2011 IEEE Projects

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S.NO 1.

TITLE -2010 A Machine Learning Approach to TCP Throughput Prediction

ABSTRACT

DOMAIN

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2. Feedback-Based Scheduling for LoadBalanced Two-Stage Switches

TCP throughput prediction is an important capability for Networking networks where multiple paths exist between data senders and receivers. In this paper, we describe a new lightweight method for TCP throughput prediction. Our predictor uses Support Vector Regression (SVR); prediction is based on both prior file transfer history and measurements of simple path properties. We evaluate our predictor in a laboratory setting where ground truth can be measured with perfect accuracy. We report the performance of our predictor for oracular and practical measurements of path properties over a wide range of traffic conditions and transfer sizes. For bulk transfers in heavy traffic using oracular measurements, TCP throughput is predicted within 10% of the actual value 87% of the time, representing nearly a threefold improvement in accuracy over prior history-based methods. For practical measurements of path properties, predictions can be made within 10% of the actual value nearly 50% of the time, approximately a 60% improvement over history-based methods, and with much lower measurement traffic overhead. We implement our predictor in a tool called PathPerf, test it in the wide area, and show that PathPerf predicts TCP throughput accurately over diverse wide area paths. A framework for designing feedback-based scheduling algorithms is proposed for elegantly solving the notorious packet missequencing problem of a load balanced switch. Unlike existing approaches, we show that the efforts made in load balancing and keeping packets in order can complement each other. Specifically, at each middle-stage port between the two switch fabrics of a load-balanced switch, only a single-packet buffer for each virtual output queueing (VOQ)is required. Although packets belonging to the same flow pass through different middle-stage VOQs, the delays they experience at different middle-stage ports will be identical. This is made possible by properly selecting and coordinating the two sequences of switch configurations to form a joint sequence with both staggered symmetry property and in-order packet delivery property. Based on the staggered symmetry property, an efficient feedback mechanism is designed to allow the right middle-stage port occupancy vector to be delivered to the right input port at the right time. As a result, the performance of load balancing as well as the switch throughput is significantly improved. We further extend this feedback mechanism to support the multicabinet implementation of a load-balanced switch, where the propagation delay between switch linecards and switch fabrics is nonnegligible. As compared to the existing load -balanced switch architectures and scheduling algorithms, our solutions impose a modest requirement on switch hardware, but consistently yield better delay-throughput performance. Last but not least, some extensions and refinements are made to address the scalability, implementation, and fairness issues of our solutions.

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3. Trust management in mobile ad hoc networks using a scalable maturity based model

4. Online social networks

In this paper, we propose a human -based model which builds a trust relationship between nodes in an ad hoc network. The trust is based on previous individual experiences and on the recommendations of others. We present the Recommendation Exchange Protocol (REP) which allows nodes to exchange recommendations about their neighbors. Our proposal does not require disseminating the trust information over the entire network. Instead, nodes only need to keep and exchange trust information about nodes within the radio range. Without the need for a global trust knowledge, our proposal scales well for large networks while still reducing the number of exchanged messages and therefore the energy consumption. In addition, we mitigate the effect of colluding attacks composed of liars in the network. A key concept we introduce is the relationship maturity, which allows nodes to improve the efficiency of the proposed model for mobile scenarios. We show the correctness of our model in a single-hop network through simulations. We also extend the analysis to mobile multihop networks, showing the benefits of the maturity relationship concept. We evaluate the impact of malicious nodes that send false recommendations to degrade the efficiency of the trust model. At last, we analyze the performance of the REP protocol and show its scalability. We show that our implementation of REP can significantly reduce the number messages. OSNs applications, it is a location-based social network Network services, security and privacy of OSNs, and human mobility models based on social network OSNs online service site focuses of social networks or social relations among people, e.g., who share interests and activities. A social network service essentially consists of a representation of each user (often a profile), his/her social links, and a variety of additional services. Most social network services are web based and provide means for users to in teract over the internet, such as e-mail and instant messaging. Although online community services are sometimes considered as a social network online community services are group centered. Social networking sites allow users to share ideas, activities, events, and interests within their individual networks.

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5. SYNCHRONIZATION OF LOCAL DESKTOP TO INTERNET USING FILE TRANSFER PROTOCOL

File synchronization in computing is the process of making sure that files in two or more locations are updated through certain rules. In one-way file synchronization, also called mirroring, updated files are copied from a 'source' location to one or more 'target' locations, but no files are copied back to the source location. In two-way file synchronization, updated files are copied in both directions, usually with the purpose of keeping the two locations identical to each other. In this article, the term synchronization refers exclusively to two-way file synchronization.

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6. Intrusion Detection for Grid and Cloud Computing

Providing security in a distributed system requires more than user authentication with passwords or digital certificates and confidentiality in data transmission. The Grid and Cloud Computing Intrusion Detection System integrates knowledge and behavior analysis to detect intrusions. Transmit power and carrier sense threshold are key MAC/PHY parameters in carrier sense multiple access (CSMA) wireless networks. Transmit power control has been extensively studied in the context of topology control. However, the effect of carrier sense threshold on topology control has not been properly investigated in spite of its crucial role. Our key motivation is that the performance of a topology-controlled network may become worse than that of a network without any topology control unless carrier sense threshold is properly chosen. In order to remedy this deficiency of conventional topology control, we present a framework on how to incorporate physical carrier sense into topology control. We identify that joint control of transmit power and carrier sense threshold can be efficiently divided into topology control and carrier sense adaptation. We devise a distributed carrier sense update algorithm (DCUA), by which each node drives its carrier sense threshold toward a desirable operating point in a fully distributed manner. We derive a sufficient condition for the convergence of DCUA. To demonstrate the utility of integrating physical carrier sense into topology control, we equip a localized topology control algorithm, LMST, with the capability of DCUA. Simulation studies show that LMST-DCUA significantly outperforms LMST and the standard We model the probabilistic behavior of a system comprising a failure detector and a monitored crash recovery target. We extend failure detectors to take account of failure recovery in the target system. This involves extending QoS measures to include the recovery detection speed and proportion of failures detected. We also extend estimating the parameters of the failure detector to achieve a required QoS to configuring the crash-recovery failure detector. We investigate the impact of the dependability of the monitored process on the QoS of our failure detector. Our analysis indicates that variation in the MTTF and MTTR of the monitored process can have a significant impact on the QoS of our failure detector. Our analysis is supported by simulations that validate our theoretical results. Dependable and Security .net

7. Adaptive Physical Carrier Sense in Topology-Controlled Wireless Networks

8. On the Quality of Service of Crash-Recovery Failure Detectors

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9. Layered Approach using conditional random field

Intrusion detection faces challenges an intrusion detection system must constantly detect malicious activities in a network and must perform efficiently to cope with the large amount of network traffic. These two issues of Accuracy and Efficiency using Conditional Random Fields and Layered Approach. We show that high attack detection accuracy can be achieved by using Conditional Random Fields and high efficiency by implementing the Layered Approach. Experimental results on the benchmark KDD 99 intrusion data set show that our proposed system based on Layered Conditional Random Fields outperforms other well-known methods such as the decision trees and the naive Bayes. The improvement in attack detection accuracy is very high, particularly, for the U2R attacks (34.8 percent improvement) and the R2L attacks (34.5 percent improvement). Statistical Tests also demonstrate higher confidence in detection accuracy for our method. Finally, we show that our system is robust and is able to handle noisy data without compromising performance.

10. Privacy-Preserving Sharing of Sensitive Information

Privacy-preserving sharing of sensitive information Security and (PPSSI) is motivated by the increasing need for entities privacy (organizations or individuals) that don't fully trust each other to share sensitive information. Many types of entities need to collect, analyze, and disseminate data rapidly and accurately, without exposing sensitive information to unauthorized or untrusted parties. Although statistical methods have been used to protect data for decades, they aren't foolproof and generally involve a trusted third party. Recently, the security research community has studied and, in a few cases, deployed techniques using secure, multiparty function evaluation, encrypted keywords, and private information retrieval. However, few practical tools and technologies provide data privacy, especially when entities have certain common goals and require (or are mandated) some sharing of sensitive information. To this end, PPSSI technology aims to enable sharing information, without exposing more than the minimum necessary to complete a common task. Security and privacy issues are of most concern in pushing the success of WMNs(Wireless Mesh Networks) for their wide deployment and for supporting service-oriented applications. Despite the necessity, limited security research has been conducted toward privacy preservation in WMNs. This motivates us to develop PEACE, a novel Privacy Enhanced yet Accountable security framework, tailored for WMNs

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11. PEACE

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12. The Phish-Market Protocol: Secure Sharing Between Competitors

One way banks mitigate phishing's effects is to remove fraudulent websites or suspend abusive domain names. The removal process, called a "take -down," is often subcontracted to specialist firms, who refuse to share feeds of phishing website URLs with each other. Consequently, many phishing websites aren't removed. The take-down companies are reticent to exchange feeds, fearing that competitors with less comprehensive lists might free-ride off their efforts. Here, the authors propose the Phish -Market protocol, which enables companies to be compensated for information they provide to their competitors, encouraging them to share. The protocol is designed so that the contributing firm is compensated only for those websites affecting its competitor's clients and only those previously unknown to the receiving firm. The receiving firm, on the other hand, is guaranteed privacy for its client list. The protocol solves a more general problem of sharing between competitors; applications to data brokers in marketing, finance, energy exploration, and beyond could also benefit. Various governments have been considering mechanisms to filter out illegal or offensive Internet material. The accompanying debate raises a number of questions from a technical perspective. This article explores some of these questions, such as, w hat filtering techniques exist,are they effective in filtering out the specific content, how easy is circumventing them ,where should they be placed in the Internet architecture. Because cloud-computing environments' security vulnerabilities differ from those of traditional data centers, perimeter-security approaches will no longer work. Security must move from the perimeter to the virtual machines. Encryption keys are sometimes encrypted themselves; doing that properly requires special care. Although it might look like an oversight at first, the broadly accepted formal security definitions for cryptosystems don't allow encryption of key-dependent messages. Furthermore, key-management systems frequently use key encryption or wrapping, which might create dependencies among keys that lead to problems with simple access-control checks. Security professionals should be aware of this risk and take appropriate measures. Novel cryptosystems offer protection for key-dependent messages and should be considered for practical use. Through enhanced access control in key management systems, you can prevent security interface attacks. The notion of using context information for solving high-level vision and medical image segmentation problems has been increasingly realized in the field. However, how to learn an effective and efficient context model, together with an image appearance Pattern Analysis and Machine Intelligence

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13. Internet Filtering Issues and Challenges

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14. Can Public-Cloud Security Meet Its Unique Challenges?

