On applying Neuro - Computing in E-com Domain

Abstract: Prior studies have generally suggested that Artificial Neural Networks (ANNs) are superior to conventional statistical models in predicting consumer buying behavior. There are, however, contradicting findings which raise question over usefulness of ANNs. This paper discusses development of three neural networks for modeling consumer e-commerce behavior and compares the findings to equivalent logistic regression models. The results showed that ANNs predict e-commerce adoption slightly more accurately than logistic models but this is hardly justifiable given the added complexity. Further, ANNs seem to be highly adaptive, particularly when a small sample is coupled with a large number of nodes in hidden layers which, in turn, limits the neural networks' generalisability.


Analytical Study for Seeking Relation Between Customer Relationship Management and Enterprise Resource Planning

Abstract: Enterprise Resource Planning (ERP) is a integration of various resources of any organization. It is computer software. All kinds of organization data that is relating to each and every function of the organization are available in ERP. So most of the big business organizations are implementing ERP and some of the medium, small scale companies are also using ERP system. CRM in an organization helps to retain their existing customers as well as capturing new customers for their products. So it makes the organization to produce those goods required by their consumers. This paper focuses mainly on the merging of CRM and ERP through Neural Networks.


A comparative study of performance of fpga based mel filter bank & bark filter bank

Abstract: The sensitivity of human ear is dependent on frequency which is nonlinearly resolved across the audio spectrum .Now to improve the recognition performance in a similar non linear approach requires a front -end design, suggested by empirical evidences. A popular alternative to linear prediction based analysis is therefore filter bank analysis since this provides a much more straightforward route to obtain the desired non-linear frequency resolution. MEL filter bank and BARK filter bank are two popular filter bank analysis techniques. This paper presents FPGA based implementation of MEL filter bank and BARK filter bank with different bandwidths and different signal spectrum ranges. The designs have been implemented using VHDL, simulated and verified using Xilinx 11.1.For each filter bank, the basic building block is implemented in Spartan 3E. A comparative study among these two mentioned filter banks is also done in this paper.


Dispelling Classes Gradually to Improve Quality of Feature Reduction Approaches

Abstract: Feature reduction is an important concept which is used for reducing dimensions to decrease the computation complexity and time of classification. Since now many approaches have been proposed for solving this problem, but almost all of them just presented a fix output for each input dataset that some of them aren't satisfied cases for classification. In this we proposed an approach as processing input dataset to increase accuracy rate of each feature extraction methods. First of all, a new concept called dispelling classes gradually (DCG) is proposed to increase separability of classes based on their labels. Next, this method is used to process input dataset of the feature reduction approaches to decrease the misclassification error rate of their outputs more than when output is achieved without any processing. In addition our method has a good quality to collate with noise based on adapting dataset with feature reduction approaches. In the result part, two conditions (With process and without that) are compared to support our idea by using some of UCI datasets.


Human Arm simulation for interactive constrained environment design

Abstract: During the conceptual and prototype design stage of an industrial product, it is crucial to take assembly/disassembly and maintenance operations in advance. A well-designed system should enable relatively easy access of operating manipulators in the constrained environment and reduce musculoskeletal disorder risks for those manual handling operations. Trajectory planning comes up as an important issue for those assembly and maintenance operations under a constrained environment, since it determines the accessibility and the other ergonomics issues, such as muscle effort and its related fatigue. In this paper, a customer-oriented interactive approach is proposed to partially solve ergonomic related issues encountered during the design stage under a constrained system for the operator's convenience. Based on a single objective optimization method, trajectory planning for different operators could be generated automatically. Meanwhile, a motion capture based method assists the operator to guide the trajectory planning interactively when either a local minimum is encountered within the single objective optimization or the operator prefers guiding the virtual human manually. Besides that, a physical engine is integrated into this approach to provide physically realistic simulation in real time manner, so that collision free path and related dynamic information could be computed to determine further muscle fatigue and accessibility of a product design


A new approach to muscle fatigue evaluation for Push/Pull task

Abstract: Pushing/Pulling tasks is an important part of work in many industries. Usually, most researchers study the Push/Pull tasks by analyzing different posture conditions, force requirements, velocity factors, etc. However few studies have reported the effects of fatigue. Fatigue caused by physical loading is one of the main reasons responsible for MusculoSkeletal Disorders (MSD). In this paper, muscle groups of articulation is considered and from joint level a new approach is proposed for muscle fatigue evaluation in the arms Push/Pull operations. The objective of this work is to predict the muscle fatigue situation in the Push/Pull tasks in order to reduce the probability of MSD problems for workers. A case study is presented to use this new approach for analyzing arm fatigue in Pushing/Pulling.


Security Threats Analysis in Bluetooth-Enabled Mobile Devices

Abstract: Exponential growth of the volume of Bluetooth-enabled devices indicates that it has become a popular way of wireless interconnections for exchanging information. The main goal of this paper is to analyze the most critical Bluetooth attacks in real scenarios. In order to find out the major vulnerabilities in modern Bluetooth-enabled mobile devices several attacks have performed successfully such as- Surveillance, Obfuscation, Sniffing, Unauthorized Direct Data Access (UDDA) and Man-in-the-Middle Attack (MITM). To perform the testbed, several devices are used such as mobile phones, laptops, notebooks, wireless headsets, etc. and all the tests are carried out by pen-testing software like hcittml, braudit, spoafiooph, hridump, bluesnarfer, bluebugger and carwhisperer. KEYWORDS: Bluetooth, Security, Surveillance, Obfuscation, Sniffing, Denial of service, Man-in-the-middle.


Ordinary Search Engine Users Carrying Out Complex Search Tasks

Abstract: Web search engines have become the dominant tools for finding information on the Internet. Due to their popularity, users apply them to a wide range of search needs, from simple look-ups to rather complex information tasks. This paper presents the results of a study to investigate the characteristics of these complex information needs in the context of Web search engines. The aim of the study is to find out more about (1) what makes complex search tasks distinct from simple tasks and if it is possible to find simple measures for describing their complexity, (2) if search success for a task can be predicted by means of unique measures, and (3) if successful searchers show a different behavior than unsuccessful ones. The study includes 60 people who carried out a set of 12 search tasks with current commercial search engines. Their behavior was logged with the Search-Logger tool. The results confirm that complex tasks show significantly different characteristics than simple tasks. Yet it seems to be difficult to distinguish successful from unsuccessful search behaviors. Good searchers can be differentiated from bad searchers by means of measurable parameters. The implications of these findings for search engine vendors are discussed.


Impact of Gender and Age on performing Search Tasks Online

Abstract: More and more people use the Internet to work on duties of their daily work routine. To find the right information online, Web search engines are the tools of their choice. Apart from finding facts, people use Web search engines to also execute rather complex and time consuming search tasks. So far search engines follow the one-for-all approach to serve its users and little is known about the impact of gender and age on people's Web search behavior. In this article we present a study that examines (1) how female and male web users carry out simple and complex search tasks and what are the differences between the two user groups, and (2) how the age of the users impacts their search performance. The laboratory study was done with 56 ordinary people each carrying out 12 search tasks. Our findings confirm that age impacts behavior and search performance significantly, while gender influences were smaller than expected.


Optimizing Face Recognition Using PCA

Abstract: Principle Component Analysis PCA is a classical feature extraction and data representation technique widely used in pattern recognition. It is one of the most successful techniques in face recognition. But it has drawback of high computational especially for big size database. This paper conducts a study to optimize the time complexity of PCA (eigenfaces) that does not affects the recognition performance. The authors minimize the participated eigenvectors which consequently decreases the computational time. A comparison is done to compare the differences between the recognition time in the original algorithm and in the enhanced algorithm. The performance of the original and the enhanced proposed algorithm is tested on face94 face database. Experimental results show that the recognition time is reduced by 35% by applying our proposed enhanced algorithm. DET Curves are used to illustrate the experimental results.


Off-Line Arabic Handwriting Character Recognition Using Word Segmentation

Abstract: The ultimate aim of handwriting recognition is to make computers able to read and/or authenticate human written texts, with a performance comparable to or even better than that of humans. Reading means that the computer is given a piece of handwriting and it provides the electronic transcription of that (e.g. in ASCII format). Two types of handwriting: on-line and offline. The most important purpose of off-line handwriting recognition is in protection systems and authentication. Arabic Handwriting scripts are much more complicated in comparison to Latin scripts. This paper introduces a simple and novel methodology to authenticate Arabic handwriting characters. Reaching our aim, we built our own character database. The research methodology depends on two stages: The first is character extraction where preprocessing the word and then apply segmentation process to obtain the character. The second is the character recognition by matching the characters comprising the word with the letters in the database. Our results ensure character recognition with 81%. We eliminate FAR by using similarity percent between 45-55%. Our research is coded using MATLAB.


DEX: Self-healing Expanders

Abstract: We present a fully-distributed self-healing algorithm DEX, that maintains a constant degree expander network in a dynamic setting. To the best of our knowledge, our algorithm provides the first efficient distributed construction of expanders --- whose expansion properties hold {\em deterministically} --- that works even under an all-powerful adaptive adversary that controls the dynamic changes to the network (the adversary has unlimited computational power and knowledge of the entire network state, can decide which nodes join and leave and at what time, and knows the past random choices made by the algorithm). Previous distributed expander constructions typically provide only {\em probabilistic} guarantees on the network expansion which {\em rapidly degrade} in a dynamic setting; in particular, the expansion properties can degrade even more rapidly under {\em adversarial} insertions and deletions. Our algorithm provides efficient maintenance and incurs a low overhead per insertion/deletion by an adaptive adversary: only $O(\log n)$ rounds and $O(\log n)$ messages are needed with high probability ($n$ is the number of nodes currently in the network). The algorithm requires only a constant number of topology changes. Moreover, our algorithm allows for an efficient implementation and maintenance of a distributed hash table (DHT) on top of DEX, with only a constant additional overhead. Our results are a step towards implementing efficient self-healing networks that have \emph{guaranteed} properties (constant bounded degree and expansion) despite dynamic changes.


