Reasoning Support for Risk Prediction and Prevention in Independent Living

Abstract: In recent years there has been growing interest in solutions for the delivery of clinical care for the elderly, due to the large increase in aging population. Monitoring a patient in his home environment is necessary to ensure continuity of care in home settings, but, to be useful, this activity must not be too invasive for patients and a burden for caregivers. We prototyped a system called SINDI (Secure and INDependent lIving), focused on i) collecting a limited amount of data about the person and the environment through Wireless Sensor Networks (WSN), and ii) inferring from these data enough information to support caregivers in understanding patients' well being and in predicting possible evolutions of their health. Our hierarchical logic-based model of health combines data from different sources, sensor data, tests results, common-sense knowledge and patient's clinical profile at the lower level, and correlation rules between health conditions across upper levels. The logical formalization and the reasoning process are based on Answer Set Programming. The expressive power of this logic programming paradigm makes it possible to reason about health evolution even when the available information is incomplete and potentially incoherent, while declarativity simplifies rules specification by caregivers and allows automatic encoding of knowledge. This paper describes how these issues have been targeted in the application scenario of the SINDI system.


Working Document on Gloss Ontology

Abstract: This document describes the Gloss Ontology. The ontology and associated class model are organised into several packages. Section 2 describes each package in detail, while Section 3 contains a summary of the whole ontology.


Evolutionary Computation Algorithms for Cryptanalysis: A Study

Abstract: The cryptanalysis of various cipher problems can be formulated as NP-Hard combinatorial problem. Solving such problems requires time and/or memory requirement which increases with the size of the problem. Techniques for solving combinatorial problems fall into two broad groups - exact algorithms and Evolutionary Computation algorithms. An exact algorithms guarantees that the optimal solution to the problem will be found. The exact algorithms like branch and bound, simplex method, brute force etc methodology is very inefficient for solving combinatorial problem because of their prohibitive complexity (time and memory requirement). The Evolutionary Computation algorithms are employed in an attempt to find an adequate solution to the problem. A Evolutionary Computation algorithm - Genetic algorithm, simulated annealing and tabu search were developed to provide a robust and efficient methodology for cryptanalysis. The aim of these techniques to find sufficient "good" solution efficiently with the characteristics of the problem, instead of the global optimum solution, and thus it also provides attractive alternative for the large scale applications. This paper focuses on the methodology of Evolutionary Computation algorithms .


Critical Success factors for Enterprise Resource Planning implementation in Indian Retail Industry: An Exploratory study

Abstract: Enterprise resource Planning (ERP) has become a key business driver in today's world. Retailers are also trying to reap in the benefits of the ERP. In most large Indian Retail Industry ERP systems have replaced nonintegrated information systems with integrated and maintainable software. Retail ERP solution integrates demand and supply effectively to help improve bottom line. The implementation of ERP systems in such firms is a difficult task. So far, ERP implementations have yielded more failures than successes. Very few implementation failures are recorded in the literature because few companies wish to publicize their implementation failure. This paper explores and validates the existing literature empirically to find out the critical success factors that lead to the success of ERP in context to Indian retail industry. The findings of the results provide valuable insights for the researchers and practitioners who are interested in implementing Enterprise Resource Planning systems in retail industry, how best they can utilize their limited resources and to pay adequate attention to those factors that are most likely to have an impact upon the implementation of the ERP system.


Automated co-evolution of GMF editor models

Abstract: The Eclipse Graphical Modeling (GMF) Framework provides the major approach for implementing visual languages on top of the Eclipse platform. GMF relies on a family of modeling languages to describe different aspects of the visual language and its implementation in an editor. GMF uses a model-driven approach to map the different GMF models to Java code. The framework, as it stands, provides very little support for evolution. In particular, there is no support for propagating changes from say the domain model (i.e., the abstract syntax of the visual language) to other models. We analyze the resulting co-evolution challenge, and we provide a transformation-based solution, say GMF model adapters, that serve the propagation of abstract-syntax changes based on the interpretation of difference models.


A Unified Formal Description of Arithmetic and Set Theoretical Data Types

Abstract: We provide a "shared axiomatization" of natural numbers and hereditarily finite sets built around a polymorphic abstraction of bijective base-2 arithmetics. The "axiomatization" is described as a progressive refinement of Haskell type classes with examples of instances converging to an efficient implementation in terms of arbitrary length integers and bit operations. As an instance, we derive algorithms to perform arithmetic operations efficiently directly with hereditarily finite sets. The self-contained source code of the paper is available at http://logic.cse.unt.edu/tarau/research/2010/unified.hs .


Fatigue evaluation in maintenance and assembly operations by digital human simulation

Abstract: Virtual human techniques have been used a lot in industrial design in order to consider human factors and ergonomics as early as possible. The physical status (the physical capacity of virtual human) has been mostly treated as invariable in the current available human simulation tools, while indeed the physical capacity varies along time in an operation and the change of the physical capacity depends on the history of the work as well. Virtual Human Status is proposed in this paper in order to assess the difficulty of manual handling operations, especially from the physical perspective. The decrease of the physical capacity before and after an operation is used as an index to indicate the work difficulty. The reduction of physical strength is simulated in a theoretical approach on the basis of a fatigue model in which fatigue resistances of different muscle groups were regressed from 24 existing maximum endurance time (MET) models. A framework based on digital human modeling technique is established to realize the comparison of physical status. An assembly case in airplane assembly is simulated and analyzed under the framework. The endurance time and the decrease of the joint moment strengths are simulated. The experimental result in simulated operations under laboratory conditions confirms the feasibility of the theoretical approach.


Report on the XBase Project

Abstract: This project addressed the conceptual fundamentals of data storage, investigating techniques for provision of highly generic storage facilities that can be tailored to produce various individually customised storage infrastructures, compliant to the needs of particular applications. This requires the separation of mechanism and policy wherever possible. Aspirations include: actors, whether users or individual processes, should be able to bind to, update and manipulate data and programs transparently with respect to their respective locations; programs should be expressed independently of the storage and network technology involved in their execution; storage facilities should be structure-neutral so that actors can impose multiple interpretations over information, simultaneously and safely; information should not be discarded so that arbitrary historical views are supported; raw stored information should be open to all; where security restrictions on its use are required this should be achieved using cryptographic techniques. The key advances of the research were: 1) the identification of a candidate set of minimal storage system building blocks, which are sufficiently simple to avoid encapsulating policy where it cannot be customised by applications, and composable to build highly flexible storage architectures 2) insight into the nature of append-only storage components, and the issues arising from their application to common storage use-cases.


Observation-Driven Configuration of Complex Software Systems

Abstract: The ever-increasing complexity of software systems makes them hard to comprehend, predict and tune due to emergent properties and non-deterministic behaviour. Complexity arises from the size of software systems and the wide variety of possible operating environments: the increasing choice of platforms and communication policies leads to ever more complex performance characteristics. In addition, software systems exhibit different behaviour under different workloads. Many software systems are designed to be configurable so that policies can be chosen to meet the needs of various stakeholders. For complex software systems it can be difficult to accurately predict the effects of a change and to know which configuration is most appropriate. This thesis demonstrates that it is useful to run automated experiments that measure a selection of system configurations. Experiments can find configurations that meet the stakeholders' needs, find interesting behavioural characteristics, and help produce predictive models of the system's behaviour. The design and use of ACT (Automated Configuration Tool) for running such experiments is described, in combination a number of search strategies for deciding on the configurations to measure. Design Of Experiments (DOE) is discussed, with emphasis on Taguchi Methods. These statistical methods have been used extensively in manufacturing, but have not previously been used for configuring software systems. The novel contribution here is an industrial case study, applying the combination of ACT and Taguchi Methods to DC-Directory, a product from Data Connection Ltd (DCL). The case study investigated the applicability of Taguchi Methods for configuring complex software systems. Taguchi Methods were found to be useful for modelling and configuring DC- Directory, making them a valuable addition to the techniques available to system administrators and developers.


Approximate Robotic Mapping from sonar data by modeling Perceptions with Antonyms

Abstract: This work, inspired by the idea of "Computing with Words and Perceptions" proposed by Zadeh in 2001, focuses on how to transform measurements into perceptions for the problem of map building by Autonomous Mobile Robots. We propose to model the perceptions obtained from sonar-sensors as two grid maps: one for obstacles and another for empty spaces. The rules used to build and integrate these maps are expressed by linguistic descriptions and modeled by fuzzy rules. The main difference of this approach from other studies reported in the literature is that the method presented here is based on the hypothesis that the concepts "occupied" and "empty" are antonyms rather than complementary (as it happens in probabilistic approaches), or independent (as it happens in the previous fuzzy models). Controlled experimentation with a real robot in three representative indoor environments has been performed and the results presented. We offer a qualitative and quantitative comparison of the estimated maps obtained by the probabilistic approach, the previous fuzzy method and the new antonyms-based fuzzy approach. It is shown that the maps obtained with the antonyms-based approach are better defined, capture better the shape of the walls and of the empty-spaces, and contain less errors due to rebounds and short-echoes. Furthermore, in spite of noise and low resolution inherent to the sonar-sensors used, the maps obtained are accurate and tolerant to imprecision.


