Joint Impact of Pathloss Shadowing and Fast Fading - An Outage Formula for Wireless Networks

Abstract: In this paper, we analyse the joint impact of pathloss, shadowing and fast fading on wireless networks. Taking into account the pathloss and the shadowing, we first express the SINR distribution of a mobile located at a given distance from its serving base-station (BS). The moments of this distribution are easily computed, using the Fenton-Wilkinson method, and a fluid model that considers the cellular network as a continuum of BS. Then considering the joint impact of pathloss, shadowing and fast fading, we derive an easily computable outage probability formula, for a mobile located at any distance from its serving BS. We validate our approach by comparing all results to Monte Carlo simulations performed in a traditional hexagonal network. Indeed, we establish that the results given by the formula are close to the ones given by Monte Carlo simulations. The proposed framework is a powerful tool to study performances of cellular networks e.g. OFDMA systems (WiMAX, LTE).


Principal manifolds and graphs in practice: from molecular biology to dynamical systems

Abstract: We present several applications of non-linear data modeling, using principal manifolds and principal graphs constructed using the metaphor of elasticity (elastic principal graph approach). These approaches are generalizations of the Kohonen's self-organizing maps, a class of artificial neural networks. On several examples we show advantages of using non-linear objects for data approximation in comparison to the linear ones. We propose four numerical criteria for comparing linear and non-linear mappings of datasets into the spaces of lower dimension. The examples are taken from comparative political science, from analysis of high-throughput data in molecular biology, from analysis of dynamical systems.


The Condition of the Turking Class: Are Online Employers Fair and Honest?

Abstract: Online labor markets give people in poor countries direct access to buyers in rich countries. Economic theory and empirical evidence strongly suggest that this kind of access improves human welfare. However, critics claim that abuses are endemic in these markets and that employers exploit unprotected, vulnerable workers. I investigate part of this claim using a randomized, paired survey in which I ask workers in an online labor market (Amazon Mechanical Turk) how they perceive online employers and employers in their host country in terms of honesty and fairness. I find that, on average, workers perceive the collection of online employers as slightly fairer and more honest than offline employers, though the effect is not significant. Views are more polarized in the online employer case, with more respondents having very positive views of the online collection of employers.


Some Architectures for Chebyshev Interpolation

Abstract: Digital architectures for Chebyshev interpolation are explored and a variation which is word-serial in nature is proposed. These architectures are contrasted with equispaced system structures. Further, Chebyshev interpolation scheme is compared to the conventional equispaced interpolation vis-a-vis reconstruction error and relative number of samples. It is also shown that the use of a hybrid (or dual) Analog to Digital converter unit can reduce system power consumption by as much as 1/3rd of the original.


Characterizing Internet Worm Infection Structure

Abstract: Internet worm infection continues to be one of top security threats and has been widely used by botnets to recruit new bots. In this work, we attempt to quantify the infection ability of individual hosts and reveal the key characteristics of the underlying topology formed by worm infection, i.e., the number of children and the generation of the worm infection family tree. Specifically, we first apply probabilistic modeling methods and a sequential growth model to analyze the infection tree of a wide class of worms. We analytically and empirically find that the number of children has asymptotically a geometric distribution with parameter 0.5. As a result, on average half of infected hosts never compromise any vulnerable host, over 98% of infected hosts have no more than five children, and a small portion of infected hosts have a large number of children. We also discover that the generation follows closely a Poisson distribution and the average path length of the worm infection family tree increases approximately logarithmically with the total number of infected hosts. Next, we empirically study the infection structure of localized-scanning worms and surprisingly find that most of the above observations also apply to localized-scanning worms. Finally, we apply our findings to develop bot detection methods and study potential countermeasures for a botnet (e.g., Conficker C) that uses scan-based peer discovery to form a P2P-based botnet. Specifically, we demonstrate that targeted detection that focuses on the nodes with the largest number of children is an efficient way to expose bots. For example, our simulation shows that when 3.125% nodes are examined, targeted detection can reveal 22.36% bots. However, we also point out that future botnets may limit the maximum number of children to weaken targeted detection, without greatly slowing down the speed of worm infection.


New ways of scientific publishing and accessing human knowledge inspired by transdisciplinary approaches

Abstract: Inspired by interdisciplinary work touching biology and microtribology, the authors propose a new, dynamic way of publishing research results, the establishment of a tree of knowledge and the localisation of scientific articles on this tree. 'Technomimetics' is proposed as a new method of knowledge management in science and technology: it shall help find and organise information in an era of over-information. Such ways of presenting and managing research results would be accessible by people with different kinds of backgrounds and levels of education, and allow for full use of the ever- increasing number of scientific and technical publications. This approach would dramatically change and revolutionize the way we are doing science, and contribute to overcoming the three gaps between the world of ideas, inventors, innovators and investors as introduced by Gebeshuber, Gruber and Drack in 2009 for accelerated scientific and technological breakthroughs to improve the human condition. Inspiration for the development of above methods was the fact that - generally - tribologists and biologists do not see many overlaps of their professions. However, both deal with materials, structures and processes. Tribology is omnipresent in biology and many biological systems have impressive tribological properties. Tribologists can therefore get valuable input and inspiration from living systems. The aim of biomimetics is knowledge transfer from biology to technology and successful biomimetics in tribology needs collaboration between biologists and tribologists. Literature search shows that the number of papers regarding biotribology is steadily increasing. However, at the moment, most scientific papers of the other respective field are hard to access and hard to understand, in terms of concepts and specific wording.


Pure Parsimony Xor Haplotyping

Abstract: The haplotype resolution from xor-genotype data has been recently formulated as a new model for genetic studies. The xor-genotype data is a cheaply obtainable type of data distinguishing heterozygous from homozygous sites without identifying the homozygous alleles. In this paper we propose a formulation based on a well-known model used in haplotype inference: pure parsimony. We exhibit exact solutions of the problem by providing polynomial time algorithms for some restricted cases and a fixed-parameter algorithm for the general case. These results are based on some interesting combinatorial properties of a graph representation of the solutions. Furthermore, we show that the problem has a polynomial time k-approximation, where k is the maximum number of xor-genotypes containing a given SNP. Finally, we propose a heuristic and produce an experimental analysis showing that it scales to real-world large instances taken from the HapMap project.


Integer Reset Timed Automata: Clock Reduction and Determinizability

Abstract: In this paper, we propose a procedure that given an integer reset timed automaton (IRTA) ${\cal A}$, produces a language equivalent deterministic one clock IRTA ${\cal B}$ whose size is at most doubly exponential in the size of ${\cal A}$. We prove that this bound on the number of locations is tight. Further, if integer resets are used in stopwatch automata, a subclass of stopwatch automata which is closed under all boolean operations and for which reachability is decidable is obtained.


Boosting k-NN for categorization of natural scenes

Abstract: The k-nearest neighbors (k-NN) classification rule has proven extremely successful in countless many computer vision applications. For example, image categorization often relies on uniform voting among the nearest prototypes in the space of descriptors. In spite of its good properties, the classic k-NN rule suffers from high variance when dealing with sparse prototype datasets in high dimensions. A few techniques have been proposed to improve k-NN classification, which rely on either deforming the nearest neighborhood relationship or modifying the input space. In this paper, we propose a novel boosting algorithm, called UNN (Universal Nearest Neighbors), which induces leveraged k-NN, thus generalizing the classic k-NN rule. We redefine the voting rule as a strong classifier that linearly combines predictions from the k closest prototypes. Weak classifiers are learned by UNN so as to minimize a surrogate risk. A major feature of UNN is the ability to learn which prototypes are the most relevant for a given class, thus allowing one for effective data reduction. Experimental results on the synthetic two-class dataset of Ripley show that such a filtering strategy is able to reject "noisy" prototypes. We carried out image categorization experiments on a database containing eight classes of natural scenes. We show that our method outperforms significantly the classic k-NN classification, while enabling significant reduction of the computational cost by means of data filtering.


New Constructive Aspects of the Lovasz Local Lemma

Abstract: The Lov\'{a}sz Local Lemma (LLL) states that the probability that none of a set of "bad" events happens is nonzero if the probability of each event is small compared to the number of bad events it depends on. A series of results have provided algorithms to efficiently construct structures whose existence is (non-constructively) guaranteed by the full asymmetric LLL, culminating in the recent breakthrough of Moser & Tardos. We show that the output distribution of the Moser-Tardos procedure has sufficient randomness, leading to two classes of algorithmic applications. We first show that when an LLL application provides a small amount of slack, the running time of the Moser-Tardos algorithm is polynomial in the number of underlying independent variables (not events!), and can thus be used to give efficient constructions in cases where the underlying proof applies the LLL to super-polynomially many events (or where finding a bad event that holds is computationally hard). We demonstrate our method on applications including: the first constant-factor approximation algorithm for the Santa Claus problem, as well as efficient algorithms for acyclic edge coloring, non-repetitive graph colorings, and Ramsey-type graphs. Second, we show applications to cases where a few of the bad events can hold, leading to the first such algorithmic applications of the LLL: MAX $k$-SAT is an illustrative example of this.