Software Aging Analysis of Web Server Using Neural Networks

Abstract: Software aging is a phenomenon that refers to progressive performance degradation or transient failures or even crashes in long running software systems such as web servers. It mainly occurs due to the deterioration of operating system resource, fragmentation and numerical error accumulation. A primitive method to fight against software aging is software rejuvenation. Software rejuvenation is a proactive fault management technique aimed at cleaning up the system internal state to prevent the occurrence of more severe crash failures in the future. It involves occasionally stopping the running software, cleaning its internal state and restarting it. An optimized schedule for performing the software rejuvenation has to be derived in advance because a long running application could not be put down now and then as it may lead to waste of cost. This paper proposes a method to derive an accurate and optimized schedule for rejuvenation of a web server (Apache) by using Radial Basis Function (RBF) based Feed Forward Neural Network, a variant of Artificial Neural Networks (ANN). Aging indicators are obtained through experimental setup involving Apache web server and clients, which acts as input to the neural network model. This method is better than existing ones because usage of RBF leads to better accuracy and speed in convergence.


Performance Analysis of Unsymmetrical trimmed median as detector on image noises and its Fpga implementation

Abstract: This Paper Analyze the performance of Unsymmetrical trimmed median, which is used as detector for the detection of impulse noise, Gaussian noise and mixed noise is proposed. The proposed algorithm uses a fixed 3x3 window for the increasing noise densities. The pixels in the current window are arranged in sorting order using a improved snake like sorting algorithm with reduced comparator. The processed pixel is checked for the occurrence of outliers, if the absolute difference between processed pixels is greater than fixed threshold. Under high noise densities the processed pixel is also noisy hence the median is checked using the above procedure. if found true then the pixel is considered as noisy hence the corrupted pixel is replaced by the median of the current processing window. If median is also noisy then replace the corrupted pixel with unsymmetrical trimmed median else if the pixel is termed uncorrupted and left unaltered. The proposed algorithm (PA) is tested on varying detail images for various noises. The proposed algorithm effectively removes the high density fixed value impulse noise, low density random valued impulse noise, low density Gaussian noise and lower proportion of mixed noise. The proposed algorithm is targeted on Xc3e5000-5fg900 FPGA using Xilinx 7.1 compiler version which requires less number of slices, optimum speed and low power when compared to the other median finding architectures.


Tight Lower Bounds for Unequal Division

Abstract: Alice and Bob want to cut a cake; however, in contrast to the usual problems of fair division, they want to cut it unfairly. More precisely, they want to cut it in ratio $(a:b)$. (We can assume gcd(a,b)=1.) Let f(a,b) be the number of cuts will this take (assuming both act in their own self interest). It is known that f(a,b) \le \ceil{lg(a+b)}. We show that (1) for all a,b, f(a,b) \ge lg(lg(a+b)) + (2) for an infinite number of (a,b), f(a,b) \le 1+lg(lg(a+b).


Architecture for real time continuous sorting on large width data volume for fpga based applications

Abstract: In engineering applications sorting is an important and widely studied problem where execution speed and resources used for computation are of extreme importance, especially if we think about real time data processing. Most of the traditional sorting techniques compute the process after receiving all of the data and hence the process needs large amount of resources for data storage. So, suitable design strategy needs to be adopted if we wish to sort a large amount of data in real time, which essential means higher speed of process execution and utilization of fewer resources in most of the cases. This paper proposes a single chip scalable architecture based on Field Programmable Gate Array(FPGA), for a modified counting sort algorithm where data acquisition and sorting is being done in real time scenario. Our design promises to work efficiently, where data can be accepted in the run time scenario without any need of prior storage of data and also the execution speed of our algorithm is invariant to the length of the data stream. The proposed design is implemented and verified on Spartan 3E(XC3S500E-FG320) FPGA system. The results prove that our design is better in terms of some of the design parameters compared to the existing research works.


Network Selection Decision Based on Handover History in Heterogeneous Wireless Networks

Abstract: In recent years, the mobile devices are equipped with several wireless interfaces in heterogeneous environments which integrate a multitude of radio access technologies (RAT's). The evolution of these technologies will allow the users to benefit simultaneously from these RAT's. However, the most important issue is how to choose the most suitable access network for mobile's user which can be used as long as possible for communication. To achieve this issue, this paper proposes a new approach for network selection decision based on Saaty's Fuzzy Analytical Hierarchy Process (FAHP) and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). The FAHP method is used to determine a weight for each criterion, and the TOPSIS method is applied to rank the alternatives. Simulation results are presented to illustrate the effectiveness of our new approach for network selection.


Nagios Based Enhanced IT Management System

Abstract: The integrated management of multi-providers equipments is a key asset for telecommunication operators or service providers when selecting the appropriate network and service management platform for their network. In this paper, we present an open and adaptable platform that support fault and configuration management for next-generation networks. This platform Named Nagios based - information technology management system (NB-ITMS) leverage of the well-known Nagios platform to implement new capabilities. Offering additional features applications and management functions enable easy and low cost management of advanced services and networks technologies. The performance of our platform has been compared to existing off-the-shell platforms.


Objects and Goals Extraction from Semantic Networks : Applications of Fuzzy SetS Theory

Abstract: In this paper we present a short survey of fuzzy and Semantic approaches to Knowledge Extraction. The goal of such approaches is to define flexible Knowledge Extraction Systems able to deal with the inherent vagueness and uncertainty of the Extraction process. In this survey we address if and how some approaches met their goal.


DWDM/OOC and large spectrum sources performance in broadband access network

Abstract: This work presents a performance evaluation based on elaborated analytical expressions of error probability for broadband access network in the case of a combined technique of dense wavelength division multiplexing (DWDM) and one dimensional optical orthogonal codes (1D-OOC). Optical sources with relatively large spectrum has been considered and simulated. Besides the Multiple Access Interference (MAI) at the receiver due to the access method which is optical code division multiple access (OCDMA), the emitted radiation of these sources in a dense WDM communication link introduces additional interference. Conventional correlation receiver (CCR) and parallel interference cancellation (PIC) receiver limitations are discussed. This paper has investigated the kind of optical sources with large spectrum bandwidth which could be accepted for a targeted bit error rate (BER) and given number of users in broadband access network supporting DWDM with optical orthogonal codes.


Measure of Similarity between Fuzzy Concepts for Optimization of Fuzzy Semantic Nets

Abstract: This paper presents a method to measure the similarity between different fuzzy concepts in order to optimize Semantic networks. The problem approached is the minimization of the time of research and identification of user's Objects and Goals. Indeed, it concerns to determine to each instant the totality of Objects (respectively Goals) among which one can identify rapidly the most satisfactory for the user's Object and Goal. Alone Objects and most similar Goals to Objects and researched Goals of the viewpoint of attribute values will be processed, what will avoid the analysis of all Objects and system Goals far of needs of the user.


Low-complexity Optimal Scheduling over Correlated Fading Channels with ARQ Feedback

Abstract: We investigate the downlink scheduling problem under Markovian ON/OFF fading channels, where the instantaneous channel state information is not directly accessible, but is revealed via ARQ-type feedback. The scheduler can exploit the temporal correlation/channel memory inherent in the Markovian channels to improve network performance. However, designing low-complexity and throughput-optimal algorithms under temporal correlation is a challenging problem. In this paper, we find that under an average number of transmissions constraint, a low-complexity index policy is throughput-optimal. The policy uses Whittle's index value, which was previously used to capture opportunistic scheduling under temporally correlated channels. Our results build on the interesting finding that, under the intricate queue length and channel memory evolutions, the importance of scheduling a user is captured by a simple multiplication of its queue length and Whittle's index value. The proposed queue-based index policy has provably low complexity. Numerical results show that significant throughput gains can be realized by exploiting the channel memory using the proposed low-complexity policy.


PriSM: A Private Social Mesh for Leveraging Social Networking at Workplace

Abstract: In this work we describe the PriSM framework for decentralized deployment of a federation of autonomous social networks (ASN). The individual ASNs are centrally managed by organizations according to their institutional needs, while cross-ASN interactions are facilitated subject to security and confidentiality requirements specified by administrators and users of the ASNs. Such decentralized deployment, possibly either on private or public clouds, provides control and ownership of information/flow to individual organizations. Lack of such complete control (if third party online social networking services were to be used) has so far been a great barrier in taking full advantage of the novel communication mechanisms at workplace that have however become commonplace for personal usage with the advent of Web 2.0 platforms and online social networks. PriSM provides a practical solution for organizations to harness the advantages of online social networking both in intra/inter-organizational settings without sacrificing autonomy, security and confidentiality needs.


