A Multimodal Biometric System Using Linear Discriminant Analysis For Improved Performance

Abstract: Essentially a biometric system is a pattern recognition system which recognizes a user by determining the authenticity of a specific anatomical or behavioral characteristic possessed by the user. With the ever increasing integration of computers and Internet into daily life style, it has become necessary to protect sensitive and personal data. This paper proposes a multimodal biometric system which incorporates more than one biometric trait to attain higher security and to handle failure to enroll situations for some users. This paper is aimed at investigating a multimodal biometric identity system using Linear Discriminant Analysis as backbone to both facial and speech recognition and implementing such system in real-time using SignalWAVE.


Formal proofs in real algebraic geometry: from ordered fields to quantifier elimination

Abstract: This paper describes a formalization of discrete real closed fields in the Coq proof assistant. This abstract structure captures for instance the theory of real algebraic numbers, a decidable subset of real numbers with good algorithmic properties. The theory of real algebraic numbers and more generally of semi-algebraic varieties is at the core of a number of effective methods in real analysis, including decision procedures for non linear arithmetic or optimization methods for real valued functions. After defining an abstract structure of discrete real closed field and the elementary theory of real roots of polynomials, we describe the formalization of an algebraic proof of quantifier elimination based on pseudo-remainder sequences following the standard computer algebra literature on the topic. This formalization covers a large part of the theory which underlies the efficient algorithms implemented in practice in computer algebra. The success of this work paves the way for formal certification of these efficient methods.


Social Networks Research Aspects: A Vast and Fast Survey Focused on the Issue of Privacy in Social Network Sites

Abstract: The increasing participation of people in online activities in recent years like content publishing, and having different kinds of relationships and interactions, along with the emergence of online social networks and people's extensive tendency toward them, have resulted in generation and availability of a huge amount of valuable information that has never been available before, and have introduced some new, attractive, varied, and useful research areas to researchers. In this paper we try to review some of the accomplished research on information of SNSs (Social Network Sites), and introduce some of the attractive applications that analyzing this information has. This will lead to the introduction of some new research areas to researchers. By reviewing the research in this area we will present a categorization of research topics about online social networks. This categorization includes seventeen research subtopics or subareas that will be introduced along with some of the accomplished research in these subareas. According to the consequences (slight, significant, and sometimes catastrophic) that revelation of personal and private information has, a research area that researchers have vastly investigated is privacy in online social networks. After an overview on different research subareas of SNSs, we will get more focused on the subarea of privacy protection in social networks, and introduce different aspects of it along with a categorization of these aspects.


Processor Allocation for Optimistic Parallelization of Irregular Programs

Abstract: Optimistic parallelization is a promising approach for the parallelization of irregular algorithms: potentially interfering tasks are launched dynamically, and the runtime system detects conflicts between concurrent activities, aborting and rolling back conflicting tasks. However, parallelism in irregular algorithms is very complex. In a regular algorithm like dense matrix multiplication, the amount of parallelism can usually be expressed as a function of the problem size, so it is reasonably straightforward to determine how many processors should be allocated to execute a regular algorithm of a certain size (this is called the processor allocation problem). In contrast, parallelism in irregular algorithms can be a function of input parameters, and the amount of parallelism can vary dramatically during the execution of the irregular algorithm. Therefore, the processor allocation problem for irregular algorithms is very difficult. In this paper, we describe the first systematic strategy for addressing this problem. Our approach is based on a construct called the conflict graph, which (i) provides insight into the amount of parallelism that can be extracted from an irregular algorithm, and (ii) can be used to address the processor allocation problem for irregular algorithms. We show that this problem is related to a generalization of the unfriendly seating problem and, by extending Tur\'an's theorem, we obtain a worst-case class of problems for optimistic parallelization, which we use to derive a lower bound on the exploitable parallelism. Finally, using some theoretically derived properties and some experimental facts, we design a quick and stable control strategy for solving the processor allocation problem heuristically.


Performance Analysis of Observation Based Cooperation Enforcement in Ad Hoc Networks

Abstract: Node misbehavior due to selfish or malicious behavior could significantly degrade the performance of MANET because most existing routing protocols in MANET aim to find the most efficient path. Overhearing and reputation based cooperation schemes have been used to detect and isolate the misbehaving nodes as well as to force them to cooperate. Performance analysis has been done for the network traffic using OCEAN over DSR on ns2 while considering the low energy levels for mobile nodes. Throughput, energy level, routing packets and normalized routing overhead are analyzed for OCEAN and normal DSR to show the impact of OCEAN on the overall network performance.


An Entertaining Example for the Usage of Bitwise Operations in Programming

Abstract: The present study is meant to fill in some information gaps occurring in the most widespread and well-known educational and reference literature about programming. The stress is laid on a very useful instrument - the bitwise operations, topic which is, unfortunately, seldom dealt with in most of the well-known books on programming. In addition, the research is very useful as regards the topic of overloading operators in any Object-oriented programming course. Given some appropriate examples, with the emphasis being laid on some particular and data structures language constructions, the results are quite interesting. The algorithm of solving the popular Sudoku puzzle is one such entertaining example.


Image Labeling and Segmentation using Hierarchical Conditional Random Field Model

Abstract: The use of hierarchical Conditional Random Field model deal with the problem of labeling images . At the time of labeling a new image, selection of the nearest cluster and using the related CRF model to label this image. When one give input image, one first use the CRF model to get initial pixel labels then finding the cluster with most similar images. Then at last relabeling the input image by the CRF model associated with this cluster. This paper presents a approach to label and segment specific image having correct information.


Managing Communication Latency-Hiding at Runtime for Parallel Programming Languages and Libraries

Abstract: This work introduces a runtime model for managing communication with support for latency-hiding. The model enables non-computer science researchers to exploit communication latency-hiding techniques seamlessly. For compiled languages, it is often possible to create efficient schedules for communication, but this is not the case for interpreted languages. By maintaining data dependencies between scheduled operations, it is possible to aggressively initiate communication and lazily evaluate tasks to allow maximal time for the communication to finish before entering a wait state. We implement a heuristic of this model in DistNumPy, an auto-parallelizing version of numerical Python that allows sequential NumPy programs to run on distributed memory architectures. Furthermore, we present performance comparisons for eight benchmarks with and without automatic latency-hiding. The results shows that our model reduces the time spent on waiting for communication as much as 27 times, from a maximum of 54% to only 2% of the total execution time, in a stencil application.


A PCA-Based Super-Resolution Algorithm for Short Image Sequences

Abstract: In this paper, we present a novel, learning-based, two-step super-resolution (SR) algorithm well suited to solve the specially demanding problem of obtaining SR estimates from short image sequences. The first step, devoted to increase the sampling rate of the incoming images, is performed by fitting linear combinations of functions generated from principal components (PC) to reproduce locally the sparse projected image data, and using these models to estimate image values at nodes of the high-resolution grid. PCs were obtained from local image patches sampled at sub-pixel level, which were generated in turn from a database of high-resolution images by application of a physically realistic observation model. Continuity between local image models is enforced by minimizing an adequate functional in the space of model coefficients. The second step, dealing with restoration, is performed by a linear filter with coefficients learned to restore residual interpolation artifacts in addition to low-resolution blurring, providing an effective coupling between both steps of the method. Results on a demanding five-image scanned sequence of graphics and text are presented, showing the excellent performance of the proposed method compared to several state-of-the-art two-step and Bayesian Maximum a Posteriori SR algorithms.


A Dynamic Model for Sharing Reputation of Sellers among Buyers for Enhancing Trust in Agent Mediated e-market

Abstract: Reputation systems aim to reduce the risk of loss due to untrustworthy participants. This loss is aggravated by dishonest advisors trying to pollute the e-market environment for their self-interest. A major task of a reputation system is to promote and encourage advisors who repeatedly respond with fair advice and to apply an opinion filtering or honesty checking mechanism to detect and resist dishonest advisors. This paper provides a dynamic approach to compute the aggregated shared reputation component by filtering out unfair advice and then generating the aggregated shared reputation value. The proposed approach is dynamic in nature as it is sensitive to the behaviour of advisors, value of the current transaction and encourages the cooperation among buyers as advisors. It provides incentive to honest advisors in lieu of repeated sharing of honest opinion by increasing the weight of their opinion and by making the increase in the reputation of honest advisors monotonically proportional to the value of a transaction.


A Dichotomy for 2-Constraint Forbidden CSP Patterns

Abstract: Although the CSP (constraint satisfaction problem) is NP-complete, even in the case when all constraints are binary, certain classes of instances are tractable. We study classes of instances defined by excluding subproblems. This approach has recently led to the discovery of novel tractable classes. The complete characterisation of all tractable classes defined by forbidding patterns (where a pattern is simply a compact representation of a set of subproblems) is a challenging problem. We demonstrate a dichotomy in the case of forbidden patterns consisting of either one or two constraints. This has allowed us to discover new tractable classes including, for example, a novel generalisation of 2SAT.


