The Networked Common Goods Game

Abstract: We introduce a new class of games called the networked common goods game (NCGG), which generalizes the well-known common goods game. We focus on a fairly general subclass of the game where each agent's utility functions are the same across all goods the agent is entitled to and satisfy certain natural properties (diminishing return and smoothness). We give a comprehensive set of technical results listed as follows. * We show the optimization problem faced by a single agent can be solved efficiently in this subclass. The discrete version of the problem is however NP-hard but admits an fully polynomial time approximation scheme (FPTAS). * We show uniqueness results of pure strategy Nash equilibrium of NCGG, and that the equilibrium is fully characterized by the structure of the network and independent of the choices and combinations of agent utility functions. * We show NCGG is a potential game, and give an implementation of best/better response Nash dynamics that lead to fast convergence to an $\epsilon$-approximate pure strategy Nash equilibrium. * Lastly, we show the price of anarchy of NCGG can be as large as $\Omega(n^{1-\epsilon})$ (for any $\epsilon>0$), which means selfish behavior in NCGG can lead to extremely inefficient social outcomes.


Belief Propagation for Min-cost Network Flow: Convergence and Correctness

Abstract: Message passing type algorithms such as the so-called Belief Propagation algorithm have recently gained a lot of attention in the statistics, signal processing and machine learning communities as attractive algorithms for solving a variety of optimization and inference problems. As a decentralized, easy to implement and empirically successful algorithm, BP deserves attention from the theoretical standpoint, and here not much is known at the present stage. In order to fill this gap we consider the performance of the BP algorithm in the context of the capacitated minimum-cost network flow problem - the classical problem in the operations research field. We prove that BP converges to the optimal solution in the pseudo-polynomial time, provided that the optimal solution of the underlying problem is unique and the problem input is integral. Moreover, we present a simple modification of the BP algorithm which gives a fully polynomial-time randomized approximation scheme (FPRAS) for the same problem, which no longer requires the uniqueness of the optimal solution. This is the first instance where BP is proved to have fully-polynomial running time. Our results thus provide a theoretical justification for the viability of BP as an attractive method to solve an important class of optimization problems.


Approximate Point-to-Face Shortest Paths in R^3

Abstract: We address the point-to-face approximate shortest path problem in R: Given a set of polyhedral obstacles with a total of n vertices, a source point s, an obstacle face f, and a real positive parameter epsilon, compute a path from s to f that avoids the interior of the obstacles and has length at most (1+epsilon) times the length of the shortest obstacle avoiding path from s to f. We present three approximation algorithms that take by extending three well-known "point-to-point" shortest path algorithms.


PROBER: Ad-Hoc Debugging of Extraction and Integration Pipelines

Abstract: Complex information extraction (IE) pipelines assembled by plumbing together off-the-shelf operators, specially customized operators, and operators re-used from other text processing pipelines are becoming an integral component of most text processing frameworks. A critical task faced by the IE pipeline user is to run a post-mortem analysis on the output. Due to the diverse nature of extraction operators (often implemented by independent groups), it is time consuming and error-prone to describe operator semantics formally or operationally to a provenance system. We introduce the first system that helps IE users analyze pipeline semantics and infer provenance interactively while debugging. This allows the effort to be proportional to the need, and to focus on the portions of the pipeline under the greatest suspicion. We present a generic debugger for running post-execution analysis of any IE pipeline consisting of arbitrary types of operators. We propose an effective provenance model for IE pipelines which captures a variety of operator types, ranging from those for which full or no specifications are available. We present a suite of algorithms to effectively build provenance and facilitate debugging. Finally, we present an extensive experimental study on large-scale real-world extractions from an index of ~500 million Web documents.


On Feedback Vertex Set: New Measure and New Structures

Abstract: We present a new parameterized algorithm for the {feedback vertex set} problem ({\sc fvs}) on undirected graphs. We approach the problem by considering a variation of it, the {disjoint feedback vertex set} problem ({\sc disjoint-fvs}), which finds a feedback vertex set of size $k$ that has no overlap with a given feedback vertex set $F$ of the graph $G$. We develop an improved kernelization algorithm for {\sc disjoint-fvs} and show that {\sc disjoint-fvs} can be solved in polynomial time when all vertices in $G \setminus F$ have degrees upper bounded by three. We then propose a new branch-and-search process on {\sc disjoint-fvs}, and introduce a new branch-and-search measure. The process effectively reduces a given graph to a graph on which {\sc disjoint-fvs} becomes polynomial-time solvable, and the new measure more accurately evaluates the efficiency of the process. These algorithmic and combinatorial studies enable us to develop an $O^*(3.83^k)$-time parameterized algorithm for the general {\sc fvs} problem, improving all previous algorithms for the problem.


Effect of Weighting Scheme to QoS Properties in Web Service Discovery

Abstract: Specifying QoS properties can limit the selection of some good web services that the user will have considered; this is because the algorithm used strictly ensures that there is a match between QoS properties of the consumer with that of the available services. This is to say that, a situation may arise that some services might not have all that the user specifies but are rated high in those they have. With some tradeoffs specified in form of weight, these services will be made available to the user for consideration. This assertion is from the fact that, the user's requirements for the specified QoS properties are of varying degree i.e. he will always prefer one ahead of the other. This can be captured in form of weight i.e. the one preferred most will have the highest weight. If a consumer specifies light weight for those QoS properties that a web service is deficient in and high weight for those it has, this will minimize the difference between them. Hence the service can be returned.


Seamless Data Services for Real Time Communication in a Heterogeneous Networks using Network Tracking and Management

Abstract: Heterogeneous Networks is the integration of all existing networks under a single environment with an understanding between the functional operations and also includes the ability to make use of multiple broadband transport technologies and to support generalized mobility. It is a challenging feature for Heterogeneous networks to integrate several IP-based access technologies in a seamless way. The focus of this paper is on the requirements of a mobility management scheme for multimedia real-time communication services - Mobile Video Conferencing. Nowadays, the range of available wireless access network technologies includes cellular or wide-area wireless systems, such as cellular networks (GSM/GPRS/UMTS) or Wi-Max, local area Network or personal area wireless systems, comprising for example, WLAN (802.11 a/b/g) and Bluetooth. As the mobile video conferencing is considered, the more advanced mobile terminals are capable of having more than one interface active at the same time. In addition, the heterogeneity of access technologies and also the seamless flow of information will increase in the future, making the seamless integration of the access network a key challenge for mobility management in a heterogeneous network environment. Services must be provided to the user regardless of the particular access technology and also the type of service provider or the network used.


Fuzzy Logic of Speed and Steering Control System for Three Dimensional Line Following of an Autonomous Vehicle

Abstract: ... This paper is to describe exploratory research on the design of a modular autonomous mobile robot controller. The controller incorporates a fuzzy logic [8] [9] approach for steering and speed control [37], a FL approach for ultrasound sensing and an overall expert system for guidance. The advantages of a modular system are related to portability and transportability, i.e. any vehicle can become autonomous with minimal modifications. A mobile robot test bed has been constructed in university of Cincinnati using a golf cart base. This cart has full speed control with guidance provided by a vision system and obstacle avoidance using ultrasonic sensors. The speed and steering fuzzy logic controller is supervised through a multi-axis motion controller. The obstacle avoidance system is based on a microcontroller interfaced with ultrasonic transducers. This micro-controller independently handles all timing and distance calculations and sends distance information back to the fuzzy logic controller via the serial line. This design yields a portable independent system in which high speed computer communication is not necessary. Vision guidance has been accomplished with the use of CCD cameras judging the current position of the robot.[34] [35][36] It will be generating a good image for reducing an uncertain wrong command from ground coordinate to tackle the parameter uncertainties of the system, and to obtain good WMR dynamic response.[1] Here we Apply 3D line following mythology. It transforms from 3D to 2D and also maps the image coordinates and vice versa, leading to the improved accuracy of the WMR position. ...


A reversible high embedding capacity data hiding technique for hiding secret data in images

Abstract: As the multimedia and internet technologies are growing fast, the transmission of digital media plays an important role in communication. The various digital media like audio, video and images are being transferred through internet. There are a lot of threats for the digital data that are transferred through internet. Also, a number of security techniques have been employed to protect the data that is transferred through internet. This paper proposes a new technique for sending secret messages securely, using steganographic technique. Since the proposed system uses multiple level of security for data hiding, where the data is hidden in an image file and the stego file is again concealed in another image. Previously, the secret message is being encrypted with the encryption algorithm which ensures the achievement of high security enabled data transfer through internet.


Mining The Data From Distributed Database Using An Improved Mining Algorithm

Abstract: Association rule mining is an active data mining research area and most ARM algorithms cater to a centralized environment. Centralized data mining to discover useful patterns in distributed databases isn't always feasible because merging data sets from different sites incurs huge network communication costs. In this paper, an Improved algorithm based on good performance level for data mining is being proposed. In local sites, it runs the application based on the improved LMatrix algorithm, which is used to calculate local support counts. Local Site also finds a centre site to manage every message exchanged to obtain all globally frequent item sets. It also reduces the time of scan of partition database by using LMatrix which increases the performance of the algorithm. Therefore, the research is to develop a distributed algorithm for geographically distributed data sets that reduces communication costs, superior running efficiency, and stronger scalability than direct application of a sequential algorithm in distributed databases.