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15. Encrypting Keys Securely

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16. Auto-Context and Its Application to High-Level Vision Tasks and 3D Brain Image Segmentation

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model, remains mostly unknown. The current literature using Markov Random Fields (MRFs) and Conditional Random Fields (CRFs) often involves specific algorithm design in which the modeling and computing stages are studied in isolation. In this paper, we propose a learning algorithm, auto -context. Given a set of training images and their corresponding label maps, we first learn a classifier on local image patches. The discriminative probability (or classification confidence) maps created by the lea rned classifier are then used as context information, in addition to the original image patches, to train a new classifier. The algorithm then iterates until convergence. Auto-context integrates low-level and context information by fusing a large number oflowlevel appearance features with context and implicit shape information. The resulting discriminative algorithm is general and easy to implement. Under nearly the same parameter settings in training, we apply the algorithm to three challenging vision applications: foreground/background segregation, human body configuration estimation, and scene region labeling. Moreover, context also plays a very important role in medical/brain images where the anatomical structures are mostly constrained to relatively fixed positions. With only some slight changes resulting from using 3D instead of 2D features, the auto-context algorithm applied to brain MRI image segmentation is shown to outperform state-of-the-art algorithms specifically designed for this domain. Furthermore, the scope of the proposed algorithm goes beyond image analysis and it has the potential to be used for a wide variety of problems for structured prediction problems. 17. CSMA protocol Mitigating Performance Degradation in Congested Sensor Networks This system is developed to show the descriptive management of dreadful conditions in Congested Sensor Networks. The dreadful conditions in sensor networks or any other wired networks will happen when bandwidth differs from receiving and sending points. The channel capacity of the network may not be sufficient enough to handle the speed of packets sent. In this system, we are presenting a view, how the data can be sent through the congested channel and also the safe delivery of the packets to the destination. This System is developed using java swing technology with jdk1.6. All the nodes are developed as swing API s.Multiple API s form a sink to the destination. The packets will be sent from Source to destination, via sink. In the sink, a node will be made congested and using channel capacity, the path of data will be calculated. Based on the result of the calculation, the congestion in the sink will be dissolved and data is set free to the destination.This system is an application to maintain the free flow of data in congested sensor networks using Differentiated Routing Protocol and Priority Queues, which maintain priority in dat a-types. java

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18. Feature Analysis and Evaluation for Automatic Emotion Identification in Speech

The definition of parameters is a crucial step in the development of a system for identifying emotions in speech. Although there is no agreement on which are the best features for this task, it is generally accepted that prosody carries most of the emotional information. Most works in the field use some kind of prosodic features, often in combination with spectral and voice quality parametrizations. Nevertheless, no systematic study has been done comparing these features. This paper presents the analysis of the characteristics of features derived from prosody, spectral envelope, and voice quality as well as their capability to discriminate emotions. In addition, early fusion and late fusion techniques for combining different information sources are evaluated. The results of this analysis are validated with experimental automatic emotion identification tests. Results suggest that spectral envelope features outperform the prosodic ones. Even when different parametrizations are combined, the late fusion of long -term spectral statistics with short-term spectral envelope parameters provides an accuracy comparable to that obtained when all parametrizations are combined. Identifying off-task behaviors in intelligent tutoring systems is a practical and challenging research topic. This paper proposes a machine learning model that can automatically detect students' off-task behaviors. The proposed model only utilizes the data available from the log files that record students' actions within the system. The model utilizes a set of time features, performance features, and mouse movement features, and is compared to 1) a model that only utilizes time features and 2) a model that uses time and performance features. Different students have different types of behaviors; therefore, personalized version of the proposed model is constructed and compared to the corresponding nonpersonalized version. In order to address data sparseness problem, a robust Ridge Regression algorithm is utilized to estimate model parameters. An extensive set of experiment results demonstrates the power of using multiple types of evidence, the personalized model, and the robust Ridge Regression algorithm. Here's a sobering thought for all managers responsible for Web applications: Without proactive consideration for an application's security, attackers can bypass nearly all lower-layer security controls simply by using the application in a way its developers didn't envision. Learn how to address vu lnerabilities proactively and early on to avoid the devastating consequences of a successful attack. Trust and reputation management research is highly interdisciplinary, involving researchers from networking and communication, data management and information systems, e-commerce and service computing, artificial intelligence, and game theory, as

Multimedia

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19. Automatic Detection of OffTask Behaviors in Intelligent Tutoring Systems with Machine Learning Techniques

Learning Technologie s

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20. Web-Application Security: From Reactive to Proactive

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21. Trust and Reputation Management

INTERNET COMPUTING

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well as the social sciences and evolutionary biology. Trust and reputation management has played and will continue to play an important role in Internet and social computing systems and applications. This special issue addresses key issues in the field, such as representation, recommendation aggregation, and attack-resilient reputation systems. 22. Multi-body Structure-andMotion Segmentation by Branch-and-Bound Model Selection An efficient and robust framework is proposed for Image two-view multiple structure-and-motion segmentation Processing of unknown number of rigid objects. The segmentation problem has three unknowns, namely the object memberships, the corresponding fundamental matrices, and the number of objects. To handle this otherwise recursive problem, hypotheses for fundamental matrices are generated through local sampling. Once the hypotheses are availab a le, combinatorial selection problem is formulated to optimize a model selection cost which takes into account the hypotheses likelihoods and the model complexity. An explicit model for outliers is also added for robust segmentation. The model selection c is ost minimized through the branch-and-bound technique of combinatorial optimization. The proposed branch and-bound approach efficiently searches the solution space and guaranties optimality over the current set of hypotheses. The efficiency and the guara ntee of optimality of the method is due to its ability to reject solutions without explicitly evaluating them. The proposed approach was validated with synthetic data, and segmentation results are presented for real images. Image search reranking methods usually fail to capture the user's intention when the query term is ambiguous. Therefore, reranking with user interactions, or active reranking, is highly demanded to effectively improve the search performance. The essential problem in active reranking is how to target the user's intention. To complete this goal, this paper presents a structural information based sample selection strategy to reduce the user's labeling efforts. Furthermore, to localize the user's intention in the visual feature space, a novel local-global discriminative dimension reduction algorithm is proposed. In this algorithm, a submanifold is learned by transferring the local geometry and the discriminative information from the labelled images to the whole (global) image database. Experiments on both synthetic datasets and a real Web image search dataset demonstrate the effectiveness of the proposed active reranking scheme, including both the structural information based active sample selection strategy and the local-global discriminative dimension reduction algorithm. An innovative approach based on an evolutionary stochastic algorithm, namely the Particle Swarm Optimizer (PSO), is proposed in this paper as a .net

23. Active Image Re ranking

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24. Content Based Image Retrieval using PSO

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solution to the problem of intelligent retrieval of images in large databases. The problem is recast to an optimization one, where a suitable cost function is minimized through a customized PSO. Accordingly, the relevance-feedback is used in order to exploit the information of the user with the aim of both guiding the particles inside the search space and dynamically assigning different weights to the features. 25. Automatic Composition of Semantic Web Services An Enhanced State Space Search Approach This paper presents a novel approach for semantic web service composition based on traditional state space search approach. We regard automatic web service composition problem as an AI problem -solving problem and propose an enhanced state space search approach toward web service composition domain. This approach can not only be used for automatic service composition, but also for general problem solving domain. In addition, in order to validate the feasibility of our approach, a prototype system is implemented. Although semantic technologies aren't used in current software systems on a large scale yet, they offer high potential to significantly improve the quality of electronic services especially in the E-Government domain. This paper therefore presents an approach that not only incorporates semantic technologies but allows to create E-Government services solely based on semantic models. This multiplies the benefits of the ontology modeling efforts, minimizes development and maintenance time and costs, improves user experience and enforces transparency. This paper firstly introduces the characteristics of the Cloud current E-Learning, and then analyzes the concept and characteristics of cloud computing, and describes the computing architecture of cloud computing platform; by combining the characteristics of E-Learning and learning from current major infrastructure approach of cloud computing platform, this paper structures a relatively complete set of integration and use in one of the E-Learning platform, puts the cloud computing platform apply to the study of E-Learning, and focus on the application in order to improve the resources' stability, balance and utilization; under the conditions, this platform will meet the demand for the current teaching and research activities, improve the greatest value of the E-Learning. Cloud computing provides people a way to share large mount of distributed resources belonging to different organizations. That is a good way to share many kinds of distributed resources, but it also makes security problems more complicate and more important for users than before. In this paper, we analyze some security requirements in cloud computing environment. Since the security problems both in software and hardware, we provided a method to build a trusted computing environment for cloud .net

26. Knowledge-first web services an E-Government example

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27. The Applied Research of Cloud Computing Platform Architecture In the ELearning Area

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28. Cloud Computing System Based on Trusted Computing Platform

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computing by integrating the trusted computing platform (TCP) into cloud computing system. We propose a new prototype system, in which cloud computing system is combined with Trusted Platform Support Service (TSS) and TSS is based on Trusted Platform Module (TPM). In this design, better effect can be obtained in authentication, role based access and data protection in cloud computing environment. 29. IT Auditing to Assure a Secure Cloud Computing. In this paper we discuss the evolvement of cloud computing paradigm and present a framework for secure cloud computing through IT auditing. Our approach is to establish a general framework using checklists by following data flow and its lifecycle. The checklists are made based on the cloud deployment models and cloud services models. The contribution of the paper is to understand the implication of cloud computing and what is meant secure cloud computing via IT auditing rather than propose a new methodology and new technology to secure cloud computing. Our holistic approach has strategic value to those who are using or consider using cloud computing because it addresses concerns such as security, privacy and regulations and compliance. Advanced computing on cloud computing infrastructures can only become viable alternative for the enterprise if these infrastructures can provide proper levels of nonfunctional properties (NPFs). A company that focuses on service-oriented architectures (SOA) needs to know what configuration would provide the proper levels for individual services if they are deployed in the cloud. In this paper we present an approach for performance evaluation of cloud computing configurations. While cloud computing providers assure certain service levels, this it typically done for the platform and not for a particular service instance. Our approach focuses on NFPs of individual services and thereby provides a more relevant and granular information. An experimental evaluation in Amazon Elastic Compute Cloud (EC2) verified our approach. People can only enjoy the full benefits of Cloud computing if we can address the very real privacy and security concerns that come along with storing sensitive personal information in databases and software scattered around the Internet. There are many service provider in the internet, we can call each service as a cloud, each cloud service will exchange data with other cloud, so when the data is exchanged between the clouds, there exist the problem of disclosure of privacy. So the privacy disclosure problem about individual or company is inevitably exposed when releasing or sharing data in the cloud service. Privacy is an important issue for cloud computing, both in terms of legal compliance and user .net