Online Event Segmentation in Active Perception using Adaptive Strong Anticipation

Abstract: Most cognitive architectures rely on discrete representation, both in space (e.g., objects) and in time (e.g., events). However, a robot interaction with the world is inherently continuous, both in space and in time. The segmentation of the stream of perceptual inputs a robot receives into discrete and meaningful events poses as a challenge in bridging the gap between internal cognitive representations, and the external world. Event Segmentation Theory, recently proposed in the context of cognitive systems research, sustains that humans segment time into events based on matching perceptual input with predictions. In this work we propose a framework for online event segmentation, targeting robots endowed with active perception. Moreover, sensory processing systems have an intrinsic latency, resulting from many factors such as sampling rate, and computational processing, and which is seldom accounted for. This framework is founded on the theory of dynamical systems synchronization, where the system considered includes both the robot and the world coupled (strong anticipation). An adaption rule is used to perform simultaneous system identification and synchronization, and anticipating synchronization is employed to predict the short-term system evolution. This prediction allows for an appropriate control of the robot actuation. Event boundaries are detected once synchronization is lost (sudden increase of the prediction error). An experimental proof of concept of the proposed framework is presented, together with some preliminary results corroborating the approach.


Dynamic and Transparent Analysis of Commodity Production Systems

Abstract: We propose a framework that provides a programming interface to perform complex dynamic system-level analyses of deployed production systems. By leveraging hardware support for virtualization available nowadays on all commodity machines, our framework is completely transparent to the system under analysis and it guarantees isolation of the analysis tools running on its top. Thus, the internals of the kernel of the running system needs not to be modified and the whole platform runs unaware of the framework. Moreover, errors in the analysis tools do not affect the running system and the framework. This is accomplished by installing a minimalistic virtual machine monitor and migrating the system, as it runs, into a virtual machine. In order to demonstrate the potentials of our framework we developed an interactive kernel debugger, nicknamed HyperDbg. HyperDbg can be used to debug any critical kernel component, and even to single step the execution of exception and interrupt handlers.


RoboCast: Asynchronous Communication in Robot Networks

Abstract: This paper introduces the \emph{RoboCast} communication abstraction. The RoboCast allows a swarm of non oblivious, anonymous robots that are only endowed with visibility sensors and do not share a common coordinate system, to asynchronously exchange information. We propose a generic framework that covers a large class of asynchronous communication algorithms and show how our framework can be used to implement fundamental building blocks in robot networks such as gathering or stigmergy. In more details, we propose a RoboCast algorithm that allows robots to broadcast their local coordinate systems to each others. Our algorithm is further refined with a local collision avoidance scheme. Then, using the RoboCast primitive, we propose algorithms for deterministic asynchronous gathering and binary information exchange.


Testing SDRT's Right Frontier

Abstract: The Right Frontier Constraint (RFC), as a constraint on the attachment of new constituents to an existing discourse structure, has important implications for the interpretation of anaphoric elements in discourse and for Machine Learning (ML) approaches to learning discourse structures. In this paper we provide strong empirical support for SDRT's version of RFC. The analysis of about 100 doubly annotated documents by five different naive annotators shows that SDRT's RFC is respected about 95% of the time. The qualitative analysis of presumed violations that we have performed shows that they are either click-errors or structural misconceptions.


Computational complexity of reconstruction and isomorphism testing for designs and line graphs

Abstract: Graphs with high symmetry or regularity are the main source for experimentally hard instances of the notoriously difficult graph isomorphism problem. In this paper, we study the computational complexity of isomorphism testing for line graphs of $t$-$(v,k,\lambda)$ designs. For this class of highly regular graphs, we obtain a worst-case running time of $O(v^{\log v + O(1)})$ for bounded parameters $t,k,\lambda$. In a first step, our approach makes use of the Babai--Luks algorithm to compute canonical forms of $t$-designs. In a second step, we show that $t$-designs can be reconstructed from their line graphs in polynomial-time. The first is algebraic in nature, the second purely combinatorial. For both, profound structural knowledge in design theory is required. Our results extend earlier complexity results about isomorphism testing of graphs generated from Steiner triple systems and block designs.


Counterexample Guided Abstraction Refinement Algorithm for Propositional Circumscription

Abstract: Circumscription is a representative example of a nonmonotonic reasoning inference technique. Circumscription has often been studied for first order theories, but its propositional version has also been the subject of extensive research, having been shown equivalent to extended closed world assumption (ECWA). Moreover, entailment in propositional circumscription is a well-known example of a decision problem in the second level of the polynomial hierarchy. This paper proposes a new Boolean Satisfiability (SAT)-based algorithm for entailment in propositional circumscription that explores the relationship of propositional circumscription to minimal models. The new algorithm is inspired by ideas commonly used in SAT-based model checking, namely counterexample guided abstraction refinement. In addition, the new algorithm is refined to compute the theory closure for generalized close world assumption (GCWA). Experimental results show that the new algorithm can solve problem instances that other solutions are unable to solve.


Performance Comparison of SVM and ANN for Handwritten Devnagari Character Recognition

Abstract: Classification methods based on learning from examples have been widely applied to character recognition from the 1990s and have brought forth significant improvements of recognition accuracies. This class of methods includes statistical methods, artificial neural networks, support vector machines (SVM), multiple classifier combination, etc. In this paper, we discuss the characteristics of the some classification methods that have been successfully applied to handwritten Devnagari character recognition and results of SVM and ANNs classification method, applied on Handwritten Devnagari characters. After preprocessing the character image, we extracted shadow features, chain code histogram features, view based features and longest run features. These features are then fed to Neural classifier and in support vector machine for classification. In neural classifier, we explored three ways of combining decisions of four MLP's designed for four different features.


A Saturation Method for the Modal Mu-Calculus with Backwards Modalities over Pushdown Systems

Abstract: We present an extension of an algorithm for computing directly the denotation of a mu-calculus formula X over the configuration graph of a pushdown system to allow backwards modalities. Our method gives the first extension of the saturation technique to the full mu-calculus with backwards modalities.


Recognition of Non-Compound Handwritten Devnagari Characters using a Combination of MLP and Minimum Edit Distance

Abstract: This paper deals with a new method for recognition of offline Handwritten non-compound Devnagari Characters in two stages. It uses two well known and established pattern recognition techniques: one using neural networks and the other one using minimum edit distance. Each of these techniques is applied on different sets of characters for recognition. In the first stage, two sets of features are computed and two classifiers are applied to get higher recognition accuracy. Two MLP's are used separately to recognize the characters. For one of the MLP's the characters are represented with their shadow features and for the other chain code histogram feature is used. The decision of both MLP's is combined using weighted majority scheme. Top three results produced by combined MLP's in the first stage are used to calculate the relative difference values. In the second stage, based on these relative differences character set is divided into two. First set consists of the characters with distinct shapes and second set consists of confused characters, which appear very similar in shapes. Characters of distinct shapes of first set are classified using MLP. Confused characters in second set are classified using minimum edit distance method. Method of minimum edit distance makes use of corner detected in a character image using modified Harris corner detection technique. Experiment on this method is carried out on a database of 7154 samples. The overall recognition is found to be 90.74%.


Application of Statistical Features in Handwritten Devnagari Character Recognition

Abstract: In this paper a scheme for offline Handwritten Devnagari Character Recognition is proposed, which uses different feature extraction methodologies and recognition algorithms. The proposed system assumes no constraints in writing style or size. First the character is preprocessed and features namely : Chain code histogram and moment invariant features are extracted and fed to Multilayer Perceptrons as a preliminary recognition step. Finally the results of both MLP's are combined using weighted majority scheme. The proposed system is tested on 1500 handwritten devnagari character database collected from different people. It is observed that the proposed system achieves recognition rates 98.03% for top 5 results and 89.46% for top 1 result.


Multiple Classifier Combination for Off-line Handwritten Devnagari Character Recognition

Abstract: This work presents the application of weighted majority voting technique for combination of classification decision obtained from three Multi_Layer Perceptron(MLP) based classifiers for Recognition of Handwritten Devnagari characters using three different feature sets. The features used are intersection, shadow feature and chain code histogram features. Shadow features are computed globally for character image while intersection features and chain code histogram features are computed by dividing the character image into different segments. On experimentation with a dataset of 4900 samples the overall recognition rate observed is 92.16% as we considered top five choices results. This method is compared with other recent methods for Handwritten Devnagari Character Recognition and it has been observed that this approach has better success rate than other methods.


A Two Stage Classification Approach for Handwritten Devanagari Characters

Abstract: The paper presents a two stage classification approach for handwritten devanagari characters The first stage is using structural properties like shirorekha, spine in character and second stage exploits some intersection features of characters which are fed to a feedforward neural network. Simple histogram based method does not work for finding shirorekha, vertical bar (Spine) in handwritten devnagari characters. So we designed a differential distance based technique to find a near straight line for shirorekha and spine. This approach has been tested for 50000 samples and we got 89.12% success


Using Repeating Decimals As An Alternative To Prime Numbers In Encryption

Abstract: This article is meant to provide an additional point of view, applying known knowledge, to supply keys that have a series of non-repeating digits, in a manner that is not usually thought of. Traditionally, prime numbers are used in encryption as keys that have non-repeating sequences. Non-repetition of digits in a key is very sought after in encryption. Uniqueness in a digit sequence defeats decryption by method. In searching for methods of non-decryptable encryption as well as ways to provide unique sequences, other than using prime numbers, the idea of using repeating decimals came to me. Applied correctly, a repeating decimal series of sufficient length will stand in as well for a prime number. This is so, because only numbers prime to each other will produce repeating decimals and; within the repeating sequence there is uniqueness of digit sequence.


