The Causal Topography of Cognition

Abstract: The causal structure of cognition can be simulated but not implemented computationally, just as the causal structure of a comet can be simulated but not implemented computationally. The only thing that allows us even to imagine otherwise is that cognition, unlike a comet, is invisible (to all but the cognizer).


Image Filtering using All Neighbor Directional Weighted Pixels: Optimization using Particle Swarm Optimization

Abstract: In this paper a novel approach for de noising images corrupted by random valued impulses has been proposed. Noise suppression is done in two steps. The detection of noisy pixels is done using all neighbor directional weighted pixels (ANDWP) in the 5 x 5 window. The filtering scheme is based on minimum variance of the four directional pixels. In this approach, relatively recent category of stochastic global optimization technique i.e., particle swarm optimization (PSO) has also been used for searching the parameters of detection and filtering operators required for optimal performance. Results obtained shows better de noising and preservation of fine details for highly corrupted images.


Examining Motivations behind Paper Usage in Academia

Abstract: We carried out a qualitative study to identify the "missing pieces" in current computing devices and technologies that are preventing people from eliminating paper from their lives. Most of the existing literature has looked into the work practices of businesses, while a few have researched how high school and college students and teaching assistants at universities work with paper. We were specifically interested in analysing paper use for people in the research side of academia, and seeing how our results compare to existing work. We recruited and interviewed participants from academia to understand what kind of tasks they use paper for, what kind of tasks they use computing devices for and what motivates them to use these two media. We found that, despite having access to at least one personal computing device, the participants preferred to work with paper in many situations. This appears to be attributed to certain intrinsic qualities that paper has, such as open format, easy navigation, readability, and the aff?ordances these qualities provide. In order to eventually replace paper with devices, designers of new technology will have to successfully emulate these qualities.


A History of Flips in Combinatorial Triangulations

Abstract: Given two combinatorial triangulations, how many edge flips are necessary and sufficient to convert one into the other? This question has occupied researchers for over 75 years. We provide a comprehensive survey, including full proofs, of the various attempts to answer it.


Efficient Certificate Management in VANET

Abstract: Vehicular Ad hoc Networks is one of the most challenging research area in the field of Mobile Ad Hoc Networks, in this research We propose a flexible, simple, and scalable design for VANET certificates, and new methods for efficient certificate management, which will Reduce channel overhead by eliminating the use of CRL, and make Better certificate Revocation Management. Also it will increase the security of the network and helps in identifying the adversary vehicle.


Fairness and Stability Analysis of Congestion Control Schemes in Vehicular Ad-hoc Networks

Abstract: Cooperative vehicle safety (CVS) systems operate based on broadcast of vehicle position and safety information to neighboring cars. The communication medium of CVS is a vehicular ad-hoc network. One of the main challenges in large scale deployment of CVS systems is the issue of scalability. To address the scalability problem, several congestion control methods have been proposed and are currently under field study. These algorithms adapt transmission rate and power based on network measures such as channel busy ratio. We examine two such algorithms and study their dynamic behavior in time and space to evaluate stability (in time) and fairness (in space) properties of these algorithms. We present stability conditions and evaluate stability and fairness of the algorithms through simulation experiments. Results show that there is a trade-off between fast convergence, temporal stability and spatial fairness. The proper ranges of parameters for achieving stability are presented for the discussed algorithms. Stability is verified for all typical road density cases. Fairness is shown to be naturally achieved for some algorithms, while under the same conditions other algorithms may suffer from unfairness issues. A method for resolving unfairness is introduced and evaluated through simulations.


A Route Confidence Evaluation Method for Reliable Hierarchical Text Categorization

Abstract: Hierarchical Text Categorization (HTC) is becoming increasingly important with the rapidly growing amount of text data available in the World Wide Web. Among the different strategies proposed to cope with HTC, the Local Classifier per Node (LCN) approach attains good performance by mirroring the underlying class hierarchy while enforcing a top-down strategy in the testing step. However, the problem of embedding hierarchical information (parent-child relationship) to improve the performance of HTC systems still remains open. A confidence evaluation method for a selected route in the hierarchy is proposed to evaluate the reliability of the final candidate labels in an HTC system. In order to take into account the information embedded in the hierarchy, weight factors are used to take into account the importance of each level. An acceptance/rejection strategy in the top-down decision making process is proposed, which improves the overall categorization accuracy by rejecting a few percentage of samples, i.e., those with low reliability score. Experimental results on the Reuters benchmark dataset (RCV1- v2) confirm the effectiveness of the proposed method, compared to other state-of-the art HTC methods.


Generic Fibrational Induction

Abstract: This paper provides an induction rule that can be used to prove properties of data structures whose types are inductive, i.e., are carriers of initial algebras of functors. Our results are semantic in nature and are inspired by Hermida and Jacobs' elegant algebraic formulation of induction for polynomial data types. Our contribution is to derive, under slightly different assumptions, a sound induction rule that is generic over all inductive types, polynomial or not. Our induction rule is generic over the kinds of properties to be proved as well: like Hermida and Jacobs, we work in a general fibrational setting and so can accommodate very general notions of properties on inductive types rather than just those of a particular syntactic form. We establish the soundness of our generic induction rule by reducing induction to iteration. We then show how our generic induction rule can be instantiated to give induction rules for the data types of rose trees, finite hereditary sets, and hyperfunctions. The first of these lies outside the scope of Hermida and Jacobs' work because it is not polynomial, and as far as we are aware, no induction rules have been known to exist for the second and third in a general fibrational framework. Our instantiation for hyperfunctions underscores the value of working in the general fibrational setting since this data type cannot be interpreted as a set.


Defect Management Using Depth of Inspection and the Inspection Performance Metric

Abstract: Advancement in fundamental engineering aspects of software development enables IT enterprises to develop a more cost effective and better quality product through aptly organized defect management strategies. Inspection continues to be the most effective and efficient technique of defect management. To have an appropriate measurement of the inspection process, the process metric, Depth of Inspection (DI) and the people metric, Inspection Performance Metric (IPM) are introduced. The introduction of these pair of metrics can yield valuable information from a company in relation to the inspection process.


Generation and Optimization of Test cases for Object-Oriented Software Using State Chart Diagram

Abstract: The process of testing any software system is an enormous task which is time consuming and costly. The time and required effort to do sufficient testing grow, as the size and complexity of the software grows, which may cause overrun of the project budget, delay in the development of software system or some test cases may not be covered. During SDLC (software development life cycle), generally the software testing phase takes around 40-70% of the time and cost. State-based testing is frequently used in software testing. Test data generation is one of the key issues in software testing. A properly generated test suite may not only locate the errors in a software system, but also help in reducing the high cost associated with software testing. It is often desired that test data in the form of test sequences within a test suite can be automatically generated to achieve required test coverage. This paper proposes an optimization approach to test data generation for the state-based software testing. In this paper, first state transition graph is derived from state chart diagram. Then, all the required information are extracted from the state chart diagram. Then, test cases are generated. Lastly, a set of test cases are minimized by calculating the node coverage for each test case. It is also determined that which test cases are covered by other test cases. The advantage of our test generation technique is that it optimizes test coverage by minimizing time and cost. The proposed test data generation scheme generates test cases which satisfy transition path coverage criteria, path coverage criteria and action coverage criteria. A case study on Automatic Ticket Machine (ATM) has been presented to illustrate our approach.


UNL Based Bangla Natural Text Conversion - Predicate Preserving Parser Approach

Abstract: Universal Networking Language (UNL) is a declarative formal language that is used to represent semantic data extracted from natural language texts. This paper presents a novel approach to converting Bangla natural language text into UNL using a method known as Predicate Preserving Parser (PPP) technique. PPP performs morphological, syntactic and semantic, and lexical analysis of text synchronously. This analysis produces a semantic-net like structure represented using UNL. We demonstrate how Bangla texts are analyzed following the PPP technique to produce UNL documents which can then be translated into any other suitable natural language facilitating the opportunity to develop a universal language translation method via UNL.


Energy-Aware Task Partitioning on Heterogeneous Multiprocessor Platforms

Abstract: Efficient task partitioning plays a crucial role in achieving high performance at multiprocessor plat forms. This paper addresses the problem of energy-aware static partitioning of periodic real-time tasks on heterogeneous multiprocessor platforms. A Particle Swarm Optimization variant based on Min-min technique for task partitioning is proposed. The proposed approach aims to minimize the overall energy consumption, meanwhile avoid deadline violations. An energy-aware cost function is proposed to be considered in the proposed approach. Extensive simulations and comparisons are conducted in order to validate the effectiveness of the proposed technique. The achieved results demonstrate that the proposed partitioning scheme significantly surpasses previous approaches in terms of both number of iterations and energy savings.


Dynamic Threshold Optimization - A New Approach?

Abstract: Dynamic Threshold Optimization (DTO) adaptively "compresses" the decision space (DS) in a global search and optimization problem by bounding the objective function from below. This approach is different from "shrinking" DS by reducing bounds on the decision variables. DTO is applied to Schwefel's Problem 2.26 in 2 and 30 dimensions with good results. DTO is universally applicable, and the author believes it may be a novel approach to global search and optimization.


Pre-allocation Strategies of Computational Resources in Cloud Computing using Adaptive Resonance Theory-2

Abstract: One of the major challenges of cloud computing is the management of request-response coupling and optimal allocation strategies of computational resources for the various types of service requests. In the normal situations the intelligence required to classify the nature and order of the request using standard methods is insufficient because the arrival of request is at a random fashion and it is meant for multiple resources with different priority order and variety. Hence, it becomes absolutely essential that we identify the trends of different request streams in every category by auto classifications and organize preallocation strategies in a predictive way. It calls for designs of intelligent modes of interaction between the client request and cloud computing resource manager. This paper discusses about the corresponding scheme using Adaptive Resonance Theory-2.