30. Performance Evaluation of Cloud Computing Offerings

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31. Providing Privacy Preserving in cloud computing

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trust, and needs to be considered at every phase of design. Our paper provides some privacy preserving technologies used in cloud computing services. 32. VEBEK: Virtual EnergyBased Encryption and Keying for Wireless Sensor Networks Designing cost-efficient, secure network protocols for Wireless Wireless Sensor Networks (WSNs) is a challenging Computing problem because sensors are resource -limited wireless devices. Since the communication cost is the most dominant factor in a sensor's energy consumption, we introduce an energy -efficient Virtual Energy-Based Encryption and Keying (VEBEK) scheme for WSNs that significantly reduces the number of transmissions needed for rekeying to avoid stale keys. In addition to the goal of saving energy, minimal transmission is imperative for some military applications of WSNs where an adversary could be monitoring the wireless spectrum. VEBEK is a secure communication framework where sensed data is encoded using a scheme based on a permutation code generated via the RC4 encryption mechanism. The key to the RC4 encryption mechanism dynamically changes as a function of the residual virtual energy of the sensor. Thus, a one -time dynamic key is employed for one packet only and different keys are used for the successive packets of the stream. The intermediate nodes along the path to the sink are ableto verify the authenticity and integrity of the incoming packets using a predicted value of the key generated by the sender's virtual energy, thus requiring no need for specific rekeying messages. VEBEK is able to efficiently detect and filter false data injected into the network by malicious outsiders. The VEBEK framework consists of two operational modes (VEBEK-I and VEBEK-II), each of which is optimal for different scenarios. In VEBEK -I, each node monitors its one-hop neighbors where VEBEK-II statistically monitors downstream nodes. We have evaluated VEBEK's feasibility and performance analytically and through simulations. Our results show that VEBEK, without incurring transmission overhead (increasing packet size or sending control messages for rekeying), is able to eliminate malicious data from the network in an energy-efficient manner. We also show that our framework performs be- - tter than other comparable schemes in the literature with an overall 60-100 percent improvement in energy savings withoutthe assumption of a reliable medium access control layer. Compromised node and denial of service are two key attacks in wireless sensor networks (WSNs). In this paper, we study data delivery mechanisms that can with high probability circumvent black holes formed by these attacks. We argue that classic multipath routing approaches are vulnerable to such attacks, mainly due to their deterministic nature. So once the adversary acquires the routing algorithm, it can compute the same routes known to the source, hence, making all information sent over these routes vulnerable to its attacks. In this paper, we develop .net

33. Secure Data Collection in Wireless Sensor Networks Using Randomized Dispersive Routes

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mechanisms that generate randomized multipath routes. Under our designs, the routes taken by the ?? shares?? of different packets change over time. So even if the routing algorithm becomes known to the adversary, the adversary still cannot pinpoint the routes traversed by each packet. Besides randomness, the generated routes are also highly dispersive and energy efficient, making them quite capable of circumventing black holes. We analytically investigate the security and energy performance of the proposed schemes. We also formulate an optimization problem to minimize the end-to-end energy consumption under given security constraints. Extensive simulations are conducted to verify the validity of our mechanisms. 34. Aging Bloom Filter with Two Active Buffers for Dynamic Sets. A Bloom filter is a simple but powerful data structure Data Mining that can check membership to a static set. As Bloom filters become more popular for network applications, a membership query for a dynamic set is also required. Some network applications require high-speed processing of packets. For this purpose, Bloom filters should reside in a fast and small memory, SRAM. In this case, due to the limited memory size, stale data in the Bloom filter should be deleted to make space for new data. Namely the Bloom filter needs aging like LRU caching. In this paper, we propose a new aging scheme for Bloom filters. The proposed scheme utilizes the memory space more efficiently than double buffering, the current state of the art. We prove theoretically that the proposed scheme outperforms double buffering. We also perform experiments on real Internet traces to verify the effectiveness of the proposed scheme. The Bayesian classifier is a fundamental classification technique. In this work, we focus on programming Bayesian classifiers in SQL. We introduce two classifiers: naive Bayes and a classifier based on class decomposition using K-means clustering. We consider two complementary tasks: model computation and scoring a data set. We study several layouts for tables and several indexing alternatives. We analyze how to transform equations into efficient SQL queries and introduce several query optimizations. We conduct experiments with real and synthetic data sets to evaluate classification accuracy, query optimizations, and scalability. Our Bayesian classifier is more accurate than naive Bayes and decision trees. Distance computation is significantly accelerated with horizontal layout for tables, denormalization, and pivoting. We also compare naive Bayes implementations in SQL and C++: SQL is about four times slower. Our Bayesian classifier in SQL achieves high classification accuracy, can efficiently analyze large data sets, and has linear scalability. .net

35. Bayesian Classifiers Programmed in SQL

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36. Using a web-based tool to define and implement software process improvement initiatives in a small industrial setting

Top-down process improvement approaches provide a high-level model of what the process of a software development organisation should be. Such models are based on the consensus of a designated working group on how software should be developed or maintained. They are very useful in that they provide general guidelines on where to start improving, and in which order, to people who do not know how to do it. However, the majority of models have only worked in scenarios within large companies. The authors aim to help small software development organisations adopt an iterative approach by providing a process improvement web-based tool. This study presents research into a proposal which states that a small organisation may use this tool to assess and improve their software process, identifying and implementing a set of agile project management practices that can be strengthened using the CMMI-DEV 1.2 model as reference. Web service technology aims to enable the interoperation of heterogeneous systems and the reuse of distributed functions in an unprecedented scale and has achieved significant success. There are still, however, challenges to realize its full potential. One of these challenges is to ensure the behaviour of Web services consistent with their requirements. Monitoring events that are relevant to Web service requirements is, thus, an important technique. This paper introduces an online monitoring approach for Web service requirements. It includes a pattern-based specification of service constraints that correspond to service requirements, and a monitoring model that covers five kinds of system events relevant to client request, service response, application, resource, and management, and a monitoring framework in which different probes and agents collect events and data that are sensitive to requirements. The framework analyzes the collected information against the prespecified constraints, so as to evaluate the behaviour and use of Web services. The prototype implementation and experiments with a case study shows that our approach is effective and flexible, and the monitoring cost is affordable.

java

37. An Online Monitoring 2 Approach for Web Service Requirements (An Online Monitoring Approach for Web Service Requirements web services(ME))

Java

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S.NO 1.

TITLE -2011 Exploiting Dynamic Resource Allocation for Efficient Parallel Data Processing in the Cloud

ABSTRACT In recent years ad hoc parallel data processing has emerged to be one of the killer applications for Infrastructure-as-a-Service (IaaS) clouds. Major Cloud computing companies have started to integrate frameworks for parallel data processing in their product portfolio, making it easy for customers to access these services and to deploy their programs. However, the processing frameworks which are currently used have been designed for static, homogeneous cluster setups and disregard the particular nature of a cloud. Consequently, the allocated compute resources may be inadequate for big parts of the submitted job and unnecessarily increase processing time and cost. In this paper, we discuss the opportunities and challenges for efficient parallel data processing in clouds and present our research project Nephele. Nephele is the first data processing framework to explicitly exploit the dynamic resource allocation offered by today's IaaS clouds for both, task scheduling and execution. Particular tasks of a processing job can be assigned to different types of virtual machines which are automatically instantiated and terminated during the job execution. Based on this new framework, we perform extended evaluations of MapReduce-inspired processing jobs on an IaaS cloud system and compare the results to the popular data processing framework Hadoop. Cloud computing has been envisioned as the de-facto solution to the rising storage costs of IT Enterprises. With the high costs of data storage devices as well as the rapid rate at which data is being generated it proves costly for enterprises or individual users to frequently update their hardware. Apart from reduction in storage costs data outsourcing to the cloud also helps in reducing the maintenance. Cloud storage moves the user s data to large data centers, which are remotely located, on which user does not have any control. However, th unique is feature of the cloud poses many new security challenges which need to be clearly understood and resolved. One of the important concerns that need to be addressed is to assure the customer of the integrity i.e. correctness of his data in the cloud. As the data is physically not accessible to the user the cloud should provide a way for the user to check if the integrity of his data is maintained or is compromised. In this paper we provide a scheme which gives a proof of data integrity in the cloud w hich the customer can employ to check the correctness of his data in the cloud. This proof can be agreed upon by both the cloud and the customer and can be incorporated in the Service level agreement (SLA). This scheme ensures that the storage at the client side is minimal which will be beneficial for thin clients. In many applications, including location based services, queries are not precise. In this paper, we study the problem of efficiently computing range aggregates in a multi-dimensional space when the query location is uncertain. That is, for a set of data po ints P, an uncertain

DOMAIN Parallel Distribution

PLATFORM

2.

Data integrity proofs in cloud storage

Communicat ion System & network

3.