Flexible Data Dissemination Strategy for Effective Cache Consistency in Mobile Wireless Communication Networks

Abstract: In mobile wireless communication network, caching data items at the mobile clients is important to reduce the data access delay. However, efficient cache invalidation strategies are used to ensure the consistency between the data in the cache of mobile clients and at the database server. Servers use invalidation reports (IRs) to inform the mobile clients about data item updates. This paper proposes and implements a multicast based strategy to maintain cache consistency in mobile environment using AVI as the cache invalidation scheme. The proposed algorithm is outlined as follows - To resolve a query, the mobile client searches its cache to check if its data is valid. If yes, then query is answered, otherwise the client queries the DTA (Dynamic Transmitting Agent) for latest updates and the query is answered. If DTA doesn't have the latest updates, it gets it from the server. So, the main idea here is that DTA will be multicasting updates to the clients and hence the clients need not uplink to the server individually, thus preserving the network bandwidth. The scenario of simulation is developed in Java. The results demonstrate that the traffic generated in the proposed multicast model is simplified and it also retains cache consistency when compared to the existing methods that used broadcast strategy.


Dynamic MPLS with Feedback

Abstract: Multiprotocol Label Switching (MPLS) fasten the speed of packet forwarding by forwarding the packets based on labels and reduces the use of routing table look up from all routers to label edge routers(LER), where as the label switch routers (LSRs) uses Label Distribution Protocol (LDP) or RSVP (Resource reservation Protocol) for label allocation and Label table for packet forwarding. Dynamic protocol is implemented which carries a Updates packets for the details of Label Switch Paths, along with this feedback mechanism is also introduced which find the shortest path among MPLS network and also feedback is provided which also help to overcome congestion, this feedback mechanism is on a hop by hop basis rather than end to end thus providing a more reliable and much faster and congestion free path for the packets.


A Distributed Optimized Patient Scheduling using Partial Information

Abstract: A software agent may be a member of a Multi-Agent System (MAS) which is collectively performing a range of complex and intelligent tasks. In the hospital, scheduling decisions are finding difficult to schedule because of the dynamic changes and distribution. In order to face this problem with dynamic changes in the hospital, a new method, Distributed Optimized Patient Scheduling with Grouping (DOPSG) has been proposed. The goal of this method is that there is no necessity for knowing patient agents information globally. With minimal information this method works effectively. Scheduling problem can be solved for multiple departments in the hospital. Patient agents have been scheduled to the resource agent based on the patient priority to reduce the waiting time of patient agent and to reduce idle time of resources.


Behavioural Types for Actor Systems

Abstract: Recent mainstream programming languages such as Erlang or Scala have renewed the interest on the Actor model of concurrency. However, the literature on the static analysis of actor systems is still lacking of mature formal methods. In this paper we present a minimal actor calculus that takes as primitive the basic constructs of Scala's Actors API. More precisely, actors can send asynchronous messages, process received messages according to a pattern matching mechanism, and dynamically create new actors, whose scope can be extruded by passing actor names as message parameters. Drawing inspiration from the linear types and session type theories developed for process calculi, we put forward a behavioural type system that addresses the key issues of an actor calculus. We then study a safety property dealing with the determinism of finite actor com- munication. More precisely, we show that well typed and balanced actor systems are (i) deadlock-free and (ii) any message will eventually be handled by the target actor, and dually no actor will indefinitely wait for an expected message


Generating spherical multiquadrangulations by restricted vertex splittings and the reducibility of equilibrium classes

Abstract: A quadrangulation is a graph embedded on the sphere such that each face is bounded by a walk of length 4, parallel edges allowed. All quadrangulations can be generated by a sequence of graph operations called vertex splitting, starting from the path P_2 of length 2. We define the degree D of a splitting S and consider restricted splittings S_{i,j} with i <= D <= j. It is known that S_{2,3} generate all simple quadrangulations. Here we investigate the cases S_{1,2}, S_{1,3}, S_{1,1}, S_{2,2}, S_{3,3}. First we show that the splittings S_{1,2} are exactly the monotone ones in the sense that the resulting graph contains the original as a subgraph. Then we show that they define a set of nontrivial ancestors beyond P_2 and each quadrangulation has a unique ancestor. Our results have a direct geometric interpretation in the context of mechanical equilibria of convex bodies. The topology of the equilibria corresponds to a 2-coloured quadrangulation with independent set sizes s, u. The numbers s, u identify the primary equilibrium class associated with the body by V\'arkonyi and Domokos. We show that both S_{1,1} and S_{2,2} generate all primary classes from a finite set of ancestors which is closely related to their geometric results. If, beyond s and u, the full topology of the quadrangulation is considered, we arrive at the more refined secondary equilibrium classes. As Domokos, L\'angi and Szab\'o showed recently, one can create the geometric counterparts of unrestricted splittings to generate all secondary classes. Our results show that S_{1,2} can only generate a limited range of secondary classes from the same ancestor. The geometric interpretation of the additional ancestors defined by monotone splittings shows that minimal polyhedra play a key role in this process. We also present computational results on the number of secondary classes and multiquadrangulations.


Algebraic Characterization of the Class of Languages recognized by Measure Only Quantum Automata

Abstract: We study a model of one-way quantum automaton where only measurement operations are allowed (MOn-1qfa). We give an algebraic characterization of LMO, showing that the syntactic monoids of the languages in LMO are exactly the literal pseudovariety of J-trivial literally idempotent monoids, where J is the Green's relation determined by two-sided ideals. We also prove that LMO coincides with the literal variety of literally idempotent piecewise testable regular languages. This allows us to prove the existence of a polynomial time algorithm for deciding whether a regular language belongs to LMO.


C-Band VSAT Data Communication System and RF Impairments

Abstract: This paper is concerned with modelling and simulation of VSAT (very small aperture terminal) data messaging network operating in India at Karnataka with extended C-band. VSATs in Karnataka of KPTCL use VSATS 6.875-6.9465G Hz uplinks and 4.650- 4.7215 GHz downlinks. These frequencies are dedicated to fixed services. The Satellite is Intelsat -3A, the hub has a 7.2 m diameter antenna and uses 350W or 600W TWTA (Travelling wave Tube Amplifier). The VSAT's are 1.2 m with RF power of 1W or 2W depending on their position in the uplink beam with data rate of 64 or 128 K bit/s. The performance of the system is analysed by the error probability called BER (Bit Error Rate) and results are derived from Earth station to hub and hub to Earth station using satellite Transponder as the media of communication channel. The Link budgets are developed for a single one-way satellite link.


Softening Fuzzy Knowledge Representation Tool with the Learning of New Words in Natural Language

Abstract: The approach described here allows using membership function to represent imprecise and uncertain knowledge by learning in Fuzzy Semantic Networks. This representation has a great practical interest due to the possibility to realize on the one hand, the construction of this membership function from a simple value expressing the degree of interpretation of an Object or a Goal as compared to an other and on the other hand, the adjustment of the membership function during the apprenticeship. We show, how to use these membership functions to represent the interpretation of an Object (respectively of a Goal) user as compared to an system Object (respectively to a Goal). We also show the possibility to make decision for each representation of an user Object compared to a system Object. This decision is taken by determining decision coefficient calculates according to the nucleus of the membership function of the user Object.


A novel approach for security issues in VoIP networks in Virtualization with IVR

Abstract: VoIP (Voice over Internet Protocol) is a growing technology during last decade. It provides the audio, video streaming facility on successful implementation in the network. However, it provides the text transport facility over the network. Due to implementation of it the cost effective solution, it can be developed for the intercommunication among the employees of a prestigious organization. The proposed idea has been implemented on the audio streaming area of the VoIP technology. In the audio streaming, the security vulnerabilities are possible on the VoIP server during communication between two parties. In the proposed model, first the VoIP system has been implemented with IVR (Interactive Voice Response) as a case study and with the implementation of the security parameters provided to the asterisk server which works as a VoIP service provider. The asterisk server has been configured with different security parameters like VPN server, Firewall iptable rules, Intrusion Detection and Intrusion Prevention System. Every parameter will be monitored by the system administrator of the VoIP server along with the MySQL database. The system admin will get every update related to the attacks on the server through Mail server attached to the asterisk server. The main beauty of the proposed system is VoIP server alone is configured as a VoIP server, IVR provider, Mail Server with IDS and IPS, VPN server, connection with database server in a single asterisk server inside virtualization environment. The VoIP system is implemented for a Local Area Network inside the university system


Patience of Matrix Games

Abstract: For matrix games we study how small nonzero probability must be used in optimal strategies. We show that for nxn win-lose-draw games (i.e. (-1,0,1) matrix games) nonzero probabilities smaller than n^{-O(n)} are never needed. We also construct an explicit nxn win-lose game such that the unique optimal strategy uses a nonzero probability as small as n^{-Omega(n)}. This is done by constructing an explicit (-1,1) nonsingular nxn matrix, for which the inverse has only nonnegative entries and where some of the entries are of value n^{Omega(n)}.


Internet Advertising: An Interplay among Advertisers, Online Publishers, Ad Exchanges and Web Users

Abstract: Internet advertising is a fast growing business which has proved to be significantly important in digital economics. It is vitally important for both web search engines and online content providers and publishers because web advertising provides them with major sources of revenue. Its presence is increasingly important for the whole media industry due to the influence of the Web. For advertisers, it is a smarter alternative to traditional marketing media such as TVs and newspapers. As the web evolves and data collection continues, the design of methods for more targeted, interactive, and friendly advertising may have a major impact on the way our digital economy evolves, and to aid societal development. Towards this goal mathematically well-grounded Computational Advertising methods are becoming necessary and will continue to develop as a fundamental tool towards the Web. As a vibrant new discipline, Internet advertising requires effort from different research domains including Information Retrieval, Machine Learning, Data Mining and Analytic, Statistics, Economics, and even Psychology to predict and understand user behaviours. In this paper, we provide a comprehensive survey on Internet advertising, discussing and classifying the research issues, identifying the recent technologies, and suggesting its future directions. To have a comprehensive picture, we first start with a brief history, introduction, and classification of the industry and present a schematic view of the new advertising ecosystem. We then introduce four major participants, namely advertisers, online publishers, ad exchanges and web users; and through analysing and discussing the major research problems and existing solutions from their perspectives respectively, we discover and aggregate the fundamental problems that characterise the newly-formed research field and capture its potential future prospects.