A novel approach for handwritten Devnagari character recognition

Abstract: In this paper a method for recognition of handwritten devanagari characters is described. Here, feature vector is constituted by accumulated directional gradient changes in different segments, number of intersections points for the character, type of spine present and type of shirorekha present in the character. One Multi-layer Perceptron with conjugate-gradient training is used to classify these feature vectors. This method is applied to a database with 1000 sample characters and the recognition rate obtained is 88.12%


Classification Of Gradient Change Features Using MLP For Handwritten Character Recognition

Abstract: A novel, generic scheme for off-line handwritten English alphabets character images is proposed. The advantage of the technique is that it can be applied in a generic manner to different applications and is expected to perform better in uncertain and noisy environments. The recognition scheme is using a multilayer perceptron(MLP) neural networks. The system was trained and tested on a database of 300 samples of handwritten characters. For improved generalization and to avoid overtraining, the whole available dataset has been divided into two subsets: training set and test set. We achieved 99.10% and 94.15% correct recognition rates on training and test sets respectively. The purposed scheme is robust with respect to various writing styles and size as well as presence of considerable noise.


An Information Flow Architecture for Global Smart Spaces

Abstract: In this paper we describe an architecture which: Permits the deployment and execution of components in appropriate geographical locations. Provides security mechanisms that prevent misuse of the architecture. Supports a programming model that is familiar to application programmers. Permits installed components to share data. Permits the deployed components to communicate via communication channels. Provides evolution mechanisms permitting the dynamic rearrangement of inter-connection topologies the components that they connect. Supports the specification and deployment of distributed component deployments.


Second Set of Spaces

Abstract: This document describes the Gloss infrastructure supporting implementation of location-aware services. The document is in two parts. The first part describes software architecture for the smart space. As described in D8, a local architecture provides a framework for constructing Gloss applications, termed assemblies, that run on individual physical nodes, whereas a global architecture defines an overlay network for linking individual assemblies. The second part outlines the hardware installation for local sensing. This describes the first phase of the installation in Strathclyde University.


FPGA Based Assembling of Facial Components for Human Face Construction

Abstract: This paper aims at VLSI realization for generation of a new face from textual description. The FASY (FAce SYnthesis) System is a Face Database Retrieval and new Face generation System that is under development. One of its main features is the generation of the requested face when it is not found in the existing database. The new face generation system works in three steps - searching phase, assembling phase and tuning phase. In this paper the tuning phase using hardware description language and its implementation in a Field Programmable Gate Array (FPGA) device is presented.


Fuzzy Classification of Facial Component Parameters

Abstract: This paper presents a novel type-2 Fuzzy logic System to define the Shape of a facial component with the crisp output. This work is the part of our main research effort to design a system (called FASY) which offers a novel face construction approach based on the textual description and also extracts and analyzes the facial components from a face image by an efficient technique. The Fuzzy model, designed in this paper, takes crisp value of width and height of a facial component and produces the crisp value of Shape for different facial components. This method is designed using Matlab 6.5 and Visual Basic 6.0 and tested with the facial components extracted from 200 male and female face images of different ages from different face databases.


Cloud Scheduler: a resource manager for distributed compute clouds

Abstract: The availability of Infrastructure-as-a-Service (IaaS) computing clouds gives researchers access to a large set of new resources for running complex scientific applications. However, exploiting cloud resources for large numbers of jobs requires significant effort and expertise. In order to make it simple and transparent for researchers to deploy their applications, we have developed a virtual machine resource manager (Cloud Scheduler) for distributed compute clouds. Cloud Scheduler boots and manages the user-customized virtual machines in response to a user's job submission. We describe the motivation and design of the Cloud Scheduler and present results on its use on both science and commercial clouds.


Comments on Five Smart Card Based Password Authentication Protocols

Abstract: In this paper, we use the ten security requirements proposed by Liao et al. for a smart card based authentication protocol to examine five recent work in this area. After analyses, we found that the protocols of Juang et al.'s , Hsiang et al.'s, Kim et al.'s, and Li et al.'s all suffer from offline password guessing attack if the smart card is lost, and the protocol of Xu et al.'s is subjected to an insider impersonation attack.


Cryptanalysis on Four Two-Party Authentication Protocols

Abstract: In this paper, we analyze four authentication protocols of Bindu et al., Goriparthi et al., Wang et al. and H\"olbl et al.. After investigation, we reveal several weaknesses of these schemes. First, Bindu et al.'s protocol suffers from an insider impersonation attack if a malicious user obtains a lost smart card. Second, both Goriparthi et al.'s and Wang et al.'s protocols cannot withstand a DoS attack in the password change phase, i.e. an attacker can involve the phase to make user's password never be used in subsequent authentications. Third, H\"olbl et al.'s protocol is vulnerable to an insider attack since a legal but malevolent user can deduce KGC's secret key.


A Taxonomy and Survey of Energy-Efficient Data Centers and Cloud Computing Systems

Abstract: Traditionally, the development of computing systems has been focused on performance improvements driven by the demand of applications from consumer, scientific and business domains. However, the ever increasing energy consumption of computing systems has started to limit further performance growth due to overwhelming electricity bills and carbon dioxide footprints. Therefore, the goal of the computer system design has been shifted to power and energy efficiency. To identify open challenges in the area and facilitate future advancements it is essential to synthesize and classify the research on power and energy-efficient design conducted to date. In this work we discuss causes and problems of high power / energy consumption, and present a taxonomy of energy-efficient design of computing systems covering the hardware, operating system, virtualization and data center levels. We survey various key works in the area and map them to our taxonomy to guide future design and development efforts. This chapter is concluded with a discussion of advancements identified in energy-efficient computing and our vision on future research directions.


Survey of Nearest Neighbor Techniques

Abstract: The nearest neighbor (NN) technique is very simple, highly efficient and effective in the field of pattern recognition, text categorization, object recognition etc. Its simplicity is its main advantage, but the disadvantages can't be ignored even. The memory requirement and computation complexity also matter. Many techniques are developed to overcome these limitations. NN techniques are broadly classified into structure less and structure based techniques. In this paper, we present the survey of such techniques. Weighted kNN, Model based kNN, Condensed NN, Reduced NN, Generalized NN are structure less techniques whereas k-d tree, ball tree, Principal Axis Tree, Nearest Feature Line, Tunable NN, Orthogonal Search Tree are structure based algorithms developed on the basis of kNN. The structure less method overcome memory limitation and structure based techniques reduce the computational complexity.


A Novel Approach towards Cost Effective Region-Based Group Key Agreement Protocol for Secure Group Communication

Abstract: This paper addresses an interesting security problem in wireless ad hoc networks: the Dynamic Group Key Agreement key establishment. For secure group communication in an Ad hoc network, a group key shared by all group members is required. This group key should be updated when there are membership changes (when the new member joins or current member leaves) in the group. In this paper, We propose a novel, secure, scalable and efficient Region-Based Group Key Agreement protocol (RBGKA) for ad-hoc networks. This is implemented by a two-level structure and a new scheme of group key update. The idea is to divide the group into subgroups, each maintaining its subgroup keys using Group Diffie-Hellman (GDH) Protocol and links with other subgroups in a Tree structure using Tree-based Group Diffie-Hellman (TGDH) protocol. By introducing region-based approach, messages and key updates will be limited within subgroup and outer group; hence computation load is distributed among many hosts. Both theoretical analysis and experimental results show that this Region-based key agreement protocol performs better for the key establishment problem in ad-hoc network in terms of memory cost, computation cost and communication cost.


First Smart Spaces

Abstract: This document describes the Gloss software currently implemented. The description of the Gloss demonstrator for multi-surface interaction can be found in D17. The ongoing integration activity for the work described in D17 and D8 constitutes our development of infrastructure for a first smart space. In this report, the focus is on infrastructure to support the implementation of location aware services. A local architecture provides a framework for constructing Gloss applications, termed assemblies, that run on individual physical nodes. A global architecture defines an overlay network for linking individual assemblies. Both local and global architectures are under active development.


Channel Assortment Strategy for Reliable Communication in Multi-Hop Cognitive Radio Networks

Abstract: In this paper, we propose a channel assortment strategy for Reliable Communication in Multi-Hop Cognitive Radio Networks.


Acyclic Solos and Differential Interaction Nets

Abstract: We present a restriction of the solos calculus which is stable under reduction and expressive enough to contain an encoding of the pi-calculus. As a consequence, it is shown that equalizing names that are already equal is not required by the encoding of the pi-calculus. In particular, the induced solo diagrams bear an acyclicity property that induces a faithful encoding into differential interaction nets. This gives a (new) proof that differential interaction nets are expressive enough to contain an encoding of the pi-calculus. All this is worked out in the case of finitary (replication free) systems without sum, match nor mismatch.