Efficient Computing of Range Aggregates against Uncertain Location Based

Knowledge & data engineering

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Collections

location based query Q with location described by a probabilistic density function, we want to calculate the aggregate information (e.g., count, average} and sum) of the data points within distance \gamma to Q with probability at least \theta. We propose novel, efficient techniques to solve the problem based on a filtering -andverification framework. In particular, two novel filtering techniques are proposed to effectively and efficiently remove data points from verification. Finally, we show that our techniques can be immediately extended to solve the range query problem. Comprehensive experiments conducted on both real and synthetic data demonstrate the efficiency and scalability of our techniques. Natural phenomena show that many creatures form large social groups and move in regular patterns. However, previous works focus on finding the movement patterns of each single object or all objects. In this paper, we first propose an efficient distributed mining algorithm to jointly identify a group of moving objects and discover their movement patterns in wireless sensor networks. Afterward, we propose a compression algorithm, called 2P2D, which exploits the obtained group movement patterns to reduce the amount of delivered data. The compression algorithm includes a sequence merge and an entropy reduction phases. In the sequence merge phase, we propose a Merge algorithm to merge and compress the location data of a group of moving objects. In the entropy reduction phase, we formulate a Hit Item Replacement (HIR) problem and propose a Replace algorithm that obtains the optimal solution. Moreover, we devise three replacement rules and derive the maximum compression ratio. The experimental results show that the proposed compression algorithm leverages the group movement patterns to reduce the amount of delivered data effectively and efficiently. 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 (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 empiric al evaluation consistently shows the diversity gains of the proposed techniques using several real-world rating datasets and different rating prediction algorithms. Business processes are increasingly distributed and open, making them prone to failure. Monitoring is, therefore, an important concern not only for the processes themselves but also for the services that Knowledge & Data Engineering

4.

Exploring Application-Level Semantics for Data Compression

5.

Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques

Knowledge & Data Engineering

6.

Monitoring Service Systems from a Language-Action

Service Computing

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Perspective

comprise these processes. We present aframework for multilevel monitoring of these service systems. It formalizes interaction protocols, policies, and commitments that account for standard and extended effects following the language-action perspective, and allows specification of goals and monitors at varied abstraction levels. We demonstrate how the framework can be implemented and evaluate it with multiple scenarios that include specifying and monitoring open service policy commitments. With the emergence of the deep Web databases, searching in domains such as vehicles, real estate, etc. has become a routine task. One of the problems in this context is ranking the results of a user quer Earlier y. approaches for addressing this problem have used frequencies of database values, query logs, and user profiles. A common thread in most of these approaches is that ranking is done in a user- and/or queryindependent manner. This paper proposes anovel query- and user-dependent approach for ranking the results of Web database queries. We present a ranking model, based on two complementary notions of user and query similarity, to derive a ranking function for a given user query. This function is acquired from a sparse workload comprising of several such ranking functions derived for various user-query pairs. The proposed model is based on the intuition that similar users display comparable ranking preferences over the results of similar queries. We define these similarities formally in alternative ways and discuss their effectiveness both analytically and experimentally over two distinct Web databases. Cloud applications that offer data management services are emerging. Such clouds support caching of data in order to provide quality query services. The users can query the cloud data, paying the price for the infrastructure they use. Cloud management necessitates an economy that manages the service of multiple users in an efficient, but also, resource-economic way that allows for cloud profit. Naturally, the maximization of cloud profit given some guarantees for user satisfaction presumes an appropriate price-demand model that enables optimal pricing of query services. The model should be plausible in that it reflects the correlation of cache structures involved in the queries. Optimal pricing is achieved based on a dynamic pricing scheme that adapts to time changes. This paper pr oposes a novel price-demand model designed for a cloud cache and a dynamic pricing scheme for queries executed in the cloud cache. The pricing solution employs a novel method that estimates the correlations of the cache services in an time-efficient manner. The experimental study shows the efficiency of the solution. As a model for knowledge description and formalization, ontologies are widely used to represent user profiles in personalized web information gathering. However, when representing user profiles, many models have utilized only knowledge from either a global knowledge base or a Knowledge & data engineering

7.

One Size Does Not Fit All Towards Userand QueryDependent Ranking For Web Databases

8.

Optimal Service Pricing for a Cloud Cache

Knowledge & data engineering

9.

A Personalized Ontology Model for Web Information Gathering

Knowledge & data engineering

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10.

A Branch-and-Bound Algorithm for Solving the Multiprocessor Scheduling Problem with Improved Lower Bounding Techniques

11.

Design and Evaluation of a Proxy Cache for Peer-toPeer Traffic

12.

Robust Feature Selection for Microarray Data Based on

user local information. In this paper, a personalized ontology model is proposed for knowledge representation and reasoning over user profiles. This model learns ontological user profiles from both a world knowledge base and user local instance repositories. The ontology model is evaluated by comparing it against benchmark models in web information gathering. The results show that this ontology model is successful. In branch-and-bound (B&B) schemes for solving a minimization problem, a better lower bound could prune many meaningless branches which do not lead to an optimum solution. In this paper, we propose several techniques to refine the lower bound on the makespan in the multiprocessor scheduling problem (MSP). The key idea of our proposed method is to combine an efficient quadratic-time algorithm for calculating the Fernández's bound, which is known as the best lower bounding technique proposed in the literature with two improvements based on the notions of binary search and recursion. The proposed method was implemented as a part of a B&B algorithm for solving MSP, and was evaluated experimentally. The result of experiments indicates that the proposed method certainly improves the performance of the underlying B&B scheme. In particular, we found that it improves solutions generated by conventional heuristic schemes for more than 20 percent of randomly generated instances, and for more than 80 percent of instances, it could provide a certification of optimality of the resulting solutions, even when the execution time of the B&B scheme is limited by one minute. Peer-to-peer (P2P) systems generate a major fraction of the current Internet traffic, and they significantly increase the load on ISP networks and the cost of running and connecting customer networks (e .g., universities and companies) to the Internet. To mitigate these negative impacts, many previous works in the literature have proposed caching of P2P traffic, but very few (if any) have considered designing a caching system to actually do it. This paper demonstrates that caching P2P traffic is more complex than caching other Internet traffic, and it needs several new algorithms and storage systems. Then, the paper presents the design and evaluation of a complete, running, proxy cache for P2P traffic, called pCache. pCache transparently intercepts and serves traffic from different P2P systems. A new storage system is proposed and implemented in pCache. This storage system is optimized for storing P2P traffic, and it is shown to outperform other storage sys tems. In addition, a new algorithm to infer the information required to store and serve P2P traffic by the cache is proposed. Furthermore, extensive experiments to evaluate all aspects of pCache using actual implementation and real P2P traffic are presente d. Feature selection often aims to select a compact feature subset to build a pattern classifier with reduced complexity, so as to achieve improved classification performance. From the perspective of pattern analysis, producing stable or robust solution is also a desired

Computers

Computers

Computation al Biology and Bioinformati cs

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Multicriterion Fusion

13.

Image-Based Surface Matching Algorithm Oriented to Structural Biology

14.

Iris matching using multi-dimensional artificial neural network

15.

Real-time tracking using A* heuristic search and template

property of a feature selection algorithm. However, the issue of robustness is often overlooked in feature selection. In this study, we analyze the robustness issue existing in feature selection for high-dimensional and small-sized gene-expression data, and propose to improve robustness of feature selection algorithm by using multiple feature selection evaluation criteria. Based on this idea, a multicriterion fusion-based recursive feature elimination (MCF-RFE) algorithm is developed with the goal of improving both classification performance and stability of feature selection results. Experimental studies on five gene-expression data sets show that the MCF-RFE algorithm outperforms the commonly used benchmark feature selection algorithm SVM-RFE. Emerging technologies for structure matching based on surface descriptions have demonstrated their effectiveness in many research fields. In particular, they can be successfully applied to in silico studies of structural biology. Protein activities, in fact, are related to the external characteristics of these macromolecules and the ability to match surfaces can be important to infer information about their possible functions and interactions. In this work, we present a surface -matching algorithm, based on encoding the outer morphology of proteins in images of local description, which allows us to establish point-to-point correlations among macromolecular surfaces using image-processing functions. Discarding methods relying on biological analysis of atomic structures and expensive computational approaches based on energetic studies, this algorithm can successfully be used for macromolecular recognition by employing local surface features. Results demonstrate that the proposed algorithm can be employed both to identify surface similarities in context of macromolecular functional analysis and to screen possible protein interactions to predict pairing capability Iris recognition is one of the most widely used biometric technique for personal identification. This identification is achieved in this work by using the concept that, the iris patterns are statistically unique and suitable for biometric measurements. In this study, a novel method of recognition of these patterns of an iris is considered by using a multidimensional artificial neural network. The proposed technique has the distinct advantage of using the entire resized iris as an input at once. It is capable of excellent pattern recognition properties as the iris texture is unique for every person used for recognition. The system is trained and tested using two publicly available databases (CASIA and UBIRIS). The proposed approach shows significant promise and potential for improvements, compared with the other conventional matching techniques with regard to time and efficiency of results. Many vision problems require fast and accurate tracking of objects in dynamic scenes. In this study, we propose an A* search algorithm through the space of transformations for computing fast target 2D motion.

Computation al Biology and Bioinformati cs

Computer Vision, IET

Computer Vision, IET

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updating

16.

Integral image compression based on optical characteristic

17.

A Variational Model for Histogram Transfer of Color Images

18.

Nonlocal MumfordShah Regularizers for Color Image Restoration

Two features are combined in order to compute e fficient motion: (i) Kullback??Leibler measure as heuristic to guide the search process and (ii) incorporation of target dynamics into the search process for computing the most promising search alternatives. The result value of the quality of match computed by the A* search algorithm together with the more common views of the target object are used for verifying template updates. A template will be updated only when the target object has evolved to a transformed shape dissimilar with respect to the actual shape. The study includes experimental evaluations with video streams demonstrating the effectiveness and efficiency for real-time vision based tasks with rigid and deformable objects. The large amount of image data from the captured three dimensional integral image requires to be presented with adequate resolution. It is therefore necessary to develop compression algorithms that take advantage of the characteristics of the recorded integral image. In this study, the authors propose a new compression method that is adapted to integral imaging. According to the optical characteristics of integral imaging, most of the information of each elemental image is overlapped with that of its adjacent elemental images. Thus, the method is to achieve image compression by taking a sample from the elemental image sequence for every m elemental image to get image compression. Experimental results that are presented to illustrate the proposed compression technique prove that the proposed technique can improve the compression ratio of integral imaging In this paper, we propose a variational formulation for histogram transfer of two or more color images. We study an energy functional composed by three terms: one tends to approach the cumulative histograms of the transformed images, the other two tend to maintain the colors and geometry of the original images. By minimizing this energy, we obtain an algorithm that balances equalization and the conservation of features of the original images. As a result, they evolve while approaching an intermediate histogram between them. This intermediate histogram does not need to be specified in advance, but it is a natural result of the model. Finally, we provide experiments showing that the proposed method compares well with the state of the art. We propose here a class of restoration algorithms for color images, based upon the Mumford-Shah (MS) model and nonlocal image information. The Ambrosio Tortorelli and Shah elliptic approximations are defined to work in a small local neighborhood, which are sufficient to denoise smooth regions with sharp boundaries. However, texture is nonlocal in nature and requires semilocal/non-local information for efficient image denoising and restoration. Inspired from recent works (nonlocal means of Buades, Coll, Morel, and nonlocal total variation of Gilboa, Osher), we extend the local Ambrosio-Tortorelli and Shah approximations to MS functional (MS) to novel nonlocal formulations, for better restoration of fine structures and texture. We

Computer Vision, IET

Image Processing

Image Processing

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19.