Fuzzy Knowledge Representation, Learning and Optimization with Bayesian Analysis in Fuzzy Semantic Networks

Abstract: This paper presents a method of optimization, based on both Bayesian Analysis technical and Gallois Lattice, of a Fuzzy Semantic Networks. The technical System we use learn by interpreting an unknown word using the links created between this new word and known words. The main link is provided by the context of the query. When novice's query is confused with an unknown verb (goal) applied to a known noun denoting either an object in the ideal user's Network or an object in the user's Network, the system infer that this new verb corresponds to one of the known goal. With the learning of new words in natural language as the interpretation, which was produced in agreement with the user, the system improves its representation scheme at each experiment with a new user and, in addition, takes advantage of previous discussions with users. The semantic Net of user objects thus obtained by these kinds of learning is not always optimal because some relationships between couple of user objects can be generalized and others suppressed according to values of forces that characterize them. Indeed, to simplify the obtained Net, we propose to proceed to an inductive Bayesian analysis, on the Net obtained from Gallois lattice. The objective of this analysis can be seen as an operation of filtering of the obtained descriptive graph.


Hidden Mobile Guards in Simple Polygons

Abstract: We consider guarding classes of simple polygons using mobile guards (polygon edges and diagonals) under the constraint that no two guards may see each other. In contrast to most other art gallery problems, existence is the primary question: does a specific type of polygon admit some guard set? Types include simple polygons and the subclasses of orthogonal, monotone, and starshaped polygons. Additionally, guards may either exclude or include the endpoints (so-called open and closed guards). We provide a nearly complete set of answers to existence questions of open and closed edge, diagonal, and mobile guards in simple, orthogonal, monotone, and starshaped polygons, with some surprising results. For instance, every monotone or starshaped polygon can be guarded using hidden open mobile (edge or diagonal) guards, but not necessarily with hidden open edge or hidden open diagonal guards.


CARE: Content Aware Redundancy Elimination for Disaster Communications on Damaged Networks

Abstract: During a disaster scenario, situational awareness information, such as location, physical status and images of the surrounding area, is essential for minimizing loss of life, injury, and property damage. Today's handhelds make it easy for people to gather data from within the disaster area in many formats, including text, images and video. Studies show that the extreme anxiety induced by disasters causes humans to create a substantial amount of repetitive and redundant content. Transporting this content outside the disaster zone can be problematic when the network infrastructure is disrupted by the disaster. This paper presents the design of a novel architecture called CARE (Content-Aware Redundancy Elimination) for better utilizing network resources in disaster-affected regions. Motivated by measurement-driven insights on redundancy patterns found in real-world disaster area photos, we demonstrate that CARE can detect the semantic similarity between photos in the networking layer, thus reducing redundant transfers and improving buffer utilization. Using DTN simulations, we explore the boundaries of the usefulness of deploying CARE on a damaged network, and show that CARE can reduce packet delivery times and drops, and enables 20-40% more unique information to reach the rescue teams outside the disaster area than when CARE is not deployed.


CyberChair: A Web-Based Groupware Application to Facilitate the Paper Reviewing Process

Abstract: In this paper we describe CyberChair, a web-based groupware application that supports the review process for technical contributions to conferences. CyberChair deals with most administrative tasks that are involved in the review process, such as storing author information, abstracts, (camera-ready) papers and reviews. It generates several overviews based on the reviews which support the Program Committee (PC) in selecting the best papers. CyberChair points out conflicting reviews and offers the reviewers means to easily communicate to solve these conflicts. In his paper Identify the Champion, O. Nierstrasz describes this review process in terms of a pattern language. CyberChair supports PCs by using these patterns in its implementation.


Efficient Algorithms for Finding Tucker Patterns

Abstract: The Consecutive Ones Property is an important notion for binary matrices, both from a theoretical and applied point of view. Tucker gave in 1972 a characterization of matrices that do not satisfy the Consecutive Ones Property in terms of forbidden submatrices, the Tucker patterns. We describe here a linear time algorithm to find a Tucker pattern in a non-C1P binary matrix, which allows to extract in linear time a certificate for the non-C1P. We also describe an output-sensitive algorithm to enumerate all Tucker patterns of a non-C1P binary matrix. This paper had been withdrawn due to some missing cases in Algorithms 2 and 3.


An Enhanced Evaluation Model For Vertical Handover Algorithm In Heterogeneous Networks

Abstract: The vertical handover decision is considered an NP-Hard problem. For that reason, a large variety of vertical handoff algorithms (VHA) have been proposed to help the user to select dynamically the best access network in terms of quality of service (QoS). The objective of this paper is to provide a new approach for evaluating of the vertical handoff algorithms in order to choose the most appropriate algorithm which should be used to select the best access network. Simulation results are presented to evaluate and to test our new evaluation model


Concept of drafting detection system in Ironmans

Abstract: One of the biggest challenges for the Computer Science of today can be summed up by the paradigm "access to information from $everywhere$ at $anytime$". This is especially true for pervasive computing. With the growth of mobile devices (e.g., smart-phones), on the one hand, and the quick development of the Internet (this has become the really pervasive network of today), on the other hand, the development of real-time pervasive applications has broadened. This paper focuses on the problem of drafting detection in the Ironman triathlons which causes serious problems for the majority of organizers regarding such competitions. A concept of drafting detection system in Ironman is based on the paradigm of pervasive computing. Results of performing a test system show that this concept can along with further development of computer technologies become a reality in the near future.


Optimization of Fuzzy Semantic Networks Based on Galois Lattice and Bayesian Formalism

Abstract: This paper presents a method of optimization, based on both Bayesian Analysis technical and Galois Lattice of Fuzzy Semantic Network. The technical System we use learns by interpreting an unknown word using the links created between this new word and known words. The main link is provided by the context of the query. When novice's query is confused with an unknown verb (goal) applied to a known noun denoting either an object in the ideal user's Network or an object in the user's Network, the system infer that this new verb corresponds to one of the known goal. With the learning of new words in natural language as the interpretation, which was produced in agreement with the user, the system improves its representation scheme at each experiment with a new user and, in addition, takes advantage of previous discussions with users. The semantic Net of user objects thus obtained by learning is not always optimal because some relationships between couple of user objects can be generalized and others suppressed according to values of forces that characterize them. Indeed, to simplify the obtained Net, we propose to proceed to an Inductive Bayesian Analysis, on the Net obtained from Galois lattice. The objective of this analysis can be seen as an operation of filtering of the obtained descriptive graph.


Perceptual quality comparison between single-layer and scalable videos at the same spatial, temporal and amplitude resolutions

Abstract: In this paper, the perceptual quality difference between scalable and single-layer videos coded at the same spatial, temporal and amplitude resolution (STAR) is investigated through a subjective test using a mobile platform. Three source videos are considered and for each source video single-layer and scalable video are compared at 9 different STARs. We utilize paired comparison methods with and without tie option. Results collected from 10 subjects in the without "tie" option and 6 subjects in the with "tie" option show that there is no significant quality difference between scalable and singlelayer video when coded at the same STAR. An analysis of variance (ANOVA) test is also performed to further confirm the finding.


Escaping Prisoner's Dilemmas: From Discord to Harmony in the Landscape of 2x2 Games

Abstract: Changes in payoffs can transform Prisoner's Dilemma and other social dilemmas into harmonious win-win games. Using the Robinson-Goforth topology of 2x2 games, this paper analyzes how payoff swaps turn Prisoner's Dilemma into other games, compares Prisoner's Dilemmas with other families of games, traces paths that affect the difficulty of transforming Prisoner's Dilemma and other social dilemmas into win-win games, and shows how ties connect simpler and more complex games. Charts illustrate the relationships between the 144 strict ordinal 2x2 games, the 38 symmetric 2x2 ordinal games with and without ties, and the complete set of 1,413 2x2 ordinal games. Payoffs from the symmetric ordinal 2x2 games combine to form asymmetric games, generating coordinates for a simple labeling scheme to uniquely identify and locate all asymmetric ordinal 2x2 games. The expanded topology elegantly maps relationships between 2x2 games with and without ties, enables a systematic understanding of the potential for transformations in social dilemmas and other strategic interactions, offers a tool for institutional analysis and design, and locates a variety of interesting games for further research.


TTMA: Traffic-adaptive Time-division Multiple Access Protocol Wireless Sensor Networks

Abstract: This paper has been withdrawn by arXiv. arXiv admin note: author list truncated due to disputed authorship and content. This submission repeats large portions of text from this http URL by other authors. Duty cycle mode in WSN improves energy-efficiency, but also introduces packet delivery latency. Several duty-cycle based MAC schemes have been proposed to reduce latency, but throughput is limited by duty-cycled scheduling performance. In this paper, a Traffic-adaptive Time-division Multiple Access (TTMA), a distributed TDMA-based MAC protocol is introduced to improves the throughput by traffic-adaptive time-slot scheduling that increases the channel utilisation efficiency. The proposed time-slot scheduling method first avoids time-slots assigned to nodes with no traffic through fast traffic notification. It then achieves better channel utilisation among nodes having traffic through an ordered schedule negotiation scheme. By decomposing traffic notification and data transmission scheduling into two phases leads each phase to be simple and efficient. The performance evaluation shows that the two-phase design significantly improves the throughput and outperforms the time division multiple access (TDMA) control with slot stealing.