Il rapporto tra ICT e PMI italiane e le problematiche economico-organizzative dell'OS

Abstract: This technical report summarizes the preliminary findings of a project that has been developed in 2007 by an Italian company (Datanet, based in Siracusa, Italy) togheter with two Italian research institutions: Universita' degli Studi di Milano and Universita' Bocconi. The main aim of the OS4E (Open Source for Enterprises) project, has been to investigate if and how open source solutions could be profitabily and effectively exploited by an IT company based in the South of Italy, whose core business is IT systems integration and which operates in the market of Italian SMEs (Small and Medium Enterprises). Beside this goal, the project also aimed at developing effective tools and methodologies to support decision making processes while evaluating different alternative software applications based on OSS (open source software).


Decisional Processes with Boolean Neural Network: the Emergence of Mental Schemes

Abstract: Human decisional processes result from the employment of selected quantities of relevant information, generally synthesized from environmental incoming data and stored memories. Their main goal is the production of an appropriate and adaptive response to a cognitive or behavioral task. Different strategies of response production can be adopted, among which haphazard trials, formation of mental schemes and heuristics. In this paper, we propose a model of Boolean neural network that incorporates these strategies by recurring to global optimization strategies during the learning session. The model characterizes as well the passage from an unstructured/chaotic attractor neural network typical of data-driven processes to a faster one, forward-only and representative of schema-driven processes. Moreover, a simplified version of the Iowa Gambling Task (IGT) is introduced in order to test the model. Our results match with experimental data and point out some relevant knowledge coming from psychological domain.


A framework to model real-time databases

Abstract: Real-time databases deal with time-constrained data and time-constrained transactions. The design of this kind of databases requires the introduction of new concepts to support both data structures and the dynamic behaviour of the database. In this paper, we give an overview about different aspects of real-time databases and we clarify requirements of their modelling. Then, we present a framework for real-time database design and describe its fundamental operations. A case study demonstrates the validity of the structural model and illustrates SQL queries and Java code generated from the classes of the model


Multi-Armed Bandit Mechanisms for Multi-Slot Sponsored Search Auctions

Abstract: In pay-per click sponsored search auctions which are currently extensively used by search engines, the auction for a keyword involves a certain number of advertisers (say k) competing for available slots (say m) to display their ads. This auction is typically conducted for a number of rounds (say T). There are click probabilities mu_ij associated with each agent-slot pairs. The goal of the search engine is to maximize social welfare of the advertisers, that is, the sum of values of the advertisers. The search engine does not know the true values advertisers have for a click to their respective ads and also does not know the click probabilities mu_ij s. A key problem for the search engine therefore is to learn these click probabilities during the T rounds of the auction and also to ensure that the auction mechanism is truthful. Mechanisms for addressing such learning and incentives issues have recently been introduced and are aptly referred to as multi-armed-bandit (MAB) mechanisms. When m = 1, characterizations for truthful MAB mechanisms are available in the literature and it has been shown that the regret for such mechanisms will be O(T^{2/3}). In this paper, we seek to derive a characterization in the realistic but non-trivial general case when m > 1 and obtain several interesting results.


JBotSim, a Tool for Fast Prototyping of Distributed Algorithms in Dynamic Networks

Abstract: JBotSim is a java library that offers basic primitives for prototyping, running, and visualizing distributed algorithms in dynamic networks. With JBotSim, one can implement an idea in minutes and interact with it ({\it e.g. }, add, move, or delete nodes) while it is running. JBotSim is well suited to prepare live demonstrations of your algorithms to colleagues or students; it can also be used to evaluate performance at the algorithmic level (number of messages, number of rounds, etc.). Unlike most tools, JBotSim is not an integrated environment. It is a lightweight library to be used in your program. In this paper, we present an overview of its distinctive features and architecture.


Efficient Upload Bandwidth Estimation and Communication Resource Allocation Techniques

Abstract: In this paper we address two problems, for which we present novel, efficient, algorithmic solutions. The first problem is motivated by practical situations and is concerned with the efficient estimation of the upload bandwidth of a machine, particularly in the context of a peer-to-peer content sharing and distribution application. The second problem is more of a theoretical nature and considers a constrained communication resource allocation situation.


Multidimensional Data Structures and Techniques for Efficient Decision Making

Abstract: In this paper we present several novel efficient techniques and multidimensional data structures which can improve the decision making process in many domains. We consider online range aggregation, range selection and range weighted median queries; for most of them, the presented data structures and techniques can provide answers in polylogarithmic time. The presented results have applications in many business and economic scenarios, some of which are described in detail in the paper.


A New Approximation Technique for Resource-Allocation Problems

Abstract: We develop a rounding method based on random walks in polytopes, which leads to improved approximation algorithms and integrality gaps for several assignment problems that arise in resource allocation and scheduling. In particular, it generalizes the work of Shmoys and Tardos on the generalized assignment problem to the setting where some jobs can be dropped. New concentration bounds for random bipartite matching are developed as well.


A Reduced Offset Based Method for Fast Computation of the Prime Implicants Covering a Given Cube

Abstract: In order to generate prime implicants for a given cube (minterm), most of minimization methods increase the dimension of this cube by removing one literal from it at a time. But there are two problems of exponential complexity. One of them is the selection of the order in which the literals are to be removed from the implicant at hand. The latter is the mechanism that checks whether a tentative literal removal is acceptable. The reduced Offset concept has been developed to avoid of these problems. This concept is based on positional-cube representation where each cube is represented by two n-bit strings. We show that each reduced Off-cube may be represented by a single n-bit string and propose a set of bitwise operations to be performed on such strings. The experiments on single-output benchmarks show that this approach can significantly speed up the minimization process, improve the quality of its results and reduce the amount of memory required for this aim.


Random Access to Grammar Compressed Strings

Abstract: Grammar based compression, where one replaces a long string by a small context-free grammar that generates the string, is a simple and powerful paradigm that captures many popular compression schemes. In this paper, we present a novel grammar representation that allows efficient random access to any character or substring without decompressing the string. Let $S$ be a string of length $N$ compressed into a context-free grammar $\mathcal{S}$ of size $n$. We present two representations of $\mathcal{S}$ achieving $O(\log N)$ random access time, and either $O(n\cdot \alpha_k(n))$ construction time and space on the pointer machine model, or $O(n)$ construction time and space on the RAM. Here, $\alpha_k(n)$ is the inverse of the $k^{th}$ row of Ackermann's function. Our representations also efficiently support decompression of any substring in $S$: we can decompress any substring of length $m$ in the same complexity as a single random access query and additional $O(m)$ time. Combining these results with fast algorithms for uncompressed approximate string matching leads to several efficient algorithms for approximate string matching on grammar-compressed strings without decompression. For instance, we can find all approximate occurrences of a pattern $P$ with at most $k$ errors in time $O(n(\min\{|P|k, k^4 + |P|\} + \log N) + occ)$, where $occ$ is the number of occurrences of $P$ in $S$. Finally, we generalize our results to navigation and other operations on grammar-compressed ordered trees. All of the above bounds significantly improve the currently best known results. To achieve these bounds, we introduce several new techniques and data structures of independent interest, including a predecessor data structure, two "biased" weighted ancestor data structures, and a compact representation of heavy paths in grammars.


Fooling functions of halfspaces under product distributions

Abstract: We construct pseudorandom generators that fool functions of halfspaces (threshold functions) under a very broad class of product distributions. This class includes not only familiar cases such as the uniform distribution on the discrete cube, the uniform distribution on the solid cube, and the multivariate Gaussian distribution, but also includes any product of discrete distributions with probabilities bounded away from 0. Our first main result shows that a recent pseudorandom generator construction of Meka and Zuckerman [MZ09], when suitably modifed, can fool arbitrary functions of d halfspaces under product distributions where each coordinate has bounded fourth moment. To eps-fool any size-s, depth-d decision tree of halfspaces, our pseudorandom generator uses seed length O((d log(ds/eps)+log n) log(ds/eps)). For monotone functions of d halfspaces, the seed length can be improved to O((d log(d/eps)+log n) log(d/eps)). We get better bounds for larger eps; for example, to 1/polylog(n)-fool all monotone functions of (log n)= log log n halfspaces, our generator requires a seed of length just O(log n). Our second main result generalizes the work of Diakonikolas et al. [DGJ+09] to show that bounded independence suffices to fool functions of halfspaces under product distributions. Assuming each coordinate satisfies a certain stronger moment condition, we show that any function computable by a size-s, depth-d decision tree of halfspaces is eps-fooled by O(d^4s^2/eps^2)-wise independence.


Steps towards a theory and calculus of aliasing

Abstract: A theory, graphical notation, mathematical calculus and implementation for finding whether two given expressions can, at execution time, denote references attached to the same object. Intended as the basis for a comprehensive solution to the "frame problem" and as a complement to, or even a replacement for, separation logic, shape analysis, ownership types and dynamic frames.


On the boundedness of an iteration involving points on the hypersphere

Abstract: For a finite set of points $X$ on the unit hypersphere in $\mathbb{R}^d$ we consider the iteration $u_{i+1}=u_i+\chi_i$, where $\chi_i$ is the point of $X$ farthest from $u_i$. Restricting to the case where the origin is contained in the convex hull of $X$ we study the maximal length of $u_i$. We give sharp upper bounds for the length of $u_i$ independently of $X$. Precisely, this upper bound is infinity for $d\ge 3$ and $\sqrt2$ for $d=2$.