Adaptive and occupancy-based channel selection for unreliable cognitive radio networks

Abstract: In this paper, we propose an adaptive and occupancy-based channel selection for unreliable cognitive radio networks.


A Cognitive Radio Based Internet Access Framework for Disaster Response Network Deployment

Abstract: In this paper, we propose a cognitive radio based Internet access framework for disaster response network deployment in challenged environments. The proposed architectural framework is designed to help the existent but partially damaged networks to restore their connectivity and to connect them to the global Internet. This architectural framework provides the basis to develop algorithms and protocols for the future cognitive radio network deployments in challenged environments.


Toward Reliable Contention-aware Data Dissemination in Multi-hop Cognitive Radio Ad Hoc Networks

Abstract: This paper introduces a new channel selection strategy for reliable contentionaware data dissemination in multi-hop cognitive radio network. The key challenge here is to select channels providing a good tradeoff between connectivity and contention. In other words, channels with good opportunities for communication due to (1) low primary radio nodes (PRs) activities, and (2) limited contention of cognitive ratio nodes (CRs) acceding that channel, have to be selected. Thus, by dynamically exploring residual resources on channels and by monitoring the number of CRs on a particular channel, SURF allows building a connected network with limited contention where reliable communication can take place. Through simulations, we study the performance of SURF when compared with three other related approaches. Simulation results confirm that our approach is effective in selecting the best channels for efficient and reliable multi-hop data dissemination.


Detecting Inconsistencies in Large Biological Networks with Answer Set Programming

Abstract: We introduce an approach to detecting inconsistencies in large biological networks by using Answer Set Programming (ASP). To this end, we build upon a recently proposed notion of consistency between biochemical/genetic reactions and high-throughput profiles of cell activity. We then present an approach based on ASP to check the consistency of large-scale data sets. Moreover, we extend this methodology to provide explanations for inconsistencies by determining minimal representations of conflicts. In practice, this can be used to identify unreliable data or to indicate missing reactions.


From matrix interpretations over the rationals to matrix interpretations over the naturals

Abstract: Matrix interpretations generalize linear polynomial interpretations and have been proved useful in the implementation of tools for automatically proving termination of Term Rewriting Systems. In view of the successful use of rational coefficients in polynomial interpretations, we have recently generalized traditional matrix interpretations (using natural numbers in the matrix entries) to incorporate real numbers. However, existing results which formally prove that polynomials over the reals are more powerful than polynomials over the naturals for proving termination of rewrite systems failed to be extended to matrix interpretations. In this paper we get deeper into this problem. We show that, under some conditions, it is possible to transform a matrix interpretation over the rationals satisfying a set of symbolic constraints into a matrix interpretation over the naturals (using bigger matrices) which still satisfies the constraints.


Empowering Collections with Swarm Behavior

Abstract: Often, when modelling a system there are properties and operations that are related to a group of objects rather than to a single object. In this paper we extend Java with Swarm Behavior, a new composition operator that associates behavior with a collection of instances. The lookup resolution of swarm behavior is based on the element type of a collection and is thus orthogonal to the collection hierarchy.


A Bound on the Sum of Weighted Pairwise Distances of Points Constrained to Balls

Abstract: We consider the problem of choosing Euclidean points to maximize the sum of their weighted pairwise distances, when each point is constrained to a ball centered at the origin. We derive a dual minimization problem and show strong duality holds (i.e., the resulting upper bound is tight) when some locally optimal configuration of points is affinely independent. We sketch a polynomial time algorithm for finding a near-optimal set of points.


Applying l-Diversity in anonymizing collaborative social network

Abstract: To date publish of a giant social network jointly from different parties is an easier collaborative approach. Agencies and researchers who collect such social network data often have a compelling interest in allowing others to analyze the data. In many cases the data describes relationships that are private and sharing the data in full can result in unacceptable disclosures. Thus, preserving privacy without revealing sensitive information in the social network is a serious concern. Recent developments for preserving privacy using anonymization techniques are focused on relational data only. Preserving privacy in social networks against neighborhood attacks is an initiation which uses the definition of privacy called k-anonymity. k-anonymous social network still may leak privacy under the cases of homogeneity and background knowledge attacks. To overcome, we find a place to use a new practical and efficient definition of privacy called ldiversity. In this paper, we take a step further on preserving privacy in collaborative social network data with algorithms and analyze the effect on the utility of the data for social network analysis.


Certification Authority Monitored Multilevel and Stateful Policy Based Authorization in Services Oriented Grids

Abstract: Services oriented grids will be more prominent among other kinds of grids in the present distributed environments. With the advent of online government services the governmental grids will come up in huge numbers. Apart from common security issues as in other grids, the authorization in service oriented grids faces certain shortcomings and needs to be looked upon differently. The CMMS model presented here overcomes all these shortcomings and adds to the simplicity of implementation because of its tight similarities with certain government services and their functioning. The model is used to prototype a State Police Information Grid (SPIG). Small technological restructuring is required in PKIX and X.509 certificates.


The Application of AHP Model to Guide Decision Makers: A Case Study of E-banking Security

Abstract: Changes in technology have resulted in new ways for bankers to deliver their services to costumers. Electronic banking systems in various forms are the evidence of such advancement. However, information security threats also evolving along this trend. This paper proposes the application of Analytic Hierarchy Process (AHP) methodology to guide decision makers in banking industries to deal with information security policy. The model is structured according aspects of information security policy in conjunction with information security elements. We found that cultural aspect is valued on the top priority among other security aspects, while confidentiality is considered as the most important factor in terms of information security elements.


Repairing People Trajectories Based on Point Clustering

Abstract: This paper presents a method for improving any object tracking algorithm based on machine learning. During the training phase, important trajectory features are extracted which are then used to calculate a confidence value of trajectory. The positions at which objects are usually lost and found are clustered in order to construct the set of 'lost zones' and 'found zones' in the scene. Using these zones, we construct a triplet set of zones i.e. three zones: In/Out zone (zone where an object can enter or exit the scene), 'lost zone' and 'found zone'. Thanks to these triplets, during the testing phase, we can repair the erroneous trajectories according to which triplet they are most likely to belong to. The advantage of our approach over the existing state of the art approaches is that (i) this method does not depend on a predefined contextual scene, (ii) we exploit the semantic of the scene and (iii) we have proposed a method to filter out noisy trajectories based on their confidence value.


Autonomic Management in a Distributed Storage System

Abstract: This thesis investigates the application of autonomic management to a distributed storage system. Effects on performance and resource consumption were measured in experiments, which were carried out in a local area test-bed. The experiments were conducted with components of one specific distributed storage system, but seek to be applicable to a wide range of such systems, in particular those exposed to varying conditions. The perceived characteristics of distributed storage systems depend on their configuration parameters and on various dynamic conditions. For a given set of conditions, one specific configuration may be better than another with respect to measures such as resource consumption and performance. Here, configuration parameter values were set dynamically and the results compared with a static configuration. It was hypothesised that under non-changing conditions this would allow the system to converge on a configuration that was more suitable than any that could be set a priori. Furthermore, the system could react to a change in conditions by adopting a more appropriate configuration. Autonomic management was applied to the peer-to-peer (P2P) and data retrieval components of ASA, a distributed storage system. The effects were measured experimentally for various workload and churn patterns. The management policies and mechanisms were implemented using a generic autonomic management framework developed during this work. The experimental evaluations of autonomic management show promising results, and suggest several future research topics. The findings of this thesis could be exploited in building other distributed storage systems that focus on harnessing storage on user workstations, since these are particularly likely to be exposed to varying, unpredictable conditions.


Randomized Rounding for Routing and Covering Problems: Experiments and Improvements

Abstract: Following previous theoretical work by Srinivasan (FOCS 2001) and the first author (STACS 2006) and a first experimental evaluation on random instances (ALENEX 2009), we investigate how the recently developed different approaches to generate randomized roundings satisfying disjoint cardinality constraints behave when used in two classical algorithmic problems, namely low-congestion routing in networks and max-coverage problems in hypergraphs. We generally find that all randomized rounding algorithms work well, much better than what is guaranteed by existing theoretical work. The derandomized versions produce again significantly better rounding errors, with running times still negligible compared to the one for solving the corresponding LP. It thus seems worth preferring them over the randomized variants. The data created in these experiments lets us propose and investigate the following new ideas. For the low-congestion routing problems, we suggest to solve a second LP, which yields the same congestion, but aims at producing a solution that is easier to round. Experiments show that this reduces the rounding errors considerably, both in combination with randomized and derandomized rounding. For the max-coverage instances, we generally observe that the greedy heuristics also performs very good. We develop a strengthened method of derandomized rounding, and a simple greedy/rounding hybrid approach using greedy and LP-based rounding elements, and observe that both these improvements yield again better solutions than both earlier approaches on their own. For unit disk max-domination, we also develop a PTAS. Contrary to all other algorithms investigated, it performs not much better in experiments than in theory; thus, unless extremely good solutions are to be obtained with huge computational resources, greedy, LP-based rounding or hybrid approaches are preferable.