Mechanism Designs for Stochastic Resources for Renewable Energy Integration

Abstract: Among the many challenges of integrating renewable energy sources into the existing power grid, is the challenge of integrating renewable energy generators into the power systems economy. Electricity markets currently are run in a way that participating generators must supply contracted amounts. And yet, renewable energy generators such as wind power generators cannot supply contracted amounts with certainty. Thus, alternative market architectures must be considered where there are aggregator entities who participate in the electricity market by buying power from the renewable energy generators, and assuming risk of any shortfall from contracted amounts. In this paper, we propose auction mechanisms that can be used by the aggregators for procuring stochastic resources, such as wind power. The nature of stochastic resources is different from classical resources in that such a resource is only available stochastically. The distribution of the generation is private information, and the system objective is to truthfully elicit such information. We introduce a variant of the VCG mechanism for this problem. We also propose a non-VCG mechanism with a contracted-payment-plus-penalty payoff structure. We generalize the basic mechanisms in various ways. We then consider the setting where there are two classes of players to demonstrate the difficulty of auction design in such scenarios. We also consider an alternative architecture where the generators need to fulfill any shortfall from the contracted amount by buying from the spot market.


Routing Protocols for Mobile and Vehicular Ad-Hoc Networks: A Comparative Analysis

Abstract: We present comparative analysis of MANET (Mobile Ad-Hoc Network) and VANET (Vehicular Ad-Hoc Network) routing protocols, in this paper. The analysis is based on various design factors. The traditional routing protocols of AODV (Ad hoc On-Demand Distance Vector), DSR (Dynamic Source Routing), and DSDV (Destination-Sequenced Distance-Vector) of MANET are utilizing node centric routing which leads to frequent breaking of routes, causing instability in routing. Usage of these protocols in high mobility environment like VANET may eventually cause many packets to drop. Route repairs and failures notification overheads increase significantly leading to low throughput and long delays. Such phenomenon is not suitable for Vehicular Ad hoc Networks (VANET) due to high mobility of nodes where network can be dense or sparse. Researchers have proposed various routing algorithms or mechanism for MANET and VANET. This paper describes the relevant protocols, associated algorithm and the strength and weakness of these routing protocols.


Completeness for Two Left-Sequential Logics

Abstract: Left-sequential logics provide a means for reasoning about (closed) propositional terms with atomic propositions that may have side effects and that are evaluated sequentially from left to right. Such propositional terms are commonly used in programming languages to direct the flow of a program. In this thesis we explore two such left-sequential logics. First we discuss Fully Evaluated Left-Sequential Logic, which employs a full evaluation strategy, i.e., to evaluate a term every one of its atomic propositions is evaluated causing its possible side effects to occur. We then turn to Short-Circuit (Left-Sequential) Logic as presented in [BP10b], where the evaluation may be 'short-circuited', thus preventing some, if not all, of the atomic propositions in a term being evaluated. We propose evaluation trees as a natural semantics for both logics and provide axiomatizations for the least identifying variant of each. From this, we define a logic with connectives that prescribe a full evaluation strategy as well as connectives that prescribe a short-circuit evaluation strategy.


MediaWise - Designing a Smart Media Cloud

Abstract: The MediaWise project aims to expand the scope of existing media delivery systems with novel cloud, personalization and collaboration capabilities that can serve the needs of more users, communities, and businesses. The project develops a MediaWise Cloud platform that supports do-it-yourself creation, search, management, and consumption of multimedia content. The MediaWise Cloud supports pay-as-you-go models and elasticity that are similar to those offered by commercially available cloud services. However, unlike existing commercial CDN services providers such as Limelight Networks and Akamai the MediaWise Cloud require no ownerships of computing infrastructure and instead rely on the public Internet and public cloud services (e.g., commercial cloud storage to store its content). In addition to integrating such public cloud services into a public cloud-based Content Delivery Network, the MediaWise Cloud also provides advanced Quality of Service (QoS) management as required for the delivery of streamed and interactive high resolution multimedia content. In this paper, we give a brief overview of MediaWise Cloud architecture and present a comprehensive discussion on research objectives related to its service components. Finally, we also compare the features supported by the existing CDN services against the envisioned objectives of MediaWise Cloud.


Improvement of Loadability in Distribution System Using Genetic Algorithm

Abstract: Generally during recent decades due to development of power systems, the methods for delivering electrical energy to consumers, and because of voltage variations is a very important problem, the power plants follow this criteria. The good solution for improving transfer and distribution of electrical power the majority of consumers prefer to use energy near the loads .So small units that are connected to distribution system named "Decentralized Generation" or "Dispersed Generation". Deregulated in power industry and development of renewable energies are the most important factors in developing this type of electricity generation. Today DG has a key role in electrical distribution systems. For example we can refer to improving reliability indices, improvement of stability and reduction of losses in power system. One of the key problems in using DG's, is allocation of these sources in distribution networks. Load ability in distribution systems and its improvement has an effective role in the operation of power systems. However, placement of distributed generation sources in order to improve the distribution system load ability index was not considered, we show DG placement and allocation with genetic algorithm optimization method maximize load ability of power systems .This method implemented on the IEEE Standard bench marks. The results show the effectiveness of the proposed algorithm .Another benefits of DG in selected positions are also studied and compared.


A Survey on Various Data Hiding Techniques and their Comparative Analysis

Abstract: With the explosive growth of internet and the fast communication techniques in recent years the security and the confidentiality of the sensitive data has become of prime and supreme importance and concern. To protect this data from unauthorized access and tampering various methods for data hiding like cryptography, hashing, authentication have been developed and are in practice today. In this paper we will be discussing one such data hiding technique called Steganography. Steganography is the process of concealing sensitive information in any media to transfer it securely over the underlying unreliable and unsecured communication network. Our paper presents a survey on various data hiding techniques in Steganography that are in practice today along with the comparative analysis of these techniques.


A novel 2.5D approach for interfacing with web applications

Abstract: Web applications need better user interface to be interactive and attractive. A new approach/concept of dimensional enhancement - 2.5D "a 2D display of a virtual 3D environment", which can be implemented in social networking sites and further in other system applications.


Implementation of EasyTime Formal Semantics using a LISA Compiler Generator

Abstract: A manual measuring time tool in mass sporting competitions would not be imaginable nowadays, because many modern disciplines, such as IRONMAN, last a long-time and, therefore, demand additional reliability. Moreover, automatic timing-devices based on RFID technology, have become cheaper. However, these devices cannot operate as stand-alone because they need a computer measuring system that is capable of processing incoming events, encoding the results, assigning them to the correct competitor, sorting the results according to the achieved times, and then providing a printout of the results. This article presents the domain-specific language EasyTime, which enables the controlling of an agent by writing the events within a database. It focuses, in particular, on the implementation of EasyTime with a LISA tool that enables the automatic construction of compilers from language specifications, using Attribute Grammars.


A Connectionist Network Approach to Find Numerical Solutions of Diophantine Equations

Abstract: The paper introduces a connectionist network approach to find numerical solutions of Diophantine equations as an attempt to address the famous Hilbert's tenth problem. The proposed methodology uses a three layer feed forward neural network with back propagation as sequential learning procedure to find numerical solutions of a class of Diophantine equations. It uses a dynamically constructed network architecture where number of nodes in the input layer is chosen based on the number of variables in the equation. The powers of the given Diophantine equation are taken as input to the input layer. The training of the network starts with initial random integral weights. The weights are updated based on the back propagation of the error values at the output layer. The optimization of weights is augmented by adding a momentum factor into the network. The optimized weights of the connection between the input layer and the hidden layer are taken as numerical solution of the given Diophantine equation. The procedure is validated using different Diophantine Equations of different number of variables and different powers.


Visibility-Monotonic Polygon Deflation

Abstract: A deflated polygon is a polygon with no visibility crossings. We answer a question posed by Devadoss et al. (2012) by presenting a polygon that cannot be deformed via continuous visibility-decreasing motion into a deflated polygon. We show that the least n for which there exists such an n-gon is seven. In order to demonstrate non-deflatability, we use a new combinatorial structure for polygons, the directed dual, which encodes the visibility properties of deflated polygons. We also show that any two deflated polygons with the same directed dual can be deformed, one into the other, through a visibility-preserving deformation.


Energy-Aware Scheduling using Dynamic Voltage-Frequency Scaling

Abstract: The energy consumption issue in distributed computing systems has become quite critical due to environmental concerns. In response to this, many energy-aware scheduling algorithms have been developed primarily by using the dynamic voltage-frequency scaling (DVFS) capability incorporated in recent commodity processors. The majority of these algorithms involve two passes: schedule generation and slack reclamation. The latter is typically achieved by lowering processor frequency for tasks with slacks. In this article, we study the latest papers in this area and develop them. This study has been evaluated based on results obtained from experiments with 1,500 randomly generated task graphs.