Multi-User Scheduling in the 3GPP LTE Cellular Uplink

Abstract: In this paper, we consider resource allocation in the 3GPP Long Term Evolution (LTE) cellular uplink, which will be the most widely deployed next generation cellular uplink. The key features of the 3GPP LTE uplink (UL) are that it is based on a modified form of the orthogonal frequency division multiplexing based multiple access (OFDMA) which enables channel dependent frequency selective scheduling, and that it allows for multi-user (MU) scheduling wherein multiple users can be assigned the same time-frequency resource. In addition to the considerable spectral efficiency improvements that are possible by exploiting these two features, the LTE UL allows for transmit antenna selection together with the possibility to employ advanced receivers at the base-station, which promise further gains. However, several practical constraints that seek to maintain a low signaling overhead, are also imposed. In this paper, we show that the resulting resource allocation problem is APX-hard and then propose a local ratio test (LRT) based constant-factor polynomial-time approximation algorithm. We then propose two enhancements to this algorithm as well as a sequential LRT based MU scheduling algorithm that offers a constant-factor approximation and is another useful choice in the complexity versus performance tradeoff. Further, user pre-selection, wherein a smaller pool of good users is pre-selected and a sophisticated scheduling algorithm is then employed on the selected pool, is also examined. We suggest several such user pre-selection algorithms, some of which are shown to offer constant-factor approximations to the pre-selection problem. Detailed evaluations reveal that the proposed algorithms and their enhancements offer significant gains.


Modelling and simulation of complex systems: an approach based on multi-level agents

Abstract: A complex system is made up of many components with many interactions. So the design of systems such as simulation systems, cooperative systems or assistance systems includes a very accurate modelling of interactional and communicational levels. The agent-based approach provides an adapted abstraction level for this problem. After having studied the organizational context and communicative capacities of agentbased systems, to simulate the reorganization of a flexible manufacturing, to regulate an urban transport system, and to simulate an epidemic detection system, our thoughts on the interactional level were inspired by human-machine interface models, especially those in "cognitive engineering". To provide a general framework for agent-based complex systems modelling, we then proposed a scale of four behaviours that agents may adopt in their complex systems (reactive, routine, cognitive, and collective). To complete the description of multi-level agent models, which is the focus of this paper, we illustrate our modelling and discuss our ongoing work on each level.


Agent-Based {\mu}-Tools Integrated into a Co-Design Platform

Abstract: In this paper we present successively the proposition and the design of: 1) {\mu}-tools adapted to collaborative activity of design, and 2) a multi-agent platform adapted to innovative and distributed design of products or services. This platform called PLACID (innovating and distributed design platform) must support applications of assistance to actors implies in a design process that we have called {\mu}-tools. {\mu}-tools are developed with an aim of bringing assistance to Co-design. The use of the paradigm agent as well relates to the modeling and the development of various layers of the platform, that those of the human-computer interfaces. With these objectives, constraints are added to facilitate the integration of new co-operative tools.


Dynamic Shared Context Processing in an E-Collaborative Learning Environment

Abstract: In this paper, we propose a dynamic shared context processing method based on DSC (Dynamic Shared Context) model, applied in an e-collaborative learning environment. Firstly, we present the model. This is a way to measure the relevance between events and roles in collaborative environments. With this method, we can share the most appropriate event information for each role instead of sharing all information to all roles in a collaborative work environment. Then, we apply and verify this method in our project with Google App supported e-learning collaborative environment. During this experiment, we compared DSC method measured relevance of events and roles to manual measured relevance. And we describe the favorable points from this comparison and our finding. Finally, we discuss our future research of a hybrid DSC method to make dynamical information shared more effective in a collaborative work environment.


Elasticity on Ontology Matching of Folksodriven Structure Network

Abstract: Nowadays folksonomy tags are used not just for personal organization, but for communication and sharing between people sharing their own local interests. In this paper is considered the new concept structure called "Folksodriven" to represent folksonomies. The Folksodriven Structure Network (FSN) was thought as folksonomy tags suggestions for the user on a dataset built on chosen websites - based on Natural Language Processing (NLP). Morphological changes, such as changes in folksonomy tags chose have direct impact on network connectivity (structural plasticity) of the folksonomy tags considered. The goal of this paper is on defining a base for a FSN plasticity theory to analyze. To perform such goal it is necessary a systematic mathematical analysis on deformation and fracture for the ontology matching on the FSN. The advantages of that approach could be used on a new interesting method to be employed by a knowledge management system.


The Call-by-need Lambda Calculus, Revisited

Abstract: The existing call-by-need lambda calculi describe lazy evaluation via equational logics. A programmer can use these logics to safely ascertain whether one term is behaviorally equivalent to another or to determine the value of a lazy program. However, neither of the existing calculi models evaluation in a way that matches lazy implementations. Both calculi suffer from the same two problems. First, the calculi never discard function calls, even after they are completely resolved. Second, the calculi include re-association axioms even though these axioms are merely administrative steps with no counterpart in any implementation. In this paper, we present an alternative axiomatization of lazy evaluation using a single axiom. It eliminates both the function call retention problem and the extraneous re-association axioms. Our axiom uses a grammar of contexts to describe the exact notion of a needed computation. Like its predecessors, our new calculus satisfies consistency and standardization properties and is thus suitable for reasoning about behavioral equivalence. In addition, we establish a correspondence between our semantics and Launchbury's natural semantics.


Floating-Point Arithmetic on Round-to-Nearest Representations

Abstract: Recently we introduced a class of number representations denoted RN-representations, allowing an un-biased rounding-to-nearest to take place by a simple truncation. In this paper we briefly review the binary fixed-point representation in an encoding which is essentially an ordinary 2's complement representation with an appended round-bit. Not only is this rounding a constant time operation, so is also sign inversion, both of which are at best log-time operations on ordinary 2's complement representations. Addition, multiplication and division is defined in such a way that rounding information can be carried along in a meaningful way, at minimal cost. Based on the fixed-point encoding we here define a floating point representation, and describe to some detail a possible implementation of a floating point arithmetic unit employing this representation, including also the directed roundings.


About Instruction Sequence Testing

Abstract: Software testing is presented as a so-called theme within which different authors and groups have defined different subjects each of these subjects having a different focus on testing. A uniform concept of software testing is non-existent and the space of possible coherent perspectives on software testing, each fitting within the theme, is viewed as being spanned by five dimensions, each dimension representing two opposite views with a variety of intermediate views in between. Instruction sequences are used as a simple theoretical conceptualization of computer programs. A theory of instruction sequence testing may serve as a model for a theory of software testing. Instruction sequences testing is considered a new topic for which definitions may be freely contemplated without being restricted by existing views on software testing. The problem of developing a theory of instruction sequence testing is posed. A survey is given of motivations and scenarios for developing a theory of instruction sequence testing.


QGRP: A Novel QoS-Geographic Routing Protocol for Multimedia Wireless Sensor Networks

Abstract: Thanks to the potential they hold and the variety of their application domains, Multimedia Wireless Sensor Networks (MWSN) are forecast to become highly integrated into our daily activities. Due to the carried content nature, mainly composed of images and/or video streams with high throughput and delay constraints, Quality of Service in the context of MWSN is a crucial issue. In this paper, we propose a QoS and energy aware geographic routing protocol for MWSN: QGRP. The proposed protocol addresses bandwidth, delay and energy constraints associated with MWSN. QGRP adopts an analytical model of IEEE 802.11 Distributed Coordination Function (DCF) to estimate available bandwidth and generates loop-free routing paths.


Congestion Control and Routing over Challenged Networks

Abstract: This dissertation is a study on the design and analysis of novel, optimal routing and rate control algorithms in wireless, mobile communication networks. Congestion control and routing algorithms upto now have been designed and optimized for wired or wireless mesh networks. In those networks, optimal algorithms (optimal in the sense that either the throughput is maximized or delay is minimized, or the network operation cost is minimized) can be engineered based on the classic time scale decomposition assumption that the dynamics of the network are either fast enough so that these algorithms essentially see the average or slow enough that any changes can be tracked to allow the algorithms to adapt over time. However, as technological advancements enable integration of ever more mobile nodes into communication networks, any rate control or routing algorithms based, for example, on averaging out the capacity of the wireless mobile link or tracking the instantaneous capacity will perform poorly. The common element in our solution to engineering efficient routing and rate control algorithms for mobile wireless networks is to make the wireless mobile links seem as if they are wired or wireless links to all but few nodes that directly see the mobile links (either the mobiles or nodes that can transmit to or receive from the mobiles) through an appropriate use of queuing structures at these selected nodes. This approach allows us to design end-to-end rate control or routing algorithms for wireless mobile networks so that neither averaging nor instantaneous tracking is necessary.


A Novel Approach to Fast Image Filtering Algorithm of Infrared Images based on Intro Sort Algorithm

Abstract: In this study we investigate the fast image filtering algorithm based on Intro sort algorithm and fast noise reduction of infrared images. Main feature of the proposed approach is that no prior knowledge of noise required. It is developed based on Stefan- Boltzmann law and the Fourier law. We also investigate the fast noise reduction approach that has advantage of less computation load. In addition, it can retain edges, details, text information even if the size of the window increases. Intro sort algorithm begins with Quick sort and switches to heap sort when the recursion depth exceeds a level based on the number of elements being sorted. This approach has the advantage of fast noise reduction by reducing the comparison time. It also significantly speed up the noise reduction process and can apply to real-time image processing. This approach will extend the Infrared images applications for medicine and video conferencing.