Multipath Routing With Novel Packet Scheduling Approach In Wireless Sensor Networks

Abstract: Wireless sensor networks sense and monitor real-time events. They supervise a geographic area where a phenomenon is to be monitored. The data in sensor networks have different levels of priority and hence their criticality differs. In order to keep up the real time commitment, the applications need higher transmission rates and reliability in information delivery. In this work we propose a multipath routing algorithm which enables the reliable delivery of data. By controlling the scheduling rate, it is possible to prevent congestion and packet loss in the network. The algorithm provides an efficient way to prevent the packet loss at each node. This results in congestion management in the sensor networks. This protocol prevents packet clustering and provides smoothness to the traffic. Through monitoring and controlling the scheduling rate the flow control and congestion control are managed.


Transformation of Traditional Marketing Communications in to Paradigms of Social Media Networking

Abstract: Effective Communication for marketing is a vital field in business organizations, which is used to convey the details about their products and services to the market segments and subsequently to build long lasting customer relationships. This paper focuses on an emerging component of the integrated marketing communication, ie. social media networking, as it is increasingly becoming the trend. In 21st century, the marketing communication platforms show a tendency to shift towards innovative technology bound people networking which is becoming an acceptable domain of interaction. Though the traditional channels like TV, print media etc. are still active and prominent in marketing communication, the presences of the Internet and more specifically the Social Media Networking, has started influencing the way individuals and business enterprises communicate. It has become evident that more individuals and business enterprises are engaging the social media networking sites either to accelerate the sales of their products and services or to provide post-purchase feedbacks. This shift in scenario has motivated this research which took six months (June 2011 - December 2011), using empirical analysis which is carried out based on several primary and secondary evidences. The research paper also analyzes the factors that govern the social media networking sites to influence consumers and subsequently enable their purchase decisions. The secondary data presented for this research were those pertaining to the period between the year 2005 and year 2011. The study revealed promising facts like the transition to marketing through SMN gives visible advantages like bidirectional communication, interactive product presentation, and a firm influence on customer who has a rudimentary interest...


Congestion Games on Weighted Directed Graphs, with Applications to Spectrum Sharing

Abstract: With the advance of complex large-scale networks, it is becoming increasingly important to understand how selfish and spatially distributed individuals will share network resources without centralized coordinations. In this paper, we introduce the graphical congestion game with weighted edges (GCGWE) as a general theoretical model to study this problem. In GCGWE, we view the players as vertices in a weighted graph. The amount of negative impact (e.g. congestion) caused by two close-by players to each other is determined by the weight of the edge linking them. The GCGWE unifies and significantly generalizes several simpler models considered in the previous literature, and is well suited for modeling a wide range of networking scenarios. One good example is to use the GCGWE to model spectrum sharing in wireless networks, where we can properly define the edge weights and payoff functions to capture the rather complicated interference relationship between wireless nodes. By identifying which GCGWEs possess pure Nash equilibria and the very desirable finite improvement property, we gain insight into when spatially distributed wireless nodes will be able to self-organize into a mutually acceptable resource allocation. We also consider the efficiency of the pure Nash equilibria, and the computational complexity of finding them.


Implementation of a Real Time Passenger Information System

Abstract: Intelligent Transportation Systems (ITS) are gaining recognition in developing countries like India. This paper describes the various components of our prototype implementation of a Real-time Passenger Information System (RTPIS) for a public transport system like a fleet of buses. Vehicle-mounted units, bus station units and a server located at the transport company premises comprise the system. The vehicle unit reports the current position of the vehicle to a central server periodically via General Packet Radio Service (GPRS). An Estimated Time of Arrival (ETA) algorithm running on the server predicts the arrival times of buses at their stops based on real-time observations of the buses' current Global Positioning System (GPS) coordinates. This information is displayed and announced to passengers at stops using station units, which periodically fetch the required ETA from the server via GPRS. Novel features of our prototype include: (a) a route creator utility which automatically creates new routes from scratch when a bus is driven along the new route, and (b) voice tagging of stops and points of interest along any route. Besides, the prototype provides: (i) web-based applications for passengers, providing useful information like a snapshot of present bus locations on the streets, and (ii) web-based analysis tools for the transport authority, providing information useful for fleet management, like number of trips undertaken by a specific bus. The prototype has been demonstrated in a campus environment, with four-wheelers and two-wheelers emulating buses. The automatic real-time passenger information system has the potential of making the public transport system an attractive alternative for city-dwellers, thereby contributing to fewer private vehicles on the road, leading to lower congestion levels and less pollution.


Real-Time Bid Optimization for Group-Buying Ads

Abstract: Group-buying ads seeking a minimum number of customers before the deal expiry are increasingly used by the daily-deal providers. Unlike the traditional web ads, the advertiser's profits for group-buying ads depends on the time to expiry and additional customers needed to satisfy the minimum group size. Since both these quantities are time-dependent, optimal bid amounts to maximize profits change with every impression. Consequently, traditional static bidding strategies are far from optimal. Instead, bid values need to be optimized in real-time to maximize expected bidder profits. This online optimization of deal profits is made possible by the advent of ad exchanges offering real-time (spot) bidding. To this end, we propose a real-time bidding strategy for group-buying deals based on the online optimization of bid values. We derive the expected bidder profit of deals as a function of the bid amounts, and dynamically vary bids to maximize profits. Further, to satisfy time constraints of the online bidding, we present methods of minimizing computation timings. Subsequently, we derive the real time ad selection, admissibility, and real time bidding of the traditional ads as the special cases of the proposed method. We evaluate the proposed bidding, selection and admission strategies on a multi-million click stream of 935 ads. The proposed real-time bidding, selection and admissibility show significant profit increases over the existing strategies. Further the experiments illustrate the robustness of the bidding and acceptable computation timings.


Synergy-based Hand Pose Sensing: Reconstruction Enhancement

Abstract: Low-cost sensing gloves for reconstruction posture provide measurements which are limited under several regards. They are generated through an imperfectly known model, are subject to noise, and may be less than the number of Degrees of Freedom (DoFs) of the hand. Under these conditions, direct reconstruction of the hand posture is an ill-posed problem, and performance can be very poor. This paper examines the problem of estimating the posture of a human hand using(low-cost) sensing gloves, and how to improve their performance by exploiting the knowledge on how humans most frequently use their hands. To increase the accuracy of pose reconstruction without modifying the glove hardware - hence basically at no extra cost - we propose to collect, organize, and exploit information on the probabilistic distribution of human hand poses in common tasks. We discuss how a database of such an a priori information can be built, represented in a hierarchy of correlation patterns or postural synergies, and fused with glove data in a consistent way, so as to provide a good hand pose reconstruction in spite of insufficient and inaccurate sensing data. Simulations and experiments on a low-cost glove are reported which demonstrate the effectiveness of the proposed techniques.


Synergy-Based Hand Pose Sensing: Optimal Glove Design

Abstract: In this paper we study the problem of improving human hand pose sensing device performance by exploiting the knowledge on how humans most frequently use their hands in grasping tasks. In a companion paper we studied the problem of maximizing the reconstruction accuracy of the hand pose from partial and noisy data provided by any given pose sensing device (a sensorized "glove") taking into account statistical a priori information. In this paper we consider the dual problem of how to design pose sensing devices, i.e. how and where to place sensors on a glove, to get maximum information about the actual hand posture. We study the continuous case, whereas individual sensing elements in the glove measure a linear combination of joint angles, the discrete case, whereas each measure corresponds to a single joint angle, and the most general hybrid case, whereas both continuous and discrete sensing elements are available. The objective is to provide, for given a priori information and fixed number of measurements, the optimal design minimizing in average the reconstruction error. Solutions relying on the geometrical synergy definition as well as gradient flow-based techniques are provided. Simulations of reconstruction performance show the effectiveness of the proposed optimal design.


Automatic Generation of OWL Ontology from XML Data Source

Abstract: The eXtensible Markup Language (XML) can be used as data exchange format in different domains. It allows different parties to exchange data by providing common understanding of the basic concepts in the domain. XML covers the syntactic level, but lacks support for reasoning. Ontology can provide a semantic representation of domain knowledge which supports efficient reasoning and expressive power. One of the most popular ontology languages is the Web Ontology Language (OWL). It can represent domain knowledge using classes, properties, axioms and instances for the use in a distributed environment such as the World Wide Web. This paper presents a new method for automatic generation of OWL ontology from XML data sources.


O(1) Delta Component Computation Technique for the Quadratic Assignment Problem

Abstract: The paper describes a novel technique that allows to reduce by half the number of delta values that were required to be computed with complexity O(N) in most of the heuristics for the quadratic assignment problem. Using the correlation between the old and new delta values, obtained in this work, a new formula of complexity O(1) is proposed. Found result leads up to 25% performance increase in such well-known algorithms as Robust Tabu Search and others based on it.


Simple and Deterministic Matrix Sketching

Abstract: We adapt a well known streaming algorithm for approximating item frequencies to the matrix sketching setting. The algorithm receives the rows of a large matrix $A \in \R^{n \times m}$ one after the other in a streaming fashion. It maintains a sketch matrix $B \in \R^ {1/\eps \times m}$ such that for any unit vector $x$ [\|Ax\|^2 \ge \|Bx\|^2 \ge \|Ax\|^2 - \eps \|A\|_{f}^2 \.] Sketch updates per row in $A$ require $O(m/\eps^2)$ operations in the worst case. A slight modification of the algorithm allows for an amortized update time of $O(m/\eps)$ operations per row. The presented algorithm stands out in that it is: deterministic, simple to implement, and elementary to prove. It also experimentally produces more accurate sketches than widely used approaches while still being computationally competitive.