The Transfer of Evolved Artificial Immune System Behaviours between Small and Large Scale Robotic Platforms

Abstract: This paper demonstrates that a set of behaviours evolved in simulation on a miniature robot (epuck) can be transferred to a much larger scale platform (a virtual Pioneer P3-DX) that also differs in shape, sensor type, sensor configuration and programming interface. The chosen architecture uses a reinforcement learning-assisted genetic algorithm to evolve the epuck behaviours, which are encoded as a genetic sequence. This sequence is then used by the Pioneers as part of an adaptive, idiotypic artificial immune system (AIS) control architecture. Testing in three different simulated worlds shows that the Pioneer can use these behaviours to navigate and solve object-tracking tasks successfully, as long as its adaptive AIS mechanism is in place.


Additive Non-negative Matrix Factorization for Missing Data

Abstract: Non-negative matrix factorization (NMF) has previously been shown to be a useful decomposition for multivariate data. We interpret the factorization in a new way and use it to generate missing attributes from test data. We provide a joint optimization scheme for the missing attributes as well as the NMF factors. We prove the monotonic convergence of our algorithms. We present classification results for cases with missing attributes.


Parallel Chip Firing Game associated with n-cube orientations

Abstract: We study the cycles generated by the chip firing game associated with n-cube orientations. We show the existence of the cycles generated by parallel evolutions of even lengths from 2 to $2^n$ on $H_n$ (n >= 1), and of odd lengths different from 3 and ranging from 1 to $2^{n-1}-1$ on $H_n$ (n >= 4).


Approximate Counting for Complex-Weighted Boolean Constraint Satisfaction Problems

Abstract: Constraint satisfaction problems (or CSPs) have been extensively studied in, for instance, artificial intelligence, database theory, graph theory, and statistical physics. From a practical viewpoint, it is beneficial to approximately solve those CSPs. When one tries to approximate the total number of truth assignments that satisfy all Boolean-valued constraints for (unweighted) Boolean CSPs, there is a known trichotomy theorem by which all such counting problems are neatly classified into exactly three categories under polynomial-time (randomized) approximation-preserving reductions. In contrast, we obtain a dichotomy theorem of approximate counting for complex-weighted Boolean CSPs, provided that all complex-valued unary constraints are freely available to use. It is the expressive power of free unary constraints that enables us to prove such a stronger, complete classification theorem. This discovery makes a step forward in the quest for the approximation-complexity classification of all counting CSPs. To deal with complex weights, we employ proof techniques of factorization and arity reduction along the line of solving Holant problems. Moreover, we introduce a novel notion of T-constructibility that naturally induces approximation-preserving reducibility. Our result also gives an approximation analogue of the dichotomy theorem on the complexity of exact counting for complex-weighted Boolean CSPs.


Privacy in geo-social networks: proximity notification with untrusted service providers and curious buddies

Abstract: A major feature of the emerging geo-social networks is the ability to notify a user when one of his friends (also called buddies) happens to be geographically in proximity with the user. This proximity service is usually offered by the network itself or by a third party service provider (SP) using location data acquired from the users. This paper provides a rigorous theoretical and experimental analysis of the existing solutions for the location privacy problem in proximity services. This is a serious problem for users who do not trust the SP to handle their location data, and would only like to release their location information in a generalized form to participating buddies. The paper presents two new protocols providing complete privacy with respect to the SP, and controllable privacy with respect to the buddies. The analytical and experimental analysis of the protocols takes into account privacy, service precision, and computation and communication costs, showing the superiority of the new protocols compared to those appeared in the literature to date. The proposed protocols have also been tested in a full system implementation of the proximity service.


A Performance Comparison of Stability, Load-Balancing and Power-Aware Routing Protocols for Mobile Ad Hoc Networks

Abstract: The high-level contribution of this paper is a simulation-based detailed performance comparison of three different classes of routing protocols for mobile ad hoc networks: stability-based routing, power-aware routing and load-balanced routing. We choose the Flow-Oriented Routing protocol (FORP), the traffic interference based Load Balancing Routing (LBR) protocol and Min-Max Battery Cost Routing (MMBCR) as representatives of the stability-based routing, load-balancing and power-aware routing protocols respectively. Among the three routing protocols, FORP incurs the least number of route transitions; while LBR incurs the smallest hop count and lowest end-to-end delay per data packet. Energy consumed per node is the least for MMBCR, closely followed by LBR. MMBCR is the most fair in terms of node usage and hence it incurs the largest time for first node failure. FORP tends to repeatedly use nodes lying on the stable path and hence is the most unfair of the three routing protocols and it incurs the smallest value for the time of first node failure. As we measure the failure times of up to the first five nodes in the network, we observe that LBR incurs the maximum improvement in the lifetime of the nodes and MMBCR incurs the least improvement beyond the time of first node failure.


Impact Of Mobility and Transmission Range On The Performance of Backoff Algorithms For IEEE 802.11-Based Multi-hop Mobile Ad hoc Networks

Abstract: In IEEE 802.11 Wireless Local Area Networks (WLANs), network nodes experiencing collisions on the shared channel need to backoff for a random period of time, which is uniformly selected from the Contention Window (CW). This contention window is dynamically controlled by the Backoff algorithm. First step to design a an efficient backoff algorithm for multi-hop ad hoc network is to analysis of the existing backoff algorithms in multi-hop ad hoc networks. Thus, in this paper, we considered two important multi-hop adhoc network scenarios: (a) Node Mobility Scenario and (b) Transmission Range Scenario and analyze and evaluate both the impact of mobility (i.e. node speed) and the impact of transmission range of nodes on the performance of various backoff algorithms


Role of Statistical tests in Estimation of the Security of a New Encryption Algorithm

Abstract: Encryption study basically deals with three levels of algorithms. The first algorithm deals with encryption mechanism, second deals with decryption Mechanism and the third discusses about the generation of keys and sub keys used in the encryption study. In the given study, a new algorithm is discussed. The algorithm executes a series of steps and generates a sequence. This sequence is being used as sub key to be mapped to plain text to generate cipher text. The strength of the encryption & Decryption process depends on the strength of sequence generated against crypto analysis.. In this part of work some statistical tests like Uniformity tests, Universal tests & Repetition tests are tried on the sequence generated to test the strength of it.


Improving Iris Recognition Accuracy By Score Based Fusion Method

Abstract: Iris recognition technology, used to identify individuals by photographing the iris of their eye, has become popular in security applications because of its ease of use, accuracy, and safety in controlling access to high-security areas. Fusion of multiple algorithms for biometric verification performance improvement has received considerable attention. The proposed method combines the zero-crossing 1 D wavelet Euler number, and genetic algorithm based for feature extraction. The output from these three algorithms is normalized and their score are fused to decide whether the user is genuine or imposter. This new strategies is discussed in this paper, in order to compute a multimodal combined score.


An Ant Based Framework for Preventing DDoS Attack in Wireless Sensor Networks

Abstract: Security and Privacy are two important parameters that need to be considered when dealing with wireless sensor networks as WSN operate in an unattended environment and carry sensitive information critical to the application. However, applying security techniques that consume minimum resources is still a challenge and this paper makes an attempt to address the same. One of the major attacks in sensor network is denial of service attack that not only diminishes the network capacity but also affects the reliability of information being transmitted. This work is an extension of our previous work which could successfully detect DDoS using ants. However, no emphasis was made towards the prevention mechanism. In this paper an ant-based framework that exploits the significance of stateless and stateful signatures and hence preserving the legtimate packets only, thereby discarding the contaminated packets has been proposed.


Delta Learning Rule for the Active Sites Model

Abstract: This paper reports the results on methods of comparing the memory retrieval capacity of the Hebbian neural network which implements the B-Matrix approach, by using the Widrow-Hoff rule of learning. We then, extend the recently proposed Active Sites model by developing a delta rule to increase memory capacity. Also, this paper extends the binary neural network to a multi-level (non-binary) neural network.


Notes on higher-dimensional tarai functions

Abstract: We proved that for every $n\geq 3$, the $n$-dimensional tarai function terminates with call-by-need. It was also shown that the closed form for the function suggested by T. Bailey and J. Cowles is correct.


Query Strategies for Evading Convex-Inducing Classifiers

Abstract: Classifiers are often used to detect miscreant activities. We study how an adversary can systematically query a classifier to elicit information that allows the adversary to evade detection while incurring a near-minimal cost of modifying their intended malfeasance. We generalize the theory of Lowd and Meek (2005) to the family of convex-inducing classifiers that partition input space into two sets one of which is convex. We present query algorithms for this family that construct undetected instances of approximately minimal cost using only polynomially-many queries in the dimension of the space and in the level of approximation. Our results demonstrate that near-optimal evasion can be accomplished without reverse-engineering the classifier's decision boundary. We also consider general lp costs and show that near-optimal evasion on the family of convex-inducing classifiers is generally efficient for both positive and negative convexity for all levels of approximation if p=1.


An Improved Neighbourhood for the Traveling Tournament Problem

Abstract: The Traveling Tournament Problem (TTP) is a challenging combinatorial optimization problem that has attracted the interest of researchers around the world. This paper proposes an improved search neighbourhood for the TTP that has been tested in a simulated annealing context. The neighbourhood encompasses both feasible and infeasible schedules, and can be generated efficiently. For the largest TTP challenge problems with up to 40 teams, solutions found using this neighbourhood are the best currently known, and for smaller problems with 10 teams, three solutions found were subsequently proven optimal.