An Energy Driven Architecture for Wireless Sensor Networks

Abstract: Most wireless sensor networks operate with very limited energy sources-their batteries, and hence their usefulness in real life applications is severely constrained. The challenging issues are how to optimize the use of their energy or to harvest their own energy in order to lengthen their lives for wider classes of application. Tackling these important issues requires a robust architecture that takes into account the energy consumption level of functional constituents and their interdependency. Without such architecture, it would be difficult to formulate and optimize the overall energy consumption of a wireless sensor network. Unlike most current researches that focus on a single energy constituent of WSNs independent from and regardless of other constituents, this paper presents an Energy Driven Architecture (EDA) as a new architecture and indicates a novel approach for minimising the total energy consumption of a WSN


Developing a model for a text database indexed pedagogically for teaching the Arabic language

Abstract: In this memory we made the design of an indexing model for Arabic language and adapting standards for describing learning resources used (the LOM and their application profiles) with learning conditions such as levels education of students, their levels of understanding...the pedagogical context with taking into account the repre-sentative elements of the text, text's length,...in particular, we highlight the specificity of the Arabic language which is a complex language, characterized by its flexion, its voyellation and its agglutination.


Temporal expression normalisation in natural language texts

Abstract: Automatic annotation of temporal expressions is a research challenge of great interest in the field of information extraction. In this report, I describe a novel rule-based architecture, built on top of a pre-existing system, which is able to normalise temporal expressions detected in English texts. Gold standard temporally-annotated resources are limited in size and this makes research difficult. The proposed system outperforms the state-of-the-art systems with respect to TempEval-2 Shared Task (value attribute) and achieves substantially better results with respect to the pre-existing system on top of which it has been developed. I will also introduce a new free corpus consisting of 2822 unique annotated temporal expressions. Both the corpus and the system are freely available on-line.


Network Load Analysis and Provisioning of MapReduce Applications

Abstract: In this paper, we study the dependency between configuration parameters and network load of fixed-size MapReduce applications in shuffle phase and then propose an analytical method to model this dependency. Our approach consists of three key phases: profiling, modeling, and prediction. In the first stage, an application is run several times with different sets of MapReduce configuration parameters (here number of mappers and number of reducers) to profile the network load of the application in the shuffle phase on a given cluster. Then, the relation between these parameters and the network load is modeled by multivariate linear regression. For evaluation, three applications (WordCount, Exim Mainlog parsing, and TeraSort) are utilized to evaluate our technique on a 4-node MapReduce private cluster.


Timely Coordination in a Multi-Agent System

Abstract: In a distributed algorithm, multiple processes, or agents, work toward a common goal. More often than not, the actions of some agents are dependent on the previous execution (if not also on the outcome) of the actions of other agents. The resulting interdependencies between the timings of the actions of the various agents give rise to the study of methods for timely coordination of these actions. In this work, we formulate and mathematically analyze "Timely-Coordinated Response" - a novel multi-agent coordination problem in which the time difference between each pair of actions may be constrained by upper and/or lower bounds. This problem generalizes coordination problems previously studied by Halpern and Moses and by Ben-Zvi and Moses. We optimally solve timely-coordinated response in two ways: using a generalization of the fixed-point approach of Halpern and Moses, and using a generalization of the "syncausality" approach of Ben-Zvi and Moses. We constructively show the equivalence of the solutions yielded by both approaches, and by combining them, derive strengthened versions of known results for some previously-defined special cases of this problem. Our analysis is conducted under minimal assumptions: we work in a continuous-time model with possibly infinitely many agents. The general results we obtain for this model reduce to stronger ones for discrete-time models with only finitely many agents. In order to distill the properties of such models that are significant to this reduction, we define several classes of naturally-occurring models, which in a sense separate the different results. We present both a more practical optimal solution, as well as a surprisingly simple condition for solvability, for timely coordinated response under these models. Finally, we show how our results generalize the results known for previously-studied special cases of this problem.


Programming in logic without Prolog

Abstract: Logic can be made useful for programming and for databases independently of logic programming. To be useful in this way, logic has to provide a mechanism for the definition of new functions and new relations on the basis of those given in the interpretation of a logical theory. We provide this mechanism by creating a compositional semantics on top of the classical semantics. In this approach verification of computational results relies on a correspondence between logic interpretations and a class definition in languages like Java or C++. The advantage of this approach is the combination of an expressive medium for the programmer with, in the case of C++, optimal use of computer resources.


CloudMine: Multi-Party Privacy-Preserving Data Analytics Service

Abstract: An increasing number of businesses are replacing their data storage and computation infrastructure with cloud services. Likewise, there is an increased emphasis on performing analytics based on multiple datasets obtained from different data sources. While ensuring security of data and computation outsourced to a third party cloud is in itself challenging, supporting analytics using data distributed across multiple, independent clouds is even further from trivial. In this paper we present CloudMine, a cloud-based service which allows multiple data owners to perform privacy-preserved computation over the joint data using their clouds as delegates. CloudMine protects data privacy with respect to semi-honest data owners and semi-honest clouds. It furthermore ensures the privacy of the computation outputs from the curious clouds. It allows data owners to reliably detect if their cloud delegates have been lazy when carrying out the delegated computation. CloudMine can run as a centralized service on a single cloud, or as a distributed service over multiple, independent clouds. CloudMine supports a set of basic computations that can be used to construct a variety of highly complex, distributed privacy-preserving data analytics. We demonstrate how a simple instance of CloudMine (secure sum service) is used to implement three classical data mining tasks (classification, association rule mining and clustering) in a cloud environment. We experiment with a prototype of the service, the results of which suggest its practicality for supporting privacy-preserving data analytics as a (multi) cloud-based service.


Finishing Flows Quickly with Preemptive Scheduling

Abstract: Today's data centers face extreme challenges in providing low latency. However, fair sharing, a principle commonly adopted in current congestion control protocols, is far from optimal for satisfying latency requirements. We propose Preemptive Distributed Quick (PDQ) flow scheduling, a protocol designed to complete flows quickly and meet flow deadlines. PDQ enables flow preemption to approximate a range of scheduling disciplines. For example, PDQ can emulate a shortest job first algorithm to give priority to the short flows by pausing the contending flows. PDQ borrows ideas from centralized scheduling disciplines and implements them in a fully distributed manner, making it scalable to today's data centers. Further, we develop a multipath version of PDQ to exploit path diversity. Through extensive packet-level and flow-level simulation, we demonstrate that PDQ significantly outperforms TCP, RCP and D3 in data center environments. We further show that PDQ is stable, resilient to packet loss, and preserves nearly all its performance gains even given inaccurate flow information.


Comments on "On Approximating Euclidean Metrics by Weighted t-Cost Distances in Arbitrary Dimension"

Abstract: Mukherjee (Pattern Recognition Letters, vol. 32, pp. 824-831, 2011) recently introduced a class of distance functions called weighted t-cost distances that generalize m-neighbor, octagonal, and t-cost distances. He proved that weighted t-cost distances form a family of metrics and derived an approximation for the Euclidean norm in $\mathbb{Z}^n$. In this note we compare this approximation to two previously proposed Euclidean norm approximations and demonstrate that the empirical average errors given by Mukherjee are significantly optimistic in $\mathbb{R}^n$. We also propose a simple normalization scheme that improves the accuracy of his approximation substantially with respect to both average and maximum relative errors.


Abstract: We present a suite of algorithms for Dimension Independent Similarity Computation (DISCO) to compute all pairwise similarities between very high dimensional sparse vectors. All of our results are provably independent of dimension, meaning apart from the initial cost of trivially reading in the data, all subsequent operations are independent of the dimension, thus the dimension can be very large. We study Cosine, Dice, Overlap, and the Jaccard similarity measures. For Jaccard similiarity we include an improved version of MinHash. Our results are geared toward the MapReduce framework. We empirically validate our theorems at large scale using data from the social networking site Twitter. At time of writing, our algorithms are live in production at twitter.com.


Manipulation and Control Complexity of Schulze Voting

Abstract: Schulze voting is a recently introduced voting system enjoying unusual popularity and a high degree of real-world use, with users including the Wikimedia foundation, several branches of the Pirate Party, and MTV. It is a Condorcet voting system that determines the winners of an election using information about paths in a graph representation of the election. We resolve the complexity of many electoral control cases for Schulze voting. We find that it falls short of the best known voting systems in terms of control resistance, demonstrating vulnerabilities of concern to some prospective users of the system.


Extending Term Suggestion with Author Names

Abstract: Term suggestion or recommendation modules can help users to formulate their queries by mapping their personal vocabularies onto the specialized vocabulary of a digital library. While we examined actual user queries of the social sciences digital library Sowiport we could see that nearly one third of the users were explicitly looking for author names rather than terms. Common term recommenders neglect this fact. By picking up the idea of polyrepresentation we could show that in a standardized IR evaluation setting we can significantly increase the retrieval performances by adding topical-related author names to the query. This positive effect only appears when the query is additionally expanded with thesaurus terms. By just adding the author names to a query we often observe a query drift which results in worse results.


Improving Retrieval Results with discipline-specific Query Expansion

Abstract: Choosing the right terms to describe an information need is becoming more difficult as the amount of available information increases. Search-Term-Recommendation (STR) systems can help to overcome these problems. This paper evaluates the benefits that may be gained from the use of STRs in Query Expansion (QE). We create 17 STRs, 16 based on specific disciplines and one giving general recommendations, and compare the retrieval performance of these STRs. The main findings are: (1) QE with specific STRs leads to significantly better results than QE with a general STR, (2) QE with specific STRs selected by a heuristic mechanism of topic classification leads to better results than the general STR, however (3) selecting the best matching specific STR in an automatic way is a major challenge of this process.