Construction of Learning Path Using Ant Colony Optimization from a Frequent Pattern Graph

Abstract: In an e-Learning system a learner may come across multiple unknown terms, which are generally hyperlinked, while reading a text definition or theory on any topic. It becomes even harder when one tries to understand those unknown terms through further such links and they again find some new terms that have new links. As a consequence they get confused where to initiate from and what are the prerequisites. So it is very obvious for the learner to make a choice of what should be learnt before what. In this paper we have taken the data mining based frequent pattern graph model to define the association and sequencing between the words and then adopted the Ant Colony Optimization, an artificial intelligence approach, to derive a searching technique to obtain an efficient and optimized learning path to reach to a unknown term.


Learners' Quanta based Design of a Learning Management System

Abstract: In this paper IEEE Learning Technology System Architecture (LTSA) for LMS software has been analyzed. It has been observed that LTSA is too abstract to be adapted in a uniform way by LMS developers. A Learners' Quanta based high level design that satisfies the IEEE LTSA standard has been proposed for future development of efficient LMS software. A hybrid model of learning fitting into LTSA model has also been proposed while designing.


A comparison algorithm to check LTSA Layer 1 and SCORM compliance in e-Learning sites

Abstract: The success of e-Learning is largely dependent on the impact of its multimedia aided learning content on the learner over the hyper media. The e-Learning portals with different proportion of multimedia elements have different impact on the learner, as there is lack of standardization. The Learning Technology System Architecture (LTSA) Layer 1 deals with the effect of environment on the learner. From an information technology perspective it specifies learner interaction from the environment to the learner via multimedia content. Sharable Content Object Reference Model (SCROM) is a collection of standards and specifications for content of web-based e-learning and specifies how JavaScript API can be used to integrate content development. In this paper an examination is made on the design features of interactive multimedia components of the learning packages by creating an algorithm which will give a comparative study of multimedia component used by different learning packages. The resultant graph as output helps us to analysis to what extent any LMS compliance LTSA layer 1 and SCORM specification.


Fault Localization for Java Programs using Probabilistic Program Dependence Graph

Abstract: Fault localization is a process to find the location of faults. It determines the root cause of the failure. It identifies the causes of abnormal behaviour of a faulty program. It identifies exactly where the bugs are. Existing fault localization techniques are Slice based technique, Program- Spectrum based Technique, Statistics Based Technique, Program State Based Technique, Machine learning based Technique and Similarity Based Technique. In the proposed method Model Based Fault Localization Technique is used, which is called Probabilistic Program Dependence Graph . Probabilistic Program Dependence Graph (PPDG) is an innovative model that scans the internal behaviour of the project. PPDG construction is enhanced by Program Dependence Graph (PDG). PDG is achieved by the Control Flow Graph (CFG). The PPDG construction augments the structural dependences represented by a program dependence graph with estimates of statistical dependences between node states, which are computed from the test set. The PPDG is based on the established framework of probabilistic graphical models. This work presents algorithms for constructing PPDGs and applying fault localization.


Parameter Identification in a Probabilistic Setting

Abstract: Parameter identification problems are formulated in a probabilistic language, where the randomness reflects the uncertainty about the knowledge of the true values. This setting allows conceptually easily to incorporate new information, e.g. through a measurement, by connecting it to Bayes's theorem. The unknown quantity is modelled as a (may be high-dimensional) random variable. Such a description has two constituents, the measurable function and the measure. One group of methods is identified as updating the measure, the other group changes the measurable function. We connect both groups with the relatively recent methods of functional approximation of stochastic problems, and introduce especially in combination with the second group of methods a new procedure which does not need any sampling, hence works completely deterministically. It also seems to be the fastest and more reliable when compared with other methods. We show by example that it also works for highly nonlinear non-smooth problems with non-Gaussian measures.


On the Shape of Curves that are Rational in Polar Coordinates

Abstract: In this paper we provide a computational approach to the shape of curves which are rational in polar coordinates, i.e. which are defined by means of a parametrization (r(t),\theta(t)) where both r(t),\theta(t) are rational functions. Our study includes theoretical aspects on the shape of these curves, and algorithmic results which eventually lead to an algorithm for plotting the "interesting parts" of the curve, i.e. the parts showing the main geometrical features of it. On the theoretical side, we prove that these curves, with the exceptions of lines and circles, cannot be algebraic (in cartesian coordinates), we characterize the existence of infinitely many self-intersections, and we connect this with certain phenomena which are not possible in the algebraic world, namely the existence of limit circles, limit points, or spiral branches. On the practical side, we provide an algorithm which has been implemented in the computer algebra system Maple to visualize this kind of curves. Our implementation makes use (and improves some aspects of) the command polarplot currently available in Maple for plotting curves in polar form.


Progress in animation of an EMA-controlled tongue model for acoustic-visual speech synthesis

Abstract: We present a technique for the animation of a 3D kinematic tongue model, one component of the talking head of an acoustic-visual (AV) speech synthesizer. The skeletal animation approach is adapted to make use of a deformable rig controlled by tongue motion capture data obtained with electromagnetic articulography (EMA), while the tongue surface is extracted from volumetric magnetic resonance imaging (MRI) data. Initial results are shown and future work outlined.


A Description Logic Primer

Abstract: This paper provides a self-contained first introduction to description logics (DLs). The main concepts and features are explained with examples before syntax and semantics of the DL SROIQ are defined in detail. Additional sections review light-weight DL languages, discuss the relationship to the Web Ontology Language OWL and give pointers to further reading.


Image decomposition with anisotropic diffusion applied to leaf-texture analysis

Abstract: Texture analysis is an important field of investigation that has received a great deal of interest from computer vision community. In this paper, we propose a novel approach for texture modeling based on partial differential equation (PDE). Each image $f$ is decomposed into a family of derived sub-images. $f$ is split into the $u$ component, obtained with anisotropic diffusion, and the $v$ component which is calculated by the difference between the original image and the $u$ component. After enhancing the texture attribute $v$ of the image, Gabor features are computed as descriptors. We validate the proposed approach on two texture datasets with high variability. We also evaluate our approach on an important real-world application: leaf-texture analysis. Experimental results indicate that our approach can be used to produce higher classification rates and can be successfully employed for different texture applications.


Identifying Coordination Problems in Software Development: Finding Mismatches between Software and Project Team Structures

Abstract: Today's dynamic and iterative development environment brings significant challenges for software project management. In distributed project settings, "management by walking around" is no longer an option and project managers may miss out on key project insights. The TESNA (TEchnical Social Network Analysis) method and tool aims to provide project managers both a method and a tool for gaining insights and taking corrective action. TESNA achieves this by analysing a project's evolving social and technical network structures using data from multiple sources, including CVS, email and chat repositories. Using pattern theory, TESNA helps to identify areas where the current state of the project's social and technical networks conflicts with what patterns suggest. We refer to such a conflict as a Socio-Technical Structure Clash (STSC). In this paper we report on our experience of using TESNA to identify STSCs in a corporate environment through the mining of software repositories. We find multiple instances of three STSCs (Conway's Law, Code Ownership and Project Coordination) in many of the on-going development projects, thereby validating the method and tool that we have developed.


Optimal Threshold Control by the Robots of Web Search Engines with Obsolescence of Documents

Abstract: A typical web search engine consists of three principal parts: crawling engine, indexing engine, and searching engine. The present work aims to optimize the performance of the crawling engine. The crawling engine finds new web pages and updates web pages existing in the database of the web search engine. The crawling engine has several robots collecting information from the Internet. We first calculate various performance measures of the system (e.g., probability of arbitrary page loss due to the buffer overflow, probability of starvation of the system, the average time waiting in the buffer). Intuitively, we would like to avoid system starvation and at the same time to minimize the information loss. We formulate the problem as a multi-criteria optimization problem and attributing a weight to each criterion. We solve it in the class of threshold policies. We consider a very general web page arrival process modeled by Batch Marked Markov Arrival Process and a very general service time modeled by Phase-type distribution. The model has been applied to the performance evaluation and optimization of the crawler designed by INRIA Maestro team in the framework of the RIAM INRIA-Canon research project.


Iterative Approximate Byzantine Consensus in Arbitrary Directed Graphs

Abstract: In this paper, we explore the problem of iterative approximate Byzantine consensus in arbitrary directed graphs. In particular, we prove a necessary and sufficient condition for the existence of iterative byzantine consensus algorithms. Additionally, we use our sufficient condition to examine whether such algorithms exist for some specific graphs.


A Survey of Smart Data Pricing: Past Proposals, Current Plans, and Future Trends

Abstract: Traditionally, network operators have used simple flat-rate broadband data plans for both wired and wireless network access. But today, with the popularity of mobile devices and exponential growth of apps, videos, and clouds, service providers are gradually moving towards more sophisticated pricing schemes. This decade will therefore likely witness a major change in the ways in which network resources are managed, and the role of economics in allocating these resources. This survey reviews some of the well-known past broadband pricing proposals (both static and dynamic), including their current realizations in various consumer data plans around the world, and discusses several research problems and open questions. By exploring the benefits and challenges of pricing data, this paper attempts to facilitate both the industrial and the academic communities' efforts in understanding the existing literature, recognizing new trends, and shaping an appropriate and timely research agenda.