A Majorize Minimize Strategy for Subspace Optimization Applied to Image Restoration

20.

A Variational Model for Segmentation of Overlapping Objects With Additive Intensity Value.

21.

Image Segmentation Using Fuzzy Region Competition and Spatial/Frequency Information

present several applications of the proposed nonlocal MS regularizers in image processing such as color image denoising, color image deblurring in the presence of Gaussian or impulse noise, color image inpainting, color image super-resolution, and color filter array demosaicing. In all the applications, the proposed nonlocal regularizers produce superior results over the local ones, especially in image inpainting with large missing regions. We also prove several characterizations of minimizers based upon dual norm Formulat ions. This paper proposes accelerated subspace optimization methods in the context of image restoration. Subspace optimization methods belong to the class of iterative descent algorithms for unconstrained optimization. At each iteration of such methods, a stepsize vector allowing the best combination of several search directions is computed through a multidimensional search. It is usually obtained by an inner iterative second-order method ruled by a stopping criterion that guarantees the convergence of the outer algorithm. As an alternative, we propose an original multidimensional search strategy based on the majorize-minimize principle. It leads to a closed-form stepsize formula that ensures the convergence of the subspace algorithm whatever the number of inner iterations. The practical efficiency of the proposed scheme is illustrated in the context of edge-preserving image restoration. We propose a variant of the Mumford-Shah model for the segmentation of a pair of overlapping objects with additive intensity value. Unlike standard segmentation models, it does not only determine distinct objects in the image, but also recover the possibly multiple membership of the pixels. To accomplish this, some a priori knowledge about the smoothness of the object boundary is integrated into the model. Additi ity is v imposed through a soft constraint which allows the user to control the degree of additivity and is more robust than the hard constraint. We also show analytically that the additivity parameter can be chosen to achieve some stability conditions. To solve the optimization problem involving geometric quantities efficiently, we apply a multiphase level set method. Segmentation results on synthetic and real images validate the good performance of our model, and demonstrate the model's applicability to images with multiple channels and multiple objects. This paper presents a multiphase fuzzy region competition model that takes into account spatial and frequency information for image segmentation. In the proposed energy functional, each region is represented by a fuzzy membership function and a data fideli y term t that measures the conformity of spatial and frequency data within each region to (generalized) Gaussian densities whose parameters are determined jointly with the segmentation process. Compared with the classical region competition model, our approach gives soft segmentation results via the fuzzy membership functions, and moreover, the use of frequency data provides additional region information that can improve the overall segmentation result. To efficiently solve the

Image Processing

Image Processing

Image Processing

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22.

23.

24.

minimization of the energy functional, we adopt an alternate minimization procedure and make use of Chambolle's fast duality projection algorithm. We apply the proposed method to synthetic and natural textures as well as real-world natural images. Experimental results show that our proposed method has very promising segmentation performance compared with the current state-of-the-art approaches. H.264 video More people are studying digital video stream watermarking with transference via networks. However, frequent Internet secret image sharing use increases the requirement for copyright protection and security. As a consequence, to prevent video streams that belong to rightful owners from being intentionally or unknowingly used by others, information protection is indispensable. The authors propose a novel method for video watermarking that is specifically designed for H.264 video. For the experiment, a low -energy signal can relatively guard against low-pass filter attacks. Conversely, a high-energy signal in the host signal can relatively guard against the high-frequency noise attack. In view of these facts, the proposed system design embedding algorithm provides high-energy and lowenergy blocks. The blocks in the host image frame are divided into two different groups by estimating the block energy. The existing singular value decomposition methods were employed to calculate the watermark information. In order to enhance the security, the proposed system also employs torus automorphisms to encrypt the watermark. To achieve better robustness, the encrypted results use secret image sharing technology embedded into different I-frames in the video stream. Rotation, scaling, and Traditional watermarking schemes are sensitive to translation resilient geometric distortions, in which synchronisation for recovering embedded information is a challenging task watermarking for because of the disorder caused by rotation, scaling or images translation (RST). The existing RST-resistant watermarking methods still have limitations with respect to robustness, capacity or fidelity. In this study, the authors address several major problems in RST invariant watermarking. The first point is how to take advantage of the high RST resilience of scale -invariant feature transform (SIFT) features, which show good performance in terms of RSTresistant pattern recognition. Since many keypoint-based watermarking methods do not discuss cropping attacks, the second issue discussed in this study is how to resist cropping using a human visual system (HVS), which also helps us to eliminate computational complexity. The third issue is the investigation of an HVS-based watermarking strategy for extracting only feature points in the human attentive area. Lastly, a variable-length watermark synchronisation algorithm using dynamic programming is proposed. Experimental results show that the proposed algorithms are practical and show superior performance in comparison with many existing works in terms of watermark capacity, watermark transparency, and the resistance to RST attacks. Improvements on For classification problems, the generalized eigenvalue

Image Processing, IET

Image Processing

Neural

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25.

26.

27.

proximal support vector machine (GEPSVM) and twin support vector machine (TWSVM) are regarde as d milestones in the development of the powerful SVMs, as they use the nonparallel hyperplane classifiers. In this brief, we propose an improved version, named twin bounded support vector machines (TBSVM), based on TWSVM. The significant advantage of ourTBSVM over TWSVM is that the structural risk minimization principle is implemented by introducing the regularization term. This embodies the marrow of statistical learning theory, so this modification can improve the performance of classification. In addition, the successive overrelaxation technique is used to solve the optimization problems to speed up the training procedure. Experimental results show the effectiveness of our method in both computation time and classification accuracy, and therefore confirm the above conclusion further. Feature Selection This paper presents a new wrapper -based feature Using Probabilistic selection method for support vector regression (SVR) Prediction of Support using its probabilistic predictions. The method computes the importance of a feature by aggregating the difference, Vector Regression over the feature space, of the conditional density functions of the SVR prediction with and without the feature. As the exact computation of this importance measure is expensive, two approximations are proposed. The effectiveness of the measure using these approximations, in comparison to several other existing feature selection methods for SVR, is evaluated on both artificial and real-world problems. The result of the experiments show that the proposed method generally performs better than, or at least as well as, the existing methods, with notable advantage when the dataset is sparse. Energy-Efficient In cooperative networks, transmitting and receiving Protocol for nodes recruit neighboring nodes to assist in Cooperative communication. We model a cooperative transmission link in wireless networks as a transmitter cluster and a Networks receiver cluster. We then propose a cooperative communication protocol for establishment of these clusters and for cooperative transmission of data. We derive the upper bound of the capacity of the protocol, and we analyze the end-to-end robustness of the protocol to data-packet loss, along with the tradeoff between energy consumption and error rate. The analysis results are used to compare the energy savings and the end-to-end robustness of our protocol with two non-cooperative schemes, as well as to another cooperative protocol published in the technical literature. The comparison results show that, when nodes are positioned on a grid, there is a reduction in the probability of packet delivery failure by two orders of magnitude for the values of parameters considered. Up to 80% in energy savings can be achiev for a grid ed topology, while for random node placement our cooperative protocol can save up to 40% in energy consumption relative to the other protocols. The reduction in error rate and the energy savings translate into increased lifetime of cooperative sensor networks. Parametric Methods This paper develops parametric methods to detect Twin Support Vector Machines

Networks

Neural Networks

Networking

Networking

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for Anomaly Detection in Aggregate Traffic

28.

Peering Equilibrium Multipath Routing: A Game Theory Framework for Internet Peering Settlements

29.

Impact of File Arrivals and Departures on Buffer

network anomalies using only aggregate traffic statistics, in contrast to other works requiring flow separation, even when the anomaly is a small fraction of the total traffic. By adopting simple statistical models for anomalous and background traffic in the time domain, one can estimate model parameters in real time, thus obviating the need for a long training phaseor manual parameter tuning. The proposed bivariate parametric detection mechanism (bPDM) uses a sequential probability ratio test, allowing for control over the false positive rate while examining the tradeoff between detection time and the strength of an anomaly. Additionally, it uses both traffic-rate and packet-size statistics, yielding a bivariate model that eliminates most false positives. The method is analyzed using the bit rate signal-to-noise ratio (SNR) metric, which is shown to be an effective metric for anomaly detection. The performance of the bPDM is evaluated in three ways. First, synthetically generated traffic provides for a controlled comparison of detection time as a function of the anomalous level of traffic. Second, the approach is shown to be able to detect controlled artificial attacks over the University of Southern California (USC), Los Angeles, campus network in varying real traffic mixes. Third, the proposed algorithm achieves rapid detection of real denial-of-service attacks as determined by the replay of previously captured network traces. The method developed in this paper is able to detect all attacks in these scenarios in a few seconds or less. It is generally admitted that interdomain peering links represent nowadays the main bottleneck of the Internet, particularly because of lack of coordination between providers, which use independent and selfish routing policies. We are interested in identifying possible light coordination strategies that would allow carriers to better control their peering links while preserving their independence and respective interests. We propose a robust multipath routing coordination framework for peering carriers, which relies on the multiple-exit discriminator (MED) attribute of Border Gateway Protocol (BGP) as signaling medium. Our scheme relies on a game theory modeling, with a non -cooperative potential game considering both routing and congestions costs. Peering equilibrium multipath (PEMP) coordination policies can be implemented by selecting Pareto-superior Nash equilibria at each carrier. We compare different PEMP policies to BGP Multipath schemes by emulating a realistic peering scenario. Our results show that the routing cost can be decreased by roughly 10% with PEMP. We also show that the stability of routes can be significantly improved and that congestion can be practically avoided on the peering links. Finally, we discuss practical implementation aspects and extend the model to multiple players highlighting the possible incentives for the resulting extended peering framework. Traditionally, it had been assumed that the efficiency requirements of TCP dictate that the buffer size at the router must be of the order of the bandwidth -delay (C ×

Networking

Networking

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Sizing in Core Routers

30.