To Compress or Not To Compress: Processing vs Transmission Tradeoffs for Energy Constrained Sensor Networking

Abstract: In the past few years, lossy compression has been widely applied in the field of wireless sensor networks (WSN), where energy efficiency is a crucial concern due to the constrained nature of the transmission devices. Often, the common thinking among researchers and implementers is that compression is always a good choice, because the major source of energy consumption in a sensor node comes from the transmission of the data. Lossy compression is deemed a viable solution as the imperfect reconstruction of the signal is often acceptable in WSN. In this paper, we thoroughly review a number of lossy compression methods from the literature, and analyze their performance in terms of compression efficiency, computational complexity and energy consumption. We consider two different scenarios, namely, wireless and underwater communications, and show that signal compression may or may not help in the reduction of the overall energy consumption, depending on factors such as the compression algorithm, the signal statistics and the hardware characteristics, i.e., micro-controller and transmission technology. The lesson that we have learned, is that signal compression may in fact provide some energy savings. However, its usage should be carefully evaluated, as in quite a few cases processing and transmission costs are of the same order of magnitude, whereas, in some other cases, the former may even dominate the latter. In this paper, we show quantitative comparisons to assess these tradeoffs in the above mentioned scenarios. Finally, we provide formulas, obtained through numerical fittings, to gauge computational complexity, overall energy consumption and signal representation accuracy for the best performing algorithms as a function of the most relevant system parameters.


RepTFD: Replay Based Transient Fault Detection

Abstract: The advances in IC process make future chip multiprocessors (CMPs) more and more vulnerable to transient faults. To detect transient faults, previous core-level schemes provide redundancy for each core separately. As a result, they may leave transient faults in the uncore parts, which consume over 50% area of a modern CMP, escaped from detection. This paper proposes RepTFD, the first core-level transient fault detection scheme with 100% coverage. Instead of providing redundancy for each core separately, RepTFD provides redundancy for a group of cores as a whole. To be specific, it replays the execution of the checked group of cores on a redundant group of cores. Through comparing the execution results between the two groups of cores, all malignant transient faults can be caught. Moreover, RepTFD adopts a novel pending period based record-replay approach, which can greatly reduce the number of execution orders that need to be enforced in the replay-run. Hence, RepTFD brings only 4.76% performance overhead in comparison to the normal execution without fault-tolerance according to our experiments on the RTL design of an industrial CMP named Godson-3. In addition, RepTFD only consumes about 0.83% area of Godson-3, while needing only trivial modifications to existing components of Godson-3.


An Empirical Study of the Repair Performance of Novel Coding Schemes for Networked Distributed Storage Systems

Abstract: Erasure coding techniques are getting integrated in networked distributed storage systems as a way to provide fault-tolerance at the cost of less storage overhead than traditional replication. Redundancy is maintained over time through repair mechanisms, which may entail large network resource overheads. In recent years, several novel codes tailor-made for distributed storage have been proposed to optimize storage overhead and repair, such as Regenerating Codes that minimize the per repair traffic, or Self-Repairing Codes which minimize the number of nodes contacted per repair. Existing studies of these coding techniques are however predominantly theoretical, under the simplifying assumption that only one object is stored. They ignore many practical issues that real systems must address, such as data placement, de/correlation of multiple stored objects, or the competition for limited network resources when multiple objects are repaired simultaneously. This paper empirically studies the repair performance of these novel storage centric codes with respect to classical erasure codes by simulating realistic scenarios and exploring the interplay of code parameters, failure characteristics and data placement with respect to the trade-offs of bandwidth usage and speed of repairs.


Reachability-based Acyclicity Analysis by Abstract Interpretation

Abstract: In programming languages with dynamic use of memory, such as Java, knowing that a reference variable x points to an acyclic data structure is valuable for the analysis of termination and resource usage (e.g., execution time or memory consumption). For instance, this information guarantees that the depth of the data structure to which x points is greater than the depth of the data structure pointed to by x.f for any field f of x. This, in turn, allows bounding the number of iterations of a loop which traverses the structure by its depth, which is essential in order to prove the termination or infer the resource usage of the loop. The present paper provides an Abstract-Interpretation-based formalization of a static analysis for inferring acyclicity, which works on the reduced product of two abstract domains: reachability, which models the property that the location pointed to by a variable w can be reached by dereferencing another variable v (in this case, v is said to reach w); and cyclicity, modeling the property that v can point to a cyclic data structure. The analysis is proven to be sound and optimal with respect to the chosen abstraction.


Communication-Efficient Parallel Belief Propagation for Latent Dirichlet Allocation

Abstract: This paper presents a novel communication-efficient parallel belief propagation (CE-PBP) algorithm for training latent Dirichlet allocation (LDA). Based on the synchronous belief propagation (BP) algorithm, we first develop a parallel belief propagation (PBP) algorithm on the parallel architecture. Because the extensive communication delay often causes a low efficiency of parallel topic modeling, we further use Zipf's law to reduce the total communication cost in PBP. Extensive experiments on different data sets demonstrate that CE-PBP achieves a higher topic modeling accuracy and reduces more than 80% communication cost than the state-of-the-art parallel Gibbs sampling (PGS) algorithm.


Near-Linear-Time Deterministic Plane Steiner Spanners and TSP Approximation for Well-Spaced Point Sets

Abstract: We describe an algorithm that takes as input n points in the plane and a parameter {\epsilon}, and produces as output an embedded planar graph having the given points as a subset of its vertices in which the graph distances are a (1 + {\epsilon})-approximation to the geometric distances between the given points. For point sets in which the Delaunay triangulation has bounded sharpest angle, our algorithm's output has O(n) vertices, its weight is O(1) times the minimum spanning tree weight, and the algorithm's running time is bounded by O(n \sqrt{log log n}). We use this result in a similarly fast deterministic approximation scheme for the traveling salesperson problem.


Better bounds for matchings in the streaming model

Abstract: In this paper we present improved bounds for approximating maximum matchings in bipartite graphs in the streaming model. First, we consider the question of how well maximum matching can be approximated in a single pass over the input using $\tilde O(n)$ space, where $n$ is the number of vertices in the input graph. Two natural variants of this problem have been considered in the literature: (1) the edge arrival setting, where edges arrive in the stream and (2) the vertex arrival setting, where vertices on one side of the graph arrive in the stream together with all their incident edges. The latter setting has also been studied extensively in the context of online algorithms, where each arriving vertex has to either be matched irrevocably or discarded upon arrival. In the online setting, the celebrated algorithm of Karp-Vazirani-Vazirani achieves a $1-1/e$ approximation. Despite the fact that the streaming model is less restrictive in that the algorithm is not constrained to match vertices irrevocably upon arrival, the best known approximation in the streaming model with vertex arrivals and $\tilde O(n)$ space is the same factor of $1-1/e$. We show that no single pass streaming algorithm that uses $\tilde O(n)$ space can achieve a better than $1-1/e$ approximation to maximum matching, even in the vertex arrival setting. This leads to the striking conclusion that no single pass streaming algorithm can do better than online algorithms unless it uses significantly more than $\tilde O(n)$ space. Additionally, our bound yields the best known impossibility result for approximating matchings in the edge arrival model. We also give a simple algorithm that achieves approximation ratio $1-e^{-k}k^{k-1}/(k-1)!=1-\frac1{\sqrt{2\pi k}}+o(1/k)$ in $k$ passes in the vertex arrival model using linear space, improving upon previously best known convergence.


The Use of Fuzzy Cognitive Maps in Analyzing and Implementation of ITIL Processes

Abstract: Information Technology Infrastructure Library (ITIL) is series of best practices that helps Information technology Organizations to provide Information technology (IT) services for their customers with better performances and quality. This article is looking for a way to implement ITIL in an organization and also using Fuzzy Cognitive Maps (FCM) to model the problem for better understanding of environment. ITIL helps to improve the performance of IT services in order to gain business objectives and Fuzzy Cognitive Maps will help to model the problem of needing ITIL processes for those objectives. First, it defines the concept of FCM and ITIL in two separate sections and then, it will describe the relationship and the way that FCM helps to implement ITIL. The paper will measure the cost of service support that is depended on the metrics like changes Authorization Degree, Process Oriented activities degree, Response time and Interrupt time. This paper will be used as a part of gap analyzes step in implementing ITIL in each organizations.


A PAXOS based State Machine Replication System for Anomaly Detection

Abstract: A number of systems in recent times suffer from attacks like DDoS and Ping of Death. Such attacks result in loss of critical system resources and CPU cycles, as these compromised systems behave in an abnormal manner. The effect of such abnormalities is worse in case of compromised systems handling financial transaction, since it leads to severe monetary losses. In this paper we propose a system that uses the Replicated State Machine approach to detect abnormality in system usage. The suggested system is based on PAXOS algorithm, an algorithm for solving the consensus problem in a network of unreliable processors.