Assessing Cognitive Load on Web Search Tasks

Abstract: Assessing cognitive load on web search is useful for characterizing search system features and search tasks with respect to their demands on the searcher's mental effort. It is also helpful for examining how individual differences among searchers (e.g. cognitive abilities) affect the search process. We examined cognitive load from the perspective of primary and secondary task performance. A controlled web search study was conducted with 48 participants. The primary task performance components were found to be significantly related to both the objective and the subjective task difficulty. However, the relationship between objective and subjective task difficulty and the secondary task performance measures was weaker than expected. The results indicate that the dual-task approach needs to be used with caution.


Combinatorial Auctions with Budgets

Abstract: We consider budget constrained combinatorial auctions where bidder $i$ has a private value $v_i$, a budget $b_i$, and is interested in all the items in $S_i$. The value to agent $i$ of a set of items $R$ is $|R \cap S_i| \cdot v_i$. Such auctions capture adword auctions, where advertisers offer a bid for ads in response to an advertiser-dependent set of adwords, and advertisers have budgets. It is known that even of all items are identical and all budgets are public it is not possible to be truthful and efficient. Our main result is a novel auction that runs in polynomial time, is incentive compatible, and ensures Pareto-optimality for such auctions when the valuations are private and the budgets are public knowledge. This extends the result of Dobzinski et al. (FOCS 2008) for auctions of multiple {\sl identical} items and public budgets to single-valued {\sl combinatorial} auctions with public budgets.


Internet voting protocol based on implicit data security

Abstract: This paper presents a new protocol for Internet voting based on implicit data security. This protocol allows recasting of votes, which permits a change of mind by voters either during the time window over which polling is open or during a shorter period over which recasting is permitted. The security of votes depends on multiple servers such that each vote is divided into partitions and these partitions are distributed among the servers, all of which need to be brought together to reconstruct the votes. Such a protocol has potential applications in bargaining and electronic commerce.


Tiling for Performance Tuning on Different Models of GPUs

Abstract: The strategy of using CUDA-compatible GPUs as a parallel computation solution to improve the performance of programs has been more and more widely approved during the last two years since the CUDA platform was released. Its benefit extends from the graphic domain to many other computationally intensive domains. Tiling, as the most general and important technique, is widely used for optimization in CUDA programs. New models of GPUs with better compute capabilities have, however, been released, new versions of CUDA SDKs were also released. These updated compute capabilities must to be considered when optimizing using the tiling technique. In this paper, we implement image interpolation algorithms as a test case to discuss how different tiling strategies affect the program's performance. We especially focus on how the different models of GPUs affect the tiling's effectiveness by executing the same program on two different models of GPUs equipped testing platforms. The results demonstrate that an optimized tiling strategy on one GPU model is not always a good solution when execute on other GPU models, especially when some external conditions were changed.


Designing a Truly Integrated (Onsite and Online) Conference: Concept, Processes, Solutions

Abstract: Web conferencing tools have entered the mainstream of business applications. Using web conferencing for IEEE conferences has a good potential of adding value to both organizers and participants. Authors propose a concept of Truly Integrated Conference (TIC) according to which a multi-point worldwide-distributed network of conference online authors/participants will enhance the standard (centralized) IEEE conference model, which requires attendance of the participants in person at the main conference location. The concept entails seamless integration of the onsite and online conference systems, including data/presentation, video, audio channels. Benefits and challenges of the TIC concept are analyzed. Requirements to the web conferencing system capable of supporting the TIC conference are presented and reviewed against commercial web conferencing tools. Case study of the IEEE Toronto International Conference ? Science and Technology for Humanity, which was the first realization of TIC, is presented which analyzes various aspects (organizational, technological, and financial) of the integrated conference.


Fusion Discrete Logarithm Problems

Abstract: The Discrete Logarithm Problem is well-known among cryptographers, for its computational hardness that grants security to some of the most commonly used cryptosystems these days. Still, many of these are limited to a small number of candidate algebraic structures which permit implementing the algorithms. In order to extend the applicability of discrete-logarithm-based cryptosystems to a much richer class of algebraic structures, we present a generalized form of exponential function. Our extension relaxes some assumptions on the exponent, which is no longer required to be an integer. Using an axiomatic characterization of the exponential function, we show how to construct mappings that obey the same rules as exponentials, but can raise vectors to the power of other vectors in an algebraically sound manner. At the same time, computational hardness is not affected (in fact, the problem could possibly be strengthened). Setting up standard cryptosystems in terms of our generalized exponential function is simple and requires no change to the existing security proofs. This opens the field for building much more general schemes than the ones known so far.


Genealogical Information Search by Using Parent Bidirectional Breadth Algorithm and Rule Based Relationship

Abstract: Genealogical information is the best histories resources for culture study and cultural heritage. The genealogical research generally presents family information and depict tree diagram. This paper presents Parent Bidirectional Breadth Algorithm (PBBA) to find consanguine relationship between two persons. In addition, the paper utilizes rules based system in order to identify consanguine relationship. The study reveals that PBBA is fast to solve the genealogical information search problem and the Rule Based Relationship provides more benefits in blood relationship identification.


Web-Based Expert System for Civil Service Regulations: RCSES

Abstract: Internet and expert systems have offered new ways of sharing and distributing knowledge, but there is a lack of researches in the area of web based expert systems. This paper introduces a development of a web-based expert system for the regulations of civil service in the Kingdom of Saudi Arabia named as RCSES. It is the first time to develop such system (application of civil service regulations) as well the development of it using web based approach. The proposed system considers 17 regulations of the civil service system. The different phases of developing the RCSES system are presented, as knowledge acquiring and selection, ontology and knowledge representations using XML format. XML Rule-based knowledge sources and the inference mechanisms were implemented using ASP.net technique. An interactive tool for entering the ontology and knowledge base, and the inferencing was built. It gives the ability to use, modify, update, and extend the existing knowledge base in an easy way. The knowledge was validated by experts in the domain of civil service regulations, and the proposed RCSES was tested, verified, and validated by different technical users and the developers staff. The RCSES system is compared with other related web based expert systems, that comparison proved the goodness, usability, and high performance of RCSES.


The Peacock Encryption Method

Abstract: Here is described a preliminary method that enables secure 'anti-search-engine' encryption, where the middleman can participate in the encrypted information exchange, without being able to understand the exchanged information, encrypted using a one-way function, as well as being unaware of one of two main exchange participants.


OMI4papps: Optimisation, Modelling and Implementation for Highly Parallel Applications

Abstract: This article reports on first results of the KONWIHR-II project OMI4papps at the Leibniz Supercomputing Centre (LRZ). The first part describes Apex-MAP, a tunable synthetic benchmark designed to simulate the performance of typical scientific applications. Apex-MAP mimics common memory access patterns and different computational intensity of scientific codes. An approach for modelling LRZ's application mix is given whichh makes use of performance counter measurements of real applications running on "HLRB II", an SGI Altix system based on 9728 Intel Montecito dual-cores. The second part will show how the Apex-MAP benchmark could be used to simulate the performance of two mathematical kernels frequently used in scientific applications: a dense matrix-matrix multiplication and a sparse matrix-vector multiplication. The performance of both kernels has been intensively studied on x86 cores and hardware accelerators. We will compare the predicted performance with measured data to validate our Apex-MAP approach.


How not to share a set of secrets

Abstract: This note analyses one of the existing space efficient secret sharing schemes and suggests vulnerabilities in its design. We observe that the said algorithm fails for certain choices of the set of secrets and there is no reason for preferring this particular scheme over alternative schemes. The paper also elaborates the adoption of a scheme proposed by Hugo Krawczyk as an extension of Shamir's scheme, for a set of secrets. Such an implementation is space optimal and works for all choices of secrets. We also propose two new methods of attack which are valid under certain assumptions and observe that it is the elimination of random values that facilitates these kinds of attacks.


Cheating for Problem Solving: A Genetic Algorithm with Social Interactions

Abstract: We propose a variation of the standard genetic algorithm that incorporates social interaction between the individuals in the population. Our goal is to understand the evolutionary role of social systems and its possible application as a non-genetic new step in evolutionary algorithms. In biological populations, ie animals, even human beings and microorganisms, social interactions often affect the fitness of individuals. It is conceivable that the perturbation of the fitness via social interactions is an evolutionary strategy to avoid trapping into local optimum, thus avoiding a fast convergence of the population. We model the social interactions according to Game Theory. The population is, therefore, composed by cooperator and defector individuals whose interactions produce payoffs according to well known game models (prisoner's dilemma, chicken game, and others). Our results on Knapsack problems show, for some game models, a significant performance improvement as compared to a standard genetic algorithm.


A complete characterization of group-strategyproof mechanisms of cost-sharing

Abstract: We study the problem of designing group-strategyproof cost-sharing mechanisms. The players report their bids for getting serviced and the mechanism decides which players are going to be serviced and how much each one of them is going to pay. We determine three conditions: \emph{Fence Monotonicity}, \emph{Stability} of the allocation and \emph{Validity} of the tie-breaking rule that are necessary and sufficient for group-strategyproofness, regardless of the cost function. Fence Monotonicity puts restrictions only on the payments of the mechanism and stability only on the allocation. Consequently Fence Monotonicity characterizes group-strategyproof cost-sharing schemes. Finally, we use our results to prove that there exist families of cost functions, where any group-strategyproof mechanism has unbounded approximation ratio.


