OpenRaiser/Intern-Atlas huggingface dataset:
Data Secrecy in Distributed Storage Systems under Exact Repair
Venue: arXiv cs.IT 2013
Year: 2013

The problem of securing data against eavesdropping in distributed storage systems is studied. The focus is on systems that use linear codes and implement exact repair to recover from node failures.The maximum file size that can be stored securely is determined for systems in which all the available nodes help in repair (i.e., repair degree $d=n-1$, where $n$ is the total number of nodes) and for any number of compromised nodes. Similar results in the literature are restricted to the case of at most two compromised nodes. Moreover, new explicit upper bounds are given on the maximum secure file size for systems with $d<n-1$. The key ingredients for the contribution of this paper are new results on subspace intersection for the data downloaded during repair. The new bounds imply the interesting fact that the maximum data that can be stored securely decreases exponentially with the number of compromised nodes.
Analysis of Citizens Acceptance for E-government Services: Applying the UTAUT Model
Venue: arXiv cs.CY 2013
Year: 2013

E-government services aims to provide citizens with more accessible, accurate, real-time and high quality services and information. Although the public sectors in Kingdom of Saudi Arabia (KSA) have promoted their e-Government services for many years, its uses and achievements are few. Therefore, this paper explores the key factors of Saudi citizens acceptance through a research survey and by gathering empirical evidence based on the Unified Theory of Acceptance and the Use of Technology (UTAUT). Survey Data collected from 400 respondents was examined using structural equation modelling (SEM) technique and utilized AMOS tools. The study results explored the factors that affect the acceptance of e-government services in KSA based on UTAUT model. Moreover, as a result of this study an amended UTAUT model was proposed. Such a model contributes to the discussion and development of adoption models for technology.
Joint Precoding and Multivariate Backhaul Compression for the Downlink of Cloud Radio Access Networks
Venue: arXiv cs.IT 2013
Year: 2013

This work studies the joint design of precoding and backhaul compression strategies for the downlink of cloud radio access networks. In these systems, a central encoder is connected to multiple multi-antenna base stations (BSs) via finite-capacity backhaul links. At the central encoder, precoding is followed by compression in order to produce the rate-limited bit streams delivered to each BS over the corresponding backhaul link. In current state-of-the-art approaches, the signals intended for different BSs are compressed independently. In contrast, this work proposes to leverage joint compression, also referred to as multivariate compression, of the signals of different BSs in order to better control the effect of the additive quantization noises at the mobile stations (MSs). The problem of maximizing the weighted sum-rate with respect to both the precoding matrix and the joint correlation matrix of the quantization noises is formulated subject to power and backhaul capacity constraints. An iterative algorithm is proposed that achieves a stationary point of the problem. Moreover, in order to enable the practical implementation of multivariate compression across BSs, a novel architecture is proposed based on successive steps of minimum mean-squared error (MMSE) estimation and per-BS compression. Robust design with respect to imperfect channel state information is also discussed. From numerical results, it is confirmed that the proposed joint precoding and compression strategy outperforms conventional approaches based on the separate design of precoding and compression or independent compression across the BSs.
From Constraints to Resolution Rules, Part II: chains, braids, confluence and T&E
Venue: arXiv cs.AI 2013
Year: 2013

In this Part II, we apply the general theory developed in Part I to a detailed analysis of the Constraint Satisfaction Problem (CSP). We show how specific types of resolution rules can be defined. In particular, we introduce the general notions of a chain and a braid. As in Part I, these notions are illustrated in detail with the Sudoku example - a problem known to be NP-complete and which is therefore typical of a broad class of hard problems. For Sudoku, we also show how far one can go in 'approximating' a CSP with a resolution theory and we give an empirical statistical analysis of how the various puzzles, corresponding to different sets of entries, can be classified along a natural scale of complexity. For any CSP, we also prove the confluence property of some Resolution Theories based on braids and we show how it can be used to define different resolution strategies. Finally, we prove that, in any CSP, braids have the same solving capacity as Trial-and-Error (T&E) with no guessing and we comment this result in the Sudoku case.
A polytime complexity analyser for Probabilistic Polynomial Time over imperative stack programs
Venue: arXiv cs.LO 2013
Year: 2013

We present iSAPP (Imperative Static Analyser for Probabilistic Polynomial Time), a complexity verifier tool that is sound and extensionally complete for the Probabilistic Polynomial Time (PP) complexity class. iSAPP works on an imperative programming language for stack machines. The certificate of polynomiality can be built in polytime, with respect to the number of stacks used.
Extension of hidden markov model for recognizing large vocabulary of sign language
Venue: arXiv cs.CL 2013
Year: 2013