A simple D^2-sampling based PTAS for k-means and other Clustering Problems

Abstract: Given a set of points $P \subset \mathbb{R}^d$, the $k$-means clustering problem is to find a set of $k$ {\em centers} $C = \{c_1,...,c_k\}, c_i \in \mathbb{R}^d,$ such that the objective function $\sum_{x \in P} d(x,C)^2$, where $d(x,C)$ denotes the distance between $x$ and the closest center in $C$, is minimized. This is one of the most prominent objective functions that have been studied with respect to clustering. $D^2$-sampling \cite{ArthurV07} is a simple non-uniform sampling technique for choosing points from a set of points. It works as follows: given a set of points $P \subseteq \mathbb{R}^d$, the first point is chosen uniformly at random from $P$. Subsequently, a point from $P$ is chosen as the next sample with probability proportional to the square of the distance of this point to the nearest previously sampled points. $D^2$-sampling has been shown to have nice properties with respect to the $k$-means clustering problem. Arthur and Vassilvitskii \cite{ArthurV07} show that $k$ points chosen as centers from $P$ using $D^2$-sampling gives an $O(\log{k})$ approximation in expectation. Ailon et. al. \cite{AJMonteleoni09} and Aggarwal et. al. \cite{AggarwalDK09} extended results of \cite{ArthurV07} to show that $O(k)$ points chosen as centers using $D^2$-sampling give $O(1)$ approximation to the $k$-means objective function with high probability. In this paper, we further demonstrate the power of $D^2$-sampling by giving a simple randomized $(1 + \epsilon)$-approximation algorithm that uses the $D^2$-sampling in its core.


Collaborative Personalized Web Recommender System using Entropy based Similarity Measure

Abstract: On the internet, web surfers, in the search of information, always strive for recommendations. The solutions for generating recommendations become more difficult because of exponential increase in information domain day by day. In this paper, we have calculated entropy based similarity between users to achieve solution for scalability problem. Using this concept, we have implemented an online user based collaborative web recommender system. In this model based collaborative system, the user session is divided into two levels. Entropy is calculated at both the levels. It is shown that from the set of valuable recommenders obtained at level I; only those recommenders having lower entropy at level II than entropy at level I, served as trustworthy recommenders. Finally, top N recommendations are generated from such trustworthy recommenders for an online user.


Exact Safety Verification of Hybrid Systems Based on Bilinear SOS Representation

Abstract: In this paper, we address the problem of safety verification of nonlinear hybrid systems. A hybrid symbolic-numeric method is presented to compute exact inequality invariants of hybrid systems efficiently. Some numerical invariants of a hybrid system can be obtained by solving a bilinear SOS programming via PENBMI solver or iterative method, then the modified Newton refinement and rational vector recovery techniques are applied to obtain exact polynomial invariants with rational coefficients, which {\it exactly} satisfy the conditions of invariants. Experiments on some benchmarks are given to illustrate the efficiency of our algorithm.


Secondary use of data in EHR systems

Abstract: We show how to use aspect-oriented programming to separate security and trust issues from the logical design of mobile, distributed systems. The main challenge is how to enforce various types of security policies, in particular predictive access control policies - policies based on the future behavior of a program. A novel feature of our approach is that advice is able to analyze the future use of data. We consider a number of different security policies, concerning both primary and secondary use of data, some of which can only be enforced by analysis of process continuations.


\'Evaluation et consolidation d'un r\'eseau lexical via un outil pour retrouver le mot sur le bout de la langue

Abstract: Since September 2007, a large scale lexical network for French is under construction through methods based on some kind of popular consensus by means of games (JeuxDeMots project). Human intervention can be considered as marginal. It is limited to corrections, adjustments and validation of the senses of terms, which amounts to less than 0,5 % of the relations in the network. To appreciate the quality of this resource built by non-expert users (players of the game), we use a similar approach to its construction. The resource must be validated by laymen, persistent in time, on open class vocabulary. We suggest to check whether our tool is able to solve the Tip of the Tongue (TOT) problem. Just like JeuxDeMots, our tool can be considered as an on-line game. Like the former, it allows the acquisition of new relations, enriching thus the (existing) network.


Push-and-Track: Saving Infrastructure Bandwidth Through Opportunistic Forwarding

Abstract: Major wireless operators are nowadays facing network capacity issues in striving to meet the growing demands of mobile users. At the same time, 3G-enabled devices increasingly benefit from ad hoc radio connectivity (e.g., Wi-Fi). In this context of hybrid connectivity, we propose Push-and-track, a content dissemina- tion framework that harnesses ad hoc communication opportunities to minimize the load on the wireless infrastructure while guaranteeing tight delivery delays. It achieves this through a control loop that collects user-sent acknowledgements to determine if new copies need to be reinjected into the network through the 3G interface. Push-and-Track is flexible and can be applied to a variety of scenarios, including periodic message flooding and floating data. For the former, this paper examines multiple strategies to determine how many copies of the content should be injected, when, and to whom; for the latter, it examines the achievable offload ratio depending on the freshness constraints. The short delay-tolerance of common content, such as news or road traffic updates, make them suitable for such a system. Use cases with a long delay-tolerance, such as software updates, are an even better fit. Based on a realistic large-scale vehicular dataset from the city of Bologna composed of more than 10,000 vehicles, we demonstrate that Push-and-Track consistently meets its delivery objectives while reducing the use of the 3G network by about 90%.


A Robust Client Verification in cloud enabled m-Commerce using Gaining Protocol

Abstract: The proposed system highlights a novel approach of exclusive verification process using gain protocol for ensuring security among both the parties (client-service provider) in m-commerce application with cloud enabled service. The proposed system is based on the potential to verify the clients with trusted hand held device depending on the set of frequent events and actions to be carried out. The framework of the proposed work is design after collecting a real time data sets from an android enabled hand set, which when subjected to gain protocol, will result in detection of malicious behavior of illegal clients in the network. The real time experiment is performed with applicable datasets gather, which show the best result for identifying threats from last 2 months data collected.


Improving Security Levels of IEEE 802.16e Authentication By Diffie-Hellman Method

Abstract: In this paper, we proposed an authentication method according to Diffie-Hellman. First, we introduce different methods for authentication in IEEE.802.16 then we proposed an authentication method according to Diffie-Hellman and in the last we compare different methods for authentication to improve security in IEEE802.16e. CPN is a useful for simulation and compare protocol together so we use CPN tools in this paper.


Quantitative classical realizability

Abstract: Introduced by Dal Lago and Hofmann, quantitative realizability is a technique used to define models for logics based on Multiplicative Linear Logic. A particularity is that functions are interpreted as bounded time computable functions. It has been used to give new and uniform proofs of soundness of several type systems with respect to certain time complexity classes. We propose a reformulation of their ideas in the setting of Krivine's classical realizability. The framework obtained generalizes Dal Lago and Hofmann's realizability, and reveals deep connections between quantitative realizability and a linear variant of Cohen's forcing.


Application of Integral Value Transformation (IVT) in a Specialized Computer Network Design

Abstract: Integral Value Transformation (IVT) is a family of transformations from N0kto N0. An algebraic result has been established in p-adic IVT systems and an application of the result is described in this paper. The result in this paper provides the rule to find the pth pre image of a natural number for the Collatz like bijective functions in p-adic IVT systems. Using this result a routing algorithm is proposed. This proposed routing algorithm reduces number of address calculation.


On the intrinsic complexity of elimination problems in effective Algebraic Geometry

Abstract: The representation of polynomials by arithmetic circuits evaluating them is an alternative data structure which allowed considerable progress in polynomial equation solving in the last fifteen years. We present a circuit based computation model which captures all known symbolic elimination algorithms in effective algebraic geometry and show the intrinsically exponential complexity character of elimination in this complexity model.


Participatory Privacy: Enabling Privacy in Participatory Sensing

Abstract: Participatory Sensing is an emerging computing paradigm that enables the distributed collection of data by self-selected participants. It allows the increasing number of mobile phone users to share local knowledge acquired by their sensor-equipped devices, e.g., to monitor temperature, pollution level or consumer pricing information. While research initiatives and prototypes proliferate, their real-world impact is often bounded to comprehensive user participation. If users have no incentive, or feel that their privacy might be endangered, it is likely that they will not participate. In this article, we focus on privacy protection in Participatory Sensing and introduce a suitable privacy-enhanced infrastructure. First, we provide a set of definitions of privacy requirements for both data producers (i.e., users providing sensed information) and consumers (i.e., applications accessing the data). Then, we propose an efficient solution designed for mobile phone users, which incurs very low overhead. Finally, we discuss a number of open problems and possible research directions.


The milling process monitoring using 3D envelope method

Abstract: This paper proposes a method to vibration analysis in order to on-line monitoring of milling process quality. Adapting envelope analysis to characterize the milling tool materials is an important contribution to the qualitative and quantitative characterization of milling capacity and a step by modeling the three-dimensional cutting process. An experimental protocol was designed and developed for the acquisition, processing and analyzing three-dimensional signal. The vibration envelope analysis is proposed to detect the cutting capacity of the tool with the optimization application of cutting parameters. The research is focused on Hilbert transform optimization to evaluate the dynamic behavior of the machine/ tool/workpiece.