Dynamic measurement-aware routing in practice

31.

Measurement and diagnosis of address misconfigured P2P traffic

RTT) product. Recently, this assumption was questioned in a number of papers, and the rule was shown tobe conservative for certain traffic models. In particular, by appealing to statistical multiplexing, it was shown that on a router with N long-lived connections, buffers of size O([(C × RTT)/( N)]) or even O(1) are sufficient. In this paper, we reexamine the buffer-size requirements of core routers when flows arrive and depart. Our conclusion is as follows: If the core-to-access-speed ratio is large, then O(1) buffers are sufficient at the core routers; otherwise, larger buffer sizes do improve the flow-level performance of the users. From a modeling point of view, our analysis offers two new insights. First, it may not be appropriate to derive buffer -sizing rules by studying a network with a fixed number of users. In fact, depending upon the core-to-access-speed ratio, the buffer size itself may affect the number of flows in the system, so these two parameters (buffer size and number of flows in the system) should not be treated as independent quantities. Second, in the regime where the core-to-access-speed ratio is large, we note that the O(1) buffer sizes are sufficient for good performance and that no loss of utilization results, as previously believed. Traffic monitoring is a critical network operation for the Networking purpose of traffic accounting, debugging or troubleshooting, forensics, and traffic engineering. Existing techniques for traffic monitoring, however, tend to be suboptimal due to poor choice of monitor location or constantly evolving monitoring objectives and traffic characteristics. One way to counteract these limitations is to use routing as a degree of freedom to enhance monitoring efficacy, which we refer to as measurement aware routing. Traffic sub-populations can be routed (rerouted) on the fly to optimally leverage existing monitoring infrastructures. Implementing dynamic measurementaware routing (DMR) in practice is riddled with challenges. Three major challenges are how to dynamically assess the importance of traffic flows; how to aggregate flows (and hence take a common action for them) in order to conserve routing table entries; and how to achieve traffic routing/rerouting in a manner that is least disruptive to normal network performance while maximizing the measurement utility. This article takes a closer look at these challenges and discusses how they manifest for different types of networks. Through an OpenFlow prototype, we show how DMR can be applied in enterprise networks. Using global iceberg detection and capture as a driving application, we demonstrate how our solutions successfully route suspected iceberg flows to a DPI box for further processing, while preserving balanced load distribution in the overall network. Through measurement study, we discover an interesting Networking phenomenon, P2P address misconfiguration, in which a large number of peers send P2P file downloading requests to a ??random?? target on the Internet. Through measuring three large datasets spanning fouryears and across five different /8 networks, we find address misconfigured P2P traffic on average contributes 38.9

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32.

Packet traffic: a good data source for wireless sensor network modeling and anomaly detection

33.

Experiences of Internet traffic monitoring with tstat

34.

Network traffic monitoring, analysis and anomaly

percent of Internet background radiation, increasing by more than 100 percent every year. To detect and diagnose such unwanted traffic, we design the P2PScope, a measurement tool. After analyzing about 2 Tbytes of data and tracking millions of peers, we found that in all the P2P systems, address misconfiguration is caused by resource mapping contamination: the sources returned for a given file ID through P2P indexing are not valid. Different P2P systems have different reasons for such contamination. For eMule, we find that the root cause is mainly a network byte-order problem in the eMule Source Exchange protocol. For BitTorrent misconfiguration, one reason is that anti-P2P companies actively inject bogus peers into the P2P system. Another reason is that the KTorrent implementation has a byte order problem. The wireless sensor network (WSN) has emerged as a Networking promising technology. In WSNs, sensor nodes are distributedly deployed to collect interesting information from the environment. Because of the mission of WSNs, most node-wide as well as network-wide activities are manifested in packet traffic. As a result, packet traffic becomes a good data source for modeling sensor node as well as sensor network behaviors. In this article, the methodology of modeling node and network behavior profiles using packet traffic is exemplified. In addition, node as well as network anomalies are shown to be detectable by monitoring the evolution of node/network behavior profiles. Since the early days of the Internet, network traffic Networking monitoring has always played a strategic role in understanding and characterizing users?? activities. In this article, we present our experience in engineering and deploying Tstat, an open source passive monitori g n tool that has been developed in the past 10 years. Started as a scalable tool to continuously monitor packets that flow on a link, Tstat has evolved into a complex application that gives network researchers and operators the possibility to derive extended and complex measurements thanks to advanced traffic classifiers. After discussing Tstat capabilities and internal design, we present some examples of measurements collected deploying Tstat at the edge of several ISP networks in past years. While other works report a continuous decline of P2P traffic with streaming and file hosting services rapidly increasing in popularity, the results presented in this article picture a different scenario. First, P2P decline has stopped, and in the last months of 2010 there was a counter tendency to increase P2P traffic over UDP, so the common belief that UDP traffic is negligible is not true anymore. Furthermore, streaming and file hosting applications have either stabilized or are experiencing decreasing traffic shares. We then discuss the scalability issues software-based tools have to cope with when deployed in real networks, showing the importance of properly identifying bottlenecks. Modern computer networks are increasingly pervasive, Networking complex, and ever-evolving due to factors like enormous growth in the number of network users, continuous

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detection [Guest Editorial]

35.

Scheduling Grid Tasks in Face of Uncertain Communication Demands

36.

Improving Application Placement for Cluster-Based Web Applications

appearance of network applications, increasing amount of data transferred, and diversity of user behaviors. Understanding and measuring such a network is a difficult yet vital task for network management and diagnosis. Network traffic monitoring, analysis, and anomaly detection provide useful tools for understanding network behavior and de termining network performance and reliability so as to effectively and promptly troubleshoot and resolve various issues in practice. Grid scheduling is essential to Quality of Service provisioning as well as to efficient management of grid resources. Grid scheduling usually considers the state of the grid resources as well application demands. However, such demands are generally unknown for highly demanding applications, since these often generate data which will be transferred during their execution. Without appropriate assessment of these demands, scheduling decisions can lead to poor performance. Thus, it is of paramount importance to consider uncertainties in the formulation of a grid scheduling problem. This paper introduces the IPDT FUZZY scheduler, a scheduler which considers the demands of grid applications with such uncertainties. The scheduler uses fuzzy optimization, and both computational and communication demands are expressed as fuzzy numbers. Its performance was evaluated, and it was shown to be attractive when communication requirements are uncertain. Its efficacy is compared, via simulation, to that of a deterministic counterpart scheduler and the results reinforceits adequacy for dealing with the lack of accuracy in the estimation of communication demands. Dynamic application placement for clustered web applications heavily influences system performance and quality of user experience. Existing approaches claim that they strive to maximize the throughput, keep resource utilization balanced across servers, and minimize the start/stop cost of application instances. However, they fail to minimize the worst case of server utilization; the load balancing performance is not optimal. What's more, some applications need to communicate with each other, which we called dependent applications; the network cost of them also should be taken into consideration. In this paper, we investigate how to minimize the resource utilization of servers in the worst case, aiming at improving load balancing among clustered servers. Our c ontribution is two-fold. First we propose and define a new optimization objectives: limiting the worst case of each individual server's utilization, formulated by a min-max problem. A novel framework based on binary search is proposed to detect an optimal load balancing solution. Second, we define system cost as the weighted combination of both placement change and inter-application communication cost. By maximizing the number of instances of dependent applications that reside in the same set of servers, the basic load-shifting and placement-change procedures are enhanced to minimize whole system cost.

Network and Service Management

Network and Service Management

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37.

Efficient Network Modification to Improve QoS Stability at Failure

38.

Spectral Models for Bitrate Measurement from Packet Sampled Traffic

39.

Vulnerability Detection Systems: Think Cyborg, Not Robot

Extensive experiments have been conducted and effectively demonstrate that: 1) the proposed framework achieves a good allocation for clustered web applications. In other words, requests are evenly allocated among servers, and throughput is still maximized; 2) the total system cost maintains at a low level; 3) our algorithm has the capacity of approximating an optimal solution within polynomial time and is promising for practical implementation in real deployments. When a link or node fails, flows are detoured around the failed portion, so the hop count of flowsand the link load could change dramatically as a result of the failure. 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. In this paper, efficient design algorithms to select the loc ations for adding link facilities are proposed and their effectiveness is evaluated using the actual backbone networks of 36 commercial ISPs. In network measurement systems, packet sampling techniques are usually adopted to reduce the overall amount of data to collect and process. Being based on a subset of packets, they introduce estimation errors that have to be properly counteracted by using a fine tuni g n of the sampling strategy and sophisticated inversion methods. This problem has been deeply investigated in the literature with particular attention to the statistical properties of packet sampling and to the recovery of the original network measurements. Herein, we propose a novel approach to predict the energy of the sampling error in the real time estimation of traffic bitrate, based on spectral analysis in the frequency domain. We start by demonstrating that the error introduced by packet sampling can be modeled as an aliasing effect in the frequency domain. Then, we derive closed -form expressions for the Signal-to-Noise Ratio (SNR) to predict the distortion of traffic bitrate estimates over time. The accuracy of the proposed SNR metric is validated by means of real packet traces. Furthermore, a comparison with respect to an analogous SNR expression derived using classic stochastic tools is proposed, showing that the frequency domain approach grants for a higher accuracy when traffic rate measurements a re carried out at fine time granularity Systems proposed in academic research have so far failed to make a significant impact on real-world vulnerability detection. Most software bugs are still found by methods with little input from static-analysis and verification research. These research area could s have a significant impact on software security, but first we need a shift in research goals and approaches. We need systems that incorporate human code auditors' knowledge and abilities, and we need evaluation methods that actually test proposed systems' usability in

Network and Service Management

Network and Service Management

Security & Privacy

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40.