Node Sensing & Dynamic Discovering Routes for Wireless Sensor Networks

Abstract: The applications of Wireless Sensor Networks (WSN) contain a wide variety of scenarios. In most of them, the network is composed of a significant number of nodes deployed in an extensive area in which not all nodes are directly connected. Then, the data exchange is supported by multihop communications. Routing protocols are in charge of discovering and maintaining the routes in the network. However, the correctness of a particular routing protocol mainly depends on the capabilities of the nodes and on the application requirements. This paper presents a dynamic discover routing method for communication between sensor nodes and a base station in WSN. This method tolerates failures of arbitrary individual nodes in the network (node failure) or a small part of the network (area failure). Each node in the network does only local routing preservation, needs to record only its neighbor nodes' information, and incurs no extra routing overhead during failure free periods. It dynamically discovers new routes when an intermediate node or a small part of the network in the path from a sensor node to a base station fails. In our planned method, every node decides its path based only on local information, such as its parent node and neighbor nodes' routing information. So, it is possible to form a loop in the routing path. We believe that the loop problem in sensor network routing is not as serious as that in the Internet routing or traditional mobile ad-hoc routing. We are trying to find all possible loops and eliminate the loops as far as possible in WSN.


A Robust Fuzzy Clustering Technique with Spatial Neighborhood Information for Effective Medical Image Segmentation

Abstract: Medical image segmentation demands an efficient and robust segmentation algorithm against noise. The conventional fuzzy c-means algorithm is an efficient clustering algorithm that is used in medical image segmentation. But FCM is highly vulnerable to noise since it uses only intensity values for clustering the images. This paper aims to develop a novel and efficient fuzzy spatial c-means clustering algorithm which is robust to noise. The proposed clustering algorithm uses fuzzy spatial information to calculate membership value. The input image is clustered using proposed ISFCM algorithm. A comparative study has been made between the conventional FCM and proposed ISFCM. The proposed approach is found to be outperforming the conventional FCM.


Design And Implementation Of Multilevel Access Control In Medical Image Transmission Using Symmetric Polynomial Based Audio Steganography

Abstract: ...The steganography scheme makes it possible to hide the medical image in different bit locations of host media without inviting suspicion. The Secret file is embedded in a cover media with a key. At the receiving end the key can be derived by all the classes which are higher in the hierarchy using symmetric polynomial and the medical image file can be retrieved. The system is implemented and found to be secure, fast and scalable. Simulation results show that the system is dynamic in nature and allows any type of hierarchy. The proposed approach performs better even during frequent member joins and leaves. The computation cost is reduced as the same algorithm is used for key computation and descendant key derivation. Steganographic technique used in this paper does not use the conventional LSB's and uses two bit positions and the hidden data occurs only from a frame which is dictated by the key that is used. Hence the quality of stego data is improved.


Enhanced Authentication and Locality Aided - Destination Mobility in Dynamic Routing Protocol for MANET

Abstract: In our proposed model, the route selection is a function of following parameters: hop count, trust level of node and security level of application. In this paper, to focus on secure neighbor detection, trust factor evaluation, operational mode, route discovery and route selection. The paper mainly address the security of geographic routing. The watchdog identifies misbehaving nodes, while the Pathselector avoids routing packets through these nodes. The watchdog, the pathselector is run by each server. In order to keep the source informed about the destination's mobility, the destination keeps sending the alert message to its previous hop telling that it has changed its position and any reference to it for data packet forwarding be informed to the VHR server.


Enhanced Ad-Hoc on Demand Multipath Distance Vector Routing protocol

Abstract: Due to mobility in Ad-Hoc network the topology of the network may change randomly, rapidly and unexpectedly, because of these aspects, the routes in the network often disappear and new to arise. To avoid frequent route discovery and route failure EAOMDV was proposed based on existing routing protocol AOMDV. The EAOMDV (Enhanced Ad-Hoc on Demand Multipath Distance Vector) Routing protocol was proposed to solve the "route failure" problem in AOMDV. EAOMDV protocol reduces the route failure problem by preemptively predicting the link failure by the signal power received by the receiver (pr). This proposed protocol controls overhead, increases throughput and reduces the delay. The EAOMDV protocol was implemented on NS-2 and evaluation results show that the EAOMDV outperformed AOMDV.


Mathematical Principles in Software Quality Engineering

Abstract: Mathematics has many useful properties for developing of complex software systems. One is that it can exactly describe a physical situation of the object or outcome of an action. Mathematics support abstraction and this is an excellent medium for modeling, since it is an exact medium there is a little possibility of ambiguity. This paper demonstrates that mathematics provides a high level of validation when it is used as a software medium. It also outlines distinguishing characteristics of structural testing which is based on the source code of the program tested. Structural testing methods are very amenable to rigorous definition, mathematical analysis and precise measurement. Finally, it also discusses functional and structural testing debate to have a sense of complete testing. Any program can be considered to be a function in the sense that program input forms its domain and program outputs form its range. In general discrete mathematics is more applicable to functional testing, while graph theory pertains more to structural testing.


An undecidable property of context-free languages

Abstract: We prove that there exists no algorithm to decide whether the language generated by a context-free grammar is dense with respect to the lexicographic ordering. As a corollary to this result, we show that it is undecidable whether the lexicographic orderings of the languages generated by two context-free grammars have the same order type.


Efficient multicore-aware parallelization strategies for iterative stencil computations

Abstract: Stencil computations consume a major part of runtime in many scientific simulation codes. As prototypes for this class of algorithms we consider the iterative Jacobi and Gauss-Seidel smoothers and aim at highly efficient parallel implementations for cache-based multicore architectures. Temporal cache blocking is a known advanced optimization technique, which can reduce the pressure on the memory bus significantly. We apply and refine this optimization for a recently presented temporal blocking strategy designed to explicitly utilize multicore characteristics. Especially for the case of Gauss-Seidel smoothers we show that simultaneous multi-threading (SMT) can yield substantial performance improvements for our optimized algorithm.


An Analytical Study on Behavior of Clusters Using K Means, EM and K* Means Algorithm

Abstract: Clustering is an unsupervised learning method that constitutes a cornerstone of an intelligent data analysis process. It is used for the exploration of inter-relationships among a collection of patterns, by organizing them into homogeneous clusters. Clustering has been dynamically applied to a variety of tasks in the field of Information Retrieval (IR). Clustering has become one of the most active area of research and the development. Clustering attempts to discover the set of consequential groups where those within each group are more closely related to one another than the others assigned to different groups. The resultant clusters can provide a structure for organizing large bodies of text for efficient browsing and searching. There exists a wide variety of clustering algorithms that has been intensively studied in the clustering problem. Among the algorithms that remain the most common and effectual, the iterative optimization clustering algorithms have been demonstrated reasonable performance for clustering, e.g. the Expectation Maximization (EM) algorithm and its variants, and the well known k-means algorithm. This paper presents an analysis on how partition method clustering techniques - EM, K -means and K* Means algorithm work on heartspect dataset with below mentioned features - Purity, Entropy, CPU time, Cluster wise analysis, Mean value analysis and inter cluster distance. Thus the paper finally provides the experimental results of datasets for five clusters to strengthen the results that the quality of the behavior in clusters in EM algorithm is far better than k-means algorithm and k*means algorithm.


Node inspection and analysis thereof in the light of area estimation and curve fitting

Abstract: In this paper, we have given an idea of area specification and its corresponding sensing of nodes in a dynamic network. We have applied the concept of Monte Carlo methods in this respect. We have cited certain statistical as well as artificial intelligence based techniques for realizing the position of a node. We have also applied curve fitting concept for node detection and relative verification.


An Improved Fixed Switching Frequency Direct Torque Control of Induction Motor Drives Fed by Direct Matrix Converter

Abstract: A few papers have been interested by the fixed switching frequency direct torque control fed by direct matrix converters, where we can find just the use of direct torque controlled space vector modulated method. In this present paper, we present an improved method used for a fixed switching frequency direct torque control (DTC) using a direct matrix converter (DMC). This method is characterized by a simple structure, a fixed switching frequency which causes minimal torque ripple and a unity input power factor. Using this strategy, we combine the direct matrix converters advantages with those of direct torque control (DTC) schemes. The used technique for constant frequency is combined with the input current space vector to create the switching table of direct matrix converter (DMC). Simulation results clearly demonstrate a better dynamic and steady state performances of the proposed method.


Internet ware cloud computing :Challenges

Abstract: After decades of engineering development and infrastructural investment, Internet connections have become commodity product in many countries, and Internet scale "cloud computing" has started to compete with traditional software business through its technological advantages and economy of scale. Cloud computing is a promising enabling technology of Internet ware Cloud Computing is termed as the next big thing in the modern corporate world. Apart from the present day software and technologies, cloud computing will have a growing impact on enterprise IT and business activities in many large organizations. This paper provides an insight to cloud computing, its impacts and discusses various issues that business organizations face while implementing cloud computing. Further, it recommends various strategies that organizations need to adopt while migrating to cloud computing. The purpose of this paper is to develop an understanding of cloud computing in the modern world and its impact on organizations and businesses. Initially the paper provides a brief description of the cloud computing model introduction and its purposes. Further it discusses various technical and non-technical issues that need to be overcome in order for the benefits of cloud computing to be realized in corporate businesses and organizations. It then provides various recommendations and strategies that businesses need to work on before stepping into new technologies.