RapidMind: Portability across Architectures and its Limitations

Abstract: Recently, hybrid architectures using accelerators like GPGPUs or the Cell processor have gained much interest in the HPC community. The RapidMind Multi-Core Development Platform is a programming environment that allows generating code which is able to seamlessly run on hardware accelerators like GPUs or the Cell processor and multicore CPUs both from AMD and Intel. This paper describes the ports of three mathematical kernels to RapidMind which are chosen as synthetic benchmarks and representatives of scientific codes. Performance of these kernels has been measured on various RapidMind backends (cuda, cell and x86) and compared to other hardware-specific implementations (using CUDA, Cell SDK and Intel MKL). The results give an insight in the degree of portability of RapidMind code and code performance across different architectures.


Quantitative Games on Probabilistic Timed Automata

Abstract: Two-player zero-sum games are a well-established model for synthesising controllers that optimise some performance criterion. In such games one player represents the controller, while the other describes the (adversarial) environment, and controller synthesis corresponds to computing the optimal strategies of the controller for a given criterion. Asarin and Maler initiated the study of quantitative games on (non-probabilistic) timed automata by synthesising controllers which optimise the time to reach a final state. The correctness and termination of their approach was dependent on exploiting the properties of a special class of functions, called simple functions, that can be finitely represented. In this paper we consider quantitative games over probabilistic timed automata. Since the concept of simple functions is not sufficient to solve games in this setting, we generalise simple functions to so-called quasi-simple functions. Then, using this class of functions, we demonstrate that the problem of solving games with either expected reachability-time or expected discounted-time criteria on probabilistic timed automata are in NEXPTIME $\cap$ co-NEXPTIME.


A Key Distribution Scheme for Sensor Networks Using Structured Graphs

Abstract: This paper presents a new key predistribution scheme for sensor networks based on structured graphs. Structured graphs are advantageous in that they can be optimized to minimize the parameter of interest. The proposed approach achieves a balance between the number of keys per node, path lengths, network diameter and the complexity of routing algorithm.


Avoiding Interruptions - QoE Trade-offs in Block-coded Streaming Media Applications

Abstract: We take an analytical approach to study Quality of user Experience (QoE) for video streaming applications. First, we show that random linear network coding applied to blocks of video frames can significantly simplify the packet requests at the network layer and save resources by avoiding duplicate packet reception. Network coding allows us to model the receiver's buffer as a queue with Poisson arrivals and deterministic departures. We consider the probability of interruption in video playback as well as the number of initially buffered packets (initial waiting time) as the QoE metrics. We characterize the optimal trade-off between these metrics by providing upper and lower bounds on the minimum initial buffer size, required to achieve certain level of interruption probability for different regimes of the system parameters. Our bounds are asymptotically tight as the file size goes to infinity.


Formal Theories for Logspace Counting

Abstract: We introduce two-sorted theories in the style of Cook and Nguyen for the complexity classes ParityL and DET, whose complete problems include determinants over GF(2) and Z, respectively. The definable functions in these theories are the functions in the corresponding complexity classes; thus each theory formalizes reasoning using concepts from its corresponding complexity class.


A Wide range Survey on Recall Based Graphical User Authentications Algorithms Based on ISO and Attack Patterns

Abstract: Nowadays, user authentication is one of the important topics in information security. Text based strong password schemes could provide with certain degree of security. However, the fact that strong passwords being difficult to memorize often leads their owners to write them down on papers or even save them in a computer file. Graphical user authentication (GUA) has been proposed as a possible alternative solution to text based authentication, motivated particularly by the fact that humans can remember images better than text. In recent years, many networks, computer systems and Internet based environments try used GUA technique for their users authentication. All of GUA algorithms have two different aspects which are usability and security. Unfortunately, none of graphical algorithms were being able to cover both of these aspects at the same time. This paper presents a wide range survey on the pure and cued recall based algorithms in GUA, based on ISO standards for usability and attack patterns standards for security. After explain usability ISO standards and attack patterns international standards, we try to collect the major attributes of usability and security in GUA. Finally, try to make comparison tables among all recall based algorithms based on usability attributes and attack patterns those we found.


A New Method to Extract Dorsal Hand Vein Pattern using Quadratic Inference Function

Abstract: Among all biometric, dorsal hand vein pattern is attracting the attention of researchers, of late. Extensive research is being carried out on various techniques in the hope of finding an efficient one which can be applied on dorsal hand vein pattern to improve its accuracy and matching time. One of the crucial step in biometric is the extraction of features. In this paper, we propose a method based on quadratic inference function to the dorsal hand vein features to extract its features. The biometric system developed was tested on a database of 100 images. The false acceptance rate (FAR), false rejection rate (FRR) and the matching time are being computed.


Architecture of Network Management Tools for Heterogeneous System

Abstract: Managing heterogeneous network systems is a difficult task because each of these networks has its own curious management system. These networks usually are constructed on independent management protocols which are not compatible with each other. This results in the coexistence of many management systems with different managing functions and services across enterprises. Incompatibility of different management systems makes management of whole system a very complex and often complicated job. Ideally, it is necessary to implement centralized metalevel management across distributed heterogeneous systems and their underlying supporting network systems where the information flow and guidance is provided via a single console or single operating panels which integrates all the management functions in spite of their individual protocols and structures. This paper attempts to provide a novel network management tool architecture which supports heterogeneous managements across many different architectural platforms. Furthermore, an architectural approach to integrate heterogeneous network is proposed. This architecture takes into account both wireless fixed and mobile nodes.


A Topological derivative based image segmentation for sign language recognition system using isotropic filter

Abstract: The need of sign language is increasing radically especially to hearing impaired community. Only few research groups try to automatically recognize sign language from video, colored gloves and etc. Their approach requires a valid segmentation of the data that is used for training and of the data that is used to be recognized. Recognition of a sign language image sequence is challenging because of the variety of hand shapes and hand motions. Here, this paper proposes to apply a combination of image segmentation with restoration using topological derivatives for achieving high recognition accuracy. Image quality measures are conceded here to differentiate the methods both subjectively as well as objectively. Experiments show that the additional use of the restoration before segmenting the postures significantly improves the correct rate of hand detection, and that the discrete derivatives yields a high rate of discrimination between different static hand postures as well as between hand postures and the scene background. Eventually, the research is to contribute to the implementation of automated sign language recognition system mainly established for the welfare purpose.


A Framework for Validation of Object Oriented Design Metrics

Abstract: A large number of metrics have been proposed for the quality of object oriented software. Many of these metrics have not been properly validated due to poor methods of validation and non acceptance of metrics on scientific grounds. In the literature, two types of validations namely internal (theoretical) and external (empirical) are recommended. In this study, the authors have used both theoretical as well as empirical validation for validating already proposed set of metrics for the five quality factors. These metrics were proposed by Kumar and Soni.


A New Image Steganography Based On First Component Alteration Technique

Abstract: In this paper, A new image steganography scheme is proposed which is a kind of spatial domain technique. In order to hide secret data in cover-image, the first component alteration technique is used. Techniques used so far focuses only on the two or four bits of a pixel in a image (at the most five bits at the edge of an image) which results in less peak to signal noise ratio and high root mean square error. In this technique, 8 bits of blue components of pixels are replaced with secret data bits. Proposed scheme can embed more data than previous schemes and shows better image quality. To prove this scheme, several experiments are performed, and are compared the experimental results with the related previous works.


Evaluating Effectiveness of Tamper Proofing on Dynamic Graph Software Watermarks

Abstract: For enhancing the protection level of dynamic graph software watermarks and for the purpose of conducting the analysis which evaluates the effect of integrating two software protection techniques such as software watermarking and tamper proofing, constant encoding technique along with the enhancement through the idea of constant splitting is proposed. In this paper Thomborson technique has been implemented with the scheme of breaking constants which enables to encode all constants without having any consideration about their values with respect to the value of watermark tree. Experimental analysis which have been conducted and provided in this paper concludes that the constant encoding process significantly increases the code size, heap space usage, and execution time, while making the tamper proofed code resilient to variety of semantic preserving program transformation attacks.


A Novel Trigon based Dual Authentication Protocol for Enhancing Security in Grid Environment

Abstract: In recent times, a necessity has been raised in order to distribute computing applications often across grids. These applications are dependent on the services like data transfer or data portal services as well as submission of jobs. Security is of utmost importance in grid computing applications as grid resources are heterogeneous, dynamic, and multidomain. Authentication remains as the significant security challenge in grid environment. In traditional authentication protocol a single server stores the sensitive user credentials, like username and password. When such a server is compromised, a large number of user passwords, will be exposed. Our proposed approach uses a dual authentication protocol in order to improve the authentication service in grid environment. The protocol utilizes the fundamental concepts of trigon and based on the parameters of the trigon the user authentication will be performed. In the proposed protocol, the password is interpreted and alienated into more than one unit and these units are stored in two different servers, namely, Authentication Server and Backend Server. Only when the combined authentication scheme from both the servers authenticates the user, the privilege of accessing the requested resources is obtained by the user. The main advantage of utilizing the dual authentication protocol in grid computing is that an adversary user cannot attain the access privilege by compromising a single consolidated server because of the fact that the split password is stored in different servers.