Dynamic QoS Management and Optimization in Service-Based Systems

41.

Seeking Quality of Web Service Composition in a Semantic Dimension

42.

Mining Cluster-Based Temporal Mobile Sequential Patterns in Location-Based Service

real situations. Without changes, academic research will continue to be ignored by the security community, and opportunities to build better tools for finding bugs and understanding software will be missed. Service-based systems that are dynamically composed at runtime to provide complex, adaptive functionality are currently one of the main development paradigms in software engineering. However, the Quality of Service (QoS) delivered by these systems remains an important concern, and needs to be managed in an equally adaptive and predictable way. To address this need, we introduce a novel, tool-supported framework for the development of adaptive service-based systems called QoSMOS (QoS Management and Optimization of Service-based systems). QoSMOS can be used to develop service -based systems that achieve their QoS requirements through dynamically adapting to changes in the system state, environment, and workload. QoSMOS service-based systems translate high-level QoS requirements specified by their administrators into probabilistic temporal logic formulae, which are then formally and automatically analyzed to identify and enforce optimal system configurations. The QoSMOS self-adaptation mechanism can handle reliability and performance-related QoS requirements, and can be integrated into newly developed solutions or legacy systems. The effectiveness and scalability of the approach are validated using simulations and a set of experiments based on an implementation of an adaptive service-based system for remote medical assistance. Ranking and optimization of web service compositions represent challenging areas of research with significant implications for the realization of the Web of Services vision. Semantic web services use formal semantic descriptions of web service functionality and interface to enable automated reasoning over web service compositions. To judge the quality of the overall composition, for example, we can start by calculating the semantic similarities between outputs and inputs of connected constituent services, and aggregate these values into a measure of semantic quality for the composition. This paper takes a specific interest in combining semantic and nonfunctional criteria such as quality of service (QoS) to evaluate quality in web services composition. It proposes a novel and extensible model balancing the new dimension of semantic quality (as a functional quality metric) with a QoS metric, and using them together as ranking and optimization criteria. It also demonstrates the utility of Genetic Algorithms to allow optimization within the context of a large number of services foreseen by the Web of Services vision. We test the performance of the overall approach using a set of simulation experiments, and discuss its advantages and weaknesses. Researches on Location-Based Service (LBS) have been emerging in recent years due to a wide range of potential applications. One of the active topics is the mining and prediction of mobile movements and associated transactions. Most of existing studies focus on

Software Engineering

Knowledge and Data Engineering

Knowledge and Data Engineering

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Environments

43.

Locally Consistent Concept Factorization for Document Clustering

discovering mobile patterns from the whole logs. However, this kind of patterns may not be precise enough for predictions since the differentiated mobile behaviors among users and temporal periods are not considered. In this paper, we propose a novelalgorithm, namely, Cluster-based Temporal Mobile Sequential Pattern Mine (CTMSP-Mine), to discover the Clusterbased Temporal Mobile Sequential Patterns (CTMSPs). Moreover, a prediction strategy is proposed to predict the subsequent mobile behaviors. In C TMSP-Mine, user clusters are constructed by a novel algorithm named Cluster-Object-based Smart Cluster Affinity Search Technique (CO-Smart-CAST) and similarities between users are evaluated by the proposed measure, Location Based Service Alignment (LBS-Alignment). Meanwhile, a time segmentation approach is presented to find segmenting time intervals where similar mobile characteristics exist. To our best knowledge, this is the first work on mining and prediction of mobile behaviors with considerations of user relations and temporal property simultaneously. Through experimental evaluation under various simulated conditions, the proposed methods are shown to deliver excellent performance. Previous studies have demonstrated that document Knowledge clustering performance can be improved significantly in and Data lower dimensional linear subspaces. Recently, matrix Engineering factorization-based techniques, such as Nonnegative Matrix Factorization (NMF) and Concept Factorization (CF), have yielded impressive results. However, both of them effectively see only the global euclidean geometry, whereas the local manifold geometry is not fully considered. In this paper, we propose a new appr oach to extract the document concepts which are consistent with the manifold geometry such that each concept corresponds to a connected component. Central to our approach is a graph model which captures the local geometry of the document submanifold. Thus,we call it Locally Consistent Concept Factorization (LCCF). By using the graph Laplacian to smooth the document toconcept mapping, LCCF can extract concepts with respect to the intrinsic manifold structure and thus documents associated with the same concept can be well clustered. The experimental results on TDT2 and Reuters -21578 have shown that the proposed approach provides a better representation and achieves better clustering results in terms of accuracy and mutual information.

44.

Knowledge Discovery in Services (KDS): Aggregating Software Services to Discover Enterprise Mashups

Service mashup is the act of integrating the resulting data of two complementary software services into a common picture. Such an approach is promising with respect to the discovery of new types of knowledge. However, before service mashup routines can be executed, it is necessary to predict which services (of an open repository) are viable candidates. Similar to Knowledge Discovery in Databases (KDD), we introduce the Knowledge Discovery in Services (KDS) process that identifies mashup candidates. In this work, the KDS

Knowledge and Data Engineering

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process is specialized to address a repository of open services that do not contain semantic annotations. In these situations, specialized techniques are required to determine equivalences among open services with reasonable precision. This paper introduces a bottom-up process for KDS that adapts to the environment of services for which it operates. Detailed experiments are discussed that evaluate KDS techniques on an open repository of services from the Internet and on a repository of services created in a controlled environment.

45.

Design and Implementation of an Intrusion Response System for Relational Databases

The intrusion response component of an overall intrusion detection system is responsible for issuing a suitable response to an anomalous request. We propose the notion of database response policies to support our intrusion response system tailored for a DBMS. Our interactive response policy language makes it very easy for the database administrators to specify appropriate response actions for different circumstances depending upon the nature of the anomalous request. The two main issues that we address in context of such response policies are that of policy matching, and policy administration. For the policy matching problem, we propose two algorithms that efficiently search the policy database for policies that match an anomalous request. We also extend the PostgreSQL DBMS with our policy matching mechanism, and report experimental results. The experimental evaluation shows that our techniques are very efficient. The other issue that we address is that of administration of response policies to prevent malicious modifications to policy objects from legitimate users. We propose a novel Joint Threshold Administration Model (JTAM) that is based on the principle of separation of duty. The key idea in JTAM is that a policy object is jointly administered by at least k database administrator (DBAs), that is, any modification made to a policy object will be invalid unless it has been authorized by at least k DBAs. We present design details of JTAM which is based on a cryptographic threshold signature scheme, and show how JTAM prevents malicious modifications to policy objects from authorized users. We also implement JTAM in the PostgreSQL DBMS, and report experimental results on the efficiency of our techniques.

Knowledge and Data Engineering

46.

Automatic Discovery of Personal Name Aliases from the Web

An individual is typically referred by numerous name Knowledge aliases on the web. Accurate identification of aliases of a and Data given person name is useful in various web related tasks Engineering such as information retrieval, sentiment analysis, personal name disambiguation, and relation extraction. We propose a method to extract aliases of a given personal name from the web. Given a personal name, the proposed method first extracts a set of candidate aliases. Second, we rank the extracted candidates according to the likelihood of a candidate being a correct alias of the

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given name. We propose a novel, automatically extracted lexical pattern-based approach to efficiently extract a large set of candidate aliases from snippets retrieved from a web search engine. We define numerous ran king scores to evaluate candidate aliases using three approaches: lexical pattern frequency, word co occurrences in an anchor text graph, and page counts on the web. To construct a robust alias detection system, we integrate the different ranking scores into a single ranking function using ranking support vector machines. We evaluate the proposed method on three data sets: an English personal names data set, an English place names data set, and a Japanese personal names data set. The proposed method outperforms numerous baselines and previously proposed name alias extraction methods, achieving a statistically significant mean reciprocal rank (MRR) of 0.67. Experiments carried out using location names and Japanese personal names suggest the possibility of extending the proposed method to extract aliases for different types of named entities, and for different languages. Moreover, the aliases extracted using the proposed method are successfully utilized in an information retrieval task and improve recall by 20 percent in a relation-detection task.

47.

Classification and Novel Class Detection in Concept-Drifting Data Streams under Time Constraints

48.

A Machine Learning Approach for Identifying DiseaseTreatment Relations in Short Texts

Most existing data stream classification techniques ignore one important aspect of stream data: arrival of a novel class. We address this issue and propose a data stream classification technique that integrates a novel class detection mechanism into traditional classifiers, enabling automatic detection of novel classes before the true labels of the novel class instances arrive. Novel class detection problem becomes more challenging in the presence of concept-drift, when the underlying data distributions evolve in streams. In order to determine whether an instance belongs to a novel class,the classification model sometimes needs to wait for more test instances to discover similarities among those instances. A maximum allowable wait time Tc is imposed as a time constraint to classify a test instance. Furthermore, most existing stream classification approaches assume that the true label of a data point can be accessed immediately after the data point is classified. In reality, a time delay Tl is involved in obtaining the true label of a data point since manual labeling is time consuming. We show how to make fast and correct classification decisions under these constraints and apply them to real benchmark data. Comparison with state-of-the-art stream classification techniques prove the superiority of our approach. The Machine Learning (ML) field has gained its momentum in almost any domain of research and just recently has become a reliable tool in the medical domain. The empirical domain of automatic learning is used in tasks such as medical decision support, medical imaging, protein-protein interaction, extraction of medical knowledge, and for overall patient management care. ML is envisioned as a tool by which computer -

Knowledge and Data Engineering

Knowledge and Data Engineering

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based systems can be integrated in the healthcare field in order to get a better, more efficient medical care. This paper describes a ML-based methodology for building an application that is capable of identifying and disseminating healthcare information. It extracts sentences from published medical papers that mention diseases and treatments, and identifies semantic relations that exist between diseases and treatments. Our evaluation results for these tasks show that the proposed methodology obtains reliable outcomes that could be integrated in an application to be used in the medical care domain. The potential value of this paper stands in the ML settings that we propose and in the fact that we outperform previous results on the same data set.

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MATLAB
1.