Mobile Database System: Role of Mobility on the Query Processing

Abstract: The rapidly expanding technology of mobile communication will give mobile users capability of accessing information from anywhere and any time. The wireless technology has made it possible to achieve continuous connectivity in mobile environment. When the query is specified as continuous, the requesting mobile user can obtain continuously changing result. In order to provide accurate and timely outcome to requesting mobile user, the locations of moving object has to be closely monitored. The objective of paper is to discuss the problem related to the role of personal and terminal mobility and query processing in the mobile environment.


Secure Iris Authentication Using Visual Cryptography

Abstract: Biometrics deal with automated methods of identifying a person or verifying the identity of a person based on physiological or behavioral characteristics. Visual cryptography is a secret sharing scheme where a secret image is encrypted into the shares which independently disclose no information about the original secret image. As biometric template are stored in the centralized database, due to security threats biometric template may be modified by attacker. If biometric template is altered authorized user will not be allowed to access the resource. To deal this issue visual cryptography schemes can be applied to secure the iris template. Visual cryptography provides great means for helping such security needs as well as extra layer of authentication.


Processor Based Active Queue Management for providing QoS in Multimedia Application

Abstract: The objective of this paper is to implement the Active Network based Active Queue Management Technique for providing Quality of Service (QoS) using Network Processor(NP) based router to enhance multimedia applications. The performance is evaluated using Intel IXP2400 NP Simulator. The results demonstrate that, Active Network based Active Queue Management has better performance than RED algorithm in case of congestion and is well suited to achieve high speed packet classification to support multimedia applications with minimum delay and Queue loss. Using simulation, we show that the proposed system can provide assurance for prioritized flows with improved network utilization where bandwidth is shared among the flows according to the levels of priority. We first analyze the feasibility and optimality of the load distribution schemes and then present separate solutions for non-delay sensitive streams and delay-sensitive streams. Rigorous simulations and experiments have been carried out to evaluate the performance.


A New Approach to Lung Image Segmentation using Fuzzy Possibilistic C-Means Algorithm

Abstract: Image segmentation is a vital part of image processing. Segmentation has its application widespread in the field of medical images in order to diagnose curious diseases. The same medical images can be segmented manually. But the accuracy of image segmentation using the segmentation algorithms is more when compared with the manual segmentation. In the field of medical diagnosis an extensive diversity of imaging techniques is presently available, such as radiography, computed tomography (CT) and magnetic resonance imaging (MRI). Medical image segmentation is an essential step for most consequent image analysis tasks. Although the original FCM algorithm yields good results for segmenting noise free images, it fails to segment images corrupted by noise, outliers and other imaging artifact. This paper presents an image segmentation approach using Modified Fuzzy C-Means (FCM) algorithm and Fuzzy Possibilistic c-means algorithm (FPCM). This approach is a generalized version of standard Fuzzy CMeans Clustering (FCM) algorithm. The limitation of the conventional FCM technique is eliminated in modifying the standard technique. The Modified FCM algorithm is formulated by modifying the distance measurement of the standard FCM algorithm to permit the labeling of a pixel to be influenced by other pixels and to restrain the noise effect during segmentation. Instead of having one term in the objective function, a second term is included, forcing the membership to be as high as possible without a maximum limit constraint of one. Experiments are conducted on real images to investigate the performance of the proposed modified FCM technique in segmenting the medical images. Standard FCM, Modified FCM, Fuzzy Possibilistic CMeans algorithm (FPCM) are compared to explore the accuracy of our proposed approach.


Protection of Web Applications from Cross-Site Scripting Attacks in Browser Side

Abstract: Cross Site Scripting (XSS) Flaws are currently the most popular security problems in modern web applications. These Flaws make use of vulnerabilities in the code of web-applications, resulting in serious consequences, such as theft of cookies, passwords and other personal credentials. Cross-Site scripting Flaws occur when accessing information in intermediate trusted sites. Client side solution acts as a web proxy to mitigate Cross Site Scripting Flaws which manually generated rules to mitigate Cross Site Scripting attempts. Client side solution effectively protects against information leakage from the user's environment. Cross Site Scripting Flaws are easy to execute, but difficult to detect and prevent. This paper provides client-side solution to mitigate cross-site scripting Flaws. The existing client-side solutions degrade the performance of client's system resulting in a poor web surfing experience. In this project provides a client side solution that uses a step by step approach to protect cross site scripting, without degrading much the user's web browsing experience.


Review of Robust Video Watermarking Algorithms

Abstract: There has been a remarkable increase in the data exchange over web and the widespread use of digital media. As a result, multimedia data transfers also had a boost up. The mounting interest with reference to digital watermarking throughout the last decade is certainly due to the increase in the need of copyright protection of digital content. This is also enhanced due to commercial prospective. Applications of video watermarking in copy control, broadcast monitoring, fingerprinting, video authentication, copyright protection etc is immensely rising. The main aspects of information hiding are capacity, security and robustness. Capacity deals with the amount of information that can be hidden. The skill of anyone detecting the information is security and robustness refers to the resistance to modification of the cover content before concealed information is destroyed. Video watermarking algorithms normally prefers robustness. In a robust algorithm it is not possible to eliminate the watermark without rigorous degradation of the cover content. In this paper, we introduce the notion of Video Watermarking and the features required to design a robust watermarked video for a valuable application. We review several algorithms, and introduce frequently used key techniques. The aim of this paper is to focus on the various domains of video watermarking techniques. The majority of the reviewed methods based on video watermarking emphasize on the notion of robustness of the algorithm.


Terrorism Event Classification Using Fuzzy Inference Systems

Abstract: Terrorism has led to many problems in Thai societies, not only property damage but also civilian casualties. Predicting terrorism activities in advance can help prepare and manage risk from sabotage by these activities. This paper proposes a framework focusing on event classification in terrorism domain using fuzzy inference systems (FISs). Each FIS is a decision-making model combining fuzzy logic and approximate reasoning. It is generated in five main parts: the input interface, the fuzzification interface, knowledge base unit, decision making unit and output defuzzification interface. Adaptive neuro-fuzzy inference system (ANFIS) is a FIS model adapted by combining the fuzzy logic and neural network. The ANFIS utilizes automatic identification of fuzzy logic rules and adjustment of membership function (MF). Moreover, neural network can directly learn from data set to construct fuzzy logic rules and MF implemented in various applications. FIS settings are evaluated based on two comparisons. The first evaluation is the comparison between unstructured and structured events using the same FIS setting. The second comparison is the model settings between FIS and ANFIS for classifying structured events. The data set consists of news articles related to terrorism events in three southern provinces of Thailand. The experimental results show that the classification performance of the FIS resulting from structured events achieves satisfactory accuracy and is better than the unstructured events. In addition, the classification of structured events using ANFIS gives higher performance than the events using only FIS in the prediction of terrorism events.


A Model of Cloud Based Application Environment for Software Testing

Abstract: Cloud computing is an emerging platform of service computing designed for swift and dynamic delivery of assured computing resources. Cloud computing provide Service-Level Agreements (SLAs) for guaranteed uptime availability for enabling convenient and on-demand network access to the distributed and shared computing resources. Though the cloud computing paradigm holds its potential status in the field of distributed computing, cloud platforms are not yet to the attention of majority of the researchers and practitioners. More specifically, still the researchers and practitioners community has fragmented and imperfect knowledge on cloud computing principles and techniques. In this context, one of the primary motivations of the work presented in this paper is to reveal the versatile merits of cloud computing paradigm and hence the objective of this work is defined to bring out the remarkable significances of cloud computing paradigm through an application environment. In this work, a cloud computing model for software testing is developed.


Joint Design of Congestion Control Routing With Distributed Multi Channel Assignment in Wireless Mesh Networks

Abstract: In Wireless Mesh Networks (WMN), a channel assignment has to balance the objectives of maintaining connectivity and increasing the aggregate bandwidth. The main aim of the channel assignment algorithm is to assign the channels to the network interfaces, from the given expected load on each virtual link. From the existing work done so far, we can examine that there is no combined solution of multi-channel assignment with routing and congestion control. In this paper, we propose a congestion control routing protocol along with multi-channel assignment. We use a traffic aware metric in this protocol in order to provide quality of service. The proposed protocol can improve the throughput and channel utilization to very high extent because it provides solution for multi-channel assignment and congestion control. The proposed algorithm assigns the channels in a way that, congestion is avoided and co-channel interference levels among links with same channel are reduced. By our simulation results in NS2, we show that the proposed protocol attains high throughput and channel utilization along with reduced latency.


Mobile Broadband Possibilities considering the Arrival of IEEE 802.16m & LTE with an Emphasis on South Asia

Abstract: This paper intends to look deeper into finding an ideal mobile broadband solution. Special stress has been put in the South Asian region through some comparative analysis. Proving their competency in numerous aspects, WiMAX and LTE already have already made a strong position in telecommunication industry. Both WiMAX and LTE are 4G technologies designed to move data rather than voice having IP networks based on OFDM technology. So, they aren't like typical technological rivals as of GSM and CDMA. But still a gesture of hostility seems to outburst long before the stable commercial launch of LTE. In this paper various aspects of WiMAX and LTE for deployment have been analyzed. Again, we tried to make every possible consideration with respect to south Asia i.e. how mass people of this region may be benefited. As a result, it might be regarded as a good source in case of making major BWA deployment decisions in this region. Besides these, it also opens the path for further research and in depth thinking in this issue.