Design and Analysis of a Spurious Switching Suppression Technique Equipped Low Power Multiplier with Hybrid Encoding Scheme

Abstract: Multiplication is an arithmetic operation that is mostly used in Digital Signal Processing (DSP) and communication applications. Efficient implementation of the multipliers is required in many applications. The design and analysis of Spurious Switching Suppression Technique (SSST) equipped low power multiplier with hybrid encoding is presented in this paper. The proposed encoding technique reduces the number of switching activity and dynamic power consumption by analyzing the bit patterns in the input data. In this proposed encoding scheme, the operation is executed depends upon the number of 1s and its position in the multiplier data. The architecture of the proposed multiplier is designed using a low power full adder which consumes less power than the other adder architectures. The switching activity of the proposed multiplier has been reduced by 86 percent and 46percent compared with conventional and Booth multiplier respectively. It is observed from the device level simulation using TANNER 12.6 EDA that the power consumption of the proposed multiplier has been reduced by 87 percent and 26 percent compared with conventional and Booth multiplier.


ICD 10 Based Medical Expert System Using Fuzzy Temporal Logic

Abstract: Medical diagnosis process involves many levels and considerable amount of time and money are invariably spent for the first level of diagnosis usually made by the physician for all the patients every time. Hence there is a need for a computer based system which not only asks relevant questions to the patients but also aids the physician by giving a set of possible diseases from the symptoms obtained using logic at inference. In this work, an ICD10 based Medical Expert System that provides advice, information and recommendation to the physician using fuzzy temporal logic. The knowledge base used in this system consists of facts of symptoms and rules on diseases. It also provides fuzzy severity scale and weight factor for symptom and disease and can vary with respect to time. The system generates the possible disease conditions based on modified Euclidean metric using Elders algorithm for effective clustering. The minimum similarity value is used as the decision parameter to identify a disease.


Multiprocessor Scheduling For Tasks With Priority Using GA

Abstract: Multiprocessors have emerged as a powerful computing means for running realtime applications, especially where a uniprocessor system would not be sufficient enough to execute all the tasks. The high performance and reliability of multiprocessors have made them a powerful computing resource. Such computing environment requires an efficient algorithm to determine when and on which processor a given task should execute. In multiprocessor systems, an efficient scheduling of a parallel program onto the processors that minimizes the entire execution time is vital for achieving a high performance. This scheduling problem is known to be NPHard. In multiprocessor scheduling problem, a given program is to be scheduled in a given multiprocessor system such that the programs execution time is minimized. The last job must be completed as early as possible. Genetic algorithm (GA) is one of the widely used techniques for constrained optimization problems. Genetic algorithms are basically search algorithms based on the mechanics of natural selection and natural genesis. The main goal behind research on genetic algorithms is robustness i.e. balance between efficiency and efficacy. This paper proposes Genetic algorithm to solve scheduling problem of multiprocessors that minimizes the make span.


An Improved Image Mining Technique For Brain Tumour Classification Using Efficient Classifier

Abstract: An improved image mining technique for brain tumor classification using pruned association rule with MARI algorithm is presented in this paper. The method proposed makes use of association rule mining technique to classify the CT scan brain images into three categories namely normal, benign and malign. It combines the low level features extracted from images and high level knowledge from specialists. The developed algorithm can assist the physicians for efficient classification with multiple keywords per image to improve the accuracy. The experimental result on prediagnosed database of brain images showed 96 percent and 93 percent sensitivity and accuracy respectively.


Hierarchical Route Optimization by Using Tree information option in a Mobile Networks

Abstract: The Networks Mobility (NEMO) Protocol is a way of managing the mobility of an entire network, and mobile internet protocol is the basic solution for Networks Mobility. A hierarchical route optimization system for mobile network is proposed to solve management of hierarchical route optimization problems. In present paper, we study Hierarchical Route Optimization Scheme using Tree Information Option (HROSTIO). The concept of optimization finding the extreme of a function that maps candidate solution to scalar values of quality, is an extremely general and useful idea. For solving this problem, we use a few salient adaptations and we also extend HROSTIO perform routing between the mobile networks.


Seeing Beyond the Surface, Understanding and Tracking Fraudulent Cyber Activities

Abstract: The malaise of electronic spam mail that solicit illicit partnership using bogus business proposals (popularly called 419 mails) remained unabated on the internet despite concerted efforts. In addition to these are the emergence and prevalence of phishing scams that use social engineering tactics to obtain online access codes such as credit card number, ATM pin numbers, bank account details, social security number and other personal information (22). In an age where dependence on electronic transaction is on the increase, the web security community will have to devise more pragmatic measures to make the cyberspace safe from these demeaning ills. Understanding the perpetrators of internet crimes and their mode of operation is a basis for any meaningful effort towards stemming these crimes. This paper discusses the nature of the criminals engaged in fraudulent cyberspace activities with special emphasis on the Nigeria 419 scam mails. Based on a qualitative analysis and experiments to trace the source of electronic spam and phishing emails received over a six months period, we provide information about the scammers personalities, motivation, methodologies and victims. We posited that popular email clients are deficient in the provision of effective mechanisms that can aid users in identifying fraud mails and protect them against phishing attacks. We demonstrate, using state of the art techniques, how users can detect and avoid fraudulent emails and conclude by making appropriate recommendations based on our findings.


On the Power of Unambiguity in Logspace

Abstract: We report progress on the \NL vs \UL problem. [-] We show unconditionally that the complexity class $\ReachFewL\subseteq\UL$. This improves on the earlier known upper bound $\ReachFewL \subseteq \FewL$. [-] We investigate the complexity of min-uniqueness - a central notion in studying the \NL vs \UL problem. We show that min-uniqueness is necessary and sufficient for showing $\NL =\UL$. We revisit the class $\OptL[\log n]$ and show that {\sc ShortestPathLength} - computing the length of the shortest path in a DAG, is complete for $\OptL[\log n]$. We introduce $\UOptL[\log n]$, an unambiguous version of $\OptL[\log n]$, and show that (a) $\NL =\UL$ if and only if $\OptL[\log n] = \UOptL[\log n]$, (b) $\LogFew \leq \UOptL[\log n] \leq \SPL$. [-] We show that the reachability problem over graphs embedded on 3 pages is complete for \NL. This contrasts with the reachability problem over graphs embedded on 2 pages which is logspace equivalent to the reachability problem in planar graphs and hence is in \UL.


Block Sensitivity of Minterm-Transitive Functions

Abstract: Boolean functions with symmetry properties are interesting from a complexity theory perspective; extensive research has shown that these functions, if nonconstant, must have high `complexity' according to various measures. In recent work of this type, Sun gave bounds on the block sensitivity of nonconstant Boolean functions invariant under a transitive permutation group. Sun showed that all such functions satisfy bs(f) = Omega(N^{1/3}), and that there exists such a function for which bs(f) = O(N^{3/7}ln N). His example function belongs to a subclass of transitively invariant functions called the minterm-transitive functions (defined in earlier work by Chakraborty). We extend these results in two ways. First, we show that nonconstant minterm-transitive functions satisfy bs(f) = Omega(N^{3/7}). Thus Sun's example function has nearly minimal block sensitivity for this subclass. Second, we give an improved example: a minterm-transitive function for which bs(f) = O(N^{3/7}ln^{1/7}N).


Message Detection and Extraction of Chaotic Optical Communication Using Time-Frequency Analysis

Abstract: The security of chaotic optical communication using time-frequency (TF) representation is analyzed in this paper. The mean scalogram ratio (MSR) of TF representation and peak sidelobe level of MSR are defined to detect message. Algorithm for message detection and extraction is presented in detail. Two typical message encryption schemes, chaos masking and chaos modulation, are analyzed. The results reveal that it is not secure to transmit message when the message frequency locates at low power on power spectrum portrait. The proposed method is very useful for estimating the security level of message masking in chaotic optical communication.


The Isomorphism Problem On Classes of Automatic Structures

Abstract: Automatic structures are finitely presented structures where the universe and all relations can be recognized by finite automata. It is known that the isomorphism problem for automatic structures is complete for $\Sigma^1_1$; the first existential level of the analytical hierarchy. Several new results on isomorphism problems for automatic structures are shown in this paper: (i) The isomorphism problem for automatic equivalence relations is complete for $\Pi^0_1$ (first universal level of the arithmetical hierarchy). (ii) The isomorphism problem for automatic trees of height $n \geq 2$ is $\Pi^0_{2n-3}$-complete. (iii) The isomorphism problem for automatic linear orders is not arithmetical. This solves some open questions of Khoussainov, Rubin, and Stephan.


Predictability of PV power grid performance on insular sites without weather stations: use of artificial neural networks

Abstract: The official meteorological network is poor on the island of Corsica: only three sites being about 50 km apart are equipped with pyranometers which enable measurements by hourly and daily step. These sites are Ajaccio (41\degree 55'N and 8\degree 48'E, seaside), Bastia (42\degree 33'N, 9\degree 29'E, seaside) and Corte (42\degree 30'N, 9\degree 15'E average altitude of 486 meters). This lack of weather station makes difficult the predictability of PV power grid performance. This work intends to study a methodology which can predict global solar irradiation using data available from another location for daily and hourly horizon. In order to achieve this prediction, we have used Artificial Neural Network which is a popular artificial intelligence technique in the forecasting domain. A simulator has been obtained using data available for the station of Ajaccio that is the only station for which we have a lot of data: 16 years from 1972 to 1987. Then we have tested the efficiency of this simulator in two places with different geographical features: Corte, a mountainous region and Bastia, a coastal region. On daily horizon, the relocation has implied fewer errors than a "na\"ive" prediction method based on the persistence (RMSE=1468 Vs 1383Wh/m^2 to Bastia and 1325 Vs 1213Wh/m^2 to Corte). On hourly case, the results were still satisfactory, and widely better than persistence (RMSE=138.8 Vs 109.3 Wh/m^2 to Bastia and 135.1 Vs 114.7 Wh/m^2 to Corte). The last experiment was to evaluate the accuracy of our simulator on a PV power grid localized at 10 km from the station of Ajaccio. We got errors very suitable (nRMSE=27.9%, RMSE=99.0 W.h) compared to those obtained with the persistence (nRMSE=42.2%, RMSE=149.7 W.h).