2011

Face Recognition by Exploring Information Jointly in Space, Scale and Orientation

Image Information jointly contained in image space, scale and orientation domains can provide rich important clues notseen in Processing either individual of these domains. The position, spatial frequency and orientation selectivity properties are believed to have an important role in visual perception. This paper proposes a novel face representation and recognition approach by exploring information jointly in image space, scale and orientation domains. Specifically, the face image is first decomposed into different scale and orientation responses by convolving multiscale and multi-orientation Gabor filters. Second, local binary pattern analysis is used to describe the neighboring relationship not only in image space, but also in different scale and orientation responses. This way, information from different domains is explored to give a good face representation for recognition. Discriminant classification is then performed based upon weighted histogram intersection or conditional mutual information with linear discriminant analysis techniques. Extensive experimental results on FERET, AR, and FRGC ver 2.0 databases show the significantadvantages of the proposed method over the existing ones. Image We present methods for the detection of sites of architectural Processing distortion in prior mammograms of interval cancer cases. We hypothesize that screening mammograms obtained prior to the detection of cancer could contain subtle signs of early stages of breast cancer, in particular, architectural distortion. The methods are based upon Gabor filters, phase portrait analysis, a novel method for the analysis of the angular spread of power, fractal analysis, Laws' texture energy measures derived from geometrically transformed regions of interest (ROIs), and Haralick's texture features. With Gabor filters and phase portrait analysis, 4224 ROIs were automatically obtained from 106 prior mammograms of 56 interval-cancer cases, including 301 truepositive ROIs related to architectural distortion, and from 52 mammograms of 13 normal cases. For each ROI, the fractal dimension, the entropy of the angular spread of power, 10 Laws' measures, and Haralick's 14 features were computed. The areas under the receiver operating characteristic curves obtained using the features selected by stepwise logistic regression and the leave-one-ROI-out method are 0.76 with the Bayesian classifier, 0.75 with Fisher linear discriminant analysis, and 0.78 with a single-layer feed-forward neural network. Free-response receiver operating characteristics indicated sensitivities of 0.80 and 0.90 at 5.8 and 8.1 false positives per image, respectively, with the Bayesian classifier and the leave -one-image-out method.

2.

Detection of Architectural Distortion in Prior Mammograms

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3.

Enhanced Assessment of the Wound-Healing Process by Accurate Multiview Tissue Classification

Image With the widespread use of digital cameras,freehand wound imaging has become common practice in clinical settings. There Processing is however still a demand for a practical tool for accurate wound healing assessment, combining dimensional measurements and tissue classification in a single user -friendly system. We achieved the first part of this objective by computing a 3 model for -D wound measurements using uncalibrated vision techniques. We focus here on tissue classification from color and texture region descriptors computed after unsupervised segmentation.Due to perspective distortions, uncontrolled lighting conditions and view points, wound assessments vary significantly between patient examinations. The main contribution of this paper is to overcome this drawback with a multiview strategy for tissue classification, relying on a 3-D model onto which tissue labels are mapped and classification results merged. The experimental classification tests demonstrate that enhanced repeatability and robustness are obtained and that metric assessment is achieved through real area and volume measurements and wound outline extraction. This innovative tool is intended for use not only in therapeutic follow-up in hospitals but also for telemedicine purposes and clinical research, where repeatability and accuracy of wound assessment are critical. This paper presents a new supervised method for blood vessel detection in digital retinal images. This method uses a neural network (NN) scheme for pixel classification and computes a 7 -D vector composed of gray-level and moment invariants-based features for pixel representation. The method was evaluated on the publicly available DRIVEand STARE databases, widely used for this purpose, since they contain retinal images where the vascular structure has been precisely marked by experts. Method performance on both sets of test images is better than other existing solutions in literature. Th method proves e especially accurate for vessel detection in STARE images. Its application to this database (even when the NN was trained on the DRIVE database) outperforms all analyzed segmentation approaches. Its effectiveness and robustness with differen t image conditions, together with its simplicity and fast implementation, make this blood vessel segmentation proposal suitable for retinal image computer analyses such as automated screening for early diabetic retinopathy detection. The histopathological examination of tissue specimens is essential for cancer diagnosis and grading. However, this examination is subject to a considerable amount of observer variability as it mainly relies on visual interpretation of pathologists. To alleviate this problem, it is very important to develop computational quantitative tools, for which image segmentation constitutes the core step. In this paper, we introduce an effective and robust algorithm for the Image Processing

4.

A New Supervised Method for Blood Vessel Segmentation in Retinal Images by Using GrayLevel and Moment Invariants-Based Features

5.

Graph Run-Length Matrices for Histopathological Image Segmentation

Image Processing

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segmentation of histopathological tissue ima ges. This algorithm incorporates the background knowledge of the tissue organization into segmentation. For this purpose, it quantifies spatial relations of cytological tissue components by constructing a graph and uses this graph to define new texture features for image segmentation. This new texture definition makes use of the idea of gray-level run-length matrices. However, it considers the runs of cytological components on a graph to form a matrix, instead of considering the runs of pixel intensities. Working with colon tissue images, our experiments demonstrate that the texture features extracted from graph run-length matrices lead to high segmentation accuracies, also providing a reasonable number of segmented regions. Compared with four other segmentation algorithms, the results show that the proposed algorithm is more effective in histopathological image segmentation. 6.

X-ray Categorization and Retrieval on the Organ and Pathology Level, Using Patch-Based Visual Words

Image In this study we present an efficient image categorization and Processing retrieval system applied to medical image databases, in particular large radiograph archives. The methodology is based on local patch representation of the image content, using a bag of visual words approach. We explore the effects of various parameters on system performance, and show best results using dense sampling of simple features with spatial content, and a nonlinear kernel-based support vector machine (SVM) classifier. In a recent international competition the system was ranked first in discriminating orientation and body regions in X images. In -ray addition to organ-level discrimination, we show an application to pathology-level categorization of chest X-ray data, the most popular examination in radiology. The system discriminates between healthy and pathological cases, and is also shown to successfully identify specific pathologies in a set of chest radiographs taken from a routine hospital examination. This is a first step towards similarity-based categorization, which has a major clinical implications for computer -assisted diagnostics Image This letter proposes a new technique of restoring images Processing distorted by random-valued impulse noise. The detection process is based on finding the optimum direction, by calculating the standard deviation in different directions in the filtering window. The tested pixel is deemed original if it is similar to the pixels in the optimum direction. Extensive simulations prove that the proposed technique has superior performance, when compared to other existing methods, especially at high noise rates. A modified decision based unsymmetrical trimmed median filter Image algorithm for the restoration of gray scale, and color images that Processing are highly corrupted by salt and pepper noise is proposed in this paper. The proposed algorithm replaces the noisy pixel by

7.

Standard Deviation for Obtaining the Optimal Direction in the Removal of Impulse Noise

8.

Removal of High Density Salt and Pepper Noise Through Modified Decision Based Unsymmetric Trimmed

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Median Filter

trimmed median value when other pixel values, 0's and 255's are present in the selected window and when all the pixel values are 0's and 255's then the noise pixel is replaced by mean value of all the elements present in the selected window. This proposed algorithm shows better results than the Standard Median Filter (MF), Decision Based Algorithm (DBA), Modified Decision Based Algorithm (MDBA), and Progressive Switched Median Filter (PSMF). The proposed algorithm is tested against different grayscale and color images and it gives better Peak Signal -toNoise Ratio (PSNR) and Image Enhancement Factor (IEF). In this correspondence, the authors propose an image resolution Image Processing enhancement technique based on interpolation of the high frequency subband images obtained by discrete wavelet transform (DWT) and the input image. The edges are enhanced by introducing an intermediate stage by using stationary wavelet transform (SWT). DWT is applied in order to decompose an input image into different subbands. Then the high frequency subbands as well as the input image are interpolated. The estimated high frequency subbands are being modified by using high frequency subband obtained through SWT. Then all these subbands are combined to generate a new high resolution image by using inverse DWT (IDWT). The quantit ative and visual results are showing the superiority of the proposed technique over the conventional and state-of-art image resolution enhancement techniques. Under the framework of computer-aided eye disease diagnosis, this paper presents an automatic optic disc (OD) detection technique. The proposed technique makes use of the unique circular brightness structure associated with the OD, i.e., the OD usually has a circular shape and is brighter than the surrounding pixels whose intensity becomes darker gradually with their distances from the OD center. A line operator is designed to capture such circular brightness structure, which evaluates the image brightness variation along multiple line segments of specific orientations that pass through each retinal image pixel. The orientation of the line segment with the minimum/maximum variation has specific pattern that can be used to locate the OD accurately. The proposed tec hnique has been tested over four public datasets that include 130, 89, 40, and 81 images of healthy and pathological retinas, respectively. Experiments show that the designed line operator is tolerant to different types of retinal lesion and imaging artifa and an cts, average OD detection accuracy of 97.4% is obtained. In this letter, we propose an efficient one -nearest-neighbor classifier of texture via the contrast of local energy histograms of all the wavelet subbands between an input texture patch and each sample texture patch in a given training set. In particular, Image Processing

9.

IMAGE Resolution Enhancement by Using Discrete and Stationary Wavelet Decomposition

10. Automatic Optic Disc Detection From Retinal Images by a Line Operator

11. Wavelet-Based Image Texture Classification Using Local Energy Histograms

Image Processing

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the contrast is realized with a discrepancy measure which is just a sum of symmetrized Kullback-Leibler divergences between the input and sample local energy histograms on all the wavelet subbands. It is demonstrated by various experiments that our proposed method obtains a satisfactory texture classification accuracy in comparison with several currentstate-of-the-art texture classification approaches. 12. A Ringing-Artifact Image This paper proposes a new ringing-artifact reduction Processing method for image resizing in a block discrete cosine transform (DCT) domain. The proposed method reduces ringing artifacts without further blurring, whereas previous approaches must find a compromise between blurring and ringing artifacts. The proposed method consists of DCTdomain filtering and image-domain post-processing, which reduces ripples on smooth regions as well as overshoot near strong edges. By generating a mask map of the overshoot regions, we combine a ripple-reduced image and an overshoot-reduced image according to the mask map in the image domain to obtain a ringing-artifact reduced image. The experimental results show that the proposed method is computationally faster and produces visually finer images than previous ringing-artifact reduction approaches.

Reduction Method for Block-DCT-Based Image Resizing