SAR Image Segmentation using Vector Quantization Technique on Entropy Images

Abstract: The development and application of various remote sensing platforms result in the production of huge amounts of satellite image data. Therefore, there is an increasing need for effective querying and browsing in these image databases. In order to take advantage and make good use of satellite images data, we must be able to extract meaningful information from the imagery. Hence we proposed a new algorithm for SAR image segmentation. In this paper we propose segmentation using vector quantization technique on entropy image. Initially, we obtain entropy image and in second step we use Kekre's Fast Codebook Generation (KFCG) algorithm for segmentation of the entropy image. Thereafter, a codebook of size 128 was generated for the Entropy image. These code vectors were further clustered in 8 clusters using same KFCG algorithm and converted into 8 images. These 8 images were displayed as a result. This approach does not lead to over segmentation or under segmentation. We compared these results with well known Gray Level Co-occurrence Matrix. The proposed algorithm gives better segmentation with less complexity.


Reversible Image data Hiding using Lifting wavelet Transform and Histogram Shifting

Abstract: A method of lossless data hiding in images using integer wavelet transform and histogram shifting for gray scale images is proposed. The method shifts part of the histogram, to create space for embedding the watermark information bits. The method embeds watermark while maintaining the visual quality well. The method is completely reversible. The original image and the watermark data can be recovered without any loss.


GIS: Geographic Information System An application for socio-economical data collection for rural area

Abstract: The country India follows the planning through planning commission. This is on the basis of information collected by traditional, tedious and manual method which is too slow to sustain. Now we are in the age of 21th century. We have seen in last few decades that the progress of information technology with leaps and bounds, which have completely changed the way of life in the developed nations. While internet has changed the established working practice and opened new vistas and provided a platform to connect, this gives the opportunity for collaborative work space that goes beyond the global boundary. We are living in the global economy and India leading towards Liberalize Market Oriented Economy (LMOE). Considering this things, focusing on GIS, we proposed a system for collection of socio economic data and water resource management information of rural area via internet.


Probabilistic Semantic Web Mining Using Artificial Neural Analysis

Abstract: Most of the web user's requirements are search or navigation time and getting correctly matched result. These constrains can be satisfied with some additional modules attached to the existing search engines and web servers. This paper proposes that powerful architecture for search engines with the title of Probabilistic Semantic Web Mining named from the methods used. With the increase of larger and larger collection of various data resources on the World Wide Web (WWW), Web Mining has become one of the most important requirements for the web users. Web servers will store various formats of data including text, image, audio, video etc., but servers can not identify the contents of the data. These search techniques can be improved by adding some special techniques including semantic web mining and probabilistic analysis to get more accurate results. Semantic web mining technique can provide meaningful search of data resources by eliminating useless information with mining process. In this technique web servers will maintain Meta information of each and every data resources available in that particular web server. This will help the search engine to retrieve information that is relevant to user given input string. This paper proposing the idea of combing these two techniques Semantic web mining and Probabilistic analysis for efficient and accurate search results of web mining. SPF can be calculated by considering both semantic accuracy and syntactic accuracy of data with the input string. This will be the deciding factor for producing results.


Document Clustering using Sequential Information Bottleneck Method

Abstract: This paper illustrates the Principal Direction Divisive Partitioning (PDDP) algorithm and describes its drawbacks and introduces a combinatorial framework of the Principal Direction Divisive Partitioning (PDDP) algorithm, then describes the simplified version of the EM algorithm called the spherical Gaussian EM (sGEM) algorithm and Information Bottleneck method (IB) is a technique for finding accuracy, complexity and time space. The PDDP algorithm recursively splits the data samples into two sub clusters using the hyper plane normal to the principal direction derived from the covariance matrix, which is the central logic of the algorithm. However, the PDDP algorithm can yield poor results, especially when clusters are not well separated from one another. To improve the quality of the clustering results problem, it is resolved by reallocating new cluster membership using the IB algorithm with different settings. IB Method gives accuracy but time consumption is more. Furthermore, based on the theoretical background of the sGEM algorithm and sequential Information Bottleneck method(sIB), it can be obvious to extend the framework to cover the problem of estimating the number of clusters using the Bayesian Information Criterion. Experimental results are given to show the effectiveness of the proposed algorithm with comparison to the existing algorithm.


Polynomial Time Algorithm for Graph Isomorphism Testing

Abstract: This article deals with new polynomial time algorithm for graph isomorphism testing.


Clustering with Spectral Norm and the k-means Algorithm

Abstract: There has been much progress on efficient algorithms for clustering data points generated by a mixture of $k$ probability distributions under the assumption that the means of the distributions are well-separated, i.e., the distance between the means of any two distributions is at least $\Omega(k)$ standard deviations. These results generally make heavy use of the generative model and particular properties of the distributions. In this paper, we show that a simple clustering algorithm works without assuming any generative (probabilistic) model. Our only assumption is what we call a "proximity condition": the projection of any data point onto the line joining its cluster center to any other cluster center is $\Omega(k)$ standard deviations closer to its own center than the other center. Here the notion of standard deviations is based on the spectral norm of the matrix whose rows represent the difference between a point and the mean of the cluster to which it belongs. We show that in the generative models studied, our proximity condition is satisfied and so we are able to derive most known results for generative models as corollaries of our main result. We also prove some new results for generative models - e.g., we can cluster all but a small fraction of points only assuming a bound on the variance. Our algorithm relies on the well known $k$-means algorithm, and along the way, we prove a result of independent interest -- that the $k$-means algorithm converges to the "true centers" even in the presence of spurious points provided the initial (estimated) centers are close enough to the corresponding actual centers and all but a small fraction of the points satisfy the proximity condition. Finally, we present a new technique for boosting the ratio of inter-center separation to standard deviation.


The Complexity of Approximately Counting Stable Matchings

Abstract: We investigate the complexity of approximately counting stable matchings in the $k$-attribute model, where the preference lists are determined by dot products of "preference vectors" with "attribute vectors", or by Euclidean distances between "preference points" and "attribute points". Irving and Leather proved that counting the number of stable matchings in the general case is $#P$-complete. Counting the number of stable matchings is reducible to counting the number of downsets in a (related) partial order and is interreducible, in an approximation-preserving sense, to a class of problems that includes counting the number of independent sets in a bipartite graph ($#BIS$). It is conjectured that no FPRAS exists for this class of problems. We show this approximation-preserving interreducibilty remains even in the restricted $k$-attribute setting when $k \geq 3$ (dot products) or $k \geq 2$ (Euclidean distances). Finally, we show it is easy to count the number of stable matchings in the 1-attribute dot-product setting.


Throughput Enhancement Using Multiple Antennas in OFDM-based Ad Hoc Networks under Transceiver Impairments

Abstract: Transceiver impairments, including phase noise, residual frequency offset, and imperfect channel estimation, significantly affect the performance of Multiple-Input Multiple-Output (MIMO) system. However, these impairments are not well addressed when analyzing the throughput performance of MIMO Ad Hoc networks. In this paper, we present an analytical framework to evaluate the throughput of MIMO OFDM system under the impairments of phase noise, residual frequency offset, and imperfect channel estimation. Using this framework, we evaluate the Maximum Sum Throughput (MST) in Ad Hoc networks by optimizing the power and modulation schemes of each user. Simulations are conducted to demonstrate not only the improvement in the MST from using multiple antennas, but also the loss in the MST due to the transceiver impairments. The proposed analytical framework is further applied for the distributed implementation of MST in Ad Hoc networks, where the loss caused by impairments is also evaluated.


Contribution Games in Social Networks

Abstract: We consider network contribution games, where each agent in a social network has a budget of effort that he can contribute to different collaborative projects or relationships. Depending on the contribution of the involved agents a relationship will flourish or drown, and to measure the success we use a reward function for each relationship. Every agent is trying to maximize the reward from all relationships that it is involved in. We consider pairwise equilibria of this game, and characterize the existence, computational complexity, and quality of equilibrium based on the types of reward functions involved. For example, when all reward functions are concave, we prove that the price of anarchy is at most 2. For convex functions the same only holds under some special but very natural conditions. A special focus of the paper are minimum effort games, where the reward of a relationship depends only on the minimum effort of any of the participants. Finally, we show tight bounds for approximate equilibria and convergence of dynamics in these games.


The Low Latency Fault Tolerance System

Abstract: The Low Latency Fault Tolerance (LLFT) system provides fault tolerance for distributed applications, using the leader-follower replication technique. The LLFT system provides application-transparent replication, with strong replica consistency, for applications that involve multiple interacting processes or threads. The LLFT system comprises a Low Latency Messaging Protocol, a Leader-Determined Membership Protocol, and a Virtual Determinizer Framework. The Low Latency Messaging Protocol provides reliable, totally ordered message delivery by employing a direct group-to-group multicast, where the message ordering is determined by the primary replica in the group. The Leader-Determined Membership Protocol provides reconfiguration and recovery when a replica becomes faulty and when a replica joins or leaves a group, where the membership of the group is determined by the primary replica. The Virtual Determinizer Framework captures the ordering information at the primary replica and enforces the same ordering at the backup replicas for major sources of non-determinism, including multi-threading, time-related operations and socket communication. The LLFT system achieves low latency message delivery during normal operation and low latency reconfiguration and recovery when a fault occurs.