What's Decidable About Sequences?

Abstract: We present a first-order theory of sequences with integer elements, Presburger arithmetic, and regular constraints, which can model significant properties of data structures such as arrays and lists. We give a decision procedure for the quantifier-free fragment, based on an encoding into the first-order theory of concatenation; the procedure has PSPACE complexity. The quantifier-free fragment of the theory of sequences can express properties such as sortedness and injectivity, as well as Boolean combinations of periodic and arithmetic facts relating the elements of the sequence and their positions (e.g., "for all even i's, the element at position i has value i+3 or 2i"). The resulting expressive power is orthogonal to that of the most expressive decidable logics for arrays. Some examples demonstrate that the fragment is also suitable to reason about sequence-manipulating programs within the standard framework of axiomatic semantics.


Sampled Longest Common Prefix Array

Abstract: When augmented with the longest common prefix (LCP) array and some other structures, the suffix array can solve many string processing problems in optimal time and space. A compressed representation of the LCP array is also one of the main building blocks in many compressed suffix tree proposals. In this paper, we describe a new compressed LCP representation: the sampled LCP array. We show that when used with a compressed suffix array (CSA), the sampled LCP array often offers better time/space trade-offs than the existing alternatives. We also show how to construct the compressed representations of the LCP array directly from a CSA.


NLHB : A Non-Linear Hopper Blum Protocol

Abstract: In this paper, we propose a light-weight provably-secure authentication protocol called the NLHB protocol, which is a variant of the HB protocol. The HB protocol uses the complexity of decoding linear codes for security against passive attacks. In contrast, security for the NLHB protocol is proved by reducing passive attacks to the problem of decoding a class of non-linear codes that are provably hard. We demonstrate that the existing passive attacks on the HB protocol family, which have contributed to considerable reduction in its effective key-size, are ineffective against the NLHB protocol. From the evidence, we conclude that smaller-key sizes are sufficient for the NLHB protocol to achieve the same level of passive attack security as the HB Protocol. Further, for this choice of parameters, we provide an implementation instance for the NLHB protocol for which the Prover/Verifier complexity is lower than the HB protocol, enabling authentication on very low-cost devices like RFID tags. Finally, in the spirit of the HB$^{+}$ protocol, we extend the NLHB protocol to the NLHB$^{+}$ protocol and prove security against the class of active attacks defined in the DET Model.


Cooperative Automated Worm Response and Detection Immune Algorithm

Abstract: The role of T-cells within the immune system is to confirm and assess anomalous situations and then either respond to or tolerate the source of the effect. To illustrate how these mechanisms can be harnessed to solve real-world problems, we present the blueprint of a T-cell inspired algorithm for computer security worm detection. We show how the three central T-cell processes, namely T-cell maturation, differentiation and proliferation, naturally map into this domain and further illustrate how such an algorithm fits into a complete immune inspired computer security system and framework.


Evolving MultiAlgebras unify all usual sequential computation models

Abstract: It is well-known that Abstract State Machines (ASMs) can simulate "step-by-step" any type of machines (Turing machines, RAMs, etc.). We aim to overcome two facts: 1) simulation is not identification, 2) the ASMs simulating machines of some type do not constitute a natural class among all ASMs. We modify Gurevich's notion of ASM to that of EMA ("Evolving MultiAlgebra") by replacing the program (which is a syntactic object) by a semantic object: a functional which has to be very simply definable over the static part of the ASM. We prove that very natural classes of EMAs correspond via "literal identifications" to slight extensions of the usual machine models and also to grammar models. Though we modify these models, we keep their computation approach: only some contingencies are modified. Thus, EMAs appear as the mathematical model unifying all kinds of sequential computation paradigms.


Comparing Simulation Output Accuracy of Discrete Event and Agent Based Models: A Quantitive Approach

Abstract: In our research we investigate the output accuracy of discrete event simulation models and agent based simulation models when studying human centric complex systems. In this paper we focus on human reactive behaviour as it is possible in both modelling approaches to implement human reactive behaviour in the model by using standard methods. As a case study we have chosen the retail sector, and here in particular the operations of the fitting room in the women wear department of a large UK department store. In our case study we looked at ways of determining the efficiency of implementing new management policies for the fitting room operation through modelling the reactive behaviour of staff and customers of the department. First, we have carried out a validation experiment in which we compared the results from our models to the performance of the real system. This experiment also allowed us to establish differences in output accuracy between the two modelling methids. In a second step a multi-scenario experiment was carried out to study the behaviour of the models when they are used for the purpose of operational improvement. Overall we have found that for our case study example both discrete event simulation and agent based simulation have the same potential to support the investigation into the efficiency of implementing new management policies.


Weighted Logics for Nested Words and Algebraic Formal Power Series

Abstract: Nested words, a model for recursive programs proposed by Alur and Madhusudan, have recently gained much interest. In this paper we introduce quantitative extensions and study nested word series which assign to nested words elements of a semiring. We show that regular nested word series coincide with series definable in weighted logics as introduced by Droste and Gastin. For this we establish a connection between nested words and the free bisemigroup. Applying our result, we obtain characterizations of algebraic formal power series in terms of weighted logics. This generalizes results of Lautemann, Schwentick and Therien on context-free languages.


Building reputation systems for better ranking

Abstract: How to rank web pages, scientists and online resources has recently attracted increasing attention from both physicists and computer scientists. In this paper, we study the ranking problem of rating systems where users vote objects by discrete ratings. We propose an algorithm that can simultaneously evaluate the user reputation and object quality in an iterative refinement way. According to both the artificially generated data and the real data from MovieLens and Amazon, our algorithm can considerably enhance the ranking accuracy. This work highlights the significance of reputation systems in the Internet era and points out a way to evaluate and compare the performances of different reputation systems.


Towards a Generic Framework to Generate Explanatory Traces of Constraint Solving and Rule-Based Reasoning

Abstract: In this report, we show how to use the Simple Fluent Calculus (SFC) to specify generic tracers, i.e. tracers which produce a generic trace. A generic trace is a trace which can be produced by different implementations of a software component and used independently from the traced component. This approach is used to define a method for extending a java based CHRor platform called CHROME (Constraint Handling Rule Online Model-driven Engine) with an extensible generic tracer. The method includes a tracer specification in SFC, a methodology to extend it, and the way to integrate it with CHROME, resulting in the platform CHROME-REF (for Reasoning Explanation Facilities), which is a constraint solving and rule based reasoning engine with explanatory traces.


DCA for Bot Detection

Abstract: Ensuring the security of computers is a non-trivial task, with many techniques used by malicious users to compromise these systems. In recent years a new threat has emerged in the form of networks of hijacked zombie machines used to perform complex distributed attacks such as denial of service and to obtain sensitive data such as password information. These zombie machines are said to be infected with a 'bot' - a malicious piece of software which is installed on a host machine and is controlled by a remote attacker, termed the 'botmaster of a botnet'. In this work, we use the biologically inspired Dendritic Cell Algorithm (DCA) to detect the existence of a single bot on a compromised host machine. The DCA is an immune-inspired algorithm based on an abstract model of the behaviour of the dendritic cells of the human body. The basis of anomaly detection performed by the DCA is facilitated using the correlation of behavioural attributes such as keylogging and packet flooding behaviour. The results of the application of the DCA to the detection of a single bot show that the algorithm is a successful technique for the detection of such malicious software without responding to normally running programs.


Biological Inspiration for Artificial Immune Systems

Abstract: Artificial immune systems (AISs) to date have generally been inspired by naive biological metaphors. This has limited the effectiveness of these systems. In this position paper two ways in which AISs could be made more biologically realistic are discussed. We propose that AISs should draw their inspiration from organisms which possess only innate immune systems, and that AISs should employ systemic models of the immune system to structure their overall design. An outline of plant and invertebrate immune systems is presented, and a number of contemporary research that more biologically-realistic AISs could have is also discussed.


On the Efficiency of Fast RSA Variants in Modern Mobile Phones

Abstract: Modern mobile phones are increasingly being used for more services that require modern security mechanisms such as the public key cryptosystem RSA. It is, however, well known that public key cryptography demands considerable computing resources and that RSA encryption is much faster than RSA decryption. It is consequently an interesting question if RSA as a whole can be executed efficiently on modern mobile phones. In this paper, we explore the efficiency on modern mobile phones of variants of the RSA cryptosystem, covering CRT, MultiPrime RSA, MultiPower RSA, Rebalanced RSA and R Prime RSA by comparing the encryption and decryption time using a simple Java implementation and a typical RSA setup.