The COMICS Tool - Computing Minimal Counterexamples for Discrete-time Markov Chains

Abstract: This report presents the tool COMICS, which performs model checking and generates counterexamples for DTMCs. For an input DTMC, COMICS computes an abstract system that carries the model checking information and uses this result to compute a critical subsystem, which induces a counterexample. This abstract subsystem can be refined and concretized hierarchically. The tool comes with a command-line version as well as a graphical user interface that allows the user to interactively influence the refinement process of the counterexample.


Modern consumerism and the waste problem

Abstract: With the advance of industrial mass production, modern micro-electronics and computers, the intervals between the release of new generations of consumer products have been dramatically reduced and so have their lifetime cycles. While it was very natural in the post-war era, that sophisticated consumer products like television sets and stereo equipment would not be replaced with a new product until they break, and usually beyond that point since it was very common to have a broken television set serviced, the habits of consumers have changed during the last quarter of the 20th century. A modern consumer product, like Apple's famous iPhone has a market life of approximately one year until a successor is announced and subsequently pushed into the market. Usually these new generations bring a bunch of new features, have a higher performance while maintaining the price or becoming even cheaper, thus the consumer greatly benefits from the reduced lifetime cycle of these products. However, electronic devices not only require a lot of of Earth's limited resources for their production, but their production processes are a major source for harmful climate gases like carbon dioxide and toxic waste like heavy metal alloys, acids and alkalis. And last but not least is every obsoleted iPhone a candidate for waste facilities unless consumers are going to sell them on the second hand market. While we can not expect consumers and manufacturers to go back to the early days of consumer products where lifetime cycles reached up to 20 years, the world record being the famous "Centennial Lightbulb" in Livermore, CA in the US, which has been lit for over 100 years, it is certainly about time to rethink modern consumerism with regard to responsibility to future generations.


New Technique for Proposing Network's Topology using GPS and GIS

Abstract: The problem of proposed topology for network comes when using Prim's algorithm with default distance (unrealistic distances) between network's nodes and don't care about the lakes, high hills, buildings, etc. This problem will cause incorrect estimations for cost (budget) of requirements like the media (optic fibre) and the number or type of Access-points, regenerator, Optic Amplifier, etc. This paper proposed a new technique of implementing Prim's algorithm to obtain realistic topology using realistic distances between network's nodes via Global Positioning System GPS and Geographic Information Systems GIS packages. Applying the new technique on academic institutes network of Erbil city from view of media (optic fibre) shows that there is disability in cost (budget) of the media which is needed (nearly) 4 times if implement default Prim's algorithm (don't using GPS & GIS) base on unrealistic distances between the nodes.


WM Program manual

Abstract: This user manual has been written to describe the open source code WM to be distributed associated with a research article submitted to the information technology journal 45001-ITJ-ANSI, entitled: "Maintenance and Reengineering of software: Creating a Visual C++ Graphical User Interface to Perform Specific Tasks Related to Soil Structure Interaction in Poroelastic Soil".


The Cost of Mitigating Power Law Delay in Random Access Networks

Abstract: Exponential backoff (EB) is a widely adopted collision resolution mechanism in many popular random-access networks including Ethernet and wireless LAN (WLAN). The prominence of EB is primarily attributed to its asymptotic throughput stability, which ensures a non-zero throughput even when the number of users in the network goes to infinity. Recent studies, however, show that EB is fundamentally unsuitable for applications that are sensitive to large delay and delay jitters, as it induces divergent second- and higher-order moments of medium access delay. Essentially, the medium access delay follows a power law distribution, a subclass of heavy-tailed distribution. To understand and alleviate the issue, this paper systematically analyzes the tail delay distribution of general backoff functions, with EB being a special case. In particular, we establish a tradeoff between the tail decaying rate of medium access delay distribution and the stability of throughput. To be more specific, convergent delay moments are attainable only when the backoff functions $g(k)$ grows slower than exponential functions, i.e., when $g(k)\in o(r^k)$ for all $r>1$. On the other hand, non-zero asymptotic throughput is attainable only when backoff functions grow at least as fast as an exponential function, i.e., $g(k)\in\Omega(r^k)$ for some $r>1$. This implies that bounded delay moments and stable throughput cannot be achieved at the same time. For practical implementation, we show that polynomial backoff (PB), where $g(k)$ is a polynomial that grows slower than exponential functions, obtains finite delay moments and good throughput performance at the same time within a practical range of user population. This makes PB a better alternative than EB for multimedia applications with stringent delay requirements.


Performance analysis of an opportunistic relay selection protocol for multi-hop networks (Technical report)

Abstract: In this technical report, we analyze the performance of an interference-aware opportunistic relay selection protocol for multi-hop line networks which is based on the following simple rule: a node always transmits if it has a packet, except when its successive node on the line is transmitting. We derive analytically the saturation throughput and the end-to-end delay for two and three hop networks, and present simulation results for higher numbers of hops. In the case of three hops, we determine the throughput-optimal relay positions.


ICT's role in e-Governance in India and Malaysia: A Review

Abstract: Information and Communication Technologies (ICTs) play a key role in Development & Economic growth of the Developing countries of the World. Political, Cultural, Socio-economic Developmental & Behavioral decisions today rests on the ability to access, gather, analyze and utilize Information and Knowledge. Government of India is having an ambitious objective of transforming the citizen-government interaction at all levels to by the electronic mode by 2020.Similarly according to the Vision 2020-The Way Forward presented by His Excellency YAB Dato' Seri Dr Mahathir Mohamad at the Malaysian Business Council "By the year 2020, Malaysia can be a united nation, with a confident Malaysian society, infused by strong moral and ethical values, living in a society that is democratic, liberal and tolerant, caring, economically just and equitable, progressive and prosperous, and in full possession of an economy that is competitive, dynamic, robust and resilient". This paper presents a comparative study and review relating to e-Governance and application of ICT development between India & Malaysia.


A numerical methodology for enforcing maximum principles and the non-negative constraint for transient diffusion equations

Abstract: Transient diffusion equations arise in many branches of engineering and applied sciences (e.g., heat transfer and mass transfer), and are parabolic partial differential equations. It is well-known that, under certain assumptions on the input data, these equations satisfy important mathematical properties like maximum principles and the non-negative constraint, which have implications in mathematical modeling. However, existing numerical formulations for these types of equations do not, in general, satisfy maximum principles and the non-negative constraint. In this paper, we present a methodology for enforcing maximum principles and the non-negative constraint for transient anisotropic diffusion equation. The method of horizontal lines (also known as the Rothe method) is applied in which the time is discretized first. This results in solving steady anisotropic diffusion equation with decay equation at every discrete time level. The proposed methodology for transient anisotropic diffusion equation will satisfy maximum principles and the non-negative constraint on general computational grids, and with no additional restrictions on the time step. We illustrate the performance and accuracy of the proposed formulation using representative numerical examples. We also perform numerical convergence of the proposed methodology. For comparison, we also present the results from the standard single-field semi-discrete formulation and the results from a popular software package, which all will violate maximum principles and the non-negative constraint.


Theoretical foundation for CMA-ES from information geometric perspective

Abstract: This paper explores the theoretical basis of the covariance matrix adaptation evolution strategy (CMA-ES) from the information geometry viewpoint. To establish a theoretical foundation for the CMA-ES, we focus on a geometric structure of a Riemannian manifold of probability distributions equipped with the Fisher metric. We define a function on the manifold which is the expectation of fitness over the sampling distribution, and regard the goal of update of the parameters of sampling distribution in the CMA-ES as maximization of the expected fitness. We investigate the steepest ascent learning for the expected fitness maximization, where the steepest ascent direction is given by the natural gradient, which is the product of the inverse of the Fisher information matrix and the conventional gradient of the function. Our first result is that we can obtain under some types of parameterization of multivariate normal distribution the natural gradient of the expected fitness without the need for inversion of the Fisher information matrix. We find that the update of the distribution parameters in the CMA-ES is the same as natural gradient learning for expected fitness maximization. Our second result is that we derive the range of learning rates such that a step in the direction of the exact natural gradient improves the parameters in the expected fitness. We see from the close relation between the CMA-ES and natural gradient learning that the default setting of learning rates in the CMA-ES seems suitable in terms of monotone improvement in expected fitness. Then, we discuss the relation to the expectation-maximization framework and provide an information geometric interpretation of the CMA-ES.


Timed Test Case Generation Using Labeled Prioritized Time Petri Nets

Abstract: Model-based testing of software and hardware systems uses behavioral and formal models of the systems. The paper presents a technique for model-based black-box conformance testing of real-time systems using Labeled Prioritized Time Petri Nets (LPrTPN). The Timed Input/Output Conformance (tioco) relation, which takes environment assumptions into account, serves as reference to decide of implementation correctness. Test suites are derived automatically from a LPrTPN made up of two concurrent sub-nets that respectively specify the system under test and its environment. The result is optimal in the sense that test cases have the shortest possible accumulated time to be executed. Test cases selection combines test purposes and structural coverage criteria associated with the model. A test purpose or a coverage criterion is specified in a SE-LTL formula. The TIme Petri Net Analyzer TINA has been extended to support concurrent composed subnets. Automatic generation of time-optimal test suites with the Tina toolbox combines the model checker selt and the path analyzer plan. selt outputs a sequence that satisfies the logic formula. plan computes the fastest execution of this sequence which will be transformed in a test cases suite.