Dynamic behavior analysis for a six axis industrial machining robot

Abstract: The six axis robots are widely used in automotive industry for their good repeatability (as defined in the ISO92983) (painting, welding, mastic deposition, handling etc.). In the aerospace industry, robot starts to be used for complex applications such as drilling, riveting, fiber placement, NDT, etc. Given the positioning performance of serial robots, precision applications require usually external measurement device with complexes calibration procedure in order to reach the precision needed. New applications in the machining field of composite material (aerospace, naval, or wind turbine for example) intend to use off line programming of serial robot without the use of calibration or external measurement device. For those applications, the position, orientation and path trajectory precision of the tool center point of the robot are needed to generate the machining operation. This article presents the different conditions that currently limit the development of robots in robotic machining applications. We analyze the dynamical behavior of a robot KUKA KR240-2 (located at the University of Bordeaux 1) equipped with a HSM Spindle (42000 rpm, 18kW). This analysis is done in three stages. The first step is determining the self-excited frequencies of the robot structure for three different configurations of work. The second phase aims to analyze the dynamical vibration of the structure as the spindle is activated without cutting. The third stage consists of vibration analysis during a milling operation.


Experimental Characterization of Robot Arm Rigidity in Order to Be Used in Machining Operation

Abstract: Attempts to install a rotating tool at the end of a robot arm poly-articulated date back twenty years, but these robots were not designed for that. Indeed, two essential features are necessary for machining: high rigidity and precision in a given workspace. The experimental results presented are the dynamic identification of a poly-articulated robot equipped with an integrated spindle. This study aims to highlight the influence of the geometric configuration of the robot arm on the overall stiffness of the system. The spindle is taken into account as an additional weight on board but also as a dynamical excitation for the robot KUKA KR_240_2. Study of the robotic machining vibrations shows the suitable directions of movement in milling process


New Approach of Envelope Dynamic Analysis for Milling Process

Abstract: This paper proposes a method to vibration analysis in order to on-line monitoring of milling process quality. Adapting envelope analysis to characterize the milling tool materials is an important contribution to the qualitative and quantitative characterization of milling capacity and a step by modeling the three-dimensional cutting process. An experimental protocol was designed and developed for the acquisition, processing and analyzing three-dimensional signal. The vibration envelope analysis is proposed to detect the cutting capacity of the tool with the optimization application of cutting parameters. The research is focused on FFT Fourier transform optimization of vibration analysis and vibration envelope to evaluate the dynamic behavior of the machine/ tool/workpiece


Faster Algorithms for Alternating Refinement Relations

Abstract: One central issue in the formal design and analysis of reactive systems is the notion of refinement that asks whether all behaviors of the implementation is allowed by the specification. The local interpretation of behavior leads to the notion of simulation. Alternating transition systems (ATSs) provide a general model for composite reactive systems, and the simulation relation for ATSs is known as alternating simulation. The simulation relation for fair transition systems is called fair simulation. In this work our main contributions are as follows: (1) We present an improved algorithm for fair simulation with B\"uchi fairness constraints; our algorithm requires $O(n^3 \cdot m)$ time as compared to the previous known $O(n^6)$-time algorithm, where $n$ is the number of states and $m$ is the number of transitions. (2) We present a game based algorithm for alternating simulation that requires $O(m^2)$-time as compared to the previous known $O((n \cdot m)^2)$-time algorithm, where $n$ is the number of states and $m$ is the size of transition relation. (3) We present an iterative algorithm for alternating simulation that matches the time complexity of the game based algorithm, but is more space efficient than the game based algorithm.


An efficient parallel algorithm for the longest path problem in meshes

Abstract: In this paper, first we give a sequential linear-time algorithm for the longest path problem in meshes. This algorithm can be considered as an improvement of [13]. Then based on this sequential algorithm, we present a constant-time parallel algorithm for the problem which can be run on every parallel machine.


A System-Level Semantics

Abstract: Game semantics is a trace-like denotational semantics for programming languages where the notion of legal observable behaviour of a term is defined combinatorially, by means of rules of a game between the term (the "Proponent") and its context (the "Opponent"). In general, the richer the computational features a language has, the less constrained the rules of the semantic game. In this paper we consider the consequences of taking this relaxation of rules to the limit, by granting the Opponent omnipotence, that is, permission to play any move without combinatorial restrictions. However, we impose an epistemic restriction by not granting Opponent omniscience, so that Proponent can have undisclosed secret moves. We introduce a basic C-like programming language and we define such a semantic model for it. We argue that the resulting semantics is an appealingly simple combination of operational and game semantics and we show how certain traces explain system-level attacks, i.e. plausible attacks that are realizable outside of the programming language itself. We also show how allowing Proponent to have secrets ensures that some desirable equivalences in the programming language are preserved.


Requirements and the baseline plan

Abstract: For each software project a plan is developed, according to a documented procedure, that covers the software activities and commitments. The requirements allocated to software form the basis for the software development plan. Estimates for critical computer resources are documented, reviewed, and agreed to. All affected groups and individuals understand the estimates and plans and commit to support them. Senior management reviews the estimates and plans before external commitments are made. Software risks associated with the cost, resources, schedule, and technical aspects of the project are identified and evaluated, and contingencies are documented. Planning and estimation data are collected for use in planning subsequent projects and for input in management oversight review meetings.


SLA-Oriented Resource Provisioning for Cloud Computing: Challenges, Architecture, and Solutions

Abstract: Cloud computing systems promise to offer subscription-oriented, enterprise-quality computing services to users worldwide. With the increased demand for delivering services to a large number of users, they need to offer differentiated services to users and meet their quality expectations. Existing resource management systems in data centers are yet to support Service Level Agreement (SLA)-oriented resource allocation, and thus need to be enhanced to realize cloud computing and utility computing. In addition, no work has been done to collectively incorporate customer-driven service management, computational risk management, and autonomic resource management into a market-based resource management system to target the rapidly changing enterprise requirements of Cloud computing. This paper presents vision, challenges, and architectural elements of SLA-oriented resource management. The proposed architecture supports integration of marketbased provisioning policies and virtualisation technologies for flexible allocation of resources to applications. The performance results obtained from our working prototype system shows the feasibility and effectiveness of SLA-based resource provisioning in Clouds.


Livelock free routing schemes

Abstract: We give a livelock free routing algorithm for any allowed network. Unlike some other solutions to this problem: 1) packets entering the network have an absolute upper bound on the time to reach their destination; 2) under light loads, packets are delivered to their destinations in nearly optimal time; 3) packets with desired paths far away from congested areas will have routing times far shorter than packets wanting to access congested areas; 4) if the network becomes congested and later clears, the network operates just as it would have when it was initially under a light load. The main ideas of this note appear in a different form in my 1994 patent 5,369,745. This note adds to those results and makes them more mathematical.


An Attack on Privacy Preserving Data Aggregation Protocol for Wireless Sensor Networks

Abstract: In-network data aggregation in Wireless Sensor Networks (WSNs) provides efficient bandwidth utilization and energy-efficient computing.Supporting efficient in-network data aggregation while preserving the privacy of the data of individual sensor nodes has emerged as an important requirement in numerous WSN applications. For privacy-preserving data aggregation in WSNs, He et al. (INFOCOM 2007) have proposed a Cluster-based Private Data Aggregation (CPDA) that uses a clustering protocol and a well-known key distribution scheme for computing an additive aggregation function in a privacy-preserving manner. In spite of the wide popularity of CPDA, it has been observed that the protocol is not secure and it is also possible to enhance its efficiency. In this paper, we first identify a security vulnerability in the existing CPDA scheme, wherein we show how a malicious participant node can launch an attack on the privacy protocol so as to get access to the private data of its neighboring sensor nodes. Next it is shown how the existing CPDA scheme can be made more efficient by suitable modification of the protocol. Further, suitable modifications in the existing protocol have been proposed so as to plug the vulnerability of the protocol.


A Multi-Path Certification Protocol for Mobile Ad Hoc Networks

Abstract: A mobile ad hoc network (MANET) is a collection of autonomous nodes that communicate with each other by forming a multi-hop radio network and maintaining connections in a decentralized manner. Security remains a major challenge for these networks due to their features of open medium, dynamically changing topologies, reliance on cooperative algorithms, absence of centralized monitoring points, and lack of clear lines of defense. Most of the routing protocols for MANETs are thus vulnerable to various types of attacks. For security, these protocols are highly dependent on cryptographic key exchange operations. This paper presents a multi-path certification protocol for efficient and reliable key exchange among the nodes in a MANET. Simulation results have shown the effectiveness and efficiency of the protocol.


Implementation of Portion Approach in Distributed Firewall Application for Network Security Framework

Abstract: The stimulate of this research seeks collaboration of firewalls which, could reach to the capability of distributed points of security policy; the front-end entity may much interact by the invaders so the separation between this entity and back-end entity to make the secure domain protection is necessary; collaborative security entity has the various task in the organization and there is a certain security policy to apply in; the entities like DPFF have to be protected from outsiders. Firewalls are utilized typically to be the main layer of security in the network framework. The research is presented the particular segment of the proposed framework that DPFF based on the developed iptable firewall to be the layers of defense, which is protected front and backend of the framework with a dynamic security and policy update to control the framework's safeguard through proposed portion approach algorithm that utilize to reduce the traffic and efficiency in detection and policy update mechanism. The policy update mechanism for DPFF is given the way of its employment. The complete framework signifies a distributed firewall, where the administrator configures the policy rules set, which could be separately or else from administration nodes' side.