Femto-Macro Cellular Interference Control with Subband Scheduling and Interference Cancelation

Abstract: A significant technical challenge in deploying femtocells is controlling the interference from the underlay of femtos onto the overlay of macros. This paper presents a novel interference control method where the macrocell bandwidth is partitioned into subbands, and the short-range femtocell links adaptively allocate their power across the subbands based on a load-spillage power control method. The scheme can improve rate distribution in the macro network while also providing opportunities for short-range communication as well. Moreover, the proposed scheme requires minimal interference coordination communication between the femtos and macros, which is one of the main challenges in femtocell systems. Also, simulations show certain advantages over simpler orthogonalization schemes or power control schemes without subband partitioning. Further modest gains may also be possible with interference cancelation.


Simulation-Checking of Real-Time Systems with Fairness Assumptions

Abstract: We investigate the simulation problem in of dense-time system. A specification simulates a model if the specification can match every transition that the model can make at a time point. We also adapt the approach of Emerson and Lei and allow for multiple strong and weak fairness assumptions in checking the simulation relation. Furthermore, we allow for fairness assumptions specified as either state-predicates or event-predicates. We focus on a subclass of the problem with at most one fairness assumption for the specification. We then present a simulation-checking algorithm for this subclass. We propose simulation of a model by a specification against a common environment. We present efficient techniques for such simulations to take the common environment into consideration. Our experiment shows that such a consideration can dramatically improve the efficiency of checking simulation. We also report the performance of our algorithm in checking the liveness properties with fairness assumptions.


Limits of responsiveness concerning human-readable knowledge bases: an operational analysis

Abstract: Introduction. The purpose of this work is the evaluation of responsiveness when remote users communicate with a human-readable knowledge base (KB). Responsiveness [R(s)] is considered here as a measure of service quality. Method. The preferred method is operational analysis, a variation of classical stochastic theory, which allows for the study of user-system interaction with minimal computational effort. Analysis. The analysis is based on well-known performance metrics, such as service ability, elapsed time, and throughput: from these metrics estimates of R(s) are derived analytically. Results. Critical points indicating congestion are obtained: these are limits on the number of admissible requests and the number of connected users. Also obtained is a sufficient condition for achieving flow balance between the KB host and the request-relaying servers. Conclusions. When R(s) is within normal limits, users should appreciate the benefits from using the services offered by their KB host. When bottlenecks are formed, R(s) declines, and the whole communication system heads for saturation. Flow balancing procedures are necessary for the elimination of bottlenecks, which leads to a better resource management.


A Fast Decision Technique for Hierarchical Hough Transform for Line Detection

Abstract: Many techniques have been proposed to speedup the performance of classic Hough Transform. These techniques are primarily based on converting the voting procedure to a hierarchy based voting method. These methods use approximate decision-making process. In this paper, we propose a fast decision making process that enhances the speed and reduces the space requirements. Experimental results demonstrate that the proposed algorithm is much faster than a similar Fast Hough Transform.


A Reinforcement Learning Model Using Neural Networks for Music Sight Reading Learning Problem

Abstract: Music Sight Reading is a complex process in which when it is occurred in the brain some learning attributes would be emerged. Besides giving a model based on actor-critic method in the Reinforcement Learning, the agent is considered to have a neural network structure. We studied on where the sight reading process is happened and also a serious problem which is how the synaptic weights would be adjusted through the learning process. The model we offer here is a computational model on which an updated weights equation to fix the weights is accompanied too.


On The Complexity and Completeness of Static Constraints for Breaking Row and Column Symmetry

Abstract: We consider a common type of symmetry where we have a matrix of decision variables with interchangeable rows and columns. A simple and efficient method to deal with such row and column symmetry is to post symmetry breaking constraints like DOUBLELEX and SNAKELEX. We provide a number of positive and negative results on posting such symmetry breaking constraints. On the positive side, we prove that we can compute in polynomial time a unique representative of an equivalence class in a matrix model with row and column symmetry if the number of rows (or of columns) is bounded and in a number of other special cases. On the negative side, we show that whilst DOUBLELEX and SNAKELEX are often effective in practice, they can leave a large number of symmetric solutions in the worst case. In addition, we prove that propagating DOUBLELEX completely is NP-hard. Finally we consider how to break row, column and value symmetry, correcting a result in the literature about the safeness of combining different symmetry breaking constraints. We end with the first experimental study on how much symmetry is left by DOUBLELEX and SNAKELEX on some benchmark problems.


Decomposition of the NVALUE constraint

Abstract: We study decompositions of the global NVALUE constraint. Our main contribution is theoretical: we show that there are propagators for global constraints like NVALUE which decomposition can simulate with the same time complexity but with a much greater space complexity. This suggests that the benefit of a global propagator may often not be in saving time but in saving space. Our other theoretical contribution is to show for the first time that range consistency can be enforced on NVALUE with the same worst-case time complexity as bound consistency. Finally, the decompositions we study are readily encoded as linear inequalities. We are therefore able to use them in integer linear programs.


Symmetry within and between solutions

Abstract: Symmetry can be used to help solve many problems. For instance, Einstein's famous 1905 paper ("On the Electrodynamics of Moving Bodies") uses symmetry to help derive the laws of special relativity. In artificial intelligence, symmetry has played an important role in both problem representation and reasoning. I describe recent work on using symmetry to help solve constraint satisfaction problems. Symmetries occur within individual solutions of problems as well as between different solutions of the same problem. Symmetry can also be applied to the constraints in a problem to give new symmetric constraints. Reasoning about symmetry can speed up problem solving, and has led to the discovery of new results in both graph and number theory.


Online Cake Cutting

Abstract: We propose an online form of the cake cutting problem. This models situations where players arrive and depart during the process of dividing a resource. We show that well known fair division procedures like cut-and-choose and the Dubins-Spanier moving knife procedure can be adapted to apply to such online problems. We propose some desirable properties that online cake cutting procedures might possess like online forms of proportionality and envy-freeness, and identify which properties are in fact possessed by the different online cake procedures.


Face Synthesis (FASY) System for Determining the Characteristics of a Face Image

Abstract: This paper aims at determining the characteristics of a face image by extracting its components. The FASY (FAce SYnthesis) System is a Face Database Retrieval and new Face generation System that is under development. One of its main features is the generation of the requested face when it is not found in the existing database, which allows a continuous growing of the database also. To generate the new face image, we need to store the face components in the database. So we have designed a new technique to extract the face components by a sophisticated method. After extraction of the facial feature points we have analyzed the components to determine their characteristics. After extraction and analysis we have stored the components along with their characteristics into the face database for later use during the face construction.


Anti-Collusion Digital Fingerprinting Codes via Partially Cover-Free Families

Abstract: Anti-collusion digital fingerprinting codes have been of significant current interest in the context of deterring unauthorized use of multimedia content by a coalition of users. In this article, partially cover-free families of sets are considered and these are employed to obtain such codes. Compared to the existing methods of construction, our methods ensure gains in terms of accommodating more users and/or reducing the number of basis vectors.


Quotient Based Multiresolution Image Fusion of Thermal and Visual Images Using Daubechies Wavelet Transform for Human Face Recognition

Abstract: This paper investigates the multiresolution level-1 and level-2 Quotient based Fusion of thermal and visual images. In the proposed system, the method-1 namely "Decompose then Quotient Fuse Level-1" and the method-2 namely "Decompose-Reconstruct then Quotient Fuse Level-2" both work on wavelet transformations of the visual and thermal face images. The wavelet transform is well-suited to manage different image resolution and allows the image decomposition in different kinds of coefficients, while preserving the image information without any loss. This approach is based on a definition of an illumination invariant signature image which enables an analytic generation of the image space with varying illumination. The quotient fused images are passed through Principal Component Analysis (PCA) for dimension reduction and then those images are classified using a multi-layer perceptron (MLP). The performances of both the methods have been evaluated using OTCBVS and IRIS databases. All the different classes have been tested separately, among them the maximum recognition result is 100%.


Fusion of Daubechies Wavelet Coefficients for Human Face Recognition

Abstract: In this paper fusion of visual and thermal images in wavelet transformed domain has been presented. Here, Daubechies wavelet transform, called as D2, coefficients from visual and corresponding coefficients computed in the same manner from thermal images are combined to get fused coefficients. After decomposition up to fifth level (Level 5) fusion of coefficients is done. Inverse Daubechies wavelet transform of those coefficients gives us fused face images. The main advantage of using wavelet transform is that it is well-suited to manage different image resolution and allows the image decomposition in different kinds of coefficients, while preserving the image information. Fused images thus found are passed through Principal Component Analysis (PCA) for reduction of dimensions and then those reduced fused images are classified using a multi-layer perceptron. For experiments IRIS Thermal/Visual Face Database was used. Experimental results show that the performance of the approach presented here achieves maximum success rate of 100% in many cases.