Secure FSM- based arithmetic codes

Abstract: Recently, arithmetic coding has attracted the attention of many scholars because of its high compression capability. Accordingly, in this paper a method which adds secrecy to this well-known source code is proposed. Finite state arithmetic code (FSAC) is used as source code to add security. Its finite state machine (FSM) characteristic is exploited to insert some random jumps during source coding process. In addition, a Huffman code is designed for each state to make decoding possible even in jumps. Being Prefix free, Huffman codes are useful in tracking correct states for an authorized user when s/he decodes with correct symmetric pseudo random key. The robustness of our proposed scheme is further reinforced by adding another extra uncertainty by swapping outputs of Huffman codes in each state. Several test images are used for inspecting the validity of the proposed Huffman Finite State Arithmetic Coding (HFSAC). The results of several experimental, key space analyses, statistical analysis, key sensitivity and plaintext sensitivity tests show that HFSAC with a little effect on compression efficiency for image cryptosystem provides an efficient and secure way for real-time image encryption and transmission.


A Mixed Observability Markov Decision Process Model for Musical Pitch

Abstract: Partially observable Markov decision processes have been widely used to provide models for real-world decision making problems. In this paper, we will provide a method in which a slightly different version of them called Mixed observability Markov decision process, MOMDP, is going to join with our problem. Basically, we aim at offering a behavioural model for interaction of intelligent agents with musical pitch environment and we will show that how MOMDP can shed some light on building up a decision making model for musical pitch conveniently.


Refining a Quantitative Information Flow Metric

Abstract: We introduce a new perspective into the field of quantitative information flow (QIF) analysis that invites the community to bound the leakage, reported by QIF quantifiers, by a range consistent with the size of a program's secret input instead of by a mathematically sound (but counter-intuitive) upper bound of that leakage. To substantiate our position, we present a refinement of a recent QIF metric that appears in the literature. Our refinement is based on slight changes we bring into the design of that metric. These changes do not affect the theoretical premises onto which the original metric is laid. However, they enable the natural association between flow results and the exhaustive search effort needed to uncover a program's secret information (or the residual secret part of that information) to be clearly established. The refinement we discuss in this paper validates our perspective and demonstrates its importance in the future design of QIF quantifiers.


New Method of Measuring TCP Performance of IP Network using Bio-computing

Abstract: The measurement of performance of Internet Protocol IP network can be done by Transmission Control Protocol TCP because it guarantees send data from one end of the connection actually gets to the other end and in the same order it was send, otherwise an error is reported. There are several methods to measure the performance of TCP among these methods genetic algorithms, neural network, data mining etc, all these methods have weakness and can't reach to correct measure of TCP performance. This paper proposed a new method of measuring TCP performance for real time IP network using Biocomputing, especially molecular calculation because it provides wisdom results and it can exploit all facilities of phylogentic analysis. Applying the new method at real time on Biological Kurdish Messenger BIOKM model designed to measure the TCP performance in two types of protocols File Transfer Protocol FTP and Internet Relay Chat Daemon IRCD. This application gives very close result of TCP performance comparing with TCP performance which obtains from Little's law using same model (BIOKM), i.e. the different percentage of utilization (Busy or traffic industry) and the idle time which are obtained from a new method base on Bio-computing comparing with Little's law was (nearly) 0.13%. KEYWORDS Bio-computing, TCP performance, Phylogenetic tree, Hybridized Model (Normalized), FTP, IRCD


A Fuzzy Approach for Pertinent Information Extraction from Web Resources

Abstract: Recent work in machine learning for information extraction has focused on two distinct sub-problems: the conventional problem of filling template slots from natural language text, and the problem of wrapper induction, learning simple extraction procedures ("wrappers") for highly structured text such as Web pages. For suitable regular domains, existing wrapper induction algorithms can efficiently learn wrappers that are simple and highly accurate, but the regularity bias of these algorithms makes them unsuitable for most conventional information extraction tasks. This paper describes a new approach for wrapping semistructured Web pages. The wrapper is capable of learning how to extract relevant information from Web resources on the basis of user supplied examples. It is based on inductive learning techniques as well as fuzzy logic rules. Experimental results show that our approach achieves noticeably better precision and recall coefficient performance measures than SoftMealy, which is one of the most recently reported wrappers capable of wrapping semi-structured Web pages with missing attributes, multiple attributes, variant attribute permutations, exceptions, and typos.


Soft Computing in Product Recovery: A Survey Focusing on Remanufacturing System

Abstract: This paper focuses on the application of soft computing in remanufacturing system, in which end-of-life products are disassembled into basic components and then remanufactured for both economic and environmental reasons. The disassembly activities include disassembly sequencing and planning, while the remanufacturing process is composed of product design, production planning & scheduling, and inventory management. This paper presents a review of the related articles and suggests the corresponding further research directions.


Non-null Infinitesimal Micro-steps: a Metric Temporal Logic Approach

Abstract: Many systems include components interacting with each other that evolve with possibly very different speeds. To deal with this situation many formal models adopt the abstraction of "zero-time transitions", which do not consume time. These however have several drawbacks in terms of naturalness and logic consistency, as a system is modeled to be in different states at the same time. We propose a novel approach that exploits concepts from non-standard analysis to introduce a notion of micro- and macro-steps in an extension of the TRIO metric temporal logic, called X-TRIO. We use X-TRIO to provide a formal semantics and an automated verification technique to Stateflow-like notations used in the design of flexible manufacturing systems.


Fuzzy Knowledge Representation Based on Possibilistic and Necessary Bayesian Networks

Abstract: Within the framework proposed in this paper, we address the issue of extending the certain networks to a fuzzy certain networks in order to cope with a vagueness and limitations of existing models for decision under imprecise and uncertain knowledge. This paper proposes a framework that combines two disciplines to exploit their own advantages in uncertain and imprecise knowledge representation problems. The framework proposed is a possibilistic logic based one in which Bayesian nodes and their properties are represented by local necessity-valued knowledge base. Data in properties are interpreted as set of valuated formulas. In our contribution possibilistic Bayesian networks have a qualitative part and a quantitative part, represented by local knowledge bases. The general idea is to study how a fusion of these two formalisms would permit representing compact way to solve efficiently problems for knowledge representation. We show how to apply possibility and necessity measures to the problem of knowledge representation with large scale data. On the other hand fuzzification of crisp certainty degrees to fuzzy variables improves the quality of the network and tends to bring smoothness and robustness in the network performance. The general aim is to provide a new approach for decision under uncertainty that combines three methodologies: Bayesian networks certainty distribution and fuzzy logic.


Possibilistic Pertinence Feedback and Semantic Networks for Goal's Extraction

Abstract: Pertinence Feedback is a technique that enables a user to interactively express his information requirement by modifying his original query formulation with further information. This information is provided by explicitly confirming the pertinent of some indicating objects and/or goals extracted by the system. Obviously the user cannot mark objects and/or goals as pertinent until some are extracted, so the first search has to be initiated by a query and the initial query specification has to be good enough to pick out some pertinent objects and/or goals from the Semantic Network. In this paper we present a short survey of fuzzy and Semantic approaches to Knowledge Extraction. The goal of such approaches is to define flexible Knowledge Extraction Systems able to deal with the inherent vagueness and uncertainty of the Extraction process. It has long been recognised that interactivity improves the effectiveness of Knowledge Extraction systems. Novice user's queries are the most natural and interactive medium of communication and recent progress in recognition is making it possible to build systems that interact with the user. However, given the typical novice user's queries submitted to Knowledge Extraction Systems, it is easy to imagine that the effects of goal recognition errors in novice user's queries must be severely destructive on the system's effectiveness. The experimental work reported in this paper shows that the use of possibility theory in classical Knowledge Extraction techniques for novice user's query processing is more robust than the use of the probability theory. Moreover, both possibilistic and probabilistic pertinence feedback can be effectively employed to improve the effectiveness of novice user's query processing.


A Cross-Layer Design Based on Geographic Information for Cooperative Wireless Networks

Abstract: Most of geographic routing approaches in wireless ad hoc and sensor networks do not take into consideration the medium access control (MAC) and physical layers when designing a routing protocol. In this paper, we focus on a cross-layer framework design that exploits the synergies between network, MAC, and physical layers. In the proposed CoopGeo, we use a beaconless forwarding scheme where the next hop is selected through a contention process based on the geographic position of nodes. We optimize this Network-MAC layer interaction using a cooperative relaying technique with a relay selection scheme also based on geographic information in order to improve the system performance in terms of reliability.


Pertinent Information retrieval based on Possibilistic Bayesian network : origin and possibilistic perspective

Abstract: In this paper we present a synthesis of work performed on tow information retrieval models: Bayesian network information retrieval model witch encode (in) dependence relation between terms and possibilistic network information retrieval model witch make use of necessity and possibility measures to represent the fuzziness of pertinence measure. It is known that the use of a general Bayesian network methodology as the basis for an IR system is difficult to tackle. The problem mainly appears because of the large number of variables involved and the computational efforts needed to both determine the relationships between variables and perform the inference processes. To resolve these problems, many models have been proposed such as BNR model. Generally, Bayesian network models doesn't consider the fuzziness of natural language in the relevance measure of a document to a given query and possibilistic models doesn't undertake the dependence relations between terms used to index documents. As a first solution we propose a hybridization of these two models in one that will undertake both the relationship between terms and the intrinsic fuzziness of natural language. We believe that the translation of Bayesian network model from the probabilistic framework to possibilistic one will allow a performance improvement of BNRM.