Ramified Structural Recursion and Corecursion

Abstract: We investigate feasible computation over a fairly general notion of data and codata. Specifically, we present a direct Bellantoni-Cook-style normal/safe typed programming formalism, RS1, that expresses feasible structural recursions and corecursions over data and codata specified by polynomial functors. (Lists, streams, finite trees, infinite trees, etc. are all directly definable.) A novel aspect of RS1 is that it embraces structure-sharing as in standard functional-programming implementations. As our data representations use sharing, our implementation of structural recursions are memoized to avoid the possibly exponentially-many repeated subcomputations a naive implementation might perform. We introduce notions of size for representations of data (accounting for sharing) and codata (using ideas from type-2 computational complexity) and establish that type-level 1 RS1-functions have polynomial-bounded runtimes and satisfy a polynomial-time completeness condition. Also, restricting RS1 terms to particular types produces characterizations of some standard complexity classes (e.g., omega-regular languages, linear-space functions) and some less-standard classes (e.g., log-space streams).


Technologie et pratiques bibliographiques associ\'ees \`a l'\'ecriture scientifique en milieu universitaire

Abstract: Observe and understand users of the Scientific and Technical Information, is preparing to offer them appropriate services. This exploratory study provides answers about the uses in the humanities, social sciences as well as technical sciences. We also observe those who assist teachers in their scientific research: librarians. Then we outline considerations and recommendations to specify functionalities of an efficient e-Linrary.


Alternatives to the Journal Impact Factor: I3 and the Top-10% (or Top-25%?) of the Most-Highly Cited Papers

Abstract: Journal Impact Factors (IFs) can be considered historically as the first attempt to normalize citation distributions by using averages over two years. However, it has been recognized that citation distributions vary among fields of science and that one needs to normalize for this. Furthermore, the mean-or any central-tendency statistics-is not a good representation of the citation distribution because these distributions are skewed. Important steps have been taken to solve these two problems during the last few years. First, one can normalize at the article level using the citing audience as the reference set. Second, one can use non-parametric statistics for testing the significance of differences among ratings. A proportion of most-highly cited papers (the top-10% or top-quartile) on the basis of fractional counting of the citations may provide an alternative to the current IF. This indicator is intuitively simple, allows for statistical testing, and accords with the state of the art.


A further step forward in measuring journals' scientific prestige: The SJR2 indicator

Abstract: A new size-independent indicator of scientific journal prestige, the SJR2 indicator, is proposed. This indicator takes into account not only the prestige of the citing scientific journal but also its closeness to the cited journal using the cosine of the angle between the vectors of the two journals' cocitation profiles. To eliminate the size effect, the accumulated prestige is divided by the fraction of the journal's citable documents, thus eliminating the decreasing tendency of this type of indicator and giving meaning to the scores. Its method of computation is described, and the results of its implementation on the Scopus 2008 dataset is compared with those of an ad hoc Journal Impact Factor, JIF(3y), and SNIP, the comparison being made both overall and within specific scientific areas. All three, the SJR2 indicator, the SNIP indicator and the JIF distributions, were found to fit well to a logarithmic law. Although the three metrics were strongly correlated, there were major changes in rank. In addition, the SJR2 was distributed more equalized than the JIF by Subject Area and almost as equalized as the SNIP, and better than both at the lower level of Specific Subject Areas. The incorporation of the cosine increased the values of the flows of prestige between thematically close journals.


Collaborative knowledge networks emergence for innovation: Factors of success analysis and comparison

Abstract: New product development needs new engineering approaches. Knowledge is a key resource that impacts traditional, organisational, economic and innovative models. Through NICT (New Information and Communication Technologies), globalisation encourages the emergence of networks that overcome traditional organisation boundaries. International enterprises, European-Community Networks of Excellence or Clusters (competitiveness poles) indicate the need to define a new way of thinking. This new way moves towards an agile, continuous innovative use of knowledge. Based on an epistemic study of knowledge management best practices, four examples show the barriers that can be encountered today. This paper aims defining the key elements that enhance collaborative networks. The analysis of best practices from collaborative environments enables the design of high standard information systems and initiate knowledge ecosystems. A balance between formalism required to share knowledge and fuzziness of social networks triggers new initiatives. This ensures the validity of information exchange through virtual collaboration. It helps to maintain group coherence despite exceeding the natural maximum number of collaborators. Finally the main success or failure factors are highlights and commented to ease the transition from economic-driven to expertise-driven models is then facilitated.


Network Coding-Based Cooperative ARQ Scheme

Abstract: In this paper we introduce a novel Automatic Repeat reQuest (ARQ) scheme for cooperative wireless networks. Our scheme adopts network coding techniques in order to enhance the total bandwidth of the network by minimizing the total number of transmissions. The performance of the proposed approach is evaluated by means of computer simulations and compared to other cooperative schemes, while an analytical solution is provided to validate the results.


Reengineering multi tiered enterprise business applications for performance enhancement and reciprocal or rectangular hyperbolic relation of variation of data transportation time with row pre-fetch size of relational database drivers


Compact Symbolic Execution

Abstract: We present a generalisation of King's symbolic execution technique called compact symbolic execution. It proceeds in two steps. First, we analyse cyclic paths in the control flow graph of a given program, independently from the rest of the program. Our goal is to compute a so called template for each such a cyclic path. A template is a declarative parametric description of all possible program states, which may leave the analysed cyclic path after any number of iterations along it. In the second step, we execute the program symbolically with the templates in hand. The result is a compact symbolic execution tree. A compact tree always carry the same information in all its leaves as the corresponding classic symbolic execution tree. Nevertheless, a compact tree is typically substantially smaller than the corresponding classic tree. There are even programs for which compact symbolic execution trees are finite while classic symbolic execution trees are infinite.


On Synergy of Metal, Slicing, and Symbolic Execution

Abstract: We introduce a novel technique for finding real errors in programs. The technique is based on a synergy of three well-known methods: metacompilation, slicing, and symbolic execution. More precisely, we instrument a given program with a code that tracks runs of state machines representing various kinds of errors. Next we slice the program to reduce its size without affecting runs of state machines. And then we symbolically execute the sliced program. Depending on the kind of symbolic execution, the technique can be applied as a stand-alone bug finding technique, or to weed out some false positives from an output of another bug-finding tool. We provide several examples demonstrating the practical applicability of our technique.


Solving the LPN problem in cube-root time

Abstract: In this paper it is shown that given a sufficient number of (noisy) random binary linear equations, the Learning from Parity with Noise (LPN) problem can be solved in essentially cube root time in the number of unknowns. The techniques used to recover the solution are known from fast correlation attacks on stream ciphers. As in fast correlation attacks, the performance of the algorithm depends on the number of equations given. It is shown that if this number exceeds a certain bound, and the bias of the noisy equations is polynomial in number of unknowns, the running time of the algorithm is reduced to almost cube root time compared to the brute force checking of all possible solutions. The mentioned bound is explicitly given and it is further shown that when this bound is exceeded, the complexity of the approach can even be further reduced.


Du TAL au TIL

Abstract: Historically two types of NLP have been investigated: fully automated processing of language by machines (NLP) and autonomous processing of natural language by people, i.e. the human brain (psycholinguistics). We believe that there is room and need for another kind, INLP: interactive natural language processing. This intermediate approach starts from peoples' needs, trying to bridge the gap between their actual knowledge and a given goal. Given the fact that peoples' knowledge is variable and often incomplete, the aim is to build bridges linking a given knowledge state to a given goal. We present some examples, trying to show that this goal is worth pursuing, achievable and at a reasonable cost.


Production System Rules as Protein Complexes from Genetic Regulatory Networks

Abstract: This short paper introduces a new way by which to design production system rules. An indirect encoding scheme is presented which views such rules as protein complexes produced by the temporal behaviour of an artificial genetic regulatory network. This initial study begins by using a simple Boolean regulatory network to produce traditional ternary-encoded rules before moving to a fuzzy variant to produce real-valued rules. Competitive performance is shown with related genetic regulatory networks and rule-based systems on benchmark problems.


Existence of Stability in Hedonic Coalition Formation Games

Abstract: In this paper, we examine \emph{hedonic coalition formation games} in which each player's preferences over partitions of players depend only on the members of his coalition. We present three main results in which restrictions on the preferences of the players guarantee the existence of stable partitions for various notions of stability. The preference restrictions pertain to \emph{top responsiveness} and \emph{bottom responsiveness} which model optimistic and pessimistic behavior of players respectively. The existence results apply to natural subclasses of \emph{additive separable hedonic games} and \emph{hedonic games with \B-preferences}. It is also shown that our existence results cannot be strengthened to the case of stronger known stability concepts.


A probabilistic methodology for multilabel classification

Abstract: Multilabel classification is a relatively recent subfield of machine learning. Unlike to the classical approach, where instances are labeled with only one category, in multilabel classification, an arbitrary number of categories is chosen to label an instance. Due to the problem complexity (the solution is one among an exponential number of alternatives), a very common solution (the binary method) is frequently used, learning a binary classifier for every category, and combining them all afterwards. The assumption taken in this solution is not realistic, and in this work we give examples where the decisions for all the labels are not taken independently, and thus, a supervised approach should learn those existing relationships among categories to make a better classification. Therefore, we show here a generic methodology that can improve the results obtained by a set of independent probabilistic binary classifiers, by using a combination procedure with a classifier trained on the co-occurrences of the labels. We show an exhaustive experimentation in three different standard corpora of labeled documents (Reuters-21578, Ohsumed-23 and RCV1), which present noticeable improvements in all of them, when using our methodology, in three probabilistic base classifiers.