Feature Level Fusion of Face and Palmprint Biometrics by Isomorphic Graph-based Improved K-Medoids Partitioning

Abstract: This paper presents a feature level fusion approach which uses the improved K-medoids clustering algorithm and isomorphic graph for face and palmprint biometrics. Partitioning around medoids (PAM) algorithm is used to partition the set of n invariant feature points of the face and palmprint images into k clusters. By partitioning the face and palmprint images with scale invariant features SIFT points, a number of clusters is formed on both the images. Then on each cluster, an isomorphic graph is drawn. In the next step, the most probable pair of graphs is searched using iterative relaxation algorithm from all possible isomorphic graphs for a pair of corresponding face and palmprint images. Finally, graphs are fused by pairing the isomorphic graphs into augmented groups in terms of addition of invariant SIFT points and in terms of combining pair of keypoint descriptors by concatenation rule. Experimental results obtained from the extensive evaluation show that the proposed feature level fusion with the improved K-medoids partitioning algorithm increases the performance of the system with utmost level of accuracy.


Maximized Posteriori Attributes Selection from Facial Salient Landmarks for Face Recognition

Abstract: This paper presents a robust and dynamic face recognition technique based on the extraction and matching of devised probabilistic graphs drawn on SIFT features related to independent face areas. The face matching strategy is based on matching individual salient facial graph characterized by SIFT features as connected to facial landmarks such as the eyes and the mouth. In order to reduce the face matching errors, the Dempster-Shafer decision theory is applied to fuse the individual matching scores obtained from each pair of salient facial features. The proposed algorithm is evaluated with the ORL and the IITK face databases. The experimental results demonstrate the effectiveness and potential of the proposed face recognition technique also in case of partially occluded faces.


A note on decidability of cellularity

Abstract: A regular language L is said to be cellular if there exists a 1-dimensional cellular automaton CA such that L is the language consisting of the finite blocks associated with CA. It is shown that cellularity of a regular language is decidable using a new characterization of cellular languages formulated by Freiling, Goldstein and Moews and implied by a deep result of Boyle in symbolic dynamics.


Analytic Tableaux for Simple Type Theory and its First-Order Fragment

Abstract: We study simple type theory with primitive equality (STT) and its first-order fragment EFO, which restricts equality and quantification to base types but retains lambda abstraction and higher-order variables. As deductive system we employ a cut-free tableau calculus. We consider completeness, compactness, and existence of countable models. We prove these properties for STT with respect to Henkin models and for EFO with respect to standard models. We also show that the tableau system yields a decision procedure for three EFO fragments.


All Ternary Permutation Constraint Satisfaction Problems Parameterized Above Average Have Kernels with Quadratic Numbers of Variables

Abstract: A ternary Permutation-CSP is specified by a subset $\Pi$ of the symmetric group $\mathcal S_3$. An instance of such a problem consists of a set of variables $V$ and a multiset of constraints, which are ordered triples of distinct variables of $V.$ The objective is to find a linear ordering $\alpha$ of $V$ that maximizes the number of triples whose ordering (under $\alpha$) follows a permutation in $\Pi$. We prove that all ternary Permutation-CSPs parameterized above average have kernels with quadratic numbers of variables.


State-Space Dynamics Distance for Clustering Sequential Data

Abstract: This paper proposes a novel similarity measure for clustering sequential data. We first construct a common state-space by training a single probabilistic model with all the sequences in order to get a unified representation for the dataset. Then, distances are obtained attending to the transition matrices induced by each sequence in that state-space. This approach solves some of the usual overfitting and scalability issues of the existing semi-parametric techniques, that rely on training a model for each sequence. Empirical studies on both synthetic and real-world datasets illustrate the advantages of the proposed similarity measure for clustering sequences.


Predictive Gain Estimation - A mathematical analysis

Abstract: In case of realization of successful business, gain analysis is essential. In this paper we have cited some new techniques of gain expectation on the basis of neural property of perceptron. Support rule and Sequence mining based artificial intelligence oriented practices have also been done in this context. In the view of above fuzzy and statistical based gain sensing is also pointed out.


An optimized recursive learning algorithm for three-layer feedforward neural networks for mimo nonlinear system identifications

Abstract: Back-propagation with gradient method is the most popular learning algorithm for feed-forward neural networks. However, it is critical to determine a proper fixed learning rate for the algorithm. In this paper, an optimized recursive algorithm is presented for online learning based on matrix operation and optimization methods analytically, which can avoid the trouble to select a proper learning rate for the gradient method. The proof of weak convergence of the proposed algorithm also is given. Although this approach is proposed for three-layer, feed-forward neural networks, it could be extended to multiple layer feed-forward neural networks. The effectiveness of the proposed algorithms applied to the identification of behavior of a two-input and two-output non-linear dynamic system is demonstrated by simulation experiments.


The Socceral Force

Abstract: We have an audacious dream, we would like to develop a simulation and virtual reality system to support the decision making in European football (soccer). In this review, we summarize the efforts that we have made to fulfil this dream until recently. In addition, an introductory version of FerSML (Footballer and Football Simulation Markup Language) is presented in this paper.


Matrix Coherence and the Nystrom Method

Abstract: The Nystrom method is an efficient technique to speed up large-scale learning applications by generating low-rank approximations. Crucial to the performance of this technique is the assumption that a matrix can be well approximated by working exclusively with a subset of its columns. In this work we relate this assumption to the concept of matrix coherence and connect matrix coherence to the performance of the Nystrom method. Making use of related work in the compressed sensing and the matrix completion literature, we derive novel coherence-based bounds for the Nystrom method in the low-rank setting. We then present empirical results that corroborate these theoretical bounds. Finally, we present more general empirical results for the full-rank setting that convincingly demonstrate the ability of matrix coherence to measure the degree to which information can be extracted from a subset of columns.


Feasibility Analysis of Sporadic Real-Time Multiprocessor Task Systems

Abstract: We give the first algorithm for testing the feasibility of a system of sporadic real-time tasks on a set of identical processors, solving one major open problem in the area of multiprocessor real-time scheduling [S.K. Baruah and K. Pruhs, Journal of Scheduling, 2009]. We also investigate the related notion of schedulability and a notion that we call online feasibility. Finally, we show that discrete-time schedules are as powerful as continuous-time schedules, which answers another open question in the above mentioned survey.


Bargaining dynamics in exchange networks

Abstract: We consider a one-sided assignment market or exchange network with transferable utility and propose a model for the dynamics of bargaining in such a market. Our dynamical model is local, involving iterative updates of 'offers' based on estimated best alternative matches, in the spirit of pairwise Nash bargaining. We establish that when a balanced outcome (a generalization of the pairwise Nash bargaining solution to networks) exists, our dynamics converges rapidly to such an outcome. We extend our results to the cases of (i) general agent 'capacity constraints', i.e., an agent may be allowed to participate in multiple matches, and (ii) 'unequal bargaining powers' (where we also find a surprising change in rate of convergence).


On Computing Groebner Basis in the Rings of Differential Operators

Abstract: Insa and Pauer presented a basic theory of Groebner basis for differential operators with coefficients in a commutative ring in 1998, and a criterion was proposed to determine if a set of differential operators is a Groebner basis. In this paper, we will give a new criterion such that Insa and Pauer's criterion could be concluded as a special case and one could compute the Groebner basis more efficiently by this new criterion.


Improved information security using robust Steganography system

Abstract: Steganography is an emerging area which is used for secured data transmission over any public media.Steganography is a process that involves hiding a message in an appropriate carrier like image or audio. It is of Greek origin and means "covered or hidden writing". The carrier can be sent to a receiver without any one except the authenticated receiver knowing the existence of this information. In this paper, a specific image based steganography technique for communicating information more securely between two locations is proposed. The author incorporated the idea of secret key and password security features for authentication at both ends in order to achieve high level of security. As a further improvement of security level, the information has been permuted, encoded and then finally embedded on an image to form the stego image. In addition segmented objects extraction and reassembly of the stego image through normalized cut method has been carried out at the sender side and receiver side respectively in order to prevent distortion of the Stego image during transmission.


Constraint-based Query Distribution Framework for an Integrated Global Schema

Abstract: Distributed heterogeneous data sources need to be queried uniformly using global schema. Query on global schema is reformulated so that it can be executed on local data sources. Constraints in global schema and mappings are used for source selection, query optimization,and querying partitioned and replicated data sources. The provided system is all XML-based which poses query in XML form, transforms, and integrates local results in an XML document. Contributions include the use of constraints in our existing global schema which help in source selection and query optimization, and a global query distribution framework for querying distributed heterogeneous data sources.


Algebraic Proofs over Noncommutative Formulas

Abstract: We study possible formulations of algebraic propositional proof systems operating with noncommutative formulas. We observe that a simple formulation gives rise to systems at least as strong as Frege---yielding a semantic way to define a Cook-Reckhow (i.e., polynomially verifiable) algebraic analog of Frege proofs, different from that given in [BIKPRS96,GH03]. We then turn to an apparently weaker system, namely, polynomial calculus (PC) where polynomials are written as ordered formulas (PC over ordered formulas, for short): an ordered polynomial is a noncommutative polynomial in which the order of products in every monomial respects a fixed linear order on variables; an algebraic formula is ordered if the polynomial computed by each of its subformulas is ordered. We show that PC over ordered formulas is strictly stronger than resolution, polynomial calculus and polynomial calculus with resolution (PCR) and admits polynomial-size refutations for the pigeonhole principle and the Tseitin's formulas. We conclude by proposing an approach for establishing lower bounds on PC over ordered formulas proofs, and related systems, based on properties of lower bounds on noncommutative formulas. The motivation behind this work is developing techniques incorporating rank arguments (similar to those used in algebraic circuit complexity) for establishing lower bounds on propositional proofs.