Black-box optimization benchmarking of IPOP-saACM-ES on the BBOB-2012 noisy testbed

Abstract: In this paper, we study the performance of IPOP-saACM-ES, recently proposed self-adaptive surrogate-assisted Covariance Matrix Adaptation Evolution Strategy. The algorithm was tested using restarts till a total number of function evaluations of $10^6D$ was reached, where $D$ is the dimension of the function search space. The experiments show that the surrogate model control allows IPOP-saACM-ES to be as robust as the original IPOP-aCMA-ES and outperforms the latter by a factor from 2 to 3 on 6 benchmark problems with moderate noise. On 15 out of 30 benchmark problems in dimension 20, IPOP-saACM-ES exceeds the records observed during BBOB-2009 and BBOB-2010.


Loopy Belief Propagation in Bayesian Networks : origin and possibilistic perspectives

Abstract: In this paper we present a synthesis of the work performed on two inference algorithms: the Pearl's belief propagation (BP) algorithm applied to Bayesian networks without loops (i.e. polytree) and the Loopy belief propagation (LBP) algorithm (inspired from the BP) which is applied to networks containing undirected cycles. It is known that the BP algorithm, applied to Bayesian networks with loops, gives incorrect numerical results i.e. incorrect posterior probabilities. Murphy and al. [7] find that the LBP algorithm converges on several networks and when this occurs, LBP gives a good approximation of the exact posterior probabilities. However this algorithm presents an oscillatory behaviour when it is applied to QMR (Quick Medical Reference) network [15]. This phenomenon prevents the LBP algorithm from converging towards a good approximation of posterior probabilities. We believe that the translation of the inference computation problem from the probabilistic framework to the possibilistic framework will allow performance improvement of LBP algorithm. We hope that an adaptation of this algorithm to a possibilistic causal network will show an improvement of the convergence of LBP.


Privacy Preserving and Ownership Authentication in Ubiquitous Computing Devices using Secure Three Way Authentication

Abstract: In todays world of technology and gadgets almost every person is having a portable device, be it a laptop or the smart phones. The user would like to have all the services at his fingertips and access them through the portable device he owns. Maybe he wants some data from the fellow user or from the service provider or maybe he wants to control his smart devices at home from wherever he is. In the present era of mobile environments, interactions between the user device and the service provider must be secure enough regardless of the type of device used to access or utilize the services. In this paper we propose a "Secure Three Way Authentication (STWA)" technique intended to preserve the user privacy and to accomplish ownership authentication in order to securely deliver the services to the user devices. This technique will also help the users or the service providers to check if the device is compromised or not with the help of the encrypted pass-phrases that are being exchanged.


Nearly optimal solutions for the Chow Parameters Problem and low-weight approximation of halfspaces

Abstract: The \emph{Chow parameters} of a Boolean function $f: \{-1,1\}^n \to \{-1,1\}$ are its $n+1$ degree-0 and degree-1 Fourier coefficients. It has been known since 1961 (Chow, Tannenbaum) that the (exact values of the) Chow parameters of any linear threshold function $f$ uniquely specify $f$ within the space of all Boolean functions, but until recently (O'Donnell and Servedio) nothing was known about efficient algorithms for \emph{reconstructing} $f$ (exactly or approximately) from exact or approximate values of its Chow parameters. We refer to this reconstruction problem as the \emph{Chow Parameters Problem.} Our main result is a new algorithm for the Chow Parameters Problem which, given (sufficiently accurate approximations to) the Chow parameters of any linear threshold function $f$, runs in time $\tilde{O}(n^2)\cdot (1/\eps)^{O(\log^2(1/\eps))}$ and with high probability outputs a representation of an LTF $f'$ that is $\eps$-close to $f$. The only previous algorithm (O'Donnell and Servedio) had running time $\poly(n) \cdot 2^{2^{\tilde{O}(1/\eps^2)}}.$ As a byproduct of our approach, we show that for any linear threshold function $f$ over $\{-1,1\}^n$, there is a linear threshold function $f'$ which is $\eps$-close to $f$ and has all weights that are integers at most $\sqrt{n} \cdot (1/\eps)^{O(\log^2(1/\eps))}$. This significantly improves the best previous result of Diakonikolas and Servedio which gave a $\poly(n) \cdot 2^{\tilde{O}(1/\eps^{2/3})}$ weight bound, and is close to the known lower bound of $\max\{\sqrt{n},$ $(1/\eps)^{\Omega(\log \log (1/\eps))}\}$ (Goldberg, Servedio). Our techniques also yield improved algorithms for related problems in learning theory.


Virtualization Implementation Model for Cost Effective & Efficient Data Centers

Abstract: Data centers form a key part of the infrastructure upon which a variety of information technology services are built. They provide the capabilities of centralized repository for storage, management, networking and dissemination of data. With the rapid increase in the capacity and size of data centers, there is a continuous increase in the demand for energy consumption. These data centers not only consume a tremendous amount of energy but are riddled with IT inefficiencies. Data center are plagued with thousands of servers as major components. These servers consume huge energy without performing useful work. In an average server environment, 30% of the servers are "dead" only consuming energy, without being properly utilized. This paper proposes a five step model using an emerging technology called virtualization to achieve energy efficient data centers. The proposed model helps Data Center managers to properly implement virtualization technology in their data centers to make them green and energy efficient so as to ensure that IT infrastructure contributes as little as possible to the emission of greenhouse gases, and helps to regain power and cooling capacity, recapture resilience and dramatically reducing energy costs and total cost of ownership.


Finite-time Convergent Gossiping

Abstract: Gossip algorithms are widely used in modern distributed systems, with applications ranging from sensor networks and peer-to-peer networks to mobile vehicle networks and social networks. A tremendous research effort has been devoted to analyzing and improving the asymptotic rate of convergence for gossip algorithms. In this work we study finite-time convergence of deterministic gossiping. We show that there exists a symmetric gossip algorithm that converges in finite time if and only if the number of network nodes is a power of two, while there always exists an asymmetric gossip algorithm with finite-time convergence, independent of the number of nodes. For $n=2^m$ nodes, we prove that a fastest convergence can be reached in $nm=n\log_2 n$ node updates via symmetric gossiping. On the other hand, under asymmetric gossip among $n=2^m+r$ nodes with $0\leq r<2^m$, it takes at least $mn+2r$ node updates for achieving finite-time convergence. It is also shown that the existence of finite-time convergent gossiping often imposes strong structural requirements on the underlying interaction graph. Finally, we apply our results to gossip algorithms in quantum networks, where the goal is to control the state of a quantum system via pairwise interactions. We show that finite-time convergence is never possible for such systems.


An Optimization Framework for Semi-Supervised and Transfer Learning using Multiple Classifiers and Clusterers

Abstract: Unsupervised models can provide supplementary soft constraints to help classify new, "target" data since similar instances in the target set are more likely to share the same class label. Such models can also help detect possible differences between training and target distributions, which is useful in applications where concept drift may take place, as in transfer learning settings. This paper describes a general optimization framework that takes as input class membership estimates from existing classifiers learnt on previously encountered "source" data, as well as a similarity matrix from a cluster ensemble operating solely on the target data to be classified, and yields a consensus labeling of the target data. This framework admits a wide range of loss functions and classification/clustering methods. It exploits properties of Bregman divergences in conjunction with Legendre duality to yield a principled and scalable approach. A variety of experiments show that the proposed framework can yield results substantially superior to those provided by popular transductive learning techniques or by naively applying classifiers learnt on the original task to the target data.


Infinite Synchronizing Words for Probabilistic Automata (Erratum)

Abstract: In [1], we introduced the weakly synchronizing languages for probabilistic automata. In this report, we show that the emptiness problem of weakly synchronizing languages for probabilistic automata is undecidable. This implies that the decidability result of [1-3] for the emptiness problem of weakly synchronizing language is incorrect.


Iterated tabu search for the circular open dimension problem

Abstract: This paper mainly investigates the circular open dimension problem (CODP), which consists of packing a set of circles of known radii into a strip of fixed width and unlimited length without overlapping. The objective is to minimize the length of the strip. An iterated tabu search approach, named ITS, is proposed. ITS starts from a randomly generated solution and attempts to gain improvements by a tabu search procedure. After that, if the obtained solution is not feasible, a perturbation operator is subsequently employed to reconstruct the incumbent solution and an acceptance criterion is implemented to determine whether or not accept the perturbed solution. This process is repeated until a feasible solution has been found or the allowed computation time has been elapsed. Computational experiments based on well-known benchmark instances show that ITS produces quite competitive results with respect to the best known results. For 18 representative CODP instances taken from the literature, ITS succeeds in improving 13 best known results within reasonable time. In addition, for another challenging related variant: the problem of packing arbitrary sized circles into a circular container, ITS also succeeds in improving many best known results. Supplementary experiments are also provided to analyze the influence of the perturbation operator, as well as the acceptance criterion.


A Machine Learning Approach For Opinion Holder Extraction In Arabic Language

Abstract: Opinion mining aims at extracting useful subjective information from reliable amounts of text. Opinion mining holder recognition is a task that has not been considered yet in Arabic Language. This task essentially requires deep understanding of clauses structures. Unfortunately, the lack of a robust, publicly available, Arabic parser further complicates the research. This paper presents a leading research for the opinion holder extraction in Arabic news independent from any lexical parsers. We investigate constructing a comprehensive feature set to compensate the lack of parsing structural outcomes. The proposed feature set is tuned from English previous works coupled with our proposed semantic field and named entities features. Our feature analysis is based on Conditional Random Fields (CRF) and semi-supervised pattern recognition techniques. Different research models are evaluated via cross-validation experiments achieving 54.03 F-measure. We publicly release our own research outcome corpus and lexicon for opinion mining community to encourage further research.