Fusion of Wavelet Coefficients from Visual and Thermal Face Images for Human Face Recognition - A Comparative Study

Abstract: In this paper we present a comparative study on fusion of visual and thermal images using different wavelet transformations. Here, coefficients of discrete wavelet transforms from both visual and thermal images are computed separately and combined. Next, inverse discrete wavelet transformation is taken in order to obtain fused face image. Both Haar and Daubechies (db2) wavelet transforms have been used to compare recognition results. For experiments IRIS Thermal/Visual Face Database was used. Experimental results using Haar and Daubechies wavelets show that the performance of the approach presented here achieves maximum success rate of 100% in many cases.


A Parallel Framework for Multilayer Perceptron for Human Face Recognition

Abstract: Artificial neural networks have already shown their success in face recognition and similar complex pattern recognition tasks. However, a major disadvantage of the technique is that it is extremely slow during training for larger classes and hence not suitable for real-time complex problems such as pattern recognition. This is an attempt to develop a parallel framework for the training algorithm of a perceptron. In this paper, two general architectures for a Multilayer Perceptron (MLP) have been demonstrated. The first architecture is All-Class-in-One-Network (ACON) where all the classes are placed in a single network and the second one is One-Class-in-One-Network (OCON) where an individual single network is responsible for each and every class. Capabilities of these two architectures were compared and verified in solving human face recognition, which is a complex pattern recognition task where several factors affect the recognition performance like pose variations, facial expression changes, occlusions, and most importantly illumination changes. Both the structures were implemented and tested for face recognition purpose and experimental results show that the OCON structure performs better than the generally used ACON ones in term of training convergence speed of the network. Unlike the conventional sequential approach of training the neural networks, the OCON technique may be implemented by training all the classes of the face images simultaneously.


Image Pixel Fusion for Human Face Recognition

Abstract: In this paper we present a technique for fusion of optical and thermal face images based on image pixel fusion approach. Out of several factors, which affect face recognition performance in case of visual images, illumination changes are a significant factor that needs to be addressed. Thermal images are better in handling illumination conditions but not very consistent in capturing texture details of the faces. Other factors like sunglasses, beard, moustache etc also play active role in adding complicacies to the recognition process. Fusion of thermal and visual images is a solution to overcome the drawbacks present in the individual thermal and visual face images. Here fused images are projected into an eigenspace and the projected images are classified using a radial basis function (RBF) neural network and also by a multi-layer perceptron (MLP). In the experiments Object Tracking and Classification Beyond Visible Spectrum (OTCBVS) database benchmark for thermal and visual face images have been used. Comparison of experimental results show that the proposed approach performs significantly well in recognizing face images with a success rate of 96% and 95.07% for RBF Neural Network and MLP respectively.


Classification of Fused Images using Radial Basis Function Neural Network for Human Face Recognition

Abstract: Here an efficient fusion technique for automatic face recognition has been presented. Fusion of visual and thermal images has been done to take the advantages of thermal images as well as visual images. By employing fusion a new image can be obtained, which provides the most detailed, reliable, and discriminating information. In this method fused images are generated using visual and thermal face images in the first step. In the second step, fused images are projected into eigenspace and finally classified using a radial basis function neural network. In the experiments Object Tracking and Classification Beyond Visible Spectrum (OTCBVS) database benchmark for thermal and visual face images have been used. Experimental results show that the proposed approach performs well in recognizing unknown individuals with a maximum success rate of 96%.


Classification of fused face images using multilayer perceptron neural network

Abstract: This paper presents a concept of image pixel fusion of visual and thermal faces, which can significantly improve the overall performance of a face recognition system. Several factors affect face recognition performance including pose variations, facial expression changes, occlusions, and most importantly illumination changes. So, image pixel fusion of thermal and visual images is a solution to overcome the drawbacks present in the individual thermal and visual face images. Fused images are projected into eigenspace and finally classified using a multi-layer perceptron. In the experiments we have used Object Tracking and Classification Beyond Visible Spectrum (OTCBVS) database benchmark thermal and visual face images. Experimental results show that the proposed approach significantly improves the verification and identification performance and the success rate is 95.07%. The main objective of employing fusion is to produce a fused image that provides the most detailed and reliable information. Fusion of multiple images together produces a more efficient representation of the image.


Classification of Log-Polar-Visual Eigenfaces using Multilayer Perceptron

Abstract: In this paper we present a simple novel approach to tackle the challenges of scaling and rotation of face images in face recognition. The proposed approach registers the training and testing visual face images by log-polar transformation, which is capable to handle complicacies introduced by scaling and rotation. Log-polar images are projected into eigenspace and finally classified using an improved multi-layer perceptron. In the experiments we have used ORL face database and Object Tracking and Classification Beyond Visible Spectrum (OTCBVS) database for visual face images. Experimental results show that the proposed approach significantly improves the recognition performances from visual to log-polar-visual face images. In case of ORL face database, recognition rate for visual face images is 89.5% and that is increased to 97.5% for log-polar-visual face images whereas for OTCBVS face database recognition rate for visual images is 87.84% and 96.36% for log-polar-visual face images.


Local search for stable marriage problems with ties and incomplete lists

Abstract: The stable marriage problem has a wide variety of practical applications, ranging from matching resident doctors to hospitals, to matching students to schools, or more generally to any two-sided market. We consider a useful variation of the stable marriage problem, where the men and women express their preferences using a preference list with ties over a subset of the members of the other sex. Matchings are permitted only with people who appear in these preference lists. In this setting, we study the problem of finding a stable matching that marries as many people as possible. Stability is an envy-free notion: no man and woman who are not married to each other would both prefer each other to their partners or to being single. This problem is NP-hard. We tackle this problem using local search, exploiting properties of the problem to reduce the size of the neighborhood and to make local moves efficiently. Experimental results show that this approach is able to solve large problems, quickly returning stable matchings of large and often optimal size.


Human Face Recognition using Line Features

Abstract: In this work we investigate a novel approach to handle the challenges of face recognition, which includes rotation, scale, occlusion, illumination etc. Here, we have used thermal face images as those are capable to minimize the affect of illumination changes and occlusion due to moustache, beards, adornments etc. The proposed approach registers the training and testing thermal face images in polar coordinate, which is capable to handle complicacies introduced by scaling and rotation. Line features are extracted from thermal polar images and feature vectors are constructed using these line. Feature vectors thus obtained passes through principal component analysis (PCA) for the dimensionality reduction of feature vectors. Finally, the images projected into eigenspace are classified using a multi-layer perceptron. In the experiments we have used Object Tracking and Classification Beyond Visible Spectrum (OTCBVS) database. Experimental results show that the proposed approach significantly improves the verification and identification performance and the success rate is 99.25%.


Tree-formed Verification Data for Trusted Platforms

Abstract: The establishment of trust relationships to a computing platform relies on validation processes. Validation allows an external entity to build trust in the expected behaviour of the platform based on provided evidence of the platform's configuration. In a process like remote attestation, the 'trusted' platform submits verification data created during a start up process. These data consist of hardware-protected values of platform configuration registers, containing nested measurement values, e.g., hash values, of loaded or started components. Commonly, the register values are created in linear order by a hardware-secured operation. Fine-grained diagnosis of components, based on the linear order of verification data and associated measurement logs, is not optimal. We propose a method to use tree-formed verification data to validate a platform. Component measurement values represent leaves, and protected registers represent roots of a hash tree. We describe the basic mechanism of validating a platform using tree-formed measurement logs and root registers and show an logarithmic speed-up for the search of faults. Secure creation of a tree is possible using a limited number of hardware-protected registers and a single protected operation. In this way, the security of tree-formed verification data is maintained.


The Latent Bernoulli-Gauss Model for Data Analysis

Abstract: We present a new latent-variable model employing a Gaussian mixture integrated with a feature selection procedure (the Bernoulli part of the model) which together form a "Latent Bernoulli-Gauss" distribution. The model is applied to MAP estimation, clustering, feature selection and collaborative filtering and fares favorably with the state-of-the-art latent-variable models.


Scheduling Periodic Real-Time Tasks with Heterogeneous Reward Requirements

Abstract: We study the problem of scheduling periodic real-time tasks so as to meet their individual minimum reward requirements. A task generates jobs that can be given arbitrary service times before their deadlines. A task then obtains rewards based on the service times received by its jobs. We show that this model is compatible to the imprecise computation models and the increasing reward with increasing service models. In contrast to previous work on these models, which mainly focus on maximize the total reward in the system, we aim to fulfill different reward requirements by different tasks, which offers better fairness and allows fine-grained tradeoff between tasks. We first derive a necessary and sufficient condition for a system, along with reward requirements of tasks, to be feasible. We also obtain an off-line feasibility optimal scheduling policy. We then studies a sufficient condition for a policy to be feasibility optimal or achieves some approximation bound. This condition can serve as a guideline for designing on-line scheduling policy and we obtains a greedy policy based on it. We prove that the on-line policy is feasibility optimal when all tasks have the same periods and also obtain an approximation bound for the policy under general cases.


A unified view of Automata-based algorithms for Frequent Episode Discovery

Abstract: Frequent Episode Discovery framework is a popular framework in Temporal Data Mining with many applications. Over the years many different notions of frequencies of episodes have been proposed along with different algorithms for episode discovery. In this paper we present a unified view of all such frequency counting algorithms. We present a generic algorithm such that all current algorithms are special cases of it. This unified view allows one to gain insights into different frequencies and we present quantitative relationships among different frequencies. Our unified view also helps in obtaining correctness proofs for various algorithms as we show here. We also point out how this unified view helps us to consider generalization of the algorithm so that they can discover episodes with general partial orders.