Q-STAR:A Perceptual Video Quality Model Considering Impact of Spatial, Temporal, and Amplitude Resolutions

Abstract: In this paper, we investigate the impact of spatial, temporal and amplitude resolution (STAR) on the perceptual quality of a compressed video. Subjective quality tests were carried out on a mobile device. Seven source sequences are included in the tests and for each source sequence we have 27 test configurations generated by JSVM encoder (3 QP levels, 3 spatial resolutions, and 3 temporal resolutions), resulting a total of 189 processed video sequences (PVSs). Videos coded at different spatial resolutions are displayed at the full screen size of the mobile platform. Subjective data reveal that the impact of spatial resolution (SR), temporal resolution (TR) and quantization stepsize (QS) can each be captured by a function with a single content-dependent parameter. The joint impact of SR, TR and QS can be accurately modeled by the product of these three functions with only three parameters. We further find that the quality decay rates with SR and QS, respectively are independent of TR, and likewise, the decay rate with TR is independent of SR and QS, respectively. However, there is a significant interaction between the effects of SR and QS. The overall quality model is further validated on five other datasets with very high accuracy. The complete model correlates well with the subjective ratings with a Pearson Correlation Coefficient (PCC) of 0.991.


Power Series Solution to Non-Linear Partial Differential equations of Mathematical Physics

Abstract: Power Series Solution method has been used traditionally for to solve Linear Differential Equations, in Ordinary and Partial form. But this method has been limited to this kind of problems. We present the solution of problems of Non Linear Partial Differential equations of Physical Mathematical using power series.


Uncertain and Approximative Knowledge Representation to Reasoning on Classification with a Fuzzy Networks Based System

Abstract: The approach described here allows to use the fuzzy Object Based Representation of imprecise and uncertain knowledge. This representation has a great practical interest due to the possibility to realize reasoning on classification with a fuzzy semantic network based system. For instance, the distinction between necessary, possible and user classes allows to take into account exceptions that may appear on fuzzy knowledge-base and facilitates integration of user's Objects in the base. This approach describes the theoretical aspects of the architecture of the whole experimental A.I. system we built in order to provide effective on-line assistance to users of new technological systems: the understanding of "how it works" and "how to complete tasks" from queries in quite natural languages. In our model, procedural semantic networks are used to describe the knowledge of an "ideal" expert while fuzzy sets are used both to describe the approximative and uncertain knowledge of novice users in fuzzy semantic networks which intervene to match fuzzy labels of a query with categories from our "ideal" expert.


A Proof Checking View of Parameterized Complexity

Abstract: The PCP Theorem is one of the most stunning results in computational complexity theory, a culmination of a series of results regarding proof checking it exposes some deep structure of computational problems. As a surprising side-effect, it also gives strong non-approximability results. In this paper we initiate the study of proof checking within the scope of Parameterized Complexity. In particular we adapt and extend the PCP[n log log n, n log log n] result of Feige et al. to several parameterized classes, and discuss some corollaries.


A Novel Windowing Technique for Efficient Computation of MFCC for Speaker Recognition

Abstract: In this paper, we propose a novel family of windowing technique to compute Mel Frequency Cepstral Coefficient (MFCC) for automatic speaker recognition from speech. The proposed method is based on fundamental property of discrete time Fourier transform (DTFT) related to differentiation in frequency domain. Classical windowing scheme such as Hamming window is modified to obtain derivatives of discrete time Fourier transform coefficients. It has been mathematically shown that the slope and phase of power spectrum are inherently incorporated in newly computed cepstrum. Speaker recognition systems based on our proposed family of window functions are shown to attain substantial and consistent performance improvement over baseline single tapered Hamming window as well as recently proposed multitaper windowing technique.


Pixastic: Steganography based Anti-Phihsing Browser Plug-in

Abstract: In spite of existence of many standard security mechanisms for ensuring secure e-Commerce business, users still fall prey for online attacks. One such simple but powerful attack is 'Phishing'. Phishing is the most alarming threat in the e-Commerce world and effective anti-phishing technique is the need of the hour. This paper focuses on a novel anti-phishing browser plug-in which uses information hiding technique - Steganography. A Robust Message based Image Steganography (RMIS) algorithm has been proposed. The same has been incorporated in the form of a browser plug-in (safari) called Pixastic. Pixastic is tested in an online banking scenario and it is compared with other well-known anti-phishing plug-in methods in practice. Various parameters such as robustness, usability and its behavior on various attacks have been analysed. From experimental results, it is evident that our method Pixastic performs well compared to other anti-phishing plug-ins.


Pareto-optimal Nash equilibrium in capacity allocation game for self-managed networks

Abstract: In this paper we introduce a capacity allocation game which models the problem of maximizing network utility from the perspective of distributed noncooperative agents. Motivated by the idea of self-managed networks, in the developed framework decision-making entities are associated with individual transmission links, deciding on the way they split capacity among concurrent flows. An efficient decentralized algorithm is given for computing strongly Pareto-optimal strategies, constituting a pure Nash equilibrium. Subsequently, we discuss the properties of the introduced game related to the Price of Anarchy and Price of Stability. The paper is concluded with an experimental study.


Search Strategies of Library Search Experts

Abstract: Search engines like Google, Yahoo or Bing are an excellent support for finding documents, but this strength also imposes a limitation. As they are optimized for document retrieval tasks, they perform less well when it comes to more complex search needs. Complex search tasks are usually described as open-ended, abstract and poorly defined information needs with a multifaceted character. In this paper we will present the results of an experiment carried out with information professionals from libraries and museums in the course of a search contest. The aim of the experiment was to analyze the search strategies of experienced information workers trying to tackle search tasks of varying complexity and get qualitative results on the impact of time pressure on such an experiment.


Architecture for Automated Tagging and Clustering of Song Files According to Mood

Abstract: Music is one of the basic human needs for recreation and entertainment. As song files are digitalized now a days, and digital libraries are expanding continuously, which makes it difficult to recall a song. Thus need of a new classification system other than genre is very obvious and mood based classification system serves the purpose very well. In this paper we will present a well-defined architecture to classify songs into different mood-based categories, using audio content analysis, affective value of song lyrics to map a song onto a psychological-based emotion space and information from online sources. In audio content analysis we will use music features such as intensity, timbre and rhythm including their subfeatures to map music in a 2-Dimensional emotional space. In lyric based classification 1-Dimensional emotional space is used. Both the results are merged onto a 2-Dimensional emotional space, which will classify song into a particular mood category. Finally clusters of mood based song files are formed and arranged according to data acquired from various Internet sources.


Improved Inapproximability for TSP

Abstract: The Traveling Salesman Problem is one of the most studied problems in computational complexity and its approximability has been a long standing open question. Currently, the best known inapproximability threshold known is 220/219 due to Papadimitriou and Vempala. Here, using an essentially different construction and also relying on the work of Berman and Karpinski on bounded occurrence CSPs, we give an alternative and simpler inapproximability proof which improves the bound to 185/184.


Performance Improvement of Heterogeneous Wireless Networks using Modified Newton Method

Abstract: The heterogeneous wireless networks where coexistence of different Radio access technology (RAT) are widely deployed for various services and support various traffic demand, channel allocation. Under heterogeneous wireless networks, a user can send data through a single or multi RATs simultaneous. The objective of this paper is to choose the optimal bandwidth for the services and power allocation to that bandwidth. The proposed distributed joint allocation algorithm using modified Newton method is adopted to maximize the total system capacity. We validate the performance of the proposed algorithm through numerical results.


Generic Subsequence Matching Framework: Modularity, Flexibility, Efficiency

Abstract: Subsequence matching has appeared to be an ideal approach for solving many problems related to the fields of data mining and similarity retrieval. It has been shown that almost any data class (audio, image, biometrics, signals) is or can be represented by some kind of time series or string of symbols, which can be seen as an input for various subsequence matching approaches. The variety of data types, specific tasks and their partial or full solutions is so wide that the choice, implementation and parametrization of a suitable solution for a given task might be complicated and time-consuming; a possibly fruitful combination of fragments from different research areas may not be obvious nor easy to realize. The leading authors of this field also mention the implementation bias that makes difficult a proper comparison of competing approaches. Therefore we present a new generic Subsequence Matching Framework (SMF) that tries to overcome the aforementioned problems by a uniform frame that simplifies and speeds up the design, development and evaluation of subsequence matching related systems. We identify several relatively separate subtasks solved differently over the literature and SMF enables to combine them in straightforward manner achieving new quality and efficiency. This framework can be used in many application domains and its components can be reused effectively. Its strictly modular architecture and openness enables also involvement of efficient solutions from different fields, for instance efficient metric-based indexes. This is an extended version of a paper published on DEXA 2012.


Assessing the Quality of Wikipedia Pages Using Edit Longevity and Contributor Centrality

Abstract: In this paper we address the challenge of assessing the quality of Wikipedia pages using scores derived from edit contribution and contributor authoritativeness measures. The hypothesis is that pages with significant contributions from authoritative contributors are likely to be high-quality pages. Contributions are quantified using edit longevity measures and contributor authoritativeness is scored using centrality metrics in either the Wikipedia talk or co-author networks. The results suggest that it is useful to take into account the contributor authoritativeness when assessing the information quality of Wikipedia content. The percentile visualization of the quality scores provides some insights about the anomalous articles, and can be used to help Wikipedia editors to identify Start and Stub articles that are of relatively good quality.


Combining configuration and recommendation to define an interactive product line configuration approach

Abstract: This paper is interested in e-commerce for complex configurable products/systems. In e-commerce, satisfying the customer needs is a vital concern. One particular way to achieve this is to offer customers a panel of options among which they can select their preferred ones. While solution exists, they are not adapted for highly complex configurable systems such as product lines. This paper proposes an approach that combines two complementary forms of guidance: configuration and recommendation, to help customers define their own products out of a product line specification. The proposed approach, called interactive configuration supports the combination by organizing the configuration process in a series of partial configurations where decisions are made by the recommendation.