A Hybrid Artificial Bee Colony Algorithm for Graph 3-Coloring

Abstract: The Artificial Bee Colony (ABC) is the name of an optimization algorithm that was inspired by the intelligent behavior of a honey bee swarm. It is widely recognized as a quick, reliable, and efficient methods for solving optimization problems. This paper proposes a hybrid ABC (HABC) algorithm for graph 3-coloring, which is a well-known discrete optimization problem. The results of HABC are compared with results of the well-known graph coloring algorithms of today, i.e. the Tabucol and Hybrid Evolutionary algorithm (HEA) and results of the traditional evolutionary algorithm with SAW method (EA-SAW). Extensive experimentations has shown that the HABC matched the competitive results of the best graph coloring algorithms, and did better than the traditional heuristics EA-SAW when solving equi-partite, flat, and random generated medium-sized graphs.


Frequent Patterns mining in time-sensitive Data Stream

Abstract: Mining frequent itemsets through static Databases has been extensively studied and used and is always considered a highly challenging task. For this reason it is interesting to extend it to data streams field. In the streaming case, the frequent patterns' mining has much more information to track and much greater complexity to manage. Infrequent items can become frequent later on and hence cannot be ignored. The output structure needs to be dynamically incremented to reflect the evolution of itemset frequencies over time. In this paper, we study this problem and specifically the methodology of mining time-sensitive data streams. We tried to improve an existing algorithm by increasing the temporal accuracy and discarding the out-of-date data by adding a new concept called the "Shaking Point". We presented as well some experiments illustrating the time and space required.


Relevance Feedback for Goal's Extraction from Fuzzy Semantic Networks

Abstract: In this paper we present a short survey of fuzzy and Semantic approaches to Knowledge Extraction. The goal of such approaches is to define flexible Knowledge Extraction Systems able to deal with the inherent vagueness and uncertainty of the Extraction process. It has long been recognised that interactivity improves the effectiveness of Knowledge Extraction systems. Novice user's queries is the most natural and interactive medium of communication and recent progress in recognition is making it possible to build systems that interact with the user. However, given the typical novice user's queries submitted to Knowledge Extraction systems, it is easy to imagine that the effects of goal recognition errors in novice user's queries must be severely destructive on the system's effectiveness. The experimental work reported in this paper shows that the use of classical Knowledge Extraction techniques for novice user's query processing is robust to considerably high levels of goal recognition errors. Moreover, both standard relevance feedback and pseudo relevance feedback can be effectively employed to improve the effectiveness of novice user's query processing.


Use of Fuzzy Sets in Semantic Nets for Providing On-Line Assistance to User of Technological Systems

Abstract: The main objective of this paper is to develop a new semantic Network structure, based on the fuzzy sets theory, used in Artificial Intelligent system in order to provide effective on-line assistance to users of new technological systems. This Semantic Networks is used to describe the knowledge of an "ideal" expert while fuzzy sets are used both to describe the approximate and uncertain knowledge of novice users who intervene to match fuzzy labels of a query with categories from an "ideal" expert. The technical system we consider is a word processor software, with Objects such as "Word" and Goals such as "Cut" or "Copy". We suggest to consider the set of the system's Goals as a set of linguistic variables to which corresponds a set of possible linguistic values based on the fuzzy set. We consider, therefore, a set of interpretation's levels for these possible values to which corresponds a set of membership functions. We also propose a method to measure the similarity degree between different fuzzy linguistic variables for the partition of the semantic network in class of similar objects to make easy the diagnosis of the user's fuzzy queries.


An IMU-Aided Carrier-Phase Differential GPS Positioning System

Abstract: We consider the problem of carrier-phase differential GPS positioning for an land vehicle navigation system (LVNS), tightly coupled with an inertial measurement unit (IMU) and a speedometer. The primary focus is to apply Bayesian network to an IMU-aided GPS positioning system based on carrier-phase differential GPS. We describe the implementation details of the positioning system that integrates GPS measurements (i.e., pseudo-range, carrier-phase and doppler), IMU measurements, and speedometer measurements. We derive the linearized state process equation and the measurement equation for GPS and speedometer. To account for constraints of land vehicle, we add two more pseudo measurements to ensure the perpendicular velocities close to zero.


Hedge detection as a lens on framing in the GMO debates: A position paper

Abstract: Understanding the ways in which participants in public discussions frame their arguments is important in understanding how public opinion is formed. In this paper, we adopt the position that it is time for more computationally-oriented research on problems involving framing. In the interests of furthering that goal, we propose the following specific, interesting and, we believe, relatively accessible question: In the controversy regarding the use of genetically-modified organisms (GMOs) in agriculture, do pro- and anti-GMO articles differ in whether they choose to adopt a "scientific" tone? Prior work on the rhetoric and sociology of science suggests that hedging may distinguish popular-science text from text written by professional scientists for their colleagues. We propose a detailed approach to studying whether hedge detection can be used to understanding scientific framing in the GMO debates, and provide corpora to facilitate this study. Some of our preliminary analyses suggest that hedges occur less frequently in scientific discourse than in popular text, a finding that contradicts prior assertions in the literature. We hope that our initial work and data will encourage others to pursue this promising line of inquiry.


Memetic Artificial Bee Colony Algorithm for Large-Scale Global Optimization

Abstract: Memetic computation (MC) has emerged recently as a new paradigm of efficient algorithms for solving the hardest optimization problems. On the other hand, artificial bees colony (ABC) algorithms demonstrate good performances when solving continuous and combinatorial optimization problems. This study tries to use these technologies under the same roof. As a result, a memetic ABC (MABC) algorithm has been developed that is hybridized with two local search heuristics: the Nelder-Mead algorithm (NMA) and the random walk with direction exploitation (RWDE). The former is attended more towards exploration, while the latter more towards exploitation of the search space. The stochastic adaptation rule was employed in order to control the balancing between exploration and exploitation. This MABC algorithm was applied to a Special suite on Large Scale Continuous Global Optimization at the 2012 IEEE Congress on Evolutionary Computation. The obtained results the MABC are comparable with the results of DECC-G, DECC-G*, and MLCC.


Zwei Anwendungen des Paillier-Kryptosystems: Blinde Signatur und Three-Pass-Protocol

Abstract: Englisch: In this paper we study the paillier cryptosystem and derive form it to new schemes. First we transform the signature of paillier in a Blind signature. Secondly we propose a three-pass protocol wich use the homomorphic property instead of the commutativity as the Shamir protocol does. German: Basierend auf dem Kryptosystem von Paillier und dem damit eingef\"uhrten Problem der zusammengesetzten Residuenklasse werden in diesem Artikel zwei kryptographische Verfahren vorgeschlagen. Zun\"achst wird die Signatur von Paillier in ein blindes Signaturverfahren umgewandelt. Des Weiteren wird mit der homomorphen Eigenschaft des Kryptosystems von Paillier ein sogenanntes Three-Pass-Protocol - auch No-Key-Protocol genannt - entwickelt.


Dynamic Verification for File Safety of Multithreaded Programs

Abstract: In this paper, we present a new semantics to check file safety of multithreaded programs. A file-safe program is one that reaches a final configuration under the proposed semantics. We extend the While language with file operations and multi-threading commands, and call the new language whilef. This paper shows that the file safety is an un-decidable property for whilef. The file safety becomes a decidable property in a special case shown in this paper. The case happens when users provide pointer information. If the file is safe we call it a strongly safe file program. We modify the syntax and the semantic of the language and called it SafeWhilef.


A Fast Distributed Approximation Algorithm for Minimum Spanning Trees in the SINR Model

Abstract: A fundamental problem in wireless networks is the \emph{minimum spanning tree} (MST) problem: given a set $V$ of wireless nodes, compute a spanning tree $T$, so that the total cost of $T$ is minimized. In recent years, there has been a lot of interest in the physical interference model based on SINR constraints. Distributed algorithms are especially challenging in the SINR model, because of the non-locality of the model. In this paper, we develop a fast distributed approximation algorithm for MST construction in an SINR based distributed computing model. For an $n$-node network, our algorithm's running time is $O(D\log{n}+\mu\log{n})$ and produces a spanning tree whose cost is within $O(\log n)$ times the optimal (MST cost), where $D$ denotes the diameter of the disk graph obtained by using the maximum possible transmission range, and $\mu=\log{\frac{d_{max}}{d_{min}}}$ denotes the "distance diversity" w.r.t. the largest and smallest distances between two nodes. (When $\frac{d_{max}}{d_{min}}$ is $n$-polynomial, $\mu = O(\log n)$.) Our algorithm's running time is essentially optimal (upto a logarithmic factor), since computing {\em any} spanning tree takes $\Omega(D)$ time; thus our algorithm produces a low cost spanning tree in time only a logarithmic factor more than the time to compute a spanning tree. The distributed scheduling complexity of the spanning tree resulted from our algorithm is $O(\mu \log n)$. Our algorithmic design techniques can be useful in designing efficient distributed algorithms for related "global" problems in wireless networks in the SINR model.