Computers still have a long way to go before they can interact with users in a truly natural fashion. From a users perspective, the most natural way to interact with a computer would be through a speech and gesture interface. Although speech recognition has made significant advances in the past ten years, gesture recognition has been lagging behind. Sign Languages (SL) are the most accomplished forms of gestural communication. Therefore, their automatic analysis is a real challenge, which is interestingly implied to their lexical and syntactic organization levels. Statements dealing with sign language occupy a significant interest in the Automatic Natural Language Processing (ANLP) domain. In this work, we are dealing with sign language recognition, in particular of French Sign Language (FSL). FSL has its own specificities, such as the simultaneity of several parameters, the important role of the facial expression or movement and the use of space for the proper utterance organization. Unlike speech recognition, Frensh sign language (FSL) events occur both sequentially and simultaneously. Thus, the computational processing of FSL is too complex than the spoken languages. We present a novel approach based on HMM to reduce the recognition complexity.
Cognitive Radio Engine Model Utilizing Soft Fusion Based Genetic Algorithm For Cooperative Spectrum Optimization
Venue: arXiv cs.NI 2013
Year: 2013

Cognitive radio (CR) is to detect the presence of primary users (PUs) reliably in order to reduce the interference to licensed communications. Genetic algorithms (GAs) are well suited for CR optimization problems to increase efficiency of bandwidth utilization by manipulating its unused portions of the apparent spectrum. In this paper, a binary genetic algorithm (BGA)-based soft fusion (SF) scheme for cooperative spectrum sensing in cognitive radio network is proposed to improve detection performance and bandwidth utilization. The BGA-based optimization method is implemented at the fusion centre of a linear SF scheme to optimize the weighting coefficients vector to maximize global probability of detection performance. Simulation results and analyses confirm that the proposed scheme meets real time requirements of cognitive radio spectrum sensing and it outperforms conventional natural deflection coefficient- (NDC-), modified deflection coefficient- (MDC-), maximal ratio combining- (MRC-) and equal gain combining- (EGC-) based SDF schemes as well as the OR-rule based hard decision fusion (HDF). The propose BGA scheme also converges fast and achieves the optimum performance, which means that BGA-based method is efficient and quite stable also.
Channel Coding and Source Coding with Increased Partial Side Information
Venue: arXiv cs.IT 2013
Year: 2013

Let (S1,i, S2,i), distributed according to i.i.d p(s1, s2), i = 1, 2, . . . be a memoryless, correlated partial side information sequence. In this work we study channel coding and source coding problems where the partial side information (S1, S2) is available at the encoder and the decoder, respectively, and, additionally, either the encoder's or the decoder's side information is increased by a limited-rate description of the other's partial side information. We derive six special cases of channel coding and source coding problems and we characterize the capacity and the rate-distortion functions for the different cases. We present a duality between the channel capacity and the rate-distortion cases we study. In order to find numerical solutions for our channel capacity and rate-distortion problems, we use the Blahut-Arimoto algorithm and convex optimization tools. As a byproduct of our work, we found a tight lower bound on the Wyner-Ziv solution by formulating its Lagrange dual as a geometric program. Previous results in the literature provide a geometric programming formulation that is only a lower bound, but not necessarily tight. Finally, we provide several examples corresponding to the channel capacity and the rate-distortion cases we presented.
A Constraint Propagation Approach to Probabilistic Reasoning
Venue: arXiv cs.AI 2013
Year: 2013

The paper demonstrates that strict adherence to probability theory does not preclude the use of concurrent, self-activated constraint-propagation mechanisms for managing uncertainty. Maintaining local records of sources-of-belief allows both predictive and diagnostic inferences to be activated simultaneously and propagate harmoniously towards a stable equilibrium.
Implementing Probabilistic Reasoning
Venue: arXiv cs.AI 2013
Year: 2013

General problems in analyzing information in a probabilistic database are considered. The practical difficulties (and occasional advantages) of storing uncertain data, of using it conventional forward- or backward-chaining inference engines, and of working with a probabilistic version of resolution are discussed. The background for this paper is the incorporation of uncertain reasoning facilities in MRS, a general-purpose expert system building tool.
An Evaluation of Two Alternatives to Minimax
Venue: arXiv cs.AI 2013
Year: 2013

In the field of Artificial Intelligence, traditional approaches to choosing moves in games involve the we of the minimax algorithm. However, recent research results indicate that minimizing may not always be the best approach. In this paper we summarize the results of some measurements on several model games with several different evaluation functions. These measurements, which are presented in detail in [NPT], show that there are some new algorithms that can make significantly better use of evaluation function values than the minimax algorithm does.
Knowledge Structures and Evidential Reasoning in Decision Analysis
Venue: arXiv cs.AI 2013
Year: 2013