An Efficient Inter Carrier Interference Cancellation Schemes for OFDM Systems

Abstract: Orthogonal Frequency Division Multiplexing (OFDM) has recently been used widely in wireless communication systems. OFDM is very effective in combating intersymbol interference and can achieve high data rate in frequency selective channel. For OFDM communication systems, the frequency offsets in mobile radio channels distort the orthogonality between subcarriers resulting in Inter Carrier Interference (ICI). ICI causes power leakage among subcarriers thus degrading the system performance. A wellknown problem of OFDM is its sensitivity to frequency offset between the transmitted and received carrier frequencies. There are two deleterious effects caused by frequency offset one is the reduction of signal amplitude in the output of the filters matched to each of the carriers and the second is introduction of ICI from the other carriers. This research work investigates three effective methods for combating the effects of ICI: ICI Self Cancellation (SC), Maximum Likelihood (ML) estimation, and Extended Kalman Filter (EKF) method. These three methods are compared in terms of bit error rate performance and bandwidth efficiency. Through simulations, it is shown that the three techniques are effective in mitigating the modulation schemes, the ML and EKF methods perform better than the SC method.


High Precision HalfWave Rectifier Circuit In Dual Phase Output Mode

Abstract: This paper present high precision halfwave rectifier circuit in dual phase output mode by 0.5 micrometer CMOS technology, plus or minus 1.5 V low voltage, it has received input signal and sent output current signal, respond in high frequency. The main structure compound with CMOS inverter circuit, common source circuit, and current mirror circuit. Simulation and confirmation quality of working by PSpice program, then it able to operating at maximum frequency about 100 MHz, maximum input current range about 400 \mu Ap p, high precision output signal, low power dissipation, and uses a little transistor.


Internal Location Based System For Mobile Devices Using Passive RFID And Wireless Technology

Abstract: This article has been withdrawn by arXiv administrators due to plagiarized content from arXiv:1009.3448.


High Precision MultiWave Rectifier Circuit Operating in Low Voltage 1.5 Volt Current Mode

Abstract: This article is present high precision multiwave rectifier circuit operating in low voltage plus or minus 1.5 Volt current modes by CMOS technology 0.5 \mum, receive input and give output in current mode, respond at high frequency period. The structure compound with high speed current comparator circuit, current mirror circuit, and CMOS inverter circuit. PSpice program used for confirmation the performance of testing. The PSpice program shows operating of circuit is able to working at maximum input current 400 \muAp p, maximum frequency responding 200 MHz, high precision and low power losses, and non-precision zero crossing output signal.


Classifying Application Phases in Asymmetric Chip Multiprocessors

Abstract: In present study, in order to improve the performance and reduce the amount of power which is dissipated in heterogeneous multicore processors, the ability of detecting the program execution phases is investigated. The programs execution intervals have been classified in different phases based on their throughput and the utilization of the cores. The results of implementing the phase detection technique are investigated on a single core processor and also on a multicore processor. To minimize the profiling overhead, an algorithm for the dynamic adjustment of the profiling intervals is presented. It is based on the behavior of the program and reduces the profiling overhead more than three fold. The results are obtained from executing multiprocessor benchmarks on a given processor. In order to show the program phases clearly, throughput and utilization of execution intervals are presented on a scatter plot. The results are presented for both fixed and variable intervals.


Syllable Analysis to Build a Dictation System in Telugu language

Abstract: In recent decades, Speech interactive systems gained increasing importance. To develop Dictation System like Dragon for Indian languages it is most important to adapt the system to a speaker with minimum training. In this paper we focus on the importance of creating speech database at syllable units and identifying minimum text to be considered while training any speech recognition system. There are systems developed for continuous speech recognition in English and in few Indian languages like Hindi and Tamil. This paper gives the statistical details of syllables in Telugu and its use in minimizing the search space during recognition of speech. The minimum words that cover maximum syllables are identified. This words list can be used for preparing a small text which can be used for collecting speech sample while training the dictation system. The results are plotted for frequency of syllables and the number of syllables in each word. This approach is applied on the CIIL Mysore text corpus which is of 3 million words.


Sinusoidal Frequency Doublers Circuit With Low Voltage 1.5 Volt CMOS Inverter

Abstract: This paper is present sinusoidal frequency doublers circuit with low voltage 1.5 volt CMOS inverter. Main structure of circuit has three parts that is CMOS inverter circuit, differential amplifier circuit, and square root circuit. This circuit has designed to receive input voltage and give output voltage use few MOS transistor, easy to understand, non complex of circuit, high precision, low error and low power. The Simulation of circuit has MOS transistor functional in active and saturation period. PSpice programmed has used to confirmation of testing and simulation.


Speech Recognition by Machine, A Review

Abstract: This paper presents a brief survey on Automatic Speech Recognition and discusses the major themes and advances made in the past 60 years of research, so as to provide a technological perspective and an appreciation of the fundamental progress that has been accomplished in this important area of speech communication. After years of research and development the accuracy of automatic speech recognition remains one of the important research challenges (e.g., variations of the context, speakers, and environment).The design of Speech Recognition system requires careful attentions to the following issues: Definition of various types of speech classes, speech representation, feature extraction techniques, speech classifiers, database and performance evaluation. The problems that are existing in ASR and the various techniques to solve these problems constructed by various research workers have been presented in a chronological order. Hence authors hope that this work shall be a contribution in the area of speech recognition. The objective of this review paper is to summarize and compare some of the well known methods used in various stages of speech recognition system and identify research topic and applications which are at the forefront of this exciting and challenging field.


An Extension for Combination of Duty Constraints in Role-Based Access Control

Abstract: Among access control models, Role Based Access Control (RBAC) is very useful and is used in many computer systems. Static Combination of Duty (SCD) and Dynamic Combination of Duty (DCD) constraints have been introduced recently for this model to handle dependent roles. These roles must be used together and can be considered as a contrary point of conflicting roles. In this paper, we propose several new types of SCD and DCD constraints. Also, we introduce strong dependent roles and define new groups of SCD constraints for these types of roles as SCD with common items and SCD with union items. In addition, we present an extension for SCD constraints in the presence of hierarchy.


An Improved Approach to High Level Privacy Preserving Itemset Mining

Abstract: Privacy preserving association rule mining has triggered the development of many privacy preserving data mining techniques. A large fraction of them use randomized data distortion techniques to mask the data for preserving. This paper proposes a new transaction randomization method which is a combination of the fake transaction randomization method and a new per transaction randomization method. This method distorts the items within each transaction and ensures a higher level of data privacy in comparison to the previous approaches. The pertransaction randomization method involves a randomization function to replace the item by a random number guarantying privacy within the transaction also. A tool has also been developed to implement the proposed approach to mine frequent itemsets and association rules from the data guaranteeing the antimonotonic property.


Call Admission Control performance model for Beyond 3G Wireless Networks

Abstract: The Next Generation Wireless Networks (NGWN) will be heterogeneous in nature where the different Radio Access Technologies (RATs) operate together .The mobile terminals operating in this heterogeneous environment will have different QoS requirements to be handled by the system. These QoS requirements are determined by a set of QoS parameters. The radio resource management is one of the key challenges in NGWN. Call admission control is one of the radio resource management technique plays instrumental role in ensure the desired QoS to the users working on different applications which have diversified QoS requirements from the wireless networks . The call blocking probability is one such QoS parameter for the wireless network. For better QoS it is desirable to reduce the call blocking probability. In this customary scenario it is highly desirable to obtain analytic Performance model. In this paper we propose a higher order Markov chain based performance model for call admission control in a heterogeneous wireless network environment. In the proposed algorithm we have considered three classes of traffic having different QoS requirements and we have considered the heterogeneous network environment which includes the RATs that can effectively handle applications like voice calls, Web browsing and file transfer applications which are with varied QoS parameters. The paper presents the call blocking probabilities for all the three types of traffic both for fixed and varied traffic scenario.


Efficient Candidacy Reduction For Frequent Pattern Mining

Abstract: Certainly, nowadays knowledge discovery or extracting knowledge from large amount of data is a desirable task in competitive businesses. Data mining is a main step in knowledge discovery process. Meanwhile frequent patterns play central role in data mining tasks such as clustering, classification, and association analysis. Identifying all frequent patterns is the most time consuming process due to a massive number of candidate patterns. For the past decade there have been an increasing number of efficient algorithms to mine the frequent patterns. However reducing the number of candidate patterns and comparisons for support counting are still two problems in this field which have made the frequent pattern mining one of the active research themes in data mining. A reasonable solution is identifying a small candidate pattern set from which can generate all frequent patterns. In this paper, a method is proposed based on a new candidate set called candidate head set or H which forms a small set of candidate patterns. The experimental results verify the accuracy of the proposed method and reduction of the number of candidate patterns and comparisons.


Application of a Fuzzy Programming Technique to Production Planning in the Textile Industry

Abstract: Many engineering optimization problems can be considered as linear programming problems where all or some of the parameters involved are linguistic in nature. These can only be quantified using fuzzy sets. The aim of this paper is to solve a fuzzy linear programming problem in which the parameters involved are fuzzy quantities with logistic membership functions. To explore the applicability of the method a numerical example is considered to determine the monthly production planning quotas and profit of a home textile group.


The Application of Mamdani Fuzzy Model for Auto Zoom Function of a Digital Camera

Abstract: Mamdani Fuzzy Model is an important technique in Computational Intelligence (CI) study. This paper presents an implementation of a supervised learning method based on membership function training in the context of Mamdani fuzzy models. Specifically, auto zoom function of a digital camera is modelled using Mamdani technique. The performance of control method is verified through a series of simulation and numerical results are provided as illustrations.