Research On Mobile Cloud Computing: Review, Trend, And Perspectives

Abstract: Mobile Cloud Computing (MCC) which combines mobile computing and cloud computing, has become one of the industry buzz words and a major discussion thread in the IT world since 2009. As MCC is still at the early stage of development, it is necessary to grasp a thorough understanding of the technology in order to point out the direction of future research. With the latter aim, this paper presents a review on the background and principle of MCC, characteristics, recent research work, and future research trends. A brief account on the background of MCC: from mobile computing to cloud computing is presented and then followed with a discussion on characteristics and recent research work. It then analyses the features and infrastructure of mobile cloud computing. The rest of the paper analyses the challenges of mobile cloud computing, summary of some research projects related to this area, and points out promising future research directions.


Comparison of the C4.5 and a Naive Bayes Classifier for the Prediction of Lung Cancer Survivability

Abstract: Numerous data mining techniques have been developed to extract information and identify patterns and predict trends from large data sets. In this study, two classification techniques, the J48 implementation of the C4.5 algorithm and a Naive Bayes classifier are applied to predict lung cancer survivability from an extensive data set with fifteen years of patient records. The purpose of the project is to verify the predictive effectiveness of the two techniques on real, historical data. Besides the performance outcome that renders J48 marginally better than the Naive Bayes technique, there is a detailed description of the data and the required pre-processing activities. The performance results confirm expectations while some of the issues that appeared during experimentation, underscore the value of having domain-specific understanding to leverage any domain-specific characteristics inherent in the data.


Evaluation and Improvement of Laruelle-Widgr\'en Inverse Banzhaf Approximation

Abstract: The goal of this paper is to critically evaluate a heuristic algorithm for the Inverse Banzhaf Index problem by Laruelle and Widgr\'en. Few qualitative results are known about the approximation quality of the heuristics for this problem. The intuition behind the operation of this approximation algorithm is analysed and evaluated. We found that the algorithm can not handle general inputs well, and often fails to improve inputs. It is also shown to diverge after only tens of iterations. We present three alternative extensions of the algorithm that do not alter the complexity but can result in up to a factor 6.5 improvement in solution quality.


Memory-Efficient Topic Modeling

Abstract: As one of the simplest probabilistic topic modeling techniques, latent Dirichlet allocation (LDA) has found many important applications in text mining, computer vision and computational biology. Recent training algorithms for LDA can be interpreted within a unified message passing framework. However, message passing requires storing previous messages with a large amount of memory space, increasing linearly with the number of documents or the number of topics. Therefore, the high memory usage is often a major problem for topic modeling of massive corpora containing a large number of topics. To reduce the space complexity, we propose a novel algorithm without storing previous messages for training LDA: tiny belief propagation (TBP). The basic idea of TBP relates the message passing algorithms with the non-negative matrix factorization (NMF) algorithms, which absorb the message updating into the message passing process, and thus avoid storing previous messages. Experimental results on four large data sets confirm that TBP performs comparably well or even better than current state-of-the-art training algorithms for LDA but with a much less memory consumption. TBP can do topic modeling when massive corpora cannot fit in the computer memory, for example, extracting thematic topics from 7 GB PUBMED corpora on a common desktop computer with 2GB memory.


Traditional sufficient conditions for Nash implementation may fail on Internet

Abstract: The Maskin's theorem is a fundamental work in the theory of mechanism design. In this paper, we propose that if agents report messages to the designer through channels (e.g., Internet), agents can construct a self-enforcing agreement such that any Pareto-inefficient social choice rule satisfying monotonicity and no-veto will not be Nash implementable when an additional condition is satisfied. The key points are: 1) The agreement is unobservable to the designer, and the designer cannot prevent the agents from constructing such agreement; 2) The agents act non-cooperatively, and the Maskin mechanism remain unchanged from the designer's perspective.


Uncertainty Analysis of the Adequacy Assessment Model of a Distributed Generation System

Abstract: Due to the inherent aleatory uncertainties in renewable generators, the reliability/adequacy assessments of distributed generation (DG) systems have been particularly focused on the probabilistic modeling of random behaviors, given sufficient informative data. However, another type of uncertainty (epistemic uncertainty) must be accounted for in the modeling, due to incomplete knowledge of the phenomena and imprecise evaluation of the related characteristic parameters. In circumstances of few informative data, this type of uncertainty calls for alternative methods of representation, propagation, analysis and interpretation. In this study, we make a first attempt to identify, model, and jointly propagate aleatory and epistemic uncertainties in the context of DG systems modeling for adequacy assessment. Probability and possibility distributions are used to model the aleatory and epistemic uncertainties, respectively. Evidence theory is used to incorporate the two uncertainties under a single framework. Based on the plausibility and belief functions of evidence theory, the hybrid propagation approach is introduced. A demonstration is given on a DG system adapted from the IEEE 34 nodes distribution test feeder. Compared to the pure probabilistic approach, it is shown that the hybrid propagation is capable of explicitly expressing the imprecision in the knowledge on the DG parameters into the final adequacy values assessed. It also effectively captures the growth of uncertainties with higher DG penetration levels.


Cumulative Step-size Adaptation on Linear Functions: Technical Report

Abstract: The CSA-ES is an Evolution Strategy with Cumulative Step size Adaptation, where the step size is adapted measuring the length of a so-called cumulative path. The cumulative path is a combination of the previous steps realized by the algorithm, where the importance of each step decreases with time. This article studies the CSA-ES on composites of strictly increasing with affine linear functions through the investigation of its underlying Markov chains. Rigorous results on the change and the variation of the step size are derived with and without cumulation. The step-size diverges geometrically fast in most cases. Furthermore, the influence of the cumulation parameter is studied.


Reciprocally induced coevolution: A computational metaphor in Mathematics

Abstract: Natural phenomenon of coevolution is the reciprocally induced evolutionary change between two or more species or population. Though this biological occurrence is a natural fact, there are only few attempts to use this as a simile in computation. This paper is an attempt to introduce reciprocally induced coevolution as a mechanism to counter problems faced by a typical genetic algorithm applied as an optimization technique. The domain selected for testing the efficacy of the procedure is the process of finding numerical solutions of Diophantine equations. Diophantine equations are polynomial equations in Mathematics where only integer solutions are sought. Such equations and its solutions are significant in three aspects-(i) historically they are important as Hilbert's tenth problem with a background of more than twenty six centuries; (ii) there are many modern application areas of Diophantine equations like public key cryptography and data dependency in super computers (iii) it has been proved that there does not exist any general method to find solutions of such equations. The proposed procedure has been tested with Diophantine equations with different powers and different number of variables.


Heavy Traffic Optimal Resource Allocation Algorithms for Cloud Computing Clusters

Abstract: Cloud computing is emerging as an important platform for business, personal and mobile computing applications. In this paper, we study a stochastic model of cloud computing, where jobs arrive according to a stochastic process and request resources like CPU, memory and storage space. We consider a model where the resource allocation problem can be separated into a routing or load balancing problem and a scheduling problem. We study the join-the-shortest-queue routing and power-of-two-choices routing algorithms with MaxWeight scheduling algorithm. It was known that these algorithms are throughput optimal. In this paper, we show that these algorithms are queue length optimal in the heavy traffic limit.


Causality, Influence, and Computation in Possibly Disconnected Dynamic Networks

Abstract: In this work, we study the propagation of influence and computation in dynamic distributed systems. We focus on broadcasting models under a worst-case dynamicity assumption which have received much attention recently. We drop for the first time in worst-case dynamic networks the common instantaneous connectivity assumption and require a minimal temporal connectivity. Our temporal connectivity constraint only requires that another causal influence occurs within every time-window of some given length. We establish that there are dynamic graphs with always disconnected instances with equivalent temporal connectivity to those with always connected instances. We present a termination criterion and also establish the computational equivalence with instantaneous connectivity networks. We then consider another model of dynamic networks in which each node has an underlying communication neighborhood and the requirement is that each node covers its local neighborhood within any time-window of some given length. We discuss several properties and provide a protocol for counting, that is for determining the number of nodes in the network.


Feature Weighting for Improving Document Image Retrieval System Performance

Abstract: Feature weighting is a technique used to approximate the optimal degree of influence of individual features. This paper presents a feature weighting method for Document Image Retrieval System (DIRS) based on keyword spotting. In this method, we weight the feature using coefficient of multiple correlations. Coefficient of multiple correlations can be used to describe the synthesized effects and correlation of each feature. The aim of this paper is to show that feature weighting increases the performance of DIRS. After applying the feature weighting method to DIRS the average precision is 93.23% and average recall become 98.66% respectively


From Pop-Up Cards to Coffee-Cup Caustics: The Knight's Visor

Abstract: As a pedagogical exercise, we derive the shape of a particularly elegant pop-up card design, and show that it connects to a classically studied plane curve that is (among other interpretations) a caustic of a circle.


Model Checking Stochastic Branching Processes

Abstract: Stochastic branching processes are a classical model for describing random trees, which have applications in numerous fields including biology, physics, and natural language processing. In particular, they have recently been proposed to describe parallel programs with stochastic process creation. In this paper, we consider the problem of model checking stochastic branching process. Given a branching process and a deterministic parity tree automaton, we are interested in computing the probability that the generated random tree is accepted by the automaton. We show that this probability can be compared with any rational number in PSPACE, and with 0 and 1 in polynomial time. In a second part, we suggest a tree extension of the logic PCTL, and develop a PSPACE algorithm for model checking a branching process against a formula of this logic. We also show that the qualitative fragment of this logic can be model checked in polynomial time.