The roles played by decision factors in making complex subject are decisions are characterized by how these factors affect the overall decision. Evidence that partially matches a factor is evaluated, and then effective computational rules are applied to these roles to form an appropriate aggregation of the evidence. The use of this technique supports the expression of deeper levels of causality, and may also preserve the cognitive structure of the decision maker better than the usual weighting methods, certainty-factor or other probabilistic models can.
Logical Stochastic Optimization
Venue: arXiv cs.AI 2013
Year: 2013

We present a logical framework to represent and reason about stochastic optimization problems based on probability answer set programming. This is established by allowing probability optimization aggregates, e.g., minimum and maximum in the language of probability answer set programming to allow minimization or maximization of some desired criteria under the probabilistic environments. We show the application of the proposed logical stochastic optimization framework under the probability answer set programming to two stages stochastic optimization problems with recourse.
Current Studies On Intrusion Detection System, Genetic Algorithm And Fuzzy Logic
Venue: arXiv cs.CR 2013
Year: 2013

Nowadays Intrusion Detection System (IDS) which is increasingly a key element of system security is used to identify the malicious activities in a computer system or network. There are different approaches being employed in intrusion detection systems, but unluckily each of the technique so far is not entirely ideal. The prediction process may produce false alarms in many anomaly based intrusion detection systems. With the concept of fuzzy logic, the false alarm rate in establishing intrusive activities can be reduced. A set of efficient fuzzy rules can be used to define the normal and abnormal behaviors in a computer network. Therefore some strategy is needed for best promising security to monitor the anomalous behavior in computer network. In this paper I present a few research papers regarding the foundations of intrusion detection systems, the methodologies and good fuzzy classifiers using genetic algorithm which are the focus of current development efforts and the solution of the problem of Intrusion Detection System to offer a realworld view of intrusion detection. Ultimately, a discussion of the upcoming technologies and various methodologies which promise to improve the capability of computer systems to detect intrusions is offered.
Global cognitive radio based communication systems: Space-time communications
Venue: arXiv cs.ET 2013
Year: 2013

Spectrum Scarcity is a global problem across the world. This paper emphasizes on the fact that a global problem has to be dealt on global basis, not just locally by applying the principle of global cognitive radio,Global Opportunistic Remote Spectrum Access. The Future Internet and Internet of Things literally scare the communication system designer regarding the available bandwidth and spectrum resources. There is absolutely no scope to waste or under utilize the available resources. Hence the proposed idea of Global Cognitive Radio Concept can undoubtedly solve the resource problems in next Generation Communications.
A Secure And High Capacity Image Steganography Technique
Venue: arXiv cs.MM 2013
Year: 2013

Steganography is the science of invisible communication. The purpose of Steganography is to maintain secret communication between two parties. The secret information can be concealed in content such as image, audio, or video. This paper provides a novel image steganography technique to hide multiple secret images and keys in color cover image using Integer Wavelet Transform (IWT). There is no visual difference between the stego image and the cover image. The extracted secret images are also similar to the original secret images. Very good PSNR (Peak Signal to Noise Ratio) values are obtained for both stego and extracted secret images. The results are compared with the results of other techniques, where single image is hidden and it is found that the proposed technique is simple and gives better PSNR values than others.
A planetary nervous system for social mining and collective awareness
Venue: arXiv cs.CY 2013
Year: 2013

We present a research roadmap of a Planetary Nervous System (PNS), capable of sensing and mining the digital breadcrumbs of human activities and unveiling the knowledge hidden in the big data for addressing the big questions about social complexity. We envision the PNS as a globally distributed, self-organizing, techno-social system for answering analytical questions about the status of world-wide society, based on three pillars: social sensing, social mining, and the idea of trust networks and privacy-aware social mining. We discuss the ingredients of a science and a technology necessary to build the PNS upon the three mentioned pillars, beyond the limitations of their respective state-of-art. Social sensing is aimed at developing better methods for harvesting the big data from the techno-social ecosystem and make them available for mining, learning and analysis at a properly high abstraction level.Social mining is the problem of discovering patterns and models of human behaviour from the sensed data across the various social dimensions by data mining, machine learning and social network analysis. Trusted networks and privacy-aware social mining is aimed at creating a new deal around the questions of privacy and data ownership empowering individual persons with full awareness and control on own personal data, so that users may allow access and use of their data for their own good and the common good. The PNS will provide a goal-oriented knowledge discovery framework, made of technology and people, able to configure itself to the aim of answering questions about the pulse of global society. Given an analytical request, the PNS activates a process composed by a variety of interconnected tasks exploiting the social sensing and mining methods within the transparent ecosystem provided by the trusted network.
Advice-Efficient Prediction with Expert Advice
Venue: arXiv cs.LG 2013
Year: 2013