Transporting Functions across Ornaments

Abstract: Programming with dependent types is a blessing and a curse. It is a blessing to be able to bake invariants into the definition of data-types: we can finally write correct-by-construction software. However, this extreme accuracy is also a curse: a data-type is the combination of a structuring medium together with a special purpose logic. These domain-specific logics hamper any effort of code reuse among similarly structured data. In this paper, we exorcise our data-types by adapting the notion of ornament to our universe of inductive families. We then show how code reuse can be achieved by ornamenting functions. Using these functional ornament, we capture the relationship between functions such as the addition of natural numbers and the concatenation of lists. With this knowledge, we demonstrate how the implementation of the former informs the implementation of the latter: the user can ask the definition of addition to be lifted to lists and she will only be asked the details necessary to carry on adding lists rather than numbers. Our presentation is formalised in a type theory with a universe of data-types and all our constructions have been implemented as generic programs, requiring no extension to the type theory.


A PSPACE-Complete First Order Fragment of Computability Logic

Abstract: In a recently launched research program for developing logic as a formal theory of (interactive) computability, several very interesting logics have been introduced and axiomatized. These fragments of the larger Computability Logic aim not only to describe "what" can be computed, but also provide a mechanism for extracting computational algorithms from proofs. Among the most expressive and fundamental of these is CL4, known to be (constructively) sound and complete with respect to the underlying computational semantics. Furthermore, the fragment of CL4 not containing blind quantifiers was shown to be decidable in polynomial space. The present work extends this result and proves that this fragment is, in fact, PSPACE-complete.


Queue-Dispatch Asynchronous Systems

Abstract: To make the development of efficient multi-core applications easier, libraries, such as Grand Central Dispatch, have been proposed. When using such a library, the programmer writes so-called blocks, which are chunks of codes, and dispatches them, using synchronous or asynchronous calls, to several types of waiting queues. A scheduler is then responsible for dispatching those blocks on the available cores. Blocks can synchronize via a global memory. In this paper, we propose Queue-Dispatch Asynchronous Systems as a mathematical model that faithfully formalizes the synchronization mechanisms and the behavior of the scheduler in those systems. We study in detail their relationships to classical formalisms such as pushdown systems, Petri nets, fifo systems, and counter systems. Our main technical contributions are precise worst-case complexity results for the Parikh coverability problem for several subclasses of our model.


Compressive Acquisition of Dynamic Scenes

Abstract: Compressive sensing (CS) is a new approach for the acquisition and recovery of sparse signals and images that enables sampling rates significantly below the classical Nyquist rate. Despite significant progress in the theory and methods of CS, little headway has been made in compressive video acquisition and recovery. Video CS is complicated by the ephemeral nature of dynamic events, which makes direct extensions of standard CS imaging architectures and signal models difficult. In this paper, we develop a new framework for video CS for dynamic textured scenes that models the evolution of the scene as a linear dynamical system (LDS). This reduces the video recovery problem to first estimating the model parameters of the LDS from compressive measurements, and then reconstructing the image frames. We exploit the low-dimensional dynamic parameters (the state sequence) and high-dimensional static parameters (the observation matrix) of the LDS to devise a novel compressive measurement strategy that measures only the dynamic part of the scene at each instant and accumulates measurements over time to estimate the static parameters. This enables us to lower the compressive measurement rate considerably. We validate our approach with a range of experiments involving both video recovery, sensing hyper-spectral data, and classification of dynamic scenes from compressive data. Together, these applications demonstrate the effectiveness of the approach.


Finding Endogenously Formed Communities

Abstract: A central problem in e-commerce is determining overlapping communities among individuals or objects in the absence of external identification or tagging. We address this problem by introducing a framework that captures the notion of communities or clusters determined by the relative affinities among their members. To this end we define what we call an affinity system, which is a set of elements, each with a vector characterizing its preference for all other elements in the set. We define a natural notion of (potentially overlapping) communities in an affinity system, in which the members of a given community collectively prefer each other to anyone else outside the community. Thus these communities are endogenously formed in the affinity system and are "self-determined" or "self-certified" by its members. We provide a tight polynomial bound on the number of self-determined communities as a function of the robustness of the community. We present a polynomial-time algorithm for enumerating these communities. Moreover, we obtain a local algorithm with a strong stochastic performance guarantee that can find a community in time nearly linear in the of size the community. Social networks fit particularly naturally within the affinity system framework -- if we can appropriately extract the affinities from the relatively sparse yet rich information from social networks, our analysis then yields a set of efficient algorithms for enumerating self-determined communities in social networks. In the context of social networks we also connect our analysis with results about $(\alpha,\beta)$-clusters introduced by Mishra, Schreiber, Stanton, and Tarjan \cite{msst}. In contrast with the polynomial bound we prove on the number of communities in the affinity system model, we show that there exists a family of networks with superpolynomial number of $(\alpha,\beta)$-clusters.


Adaptive Shortest-Path Routing under Unknown and Stochastically Varying Link States

Abstract: We consider the adaptive shortest-path routing problem in wireless networks under unknown and stochastically varying link states. In this problem, we aim to optimize the quality of communication between a source and a destination through adaptive path selection. Due to the randomness and uncertainties in the network dynamics, the quality of each link varies over time according to a stochastic process with unknown distributions. After a path is selected for communication, the aggregated quality of all links on this path (e.g., total path delay) is observed. The quality of each individual link is not observable. We formulate this problem as a multi-armed bandit with dependent arms. We show that by exploiting arm dependencies, a regret polynomial with network size can be achieved while maintaining the optimal logarithmic order with time. This is in sharp contrast with the exponential regret order with network size offered by a direct application of the classic MAB policies that ignore arm dependencies. Furthermore, our results are obtained under a general model of link-quality distributions (including heavy-tailed distributions) and find applications in cognitive radio and ad hoc networks with unknown and dynamic communication environments.


Self-Organisation of Evolving Agent Populations in Digital Ecosystems

Abstract: We investigate the self-organising behaviour of Digital Ecosystems, because a primary motivation for our research is to exploit the self-organising properties of biological ecosystems. We extended a definition for the complexity, grounded in the biological sciences, providing a measure of the information in an organism's genome. Next, we extended a definition for the stability, originating from the computer sciences, based upon convergence to an equilibrium distribution. Finally, we investigated a definition for the diversity, relative to the selection pressures provided by the user requests. We conclude with a summary and discussion of the achievements, including the experimental results.


Decentralized Lifetime Maximizing Tree with Clustering for Data Delivery in Wireless Sensor Networks

Abstract: A wireless sensor network has a wide application domain which is expanding everyday and they have been deployed pertaining to their application area. An application independent approach is yet to come to terms with the ongoing exploitation of the WSNs. In this paper we propose a decentralized lifetime maximizing tree for application independent data aggregation scheme using the clustering for data delivery in WSNs. The proposed tree will minimize the energy consumption which has been a resisting factor in the smooth working of WSNs as well as minimize the distance between the communicating nodes under the control of a sub-sink which further communicate and transfer data to the sink node.


Real Time Communication Capacity for Data Delivery in Wireless Sensor Networks

Abstract: Real-time applications are performance critical applications that require bounded service latency. In multi-hop wireless ad-hoc and sensor networks, communication delays are dominant over processing delays. Therefore, to enable real-time applications in such networks, the communication latency must be bounded. In this paper, we derive expressions of real-time capacity that characterize the ability of a network to deliver data on time as well as develop network protocols that achieve this capacity. Real-time capacity expressions are obtained and analyzed for the earliest deadline first, deadline monotonic. This paper presents a treatment of the real-time capacity limits. The limits are derived for two extreme traffic topologies namely, the load balanced topology and the convergecast (i.e., many-to-one) topology. It considers DM and EDF scheduling algorithms, and discusses the implications of the capacity limit expressions.


Gaming is a hard job, but someone has to do it!

Abstract: We establish some general schemes relating the computational complexity of a video game to the presence of certain common elements or mechanics, such as destroyable paths, collectible items, doors opened by keys or activated by buttons or pressure plates, etc. Then we apply such "metatheorems" to several video games published between 1980 and 1998, including Pac-Man, Tron, Lode Runner, Boulder Dash, Deflektor, Mindbender, Pipe Mania, Skweek, Prince of Persia, Lemmings, Doom, Puzzle Bobble~3, and Starcraft. We obtain both new results, and improvements or alternative proofs of previously known results.


A study of distributed QoS adapter in large-scale wireless networks

Abstract: Considering the comfortably establishing ad hoc networks, the use of this type of network is increasing day to day. On the other side, it is predicted that using multimedia applications will be more public in these network. As it is known, in contrary to best-effort flows, the transmission of multimedia flows in any network need support from QoS. However, the wireless ad hoc networks are severely affected by bandwidth, and establishing a QoS in these networks face problems. In this paper, we have proposed a thoroughly distributed algorithm to support the QoS in ad hoc networks. This algorithm guarantees the QoS of the real-time applications vis-a-vis each other and best-effort flows as well. The algorithm suggested in this paper dynamically regulates the Contention Window of the flows and serves the flows in terms of their requests QoS choosing the smallest CW in every node. This algorithm also uses the fixed and/or less stationary nodes for the transmission of real-time flows by increasing the QoS of the multimedia flows. This algorithm is preferred because it prioritizes the flows that are of the same class but have not obtained favorite QoS compared to other flows of the same class in addition to classifying the flows in the network and offering better services to the classes of higher priority. All this occur without the controlled packets forwarding and resource reserving and freeing method. We have proved the correctness of this algorithm using Markov's mathematical model.