Certain Bayesian Network based on Fuzzy knowledge Bases

Abstract: In this paper, we are trying to examine trade offs between fuzzy logic and certain Bayesian networks and we propose to combine their respective advantages into fuzzy certain Bayesian networks (FCBN), a certain Bayesian networks of fuzzy random variables. This paper deals with different definitions and classifications of uncertainty, sources of uncertainty, and theories and methodologies presented to deal with uncertainty. Fuzzification of crisp certainty degrees to fuzzy variables improves the quality of the network and tends to bring smoothness and robustness in the network performance. The aim is to provide a new approach for decision under uncertainty that combines three methodologies: Bayesian networks certainty distribution and fuzzy logic. Within the framework proposed in this paper, we address the issue of extending the certain networks to a fuzzy certain networks in order to cope with a vagueness and limitations of existing models for decision under imprecise and uncertain knowledge.


Security Analysis of Vehicular Ad Hoc Networks (VANET)

Abstract: Vehicular Ad Hoc Networks (VANET) has mostly gained the attention of today's research efforts, while current solutions to achieve secure VANET, to protect the network from adversary and attacks still not enough, trying to reach a satisfactory level, for the driver and manufacturer to achieve safety of life and infotainment. The need for a robust VANET networks is strongly dependent on their security and privacy features, which will be discussed in this paper. In this paper a various types of security problems and challenges of VANET been analyzed and discussed; we also discuss a set of solutions presented to solve these challenges and problems.


Finding Quality Issues in SKOS Vocabularies

Abstract: The Simple Knowledge Organization System (SKOS) is a standard model for controlled vocabularies on the Web. However, SKOS vocabularies often differ in terms of quality, which reduces their applicability across system boundaries. Here we investigate how we can support taxonomists in improving SKOS vocabularies by pointing out quality issues that go beyond the integrity constraints defined in the SKOS specification. We identified potential quantifiable quality issues and formalized them into computable quality checking functions that can find affected resources in a given SKOS vocabulary. We implemented these functions in the qSKOS quality assessment tool, analyzed 15 existing vocabularies, and found possible quality issues in all of them.


A Coverage Theory of Bistatic Radar Networks: Worst-Case Intrusion Path and Optimal Deployment

Abstract: In this paper, we study optimal radar deployment for intrusion detection, with focus on network coverage. In contrast to the disk-based sensing model in a traditional sensor network, the detection range of a bistatic radar depends on the locations of both the radar transmitter and radar receiver, and is characterized by Cassini ovals. Furthermore, in a network with multiple radar transmitters and receivers, since any pair of transmitter and receiver can potentially form a bistatic radar, the detection ranges of different bistatic radars are coupled and the corresponding network coverage is intimately related to the locations of all transmitters and receivers, making the optimal deployment design highly non-trivial. Clearly, the detectability of an intruder depends on the highest SNR received by all possible bistatic radars. We focus on the worst-case intrusion detectability, i.e., the minimum possible detectability along all possible intrusion paths. Although it is plausible to deploy radars on a shortest line segment across the field, it is not always optimal in general, which we illustrate via counter-examples. We then present a sufficient condition on the field geometry for the optimality of shortest line deployment to hold. Further, we quantify the local structure of detectability corresponding to a given deployment order and spacings of radar transmitters and receivers, building on which we characterize the optimal deployment to maximize the worst-case intrusion detectability. Our results show that the optimal deployment locations exhibit a balanced structure. We also develop a polynomial-time approximation algorithm for characterizing the worse-case intrusion path for any given locations of radars under random deployment.


Broadcast-based Directional Routing in Vehicular Ad-Hoc Networks

Abstract: An optimized broadcast protocol is proposed for VANETs. It is based on two key information: the direction to the destination and the beamforming angle {\theta}. The efficiency of this technique is demonstrated in terms of packet delivery, bandwidth gain and probability of transmission success. An analytical model is developed to calculate the transmission area. This model allows capturing the propagation shape of the forwarding area. Comparisons with simulations show that the analytical model is precise.


A Novel Advanced Heap Corruption and Security Method

Abstract: Heap security has been a major concern since the past two decades. Recently many methods have been proposed to secure heap i.e. to avoid heap overrun and attacks. The paper describes a method suggested to secure heap at the operating system level. Major emphasis is given to Solaris operating system's dynamic memory manager. When memory is required dynamically during runtime, the SysVmalloc acts as a memory allocator.Vmalloc allocates the chunks of memory in the form of splay tree structure. A self adjusting binary tree structure is reviewed in the paper, moreover major security issue to secure heap area is also suggested in the paper


Reducing Packet Overhead in Mobile IPv6

Abstract: Common Mobile IPv6 mechanisms, Bidirectional tunneling and Route optimization, show inefficient packet overhead when both nodes are mobile. Researchers have proposed methods to reduce packet overhead regarding to maintain compatible with standard mechanisms. In this paper, three mechanisms in Mobile IPv6 are discussed to show their efficiency and performance. Following discussion, a new mechanism called Improved Tunneling-based Route Optimization is proposed and due to performance analysis, it is shown that proposed mechanism has less overhead comparing to common mechanisms. Analytical results indicate that Improved Tunneling-based Route Optimization transmits more payloads due to send packets with less overhead.


An Intelligent Approach for Negotiating between chains in Supply Chain Management Systems

Abstract: Holding commercial negotiations and selecting the best supplier in supply chain management systems are among weaknesses of producers in production process. Therefore, applying intelligent systems may have an effective role in increased speed and improved quality in the selections .This paper introduces a system which tries to trade using multi-agents systems and holding negotiations between any agents. In this system, an intelligent agent is considered for each segment of chains which it tries to send order and receive the response with attendance in negotiation medium and communication with other agents .This paper introduces how to communicate between agents, characteristics of multi-agent and standard registration medium of each agent in the environment. JADE (Java Application Development Environment) was used for implementation and simulation of agents cooperation.


A weighted combination similarity measure for mobility patterns in wireless networks

Abstract: The similarity between trajectory patterns in clustering has played an important role in discovering movement behaviour of different groups of mobile objects. Several approaches have been proposed to measure the similarity between sequences in trajectory data. Most of these measures are based on Euclidean space or on spatial network and some of them have been concerned with temporal aspect or ordering types. However, they are not appropriate to characteristics of spatiotemporal mobility patterns in wireless networks. In this paper, we propose a new similarity measure for mobility patterns in cellular space of wireless network. The framework for constructing our measure is composed of two phases as follows. First, we present formal definitions to capture mathematically two spatial and temporal similarity measures for mobility patterns. And then, we define the total similarity measure by means of a weighted combination of these similarities. The truth of the partial and total similarity measures are proved in mathematics. Furthermore, instead of the time interval or ordering, our work makes use of the timestamp at which two mobility patterns share the same cell. A case study is also described to give a comparison of the combination measure with other ones.


Analysis study of time synchronization protocols in wireless sensor networks

Abstract: One of the main pervasive problems Wireless Sensor Networks (WSN) encounter is to maintain flawless communication sharing and cooperative processing between sensors via radio links to ensure a reliable treatment of information. Many applications based on these WSNs consider local clocks at each sensor node that need to be synchronized to a common notion of time. In this context, the majority of previous researches were focused on the study of protocols, and algorithms that address these issues in order to resolve synchronization problems. Previous fforts and empirical studies in wireless sensor network (WSN) proposed several solutions (algorithms). The focus of this this paper is to examine and evaluate the most important synchronization algorithms based on the positions of various quantitative and qualitative synchronization protocols for energy-efficient information processing and routing in WSNs.


The visible perimeter of an arrangement of disks

Abstract: Given a collection of n opaque unit disks in the plane, we want to find a stacking order for them that maximizes their visible perimeter---the total length of all pieces of their boundaries visible from above. We prove that if the centers of the disks form a dense point set, i.e., the ratio of their maximum to their minimum distance is O(n^1/2), then there is a stacking order for which the visible perimeter is Omega(n^2/3). We also show that this bound cannot be improved in the case of a sufficiently small n^1/2 by n^1/2 uniform grid. On the other hand, if the set of centers is dense and the maximum distance between them is small, then the visible perimeter is O(n^3/4) with respect to any stacking order. This latter bound cannot be improved either. Finally, we address the case where no more than c disks can have a point in common. These results partially answer some questions of Cabello, Haverkort, van Kreveld, and Speckmann.


Energy Consumption Model in ad hoc Mobile Network

Abstract: The aim of this work is to model the nodes battery discharge in wireless ad hoc networks. Many work focus on the energy consumption in such networks. Even, the research in the highest layers of the ISO model, takes into account the energy consumption and efficiency. Indeed, the nodes that form such network are mobiles, so no instant recharge of battery. Also with special type of ad hoc networks are wireless sensors networks using non-rechargeable batteries. All nodes with an exhausted battery are considered death and left the network. To consider the energy consumption, in this work we model using a Markov chain, the discharge of the battery considering of instant activation and deactivation distribution function of these nodes.


Distance Based Asynchronous Recovery Approach in Mobile Computing Environment

Abstract: A mobile computing system is a distributed system in which at least one of the processes is mobile. They are constrained by lack of stable storage, low network bandwidth, mobility, frequent disconnection and limited battery life. Checkpointing is one of the commonly used techniques to provide fault tolerance in mobile computing environment. In order to suit the mobile environment a distance based recovery scheme is proposed which is based on checkpointing and message logging. After the system recovers from failures, only the failed processes rollback and restart from their respective recent checkpoints, independent of the others. The salient feature of this scheme is to reduce the transfer and recovery cost. While the mobile host moves with in a specific range, recovery information is not moved and thus only be transferred nearby if the mobile host moves out of certain range.