Advice-efficient prediction with expert advice (in analogy to label-efficient prediction) is a variant of prediction with expert advice game, where on each round of the game we are allowed to ask for advice of a limited number $M$ out of $N$ experts. This setting is especially interesting when asking for advice of every expert on every round is expensive. We present an algorithm for advice-efficient prediction with expert advice that achieves $O(\sqrt{\frac{N}{M}T\ln N})$ regret on $T$ rounds of the game.
A Taxonomy of Performance Assurance Methodologies and its Application in High Performance Computer Architectures
Venue: arXiv cs.PF 2013
Year: 2013

This paper presents a systematic approach to the complex problem of high confidence performance assurance of high performance architectures based on methods used over several generations of industrial microprocessors. A taxonomy is presented for performance assurance through three key stages of a product life cycle-high level performance, RTL performance, and silicon performance. The proposed taxonomy includes two components-independent performance assurance space for each stage and a correlation performance assurance space between stages. It provides a detailed insight into the performance assurance space in terms of coverage provided taking into account capabilities and limitations of tools and methodologies used at each stage. An application of the taxonomy to cases described in the literature and to high performance Intel architectures is shown. The proposed work should be of interest to manufacturers of high performance microprocessor/chipset architectures and has not been discussed in the literature.
Improving Generalization Ability of Genetic Programming: Comparative Study
Venue: arXiv cs.NE 2013
Year: 2013

In the field of empirical modeling using Genetic Programming (GP), it is important to evolve solution with good generalization ability. Generalization ability of GP solutions get affected by two important issues: bloat and over-fitting. Bloat is uncontrolled growth of code without any gain in fitness and important issue in GP. We surveyed and classified existing literature related to different techniques used by GP research community to deal with the issue of bloat. Moreover, the classifications of different bloat control approaches and measures for bloat are discussed. Next, we tested four bloat control methods: Tarpeian, double tournament, lexicographic parsimony pressure with direct bucketing and ratio bucketing on six different problems and identified where each bloat control method performs well on per problem basis. Based on the analysis of each method, we combined two methods: double tournament (selection method) and Tarpeian method (works before evaluation) to avoid bloated solutions and compared with the results obtained from individual performance of double tournament method. It was found that the results were improved with this combination of two methods.
Network Design with Coverage Costs
Venue: arXiv cs.DS 2013
Year: 2013

We study network design with a cost structure motivated by redundancy in data traffic. We are given a graph, g groups of terminals, and a universe of data packets. Each group of terminals desires a subset of the packets from its respective source. The cost of routing traffic on any edge in the network is proportional to the total size of the distinct packets that the edge carries. Our goal is to find a minimum cost routing. We focus on two settings. In the first, the collection of packet sets desired by source-sink pairs is laminar. For this setting, we present a primal-dual based 2-approximation, improving upon a logarithmic approximation due to Barman and Chawla (2012). In the second setting, packet sets can have non-trivial intersection. We focus on the case where each packet is desired by either a single terminal group or by all of the groups, and the graph is unweighted. For this setting we present an O(log g)-approximation. Our approximation for the second setting is based on a novel spanner-type construction in unweighted graphs that, given a collection of g vertex subsets, finds a subgraph of cost only a constant factor more than the minimum spanning tree of the graph, such that every subset in the collection has a Steiner tree in the subgraph of cost at most O(log g) that of its minimum Steiner tree in the original graph. We call such a subgraph a group spanner.
Sparsity-Aware STAP Algorithms Using $L_1$-norm Regularization For Radar Systems
Venue: arXiv cs.IT 2013
Year: 2013

This article proposes novel sparsity-aware space-time adaptive processing (SA-STAP) algorithms with $l_1$-norm regularization for airborne phased-array radar applications. The proposed SA-STAP algorithms suppose that a number of samples of the full-rank STAP data cube are not meaningful for processing and the optimal full-rank STAP filter weight vector is sparse, or nearly sparse. The core idea of the proposed method is imposing a sparse regularization ($l_1$-norm type) to the minimum variance (MV) STAP cost function. Under some reasonable assumptions, we firstly propose a $l_1$-based sample matrix inversion (SMI) to compute the optimal filter weight vector. However, it is impractical due to its matrix inversion, which requires a high computational cost when in a large phased-array antenna. Then, we devise lower complexity algorithms based on conjugate gradient (CG) techniques. A computational complexity comparison with the existing algorithms and an analysis of the proposed algorithms are conducted. Simulation results with both simulated and the Mountain Top data demonstrate that fast signal-to-interference-plus-noise-ratio (SINR) convergence and good performance of the proposed algorithms are achieved.
Comparative Study and Analysis of Variability Tools
Venue: arXiv cs.SE 2013
Year: 2013

The dissertation provides a comparative analysis of a number of variability tools currently in use. It serves as a catalogue for practitioners interested in the topic. We compare a range of modelling, configuring, and management tools for product line engineering. The tools surveyed are compared against the following criteria: functional, non-functional, governance issues and Technical aspects. The outcome of the analysis is provided in tabular format.