discusses some satety solution :the credit level of web page, Trace data, analyze and filter them by large-scale statistical methods, encryption protection of user data and key management. 2012
Distributed Shared Memory as an Approach for Integrating WSNs and Cloud Computing In this paper we discuss the idea of combining wireless sensor networks and cloud computing starting with a state of the art analysis of existing approaches in this field. As result of the analysis we propose to reflect a real wireless sensor network by virtual sensors in the cloud. The main idea is to replicate data stored on the real sensor nodes also in the virtual sensors, without explicit triggering such updates from the application. We provide a short overview of the resulting architecture before explaining mechanisms to realize it. The means to ensure a certain level of consistency between the real WSN and the virtual sensors in the cloud is distributed shared memory. In order to realize DSM in WSNs we have developed a middleware named tinyDSM which is shortly introduced here and which provides means for replicating sensor data and ensuring the consistency of the replicates. Even though tinyDSM is a pretty good vehicle to realize our idea there are some open issues that need to be addressed when realizing such an architecture. We discuss these challenges in an abstract way to ensure clear separation between the idea and its specific realization. 2012
Improving resource allocation in multi-tier cloud systems Even though the adoption of cloud computing and virtualization have improved resource utilization to a great extent, the continued traditional approach of resource allocation in production environments has introduced the problem of over-provisioning of resources for enterprise-class applications hosted in cloud systems. In this paper, we address the problem and propose ways to minimize over-provisioning of IT resources in multi-tier cloud systems by adopting an innovative approach of application performance monitoring and resource allocation at individual tier levels, on the basis of criticality of the business services and availability of the resources at one’s disposal. 2012
Ensuring Distributed Accountability for Data Sharing in the Cloud Cloud computing enables highly scalable services to be easily consumed over the Internet on an as-needed basis. A major feature of the cloud services is that users’ data are usually processed remotely in unknown machines that users do not own or operate. While enjoying the convenience brought by this new emerging technology, users’ fears of losing control of their own data (particularly, financial and health data) can become a significant barrier to the wide adoption of cloud services. To address this problem, in this paper, we propose a novel highly decentralized information accountability framework to keep track of the actual usage of the users’ data in the cloud. In particular, we propose an object-centered approach that enables enclosing our logging mechanism together with users’ data and policies. We leverage the JAR programmable capabilities to both create a dynamic and traveling object, and to ensure that any access to users’ data will trigger authentication and automated logging local to the JARs. To strengthen user’s control, we also provide distributed auditing mechanisms. We provide extensive experimental studies that demonstrate the efficiency and effectiveness of the proposed approaches. 2012
Efficient information retrieval for ranked queries in cost-effective cloud environments Cloud computing as an emerging technology trend is expected to reshape the advances in information technology. In this paper, we address two fundamental issues in a cloud environment: privacy and efficiency. We first review a private keyword-based file retrieval scheme proposed by Ostrovsky et. al. Then, based on an aggregation and distribution layer (ADL), we present a scheme, termed efficient information retrieval for ranked query (EIRQ), to further reduce querying costs incurred in the cloud. Queries are classified into multiple ranks, where a higher ranked query can retrieve a higher percentage of matched files. Extensive evaluations have been conducted on an analytical model to examine the effectiveness of our scheme. 2012

 

TECHNOLOGY  : JAVA DOMAIN              : IEEE TRANSACTIONS ON MULTIMEDIA 

 

S.NO TITLES ABSTRACT YEAR
Movie2Comics: Towards a Lively Video Content Presentation This paper proposes a scheme that is able to automatically turn a movie clip to comics. Two principles are followed in the scheme: 1) optimizing the information preservation of the movie; and 2) generating outputs following the rules and the styles of comics. The scheme mainly contains three components: script-face mapping, descriptive picture extraction, and cartoonization. The script-face mapping utilizes face tracking and recognition techniques to accomplish the mapping between characters’ faces and their scripts 2012
Robust Watermarking of Compressed and Encrypted JPEG2000 Images In this paper, we propose a robust watermarking algorithm to watermark JPEG2000 compressed and encrypted images. The encryption algorithm we propose to use is a stream cipher. While the proposed technique embeds watermark in the compressed-encrypted domain, the extraction ofwatermark can be done in the decrypted domain 2012
Load-Balancing Multipath Switching System with Flow Slice Multipath Switching systems (MPS) are intensely used in state-of-the-art core routers to provide terabit or even petabit switching capacity. One of the most intractable issues in designing MPS is how to load balance traffic across its multiple paths while not disturbing the intraflow packet orders. Previous packet-based solutions either suffer from delay penalties or lead to O(N2 ) hardware complexity, hence do not scale. Flow-based hashing algorithms also perform badly due to the heavy-tailed flow-size distribution. In this paper, we develop a novel scheme, namely, Flow Slice (FS) that cuts off each flow into flow slices at every intraflow interval larger than a slicing threshold and balances the load on a finer granularity. Based on the studies of tens of real Internet traces, we show that setting a slicing threshold of 1-4 ms, the FS scheme achieves comparative load-balancing performance to the optimal one. It also limits the probability of out-of-order packets to a negligible level (10-6) on three popular MPSes at the cost of little hardware complexity and an internal speedup up to two. These results are proven by theoretical analyses and also validated through trace-driven prototype simulations
  1. 4.       
Robust Face-Name Graph Matching for Movie Character Identification Automatic face identification of characters in movies has drawn significant research interests and led to many interesting applications. It is a challenging problem due to the huge variation in the appearance of each character. Although existing methods demonstrate promising results in clean environment, the performances are limited in complex movie scenes due to the noises generated during the face tracking and face clustering process. In this paper we present two schemes of global face-name matching based framework for robust character identification. The contributions of this work include: Complex character changes are handled by simultaneously graph partition and graph matching. Beyond existing character identification approaches, we further perform an in-depth sensitivity analysis by introducing two types of simulated noises. The proposed schemes demonstrate state-of-the-art performance on movie character identification in various genres of movies. 2012
  1. 5.       
Learn to Personalized Image Search from the Photo Sharing Websites – projects 2012 Increasingly developed social sharing websites, like Flickr and Youtube, allow users to create, share, annotate and comment medias. The large-scale user-generated meta-data not only facilitate users in sharing and organizing multimedia content,but provide useful information to improve media retrieval and management. Personalized search serves as one of such
examples where the web search experience is improved by generating the returned list according to the modified user search intents. In this paper, we exploit the social annotations and propose a novel framework simultaneously considering the user and query relevance to learn to personalized image search. The basic premise is to embed the user preference and query-related
search intent into user-specific topic spaces. Since the users’ original annotation is too sparse for topic modeling, we need to enrich users’ annotation pool before user-specific topic spaces construction. The proposed framework contains two components:
2012

 

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