G\'en\'eSyst : G\'en\'eration d'un syst\`eme de transitions \'etiquet\'ees \`a partir d'une sp\'ecification B \'ev\'enementiel

Abstract: The most expensive source of errors and the more difficult to detect in a formal development is the error during specification. Hence, the first step in a formal development usually consists in exhibiting the set of all behaviors of the specification, for instance with an automaton. Starting from this observation, many researches are about the generation of a B machine from a behavioral specification, such as UML. However, no backward verification are done. This is why, we propose the GeneSyst tool, which aims at generating an automaton describing at least all behaviors of the specification. The refinement step is considered and appears as sub-automatons in the produced SLTS.


What a Difference a Tag Cloud Makes: Effects of Tasks and Cognitive Abilities on Search Results Interface Use

Abstract: The goal of this study is to expand our understanding of the relationships between selected tasks, cognitive abilities and search result interfaces. The underlying objective is to understand how to select search results presentation for tasks and user contexts. Twenty three participants conducted four search tasks of two types and used two interfaces (List and Overview) to refine and examine search results. Clickthrough data were recorded. This controlled study employed a mixed model design with two within-subject factors (task and interface) and two between-subject factors (two cognitive abilities: memory span and verbal closure). Quantitative analyses were carried out by means of the statistical package SPSS. Specifically, multivariate analysis of variance with repeated measures and non-parametric tests were performed on the collected data. The overview of search results appeared to have benefited searchers in several ways. It made them faster; it facilitated formulation of more effective queries and helped to assess search results. Searchers with higher cognitive abilities were faster in the Overview interface and in less demanding situations (on simple tasks), while at the same time they issued about the same number of queries as lower-ability searchers. In more demanding situations (on complex tasks and in the List interface), the higher ability searchers expended more search effort, although they were not significantly slower than the lower ability people in these situations. The higher search effort, however, did not result in a measurable improvement of task outcomes for high-ability searchers. These findings have implications for the design of search interfaces. They suggest benefits of providing result overviews. They also suggest the importance of considering cognitive abilities in the design of search results' presentation and interaction.


One Tree Suffices: A Simultaneous O(1)-Approximation for Single-Sink Buy-at-Bulk

Abstract: We study the single-sink buy-at-bulk problem with an unknown cost function. We wish to route flow from a set of demand nodes to a root node, where the cost of routing x total flow along an edge is proportional to f(x) for some concave, non-decreasing function f satisfying f(0)=0. We present a simple, fast, combinatorial algorithm that takes a set of demands and constructs a single tree T such that for all f the cost f(T) is a 47.45-approximation of the optimal cost for that f. This is within a factor of 2.33 of the best approximation ratio currently achievable when the tree can be optimized for a specific function. Trees achieving simultaneous O(1)-approximations for all concave functions were previously not known to exist regardless of computation time.


Addressing the P2P Bootstrap Problem for Small Networks

Abstract: P2P overlays provide a framework for building distributed applications consisting of few to many resources with features including self-configuration, scalability, and resilience to node failures. Such systems have been successfully adopted in large-scale services for content delivery networks, file sharing, and data storage. In small-scale systems, they can be useful to address privacy concerns and for network applications that lack dedicated servers. The bootstrap problem, finding an existing peer in the overlay, remains a challenge to enabling these services for small-scale P2P systems. In large networks, the solution to the bootstrap problem has been the use of dedicated services, though creating and maintaining these systems requires expertise and resources, which constrain their usefulness and make them unappealing for small-scale systems. This paper surveys and summarizes requirements that allow peers potentially constrained by network connectivity to bootstrap small-scale overlays through the use of existing public overlays. In order to support bootstrapping, a public overlay must support the following requirements: a method for reflection in order to obtain publicly reachable addresses, so peers behind network address translators and firewalls can receive incoming connection requests; communication relaying to share public addresses and communicate when direct communication is not feasible; and rendezvous for discovering remote peers, when the overlay lacks stable membership. After presenting a survey of various public overlays, we identify two overlays that match the requirements: XMPP overlays, such as Google Talk and Live Journal Talk, and Brunet, a structured overlay based upon Symphony. We present qualitative experiences with prototypes that demonstrate the ability to bootstrap small-scale private structured overlays from public Brunet or XMPP infrastructures.


Asymptotic Equivalence of Bayes Cross Validation and Widely Applicable Information Criterion in Singular Learning Theory

Abstract: In regular statistical models, the leave-one-out cross-validation is asymptotically equivalent to the Akaike information criterion. However, since many learning machines are singular statistical models, the asymptotic behavior of the cross-validation remains unknown. In previous studies, we established the singular learning theory and proposed a widely applicable information criterion, the expectation value of which is asymptotically equal to the average Bayes generalization loss. In the present paper, we theoretically compare the Bayes cross-validation loss and the widely applicable information criterion and prove two theorems. First, the Bayes cross-validation loss is asymptotically equivalent to the widely applicable information criterion as a random variable. Therefore, model selection and hyperparameter optimization using these two values are asymptotically equivalent. Second, the sum of the Bayes generalization error and the Bayes cross-validation error is asymptotically equal to $2\lambda/n$, where $\lambda$ is the real log canonical threshold and $n$ is the number of training samples. Therefore the relation between the cross-validation error and the generalization error is determined by the algebraic geometrical structure of a learning machine. We also clarify that the deviance information criteria are different from the Bayes cross-validation and the widely applicable information criterion.


Dynamical Jumping Real-Time Fault-Tolerant Routing Protocol for Wireless Sensor Networks

Abstract: In time-critical wireless sensor network (WSN) applications, a high degree of reliability is commonly required. A dynamical jumping real-time fault-tolerant routing protocol (DMRF) is proposed in this paper. Each node utilizes the remaining transmission time of the data packets and the state of the forwarding candidate node set to dynamically choose the next hop. Once node failure, network congestion or void region occurs, the transmission mode will switch to jumping transmission mode, which can reduce the transmission time delay, guaranteeing the data packets to be sent to the destination node within the specified time limit. By using feedback mechanism, each node dynamically adjusts the jumping probabilities to increase the ratio of successful transmission. Simulation results show that DMRF can not only efficiently reduce the effects of failure nodes, congestion and void region, but also yield higher ratio of successful transmission, smaller transmission delay and reduced number of control packets.


Complexity Analysis of Balloon Drawing for Rooted Trees

Abstract: In a balloon drawing of a tree, all the children under the same parent are placed on the circumference of the circle centered at their parent, and the radius of the circle centered at each node along any path from the root reflects the number of descendants associated with the node. Among various styles of tree drawings reported in the literature, the balloon drawing enjoys a desirable feature of displaying tree structures in a rather balanced fashion. For each internal node in a balloon drawing, the ray from the node to each of its children divides the wedge accommodating the subtree rooted at the child into two sub-wedges. Depending on whether the two sub-wedge angles are required to be identical or not, a balloon drawing can further be divided into two types: even sub-wedge and uneven sub-wedge types. In the most general case, for any internal node in the tree there are two dimensions of freedom that affect the quality of a balloon drawing: (1) altering the order in which the children of the node appear in the drawing, and (2) for the subtree rooted at each child of the node, flipping the two sub-wedges of the subtree. In this paper, we give a comprehensive complexity analysis for optimizing balloon drawings of rooted trees with respect to angular resolution, aspect ratio and standard deviation of angles under various drawing cases depending on whether the tree is of even or uneven sub-wedge type and whether (1) and (2) above are allowed. It turns out that some are NP-complete while others can be solved in polynomial time. We also derive approximation algorithms for those that are intractable in general.


Kinematic modelling of a 3-axis NC machine tool in linear and circular interpolation

Abstract: Machining time is a major performance criterion when it comes to high-speed machining. CAM software can help in estimating that time for a given strategy. But in practice, CAM-programmed feed rates are rarely achieved, especially where complex surface finishing is concerned. This means that machining time forecasts are often more than one step removed from reality. The reason behind this is that CAM routines do not take either the dynamic performances of the machines or their specific machining tolerances into account. The present article seeks to improve simulation of high-speed NC machine dynamic behaviour and machining time prediction, offering two models. The first contributes through enhanced simulation of three-axis paths in linear and circular interpolation, taking high-speed machine accelerations and jerks into account. The second model allows transition passages between blocks to be integrated in the simulation by adding in a polynomial transition path that caters for the true machining environment tolerances. Models are based on respect for path monitoring. Experimental validation shows the contribution of polynomial modelling of the transition passage due to the absence of a leap in acceleration. Simulation error on the machining time prediction remains below 1%.