A Knowledge Engineering Method for New Product Development

Abstract: Engineering activities involve large groups of people from different domains and disciplines. They often generate important information flows that are difficult to manage. To face these difficulties, a knowledge engineering process is necessary to structure the information and its use. This paper presents a deployment of a knowledge capitalization process based on the enrichment of MOKA methodology to support the integration of Process Planning knowledge in a CAD System. Our goal is to help different actors to work collaboratively by proposing one referential view of the domain, the context and the objectives assuming that it will help them in better decision-making.


Online Multi-Commodity Flow with High Demands

Abstract: This paper deals with the problem of computing, in an online fashion, a maximum benefit multi-commodity flow (\ONMCF), where the flow demands may be bigger than the edge capacities of the network. We present an online, deterministic, centralized, all-or-nothing, bi-criteria algorithm. The competitive ratio of the algorithm is constant, and the algorithm augments the capacities by at most a logarithmic factor. The algorithm can handle two types of flow requests: (i) low demand requests that must be routed along a path, and (ii) high demand requests that may be routed using a multi-path flow. Two extensions are discussed: requests with known durations and machine scheduling.


Word Automaticity of Tree Automatic Scattered Linear Orderings Is Decidable

Abstract: A tree automatic structure is a structure whose domain can be encoded by a regular tree language such that each relation is recognisable by a finite automaton processing tuples of trees synchronously. Words can be regarded as specific simple trees and a structure is word automatic if it is encodable using only these trees. The question naturally arises whether a given tree automatic structure is already word automatic. We prove that this problem is decidable for tree automatic scattered linear orderings. Moreover, we show that in case of a positive answer a word automatic presentation is computable from the tree automatic presentation.


Strategy Synthesis for Multi-dimensional Quantitative Objectives

Abstract: Multi-dimensional mean-payoff and energy games provide the mathematical foundation for the quantitative study of reactive systems, and play a central role in the emerging quantitative theory of verification and synthesis. In this work, we study the strategy synthesis problem for games with such multi-dimensional objectives along with a parity condition, a canonical way to express $\omega$-regular conditions. While in general, the winning strategies in such games may require infinite memory, for synthesis the most relevant problem is the construction of a finite-memory winning strategy (if one exists). Our main contributions are as follows. First, we show a tight exponential bound (matching upper and lower bounds) on the memory required for finite-memory winning strategies in both multi-dimensional mean-payoff and energy games along with parity objectives. This significantly improves the triple exponential upper bound for multi energy games (without parity) that could be derived from results in literature for games on VASS (vector addition systems with states). Second, we present an optimal symbolic and incremental algorithm to compute a finite-memory winning strategy (if one exists) in such games. Finally, we give a complete characterization of when finite memory of strategies can be traded off for randomness. In particular, we show that for one-dimension mean-payoff parity games, randomized memoryless strategies are as powerful as their pure finite-memory counterparts.


Conception and Use of Ontologies for Indexing and Searching by Semantic Contents of Video Courses

Abstract: Nowadays, the video documents like educational courses available on the web increases significantly. However, the information retrieval systems today can not return to the users (students or teachers) of parts of those videos that meet their exact needs expressed by a query consisting of semantic information. In this paper, we present a model of pedagogical knowledge of current videos. This knowledge is used throughout the process of indexing and semantic search segments instructional videos. Our experimental results show that the proposed approach is promising.


Faster and Simpler Width-Independent Parallel Algorithms for Positive Semidefinite Programming

Abstract: This paper studies the problem of finding an $(1+\epsilon)$-approximate solution to positive semidefinite programs. These are semidefinite programs in which all matrices in the constraints and objective are positive semidefinite and all scalars are non-negative. We present a simpler \NC parallel algorithm that on input with $n$ constraint matrices, requires $O(\frac{1}{\epsilon^3} log^3 n)$ iterations, each of which involves only simple matrix operations and computing the trace of the product of a matrix exponential and a positive semidefinite matrix. Further, given a positive SDP in a factorized form, the total work of our algorithm is nearly-linear in the number of non-zero entries in the factorization.


Data Mining as a Torch Bearer in Education Sector

Abstract: Every data has a lot of hidden information. The processing method of data decides what type of information data produce. In India education sector has a lot of data that can produce valuable information. This information can be used to increase the quality of education. But educational institution does not use any knowledge discovery process approach on these data. Information and communication technology puts its leg into the education sector to capture and compile low cost information. Now a day a new research community, educational data mining (EDM), is growing which is intersection of data mining and pedagogy. In this paper we present roadmap of research done in EDM in various segment of education sector.


Unsupervised Classification Using Immune Algorithm

Abstract: Unsupervised classification algorithm based on clonal selection principle named Unsupervised Clonal Selection Classification (UCSC) is proposed in this paper. The new proposed algorithm is data driven and self-adaptive, it adjusts its parameters to the data to make the classification operation as fast as possible. The performance of UCSC is evaluated by comparing it with the well known K-means algorithm using several artificial and real-life data sets. The experiments show that the proposed UCSC algorithm is more reliable and has high classification precision comparing to traditional classification methods such as K-means.


A New Local Adaptive Thresholding Technique in Binarization

Abstract: Image binarization is the process of separation of pixel values into two groups, white as background and black as foreground. Thresholding plays a major in binarization of images. Thresholding can be categorized into global thresholding and local thresholding. In images with uniform contrast distribution of background and foreground like document images, global thresholding is more appropriate. In degraded document images, where considerable background noise or variation in contrast and illumination exists, there exists many pixels that cannot be easily classified as foreground or background. In such cases, binarization with local thresholding is more appropriate. This paper describes a locally adaptive thresholding technique that removes background by using local mean and mean deviation. Normally the local mean computational time depends on the window size. Our technique uses integral sum image as a prior processing to calculate local mean. It does not involve calculations of standard deviations as in other local adaptive techniques. This along with the fact that calculations of mean is independent of window size speed up the process as compared to other local thresholding techniques.


Invertible Program Restructurings for Continuing Modular Maintenance

Abstract: When one chooses a main axis of structural decompostion for a software, such as function- or data-oriented decompositions, the other axes become secondary, which can be harmful when one of these secondary axes becomes of main importance. This is called the tyranny of the dominant decomposition. In the context of modular extension, this problem is known as the Expression Problem and has found many solutions, but few solutions have been proposed in a larger context of modular maintenance. We solve the tyranny of the dominant decomposition in maintenance with invertible program transformations. We illustrate this on the typical Expression Problem example. We also report our experiments with Java and Haskell programs and discuss the open problems with our approach.


A dependent nominal type theory

Abstract: Nominal abstract syntax is an approach to representing names and binding pioneered by Gabbay and Pitts. So far nominal techniques have mostly been studied using classical logic or model theory, not type theory. Nominal extensions to simple, dependent and ML-like polymorphic languages have been studied, but decidability and normalization results have only been established for simple nominal type theories. We present a LF-style dependent type theory extended with name-abstraction types, prove soundness and decidability of beta-eta-equivalence checking, discuss adequacy and canonical forms via an example, and discuss extensions such as dependently-typed recursion and induction principles.


An Efficient Primal-Dual Prox Method for Non-Smooth Optimization

Abstract: We study the non-smooth optimization problems in machine learning, where both the loss function and the regularizer are non-smooth functions. Previous studies on efficient empirical loss minimization assume either a smooth loss function or a strongly convex regularizer, making them unsuitable for non-smooth optimization. We develop a simple yet efficient method for a family of non-smooth optimization problems where the dual form of the loss function is bilinear in primal and dual variables. We cast a non-smooth optimization problem into a minimax optimization problem, and develop a primal dual prox method that solves the minimax optimization problem at a rate of $O(1/T)$ {assuming that the proximal step can be efficiently solved}, significantly faster than a standard subgradient descent method that has an $O(1/\sqrt{T})$ convergence rate. Our empirical study verifies the efficiency of the proposed method for various non-smooth optimization problems that arise ubiquitously in machine learning by comparing it to the state-of-the-art first order methods.


An Authoring System for Editing Lessons in Phonetic English in SMIL3.0

Abstract: One of the difficulties of teaching English is the prosody, including the stress. French learners have difficulties to encode this information about the word because it is irrelevant for them. Therefore, they have difficulty to produce this stress when they speak that language. Studies in this area have concluded that the dual-coding approach (auditory and visual) of a phonetic phenomenon helps a lot to improve its perception and memorization for novice learners. The aim of our work is to provide English teachers with an authoring named SaCoPh for editing multimedia courses that support this approach. This course is based on a template that fits the educational aspects of phonetics, exploiting the features of version 3.0 of the standard SMIL (Synchronized Multimedia Integration Language) for the publication of this course on the web.


Scrabble is PSPACE-Complete

Abstract: In this paper we study the computational complexity of the game of Scrabble. We prove the PSPACE-completeness of a derandomized model of the game, answering an open question of Erik Demaine and Robert Hearn.