Large scale probabilistic available bandwidth estimation

Abstract: The common utilization-based definition of available bandwidth and many of the existing tools to estimate it suffer from several important weaknesses: i) most tools report a point estimate of average available bandwidth over a measurement interval and do not provide a confidence interval; ii) the commonly adopted models used to relate the available bandwidth metric to the measured data are invalid in almost all practical scenarios; iii) existing tools do not scale well and are not suited to the task of multi-path estimation in large-scale networks; iv) almost all tools use ad-hoc techniques to address measurement noise; and v) tools do not provide enough flexibility in terms of accuracy, overhead, latency and reliability to adapt to the requirements of various applications. In this paper we propose a new definition for available bandwidth and a novel framework that addresses these issues. We define probabilistic available bandwidth (PAB) as the largest input rate at which we can send a traffic flow along a path while achieving, with specified probability, an output rate that is almost as large as the input rate. PAB is expressed directly in terms of the measurable output rate and includes adjustable parameters that allow the user to adapt to different application requirements. Our probabilistic framework to estimate network-wide probabilistic available bandwidth is based on packet trains, Bayesian inference, factor graphs and active sampling. We deploy our tool on the PlanetLab network and our results show that we can obtain accurate estimates with a much smaller measurement overhead compared to existing approaches.


Retransmission Steganography Applied

Abstract: This paper presents experimental results of the implementation of network steganography method called RSTEG (Retransmission Steganography). The main idea of RSTEG is to not acknowledge a successfully received packet to intentionally invoke retransmission. The retransmitted packet carries a steganogram instead of user data in the payload field. RSTEG can be applied to many network protocols that utilize retransmissions. We present experimental results for RSTEG applied to TCP (Transmission Control Protocol) as TCP is the most popular network protocol which ensures reliable data transfer. The main aim of the performed experiments was to estimate RSTEG steganographic bandwidth and detectability by observing its influence on the network retransmission level.


Is Computational Complexity a Barrier to Manipulation?

Abstract: When agents are acting together, they may need a simple mechanism to decide on joint actions. One possibility is to have the agents express their preferences in the form of a ballot and use a voting rule to decide the winning action(s). Unfortunately, agents may try to manipulate such an election by misreporting their preferences. Fortunately, it has been shown that it is NP-hard to compute how to manipulate a number of different voting rules. However, NP-hardness only bounds the worst-case complexity. Recent theoretical results suggest that manipulation may often be easy in practice. To address this issue, I suggest studying empirically if computational complexity is in practice a barrier to manipulation. The basic tool used in my investigations is the identification of computational "phase transitions". Such an approach has been fruitful in identifying hard instances of propositional satisfiability and other NP-hard problems. I show that phase transition behaviour gives insight into the hardness of manipulating voting rules, increasing concern that computational complexity is indeed any sort of barrier. Finally, I look at the problem of computing manipulation of other, related problems like stable marriage and tournament problems.


Redundancies in Dependently Typed Lambda Calculi and Their Relevance to Proof Search

Abstract: Dependently typed lambda calculi such as the Logical Framework (LF) are capable of representing relationships between terms through types. By exploiting the "formulas-as-types" notion, such calculi can also encode the correspondence between formulas and their proofs in typing judgments. As such, these calculi provide a natural yet powerful means for specifying varied formal systems. Such specifications can be transformed into a more direct form that uses predicate formulas over simply typed lambda-terms and that thereby provides the basis for their animation using conventional logic programming techniques. However, a naive use of this idea is fraught with inefficiencies arising from the fact that dependently typed expressions typically contain much redundant typing information. We investigate syntactic criteria for recognizing and, hence, eliminating such redundancies. In particular, we identify a property of bound variables in LF types called "rigidity" and formally show that checking that instantiations of such variables adhere to typing restrictions is unnecessary for the purpose of ensuring that the overall expression is well-formed. We show how to exploit this property in a translation based approach to executing specifications in the Twelf language. Recognizing redundancy is also relevant to devising compact representations of dependently typed expressions. We highlight this aspect of our work and discuss its connection with other approaches proposed in this context.


A Geometric Presentation of Probabilistic Satisfiability

Abstract: By considering probability distributions over the set of assignments the expected truth values assignment to propositional variables are extended through linear operators, and the expected truth values of the clauses at any given conjunctive form are also extended through linear maps. The probabilistic satisfiability problems are discussed in terms of the introduced linear extensions. The case of multiple truth values is also discussed.


Soft Control on Collective Behavior of a Group of Autonomous Agents by a Shill Agent

Abstract: This paper asks a new question: how can we control the collective behavior of self-organized multi-agent systems? We try to answer the question by proposing a new notion called 'Soft Control', which keeps the local rule of the existing agents in the system. We show the feasibility of soft control by a case study. Consider the simple but typical distributed multi-agent model proposed by Vicsek et al. for flocking of birds: each agent moves with the same speed but with different headings which are updated using a local rule based on the average of its own heading and the headings of its neighbors. Most studies of this model are about the self-organized collective behavior, such as synchronization of headings. We want to intervene in the collective behavior (headings) of the group by soft control. A specified method is to add a special agent, called a 'Shill', which can be controlled by us but is treated as an ordinary agent by other agents. We construct a control law for the shill so that it can synchronize the whole group to an objective heading. This control law is proved to be effective analytically and numerically. Note that soft control is different from the approach of distributed control. It is a natural way to intervene in the distributed systems. It may bring out many interesting issues and challenges on the control of complex systems.


Filtrage vaste marge pour l'\'etiquetage s\'equentiel \`a noyaux de signaux

Abstract: We address in this paper the problem of multi-channel signal sequence labeling. In particular, we consider the problem where the signals are contaminated by noise or may present some dephasing with respect to their labels. For that, we propose to jointly learn a SVM sample classifier with a temporal filtering of the channels. This will lead to a large margin filtering that is adapted to the specificity of each channel (noise and time-lag). We derive algorithms to solve the optimization problem and we discuss different filter regularizations for automated scaling or selection of channels. Our approach is tested on a non-linear toy example and on a BCI dataset. Results show that the classification performance on these problems can be improved by learning a large margin filtering.


Proceedings First International Workshop on Logics for Component Configuration

Abstract: This volume contains the papers presented at the first international workshop on Logics for Component Configuration (LoCoCo 2010) which was associated with the International Conference on Theory and Applications of Satisfiability Testing (SAT 2010) as part of the Federated Logic Conference (FLoC 2010), and which took place on July 10, 2010 in Edinburgh, UK. Modern software distributions are based on the notion of components, which denote units of independent development and deployment. Components provide the necessary flexibility when organizing a complex software distribution, but also are a challenge when it comes to selecting components from a large repository of possible choices, and configuring these components according to user needs, resource constraints, and interdependencies with other components. Representing and solving configuration problems is a hot topic of great importance for many application domains. Some well-known examples of complex systems of components are Free and Open Source software distributions like GNU/Linux, or Eclipse plugins. The LoCoCo workshop has a focus on logic-based methods for specifying and solving complex configuration problems for software components. The goal was to bring together both researchers and practitioners active in the area of component configuration of software systems, using different modeling and solving techniques, such as constraint and logic programing, description logics, satisfiability and its extensions.


Local search for stable marriage problems

Abstract: The stable marriage (SM) problem has a wide variety of practical applications, ranging from matching resident doctors to hospitals, to matching students to schools, or more generally to any two-sided market. In the classical formulation, n men and n women express their preferences (via a strict total order) over the members of the other sex. Solving a SM problem means finding a stable marriage where stability is an envy-free notion: no man and woman who are not married to each other would both prefer each other to their partners or to being single. We consider both the classical stable marriage problem and one of its useful variations (denoted SMTI) where the men and women express their preferences in the form of an incomplete preference list with ties over a subset of the members of the other sex. Matchings are permitted only with people who appear in these lists, an we try to find a stable matching that marries as many people as possible. Whilst the SM problem is polynomial to solve, the SMTI problem is NP-hard. We propose to tackle both problems via a local search approach, which exploits properties of the problems to reduce the size of the neighborhood and to make local moves efficiently. We evaluate empirically our algorithm for SM problems by measuring its runtime behaviour and its ability to sample the lattice of all possible stable marriages. We evaluate our algorithm for SMTI problems in terms of both its runtime behaviour and its ability to find a maximum cardinality stable marriage.For SM problems, the number of steps of our algorithm grows only as O(nlog(n)), and that it samples very well the set of all stable marriages. It is thus a fair and efficient approach to generate stable marriages.Furthermore, our approach for SMTI problems is able to solve large problems, quickly returning stable matchings of large and often optimal size despite the NP-hardness of this problem.


A Platform for Implementing Secure Wireless Ad Hoc Networks

Abstract: We propose a new platform for implementing secure wireless ad hoc networks. Our proposal is based on a modular architecture, with the software stack constructed directly on the Ethernet layer. Within our platform we use a new security protocol that we designed to ensure mutual authentication between nodes and a secure key exchange. The correctness of the proposed security protocol is ensured by Guttman's authentication tests.