Towards Shift Tolerant Visual Secret Sharing Schemes

Abstract: In (k, n) visual secret sharing (VSS) scheme, secret image can be visually reconstructed when k or more participants printing theirs shares on transparencies and stack them together. No secret is revealed with fewer than k shares. The alignment of the transparencies is important to the visual quality of the reconstructed secret image. In VSS scheme, each pixel of the original secret image is expanded to m sub-pixels in a share image. If a share image is printed on paper with the same size as the original secret image, the alignment or the registration of the sub-pixels, which is only m times smaller than that in the original secret, could be troublesome. Liu et al. [4] has noticed this alignment problem and observed that some information of the secret image may still be revealed even when the shares are not precisely registered in the horizontal direction. Yang et al. [9] introduced a general approach to construct a misalignment tolerant (k, n)-VSS scheme using big and small blocks for the situation where the original secret image has a certain degree of redundancy in shape accuracy. In this paper, we propose a (2, n)-VSS scheme that allows a relative shift between the shares in the horizontal direction and vertical direction. When the shares are perfectly aligned, the contrast of the reconstructed image is equal to that of traditional VSS shceme. When there is a shift, average contrast in the reconstructed image is higher than that of the traditional VSS scheme, and the scheme can still work in the cases where very little shape redundancy presents in the image. The trade-off is that our method involves a larger pixel expansion. The basic building block of our scheme is duplication and concatenation of certain rows or columns of the basic matrices. This seemingly simple but very powerful construction principle can be easily used to create more general (k, n)-VSS schemes.


GIST: A Solver for Probabilistic Games

Abstract: Gist is a tool that (a) solves the qualitative analysis problem of turn-based probabilistic games with {\omega}-regular objectives; and (b) synthesizes reasonable environment assumptions for synthesis of unrealizable specifications. Our tool provides the first and efficient implementations of several reduction-based techniques to solve turn-based probabilistic games, and uses the analysis of turn-based probabilistic games for synthesizing environment assumptions for unrealizable specifications.


Learning Deterministic Regular Expressions for the Inference of Schemas from XML Data

Abstract: Inferring an appropriate DTD or XML Schema Definition (XSD) for a given collection of XML documents essentially reduces to learning deterministic regular expressions from sets of positive example words. Unfortunately, there is no algorithm capable of learning the complete class of deterministic regular expressions from positive examples only, as we will show. The regular expressions occurring in practical DTDs and XSDs, however, are such that every alphabet symbol occurs only a small number of times. As such, in practice it suffices to learn the subclass of deterministic regular expressions in which each alphabet symbol occurs at most k times, for some small k. We refer to such expressions as k-occurrence regular expressions (k-OREs for short). Motivated by this observation, we provide a probabilistic algorithm that learns k-OREs for increasing values of k, and selects the deterministic one that best describes the sample based on a Minimum Description Length argument. The effectiveness of the method is empirically validated both on real world and synthetic data. Furthermore, the method is shown to be conservative over the simpler classes of expressions considered in previous work.


A novel pseudo-random number generator based on discrete chaotic iterations

Abstract: Security of information transmitted through the Internet, against passive or active attacks is an international concern. The use of a chaos-based pseudo-random bit sequence to make it unrecognizable by an intruder, is a field of research in full expansion. This mask of useful information by modulation or encryption is a fundamental part of the TLS Internet exchange protocol. In this paper, a new method using discrete chaotic iterations to generate pseudo-random numbers is presented. This pseudo-random number generator has successfully passed the NIST statistical test suite (NIST SP800-22). Security analysis shows its good characteristics. The application for secure image transmission through the Internet is proposed at the end of the paper.


On the Continuous CNN Problem

Abstract: In the (discrete) CNN problem, online requests appear as points in $\mathbb{R}^2$. Each request must be served before the next one is revealed. We have a server that can serve a request simply by aligning either its $x$ or $y$ coordinate with the request. The goal of the online algorithm is to minimize the total $L_1$ distance traveled by the server to serve all the requests. The best known competitive ratio for the discrete version is 879 (due to Sitters and Stougie). We study the continuous version, in which, the request can move continuously in $\mathbb{R}^2$ and the server must continuously serve the request. A simple adversarial argument shows that the lower bound on the competitive ratio of any online algorithm for the continuous CNN problem is 3. Our main contribution is an online algorithm with competitive ratio $3+2 \sqrt{3} \approx 6.464$. Our analysis is tight. The continuous version generalizes the discrete orthogonal CNN problem, in which every request must be $x$ or $y$ aligned with the previous request. Therefore, Our result improves upon the previous best competitive ratio of 9 (due to Iwama and Yonezawa).


Simulation vs. Equivalence

Abstract: For several semirings S, two weighted finite automata with multiplicities in S are equivalent if and only if they can be connected by a chain of simulations. Such a semiring S is called "proper". It is known that the Boolean semiring, the semiring of natural numbers, the ring of integers, all finite commutative positively ordered semirings and all fields are proper. The semiring S is Noetherian if every subsemimodule of a finitely generated S-semimodule is finitely generated. First, it is shown that all Noetherian semirings and thus all commutative rings and all finite semirings are proper. Second, the tropical semiring is shown not to be proper. So far there has not been any example of a semiring that is not proper.


Some Mathematicians Are Not Turing Machines

Abstract: A certain mathematician M, considering some hypothesis H, conclusion C and text P, can arrive at one of the following judgments: (1) P does not convince M of the fact that since H, it follows that C; (2) P is the proof that since H, it follows that C (judgment of the type "Proved"). Is it possible to replace such a mathematician with an arbitrary Turing machine? The paper provides a proof that the answer to the question is negative under the two following conditions: (1) M is faultless, namely his judgment "Proved" always implies that since H, it actually follows that C; (2) M recognizes a certain P' as the correct proof of the fact that for certain H' and C', if H', then C' (where P', H', and C' are stated in the paper).


How to prevent type-flaw and multi-protocol attacks on cryptographic protocols under Exclusive-OR

Abstract: Type-flaw attacks and multi-protocol attacks on security protocols have been frequently reported in the literature. Heather et al. and Guttman et al. have proven that these could be prevented by tagging encrypted components with distinct constants in a standard protocol model with free message algebra and perfect encryption. However, most "real-world" protocols such as SSL 3.0 are designed with the Exclusive-OR (XOR) operator that possesses algebraic properties, breaking the free algebra assumption. These algebraic properties induce equational theories that need to be considered when analyzing protocols that use the operator. This is the problem we consider in this paper: We prove that, under certain assumptions, tagging encrypted components still prevents type-flaw and multi-protocol attacks even in the presence of the XOR operator and its algebraic properties.


The Complexity of Codiagnosability for Discrete Event and Timed Systems

Abstract: In this paper we study the fault codiagnosis problem for discrete event systems given by finite automata (FA) and timed systems given by timed automata (TA). We provide a uniform characterization of codiagnosability for FA and TA which extends the necessary and sufficient condition that characterizes diagnosability. We also settle the complexity of the codiagnosability problems both for FA and TA and show that codiagnosability is PSPACE-complete in both cases. For FA this improves on the previously known bound (EXPTIME) and for TA it is a new result. Finally we address the codiagnosis problem for TA under bounded resources and show it is 2EXPTIME-complete.


Enhancing Curriculum Acceptance among Students with E-learning 2.0

Abstract: E-learning; enhanced by communicating and interacting is becoming increasingly accepted and this puts Web 2.0 at the center of the new educational technologies. E-Learning 2.0 emerges as an innovative method of online learning for its incorporation of Web 2.0 tools. For any academic study, the curriculum provides overview of intact learning area. The Curriculum provides overview to content of the Subject. Many institutions place student interaction as a priority of their online curriculum design. It is proved that interaction has a great effect on the students' involvement in learning and acceptance of Curriculum. Students are accepting curriculum that is designed by teacher; whereas E-learning 2.0 enabled Curriculum management system allows student to involve in learning activities. It works as a stimulus and increases their dedication to the Curriculum. While Institute adapts E-Learning 2.0 as Learning Management System, it also provides Social Networking services and provides direct and transparent interaction between students and teachers. This view of the e-Learning 2.0 shifts its focus from LMS to the students, equipping them, with the means to become ever more autonomous, accepting them to make use of these means in solving problems on their own initiative. Curriculum usage will empower student involvement and enhancing E-learning 2.0 spreading. This paper, analyzing implementation E-learning 2.0 for Curriculum management and discusses Opportunities & Challenges for Curriculum over Web 2.0.


Competitive Equilibria in Matching Markets with Budgets

Abstract: We study competitive equilibria in the classic Shapley-Shubik assignment model with indivisible goods and unit-demand buyers, with budget constraints: buyers can specify a maximum price they are willing to pay for each item, beyond which they cannot afford the item. This single discontinuity introduced by the budget constraint fundamentally changes the properties of equilibria: in the assignment model without budget constraints, a competitive equilibrium always exists, and corresponds exactly to a stable matching. With budgets, a competitive equilibrium need not always exist. In addition, there are now two distinct notions of stability, depending on whether both or only one of the buyer and seller can strictly benefit in a blocking pair, that no longer coincide due to the budget-induced discontinuity. We define weak and strong stability for the assignment model with transferable utilities, and show that competitive equilibria correspond exactly to strongly stable matchings. We consider the algorithmic question of efficiently computing competitive equilibria in an extension of the assignment model with budgets, where each buyer specifies his preferences over items using utility functions $u_{ij}$, where $u_{ij}(p_j)$ is the utility of buyer $i$ for item $j$ when its price is $p_j$. Our main result is a strongly polynomial time algorithm that decides whether or not a competitive equilibrium exists and if yes, computes a minimum one, for a general class of utility functions $u_{ij}$. This class of utility functions includes the standard quasi-linear utility model with a budget constraint, and in addition, allows modeling marketplaces where, for example, buyers only have a preference ranking amongst items subject to a maximum payment limit for each item, or where buyers want to optimize return on investment (ROI) instead of a quasi-linear utility and only know items' relative values.