Comparative Evaluation and Analysis of IAX and RSW

Abstract: Voice over IP (VoIP) is a technology to transport media over IP networks such as the Internet. VoIP has the capability of connecting people over packet switched networks instead of traditional circuit switched networks. Recently, the InterAsterisk Exchange Protocol (IAX) has emerged as a new VoIP which is gaining popularity among VoIP products. IAX is known for its simplicity, NAT friendliness, efficiency, and robustness. More recently, the Real time Switching (RSW) control criterion has emerged as a multimedia conferencing protocol. In this paper, we made a comparative evaluation and analysis of IAX and RSW using Mean Opinion Score rating (MOS) and found that they both perform well under different network packet delays in ms.


A Distributed Data Storage Scheme for Sensor Networks

Abstract: We present a data storage scheme for sensor networks that achieves the targets of encryption and distributed storage simultaneously. We partition the data to be stored into numerous pieces such that at least a specific number of them have to be brought together to recreate the data. The procedure for creation of partitions does not use any encryption key and the pieces are implicitly secure. These pieces are then distributed over random sensors for storage. Capture or malfunction of one or more (less than a threshold number of sensors) does not compromise the data. The scheme provides protection against compromise of data in specific sensors due to physical capture or malfunction.


Towards Improving the Mental Model of Software Developers through Cartographic Visualization

Abstract: Software is intangible and knowledge about software systems is typically tacit. The mental model of software developers is thus an important factor in software engineering. It is our vision that developers should be able to refer to code as being "up in the north", "over in the west", or "down-under in the south". We want to provide developers, and everyone else involved in software development, with a *shared*, spatial and stable mental model of their software project. We aim to reinforce this by embedding a cartographic visualization in the IDE (Integrated Development Environment). The visualization is always visible in the bottom-left, similar to the GPS navigation device for car drivers. For each development task, related information is displayed on the map. In this paper we present CODEMAP, an eclipse plug-in, and report on preliminary results from an ongoing user study with professional developers and students.


A Little More, a Lot Better: Improving Path Quality by a Simple Path Merging Algorithm

Abstract: Sampling-based motion planners are an effective means for generating collision-free motion paths. However, the quality of these motion paths (with respect to quality measures such as path length, clearance, smoothness or energy) is often notoriously low, especially in high-dimensional configuration spaces. We introduce a simple algorithm for merging an arbitrary number of input motion paths into a hybrid output path of superior quality, for a broad and general formulation of path quality. Our approach is based on the observation that the quality of certain sub-paths within each solution may be higher than the quality of the entire path. A dynamic-programming algorithm, which we recently developed for comparing and clustering multiple motion paths, reduces the running time of the merging algorithm significantly. We tested our algorithm in motion-planning problems with up to 12 degrees of freedom. We show that our algorithm is able to merge a handful of input paths produced by several different motion planners to produce output paths of much higher quality.


Dendritic Cells for Real-Time Anomaly Detection

Abstract: Dendritic Cells (DCs) are innate immune system cells which have the power to activate or suppress the immune system. The behaviour of human of human DCs is abstracted to form an algorithm suitable for anomaly detection. We test this algorithm on the real-time problem of port scan detection. Our results show a significant difference in artificial DC behaviour for an outgoing portscan when compared to behaviour for normal processes.


Dendritic Cells for Anomaly Detection

Abstract: Artificial immune systems, more specifically the negative selection algorithm, have previously been applied to intrusion detection. The aim of this research is to develop an intrusion detection system based on a novel concept in immunology, the Danger Theory. Dendritic Cells (DCs) are antigen presenting cells and key to the activation of the human signals from the host tissue and correlate these signals with proteins know as antigens. In algorithmic terms, individual DCs perform multi-sensor data fusion based on time-windows. The whole population of DCs asynchronously correlates the fused signals with a secondary data stream. The behaviour of human DCs is abstracted to form the DC Algorithm (DCA), which is implemented using an immune inspired framework, libtissue. This system is used to detect context switching for a basic machine learning dataset and to detect outgoing portscans in real-time. Experimental results show a significant difference between an outgoing portscan and normal traffic.


Computing the output distribution and selection probabilities of a stack filter from the DNF of its positive Boolean function

Abstract: Many nonlinear filters used in practise are stack filters. An algorithm is presented which calculates the output distribution of an arbitrary stack filter S from the disjunctive normal form (DNF) of its underlying positive Boolean function. The so called selection probabilities can be computed along the way.


On the Sets of Real Numbers Recognized by Finite Automata in Multiple Bases

Abstract: This article studies the expressive power of finite automata recognizing sets of real numbers encoded in positional notation. We consider Muller automata as well as the restricted class of weak deterministic automata, used as symbolic set representations in actual applications. In previous work, it has been established that the sets of numbers that are recognizable by weak deterministic automata in two bases that do not share the same set of prime factors are exactly those that are definable in the first order additive theory of real and integer numbers. This result extends Cobham's theorem, which characterizes the sets of integer numbers that are recognizable by finite automata in multiple bases. In this article, we first generalize this result to multiplicatively independent bases, which brings it closer to the original statement of Cobham's theorem. Then, we study the sets of reals recognizable by Muller automata in two bases. We show with a counterexample that, in this setting, Cobham's theorem does not generalize to multiplicatively independent bases. Finally, we prove that the sets of reals that are recognizable by Muller automata in two bases that do not share the same set of prime factors are exactly those definable in the first order additive theory of real and integer numbers. These sets are thus also recognizable by weak deterministic automata. This result leads to a precise characterization of the sets of real numbers that are recognizable in multiple bases, and provides a theoretical justification to the use of weak automata as symbolic representations of sets.


Virtual Private Overlays: Secure Group Commounication in NAT-Constrained Environments

Abstract: Structured P2P overlays provide a framework for building distributed applications that are self-configuring, scalable, and resilient to node failures. Such systems have been successfully adopted in large-scale Internet services such as content delivery networks and file sharing; however, widespread adoption in small/medium scales has been limited due in part to security concerns and difficulty bootstrapping in NAT-constrained environments. Nonetheless, P2P systems can be designed to provide guaranteed lookup times, NAT traversal, point-to-point overlay security, and distributed data stores. In this paper we propose a novel way of creating overlays that are both secure and private and a method to bootstrap them using a public overlay. Private overlay nodes use the public overlay's distributed data store to discover each other, and the public overlay's connections to assist with NAT hole punching and as relays providing STUN and TURN NAT traversal techniques. The security framework utilizes groups, which are created and managed by users through a web based user interface. Each group acts as a Public Key Infrastructure (PKI) relying on the use of a centrally-managed web site providing an automated Certificate Authority (CA). We present a reference implementation which has been used in a P2P VPN (Virtual Private Network). To evaluate our contributions, we apply our techniques to an overlay network modeler, event-driven simulations using simulated time delays, and deployment in the PlanetLab wide-area testbed.


On the Design and Implementation of Structured P2P VPNs

Abstract: Centralized Virtual Private Networks (VPNs) when used in distributed systems have performance constraints as all traffic must traverse through a central server. In recent years, there has been a paradigm shift towards the use of P2P in VPNs to alleviate pressure placed upon the central server by allowing participants to communicate directly with each other, relegating the server to handling session management and supporting NAT traversal using relays when necessary. Another, less common, approach uses unstructured P2P systems to remove all centralization from the VPN. These approaches currently lack the depth in security options provided by other VPN solutions, and their scalability constraints have not been well studied. In this paper, we propose and implement a novel VPN architecture, which uses a structured P2P system for peer discovery, session management, NAT traversal, and autonomic relay selection and a central server as a partially-automated public key infrastructure (PKI) via a user-friendly web interface. Our model also provides the first design and implementation of a P2P VPN with full tunneling support, whereby all non-P2P based Internet traffic routes through a trusted third party and does so in a way that is more secure than existing full tunnel techniques. To verify our model, we evaluate our reference implementation by comparing it quantitatively to other VPN technologies focusing on latency, bandwidth, and memory usage. We also discuss some of our experiences with developing, maintaining, and deploying a P2P VPN.


Multiple Access Network Information-flow And Correction codes

Abstract: The network communication scenario where one or more receivers request all the information transmitted by different sources is considered. We introduce distributed polynomial-time network codes in the presence of malicious nodes. Our codes can achieve any point inside the rate region of multiple-source multicast transmission scenarios both in the cases of coherent and non-coherent network coding. For both cases the encoding and decoding algorithm runs in poly(|E|)exp(s) time, where poly(|E|) is a polynomial function of the number of edges |E| in the network and exp(s) is an exponential function of the number of sources s. Our codes are fully distributed and different sources require no knowledge of the data transmitted by their peers. Our codes are "end-to-end", that is, all nodes apart from the sources and the receivers are oblivious to the adversaries present in the network and simply implement random linear network coding.


On the Model Transform in Stochastic Network Calculus

Abstract: Stochastic network calculus requires special care in the search of proper stochastic traffic arrival models and stochastic service models. Tradeoff must be considered between the feasibility for the analysis of performance bounds, the usefulness of performance bounds, and the ease of their numerical calculation. In theory, transform between different traffic arrival models and transform between different service models are possible. Nevertheless, the impact of the model transform on performance bounds has not been thoroughly investigated. This paper is to investigate the effect of the model transform and to provide practical guidance in the model selection in stochastic network calculus.