Symmetry within Solutions

Abstract: We define the concept of an internal symmetry. This is a symmety within a solution of a constraint satisfaction problem. We compare this to solution symmetry, which is a mapping between different solutions of the same problem. We argue that we may be able to exploit both types of symmetry when finding solutions. We illustrate the potential of exploiting internal symmetries on two benchmark domains: Van der Waerden numbers and graceful graphs. By identifying internal symmetries we are able to extend the state of the art in both cases.


Propagating Conjunctions of AllDifferent Constraints

Abstract: We study propagation algorithms for the conjunction of two AllDifferent constraints. Solutions of an AllDifferent constraint can be seen as perfect matchings on the variable/value bipartite graph. Therefore, we investigate the problem of finding simultaneous bipartite matchings. We present an extension of the famous Hall theorem which characterizes when simultaneous bipartite matchings exists. Unfortunately, finding such matchings is NP-hard in general. However, we prove a surprising result that finding a simultaneous matching on a convex bipartite graph takes just polynomial time. Based on this theoretical result, we provide the first polynomial time bound consistency algorithm for the conjunction of two AllDifferent constraints. We identify a pathological problem on which this propagator is exponentially faster compared to existing propagators. Our experiments show that this new propagator can offer significant benefits over existing methods.


Performance Evaluation of Components Using a Granularity-based Interface Between Real-Time Calculus and Timed Automata

Abstract: To analyze complex and heterogeneous real-time embedded systems, recent works have proposed interface techniques between real-time calculus (RTC) and timed automata (TA), in order to take advantage of the strengths of each technique for analyzing various components. But the time to analyze a state-based component modeled by TA may be prohibitively high, due to the state space explosion problem. In this paper, we propose a framework of granularity-based interfacing to speed up the analysis of a TA modeled component. First, we abstract fine models to work with event streams at coarse granularity. We perform analysis of the component at multiple coarse granularities and then based on RTC theory, we derive lower and upper bounds on arrival patterns of the fine output streams using the causality closure algorithm. Our framework can help to achieve tradeoffs between precision and analysis time.


W[1]-hardness of some domination-like problems parameterized by tree-width

Abstract: The concept of generalized domination unifies well-known variants of domination-like and independence problems, such as Dominating Set, Independent Set, Perfect Code, etc. A generalized domination (also called $[\sigma,\rho]$-Dominating Set}) problem consists in finding a subset of vertices in a graph such that every vertex is satisfied with respect to two given sets of constraints $\sigma$ and $\rho$. Very few problems are known not to be FPT when parameterized by tree-width, as usually this restriction allows one to write efficient algorithms to solve the considered problems. The main result of this article is a proof that for some (infinitely many) sets $\sigma$ and $\rho$, the problem $\exists[\sigma,\rho]$-Dominating Set} is W[1]-hard when parameterized by the tree-width of the input graph. This contrasts with the current knowledge on the parameterized complexity of this problem when parameterized by tree-width, which had only been studied for finite and cofinite sets $\sigma$ and $\rho$ and for which it has been shown to be FPT.


Assume-Guarantee Synthesis for Digital Contract Signing

Abstract: We study the automatic synthesis of fair non-repudiation protocols, a class of fair exchange protocols, used for digital contract signing. First, we show how to specify the objectives of the participating agents and the trusted third party (TTP) as path formulas in LTL and prove that the satisfaction of these objectives imply fairness; a property required of fair exchange protocols. We then show that weak (co-operative) co-synthesis and classical (strictly competitive) co-synthesis fail, whereas assume-guarantee synthesis (AGS) succeeds. We demonstrate the success of assume-guarantee synthesis as follows: (a) any solution of assume-guarantee synthesis is attack-free; no subset of participants can violate the objectives of the other participants; (b) the Asokan-Shoup-Waidner (ASW) certified mail protocol that has known vulnerabilities is not a solution of AGS; (c) the Kremer-Markowitch (KM) non-repudiation protocol is a solution of AGS; and (d) AGS presents a new and symmetric fair non-repudiation protocol that is attack-free. To our knowledge this is the first application of synthesis to fair non-repudiation protocols, and our results show how synthesis can both automatically discover vulnerabilities in protocols and generate correct protocols. The solution to assume-guarantee synthesis can be computed efficiently as the secure equilibrium solution of three-player graph games.


Completeness of Flat Coalgebraic Fixpoint Logics

Abstract: Modal fixpoint logics traditionally play a central role in computer science, in particular in artificial intelligence and concurrency. The mu-calculus and its relatives are among the most expressive logics of this type. However, popular fixpoint logics tend to trade expressivity for simplicity and readability, and in fact often live within the single variable fragment of the mu-calculus. The family of such flat fixpoint logics includes, e.g., LTL, CTL, and the logic of common knowledge. Extending this notion to the generic semantic framework of coalgebraic logic enables covering a wide range of logics beyond the standard mu-calculus including, e.g., flat fragments of the graded mu-calculus and the alternating-time mu-calculus (such as alternating-time temporal logic ATL), as well as probabilistic and monotone fixpoint logics. We give a generic proof of completeness of the Kozen-Park axiomatization for such flat coalgebraic fixpoint logics.


Is This a Good Title?

Abstract: Missing web pages, URIs that return the 404 "Page Not Found" error or the HTTP response code 200 but dereference unexpected content, are ubiquitous in today's browsing experience. We use Internet search engines to relocate such missing pages and provide means that help automate the rediscovery process. We propose querying web pages' titles against search engines. We investigate the retrieval performance of titles and compare them to lexical signatures which are derived from the pages' content. Since titles naturally represent the content of a document they intuitively change over time. We measure the edit distance between current titles and titles of copies of the same pages obtained from the Internet Archive and display their evolution. We further investigate the correlation between title changes and content modifications of a web page over time. Lastly we provide a predictive model for the quality of any given web page title in terms of its discovery performance. Our results show that titles return more than 60% URIs top ranked and further relevant content returned in the top 10 results. We show that titles decay slowly but are far more stable than the pages' content. We further distill stop titles than can help identify insufficiently performing search engine queries.


A Note on Fault Diagnosis Algorithms

Abstract: In this paper we review algorithms for checking diagnosability of discrete-event systems and timed automata. We point out that the diagnosability problems in both cases reduce to the emptiness problem for (timed) B\"uchi automata. Moreover, it is known that, checking whether a discrete-event system is diagnosable, can also be reduced to checking bounded diagnosability. We establish a similar result for timed automata. We also provide a synthesis of the complexity results for the different fault diagnosis problems.


Boosting Multi-Core Reachability Performance with Shared Hash Tables

Abstract: This paper focuses on data structures for multi-core reachability, which is a key component in model checking algorithms and other verification methods. A cornerstone of an efficient solution is the storage of visited states. In related work, static partitioning of the state space was combined with thread-local storage and resulted in reasonable speedups, but left open whether improvements are possible. In this paper, we present a scaling solution for shared state storage which is based on a lockless hash table implementation. The solution is specifically designed for the cache architecture of modern CPUs. Because model checking algorithms impose loose requirements on the hash table operations, their design can be streamlined substantially compared to related work on lockless hash tables. Still, an implementation of the hash table presented here has dozens of sensitive performance parameters (bucket size, cache line size, data layout, probing sequence, etc.). We analyzed their impact and compared the resulting speedups with related tools. Our implementation outperforms two state-of-the-art multi-core model checkers (SPIN and DiVinE) by a substantial margin, while placing fewer constraints on the load balancing and search algorithms.


A Geometric Approach to the Problem of Unique Decomposition of Processes

Abstract: This paper proposes a geometric solution to the problem of prime decomposability of concurrent processes first explored by R. Milner and F. Moller in [MM93]. Concurrent programs are given a geometric semantics using cubical areas, for which a unique factorization theorem is proved. An effective factorization method which is correct and complete with respect to the geometric semantics is derived from the factorization theorem. This algorithm is implemented in the static analyzer ALCOOL.


Forward Analysis and Model Checking for Trace Bounded WSTS

Abstract: We investigate a subclass of well-structured transition systems (WSTS), the bounded---in the sense of Ginsburg and Spanier (Trans. AMS 1964)---complete deterministic ones, which we claim provide an adequate basis for the study of forward analyses as developed by Finkel and Goubault-Larrecq (Logic. Meth. Comput. Sci. 2012). Indeed, we prove that, unlike other conditions considered previously for the termination of forward analysis, boundedness is decidable. Boundedness turns out to be a valuable restriction for WSTS verification, as we show that it further allows to decide all $\omega$-regular properties on the set of infinite traces of the system.


Fault Diagnosis with Dynamic Observers

Abstract: In this paper, we review some recent results about the use of dynamic observers for fault diagnosis of discrete event systems. Fault diagnosis consists in synthesizing a diagnoser that observes a given plant and identifies faults in the plant as soon as possible after their occurrence. Existing literature on this problem has considered the case of fixed static observers, where the set of observable events is fixed and does not change during execution of the system. In this paper, we consider dynamic observers: an observer can "switch" sensors on or off, thus dynamically changing the set of events it wishes to observe. It is known that checking diagnosability (i.e., whether a given observer is capable of identifying faults) can be solved in polynomial time for static observers, and we show that the same is true for dynamic ones. We also solve the problem of dynamic observers' synthesis and prove that a most permissive observer can be computed in doubly exponential time, using a game-theoretic approach. We further investigate optimization problems for dynamic observers and define a notion of cost of an observer.