OpenRaiser/Intern-Atlas huggingface dataset:
Steganalysis Using Color Model Conversion
Venue: arXiv cs.CR 2012
Year: 2012

The major threat in cyber crime for digital forensic examiner is to identify, analyze and interpret the concealed information inside digital medium such as image, audio and video. There are strong indications that hiding information inside digital medium has been used for planning criminal activities. In this way, it is important to develop a steganalysis technique which detects the existence of hidden messages inside digital medium. This paper focuses on universal image steganalysis method which uses RGB to HSI colour model conversion. Any Universal Steganalysis algorithm developed should be tested with various stego-images to prove its efficiency. The developed Universal Steganalysis algorithm is tested in stego-image database which is obtained by implementing various RGB Least Significant Bit Steganographic algorithms. Though there are many stego-image sources available on the internet it lacks in the information such as how many rows has been infected by the steganography algorithms, how many bits have been modified and which channel has been affected. These parameters are important for Steganalysis algorithms and it helps to rate its efficiency. Proposed Steganalysis using Colour Model has been tested with our Image Database and the results were affirmative.
Asymptotic Outage Probability Analysis for General Fixed-Gain Amplify-and-Forward Multihop Relay Systems
Venue: arXiv cs.IT 2012
Year: 2012

In this paper, we present an analysis of the outage probability for fixed-gain amplify-and-forward (AF) multihop relay links operating in the high SNR regime. Our analysis exploits properties of Mellin transforms to derive an asymptotic approximation that is accurate even when the per-hop channel gains adhere to completely different fading models. The main result contained in the paper is a general expression for the outage probability, which is a functional of the Mellin transforms of the per-hop channel gains. Furthermore, we explicitly calculate the asymptotic outage probability for four different systems, whereby in each system the per-hop channels adhere to either a Nakagami-m, Weibull, Rician, or Hoyt fading profile, but where the distributional parameters may differ from hop to hop. This analysis leads to our second main result, which is a semi-general closed-form formula for the outage probability of general fixed-gain AF multihop systems. We exploit this formula to analyze an example scenario for a four-hop system where the per-hop channels follow the four aforementioned fading models, i.e., the first channel is Nakagami-m fading, the second is Weibull fading, and so on. Finally, we provide simulation results to corroborate our analysis.
A Metapopulation Model for Chikungunya Including Populations Mobility on a Large-Scale Network
Venue: arXiv cs.SI 2012
Year: 2012

In this work we study the influence of populations mobility on the spread of a vector-borne disease. We focus on the chikungunya epidemic event that occurred in 2005-2006 on the Réunion Island, Indian Ocean, France, and validate our models with real epidemic data from the event. We propose a metapopulation model to represent both a high-resolution patch model of the island with realistic population densities and also mobility models for humans (based on real-motion data) and mosquitoes. In this metapopulation network, two models are coupled: one for the dynamics of the mosquito population and one for the transmission of the disease. A high-resolution numerical model is created out from real geographical, demographical and mobility data. The Island is modeled with an 18 000-nodes metapopulation network. Numerical results show the impact of the geographical environment and populations' mobility on the spread of the disease. The model is finally validated against real epidemic data from the Réunion event.
The third open Answer Set Programming competition
Venue: arXiv cs.AI 2012
Year: 2012

Answer Set Programming (ASP) is a well-established paradigm of declarative programming in close relationship with other declarative formalisms such as SAT Modulo Theories, Constraint Handling Rules, FO(.), PDDL and many others. Since its first informal editions, ASP systems have been compared in the now well-established ASP Competition. The Third (Open) ASP Competition, as the sequel to the ASP Competitions Series held at the University of Potsdam in Germany (2006-2007) and at the University of Leuven in Belgium in 2009, took place at the University of Calabria (Italy) in the first half of 2011. Participants competed on a pre-selected collection of benchmark problems, taken from a variety of domains as well as real world applications. The Competition ran on two tracks: the Model and Solve (M&S) Track, based on an open problem encoding, and open language, and open to any kind of system based on a declarative specification paradigm; and the System Track, run on the basis of fixed, public problem encodings, written in a standard ASP language. This paper discusses the format of the Competition and the rationale behind it, then reports the results for both tracks. Comparison with the second ASP competition and state-of-the-art solutions for some of the benchmark domains is eventually discussed. To appear in Theory and Practice of Logic Programming (TPLP).
Convexity Conditions for 802.11 WLANs
Venue: arXiv cs.NI 2012
Year: 2012

In this paper we characterise the maximal convex subsets of the (non-convex) rate region in 802.11 WLANs. In addition to being of intrinsic interest as a fundamental property of 802.11 WLANs, this characterisation can be exploited to allow the wealth of convex optimisation approaches to be applied to 802.11 WLANs.
Optimizing the eigenvector computation algorithm with diffusion approach
Venue: arXiv cs.NA 2012
Year: 2012

In this paper, we apply the ideas of the matrix column based diffusion approach to define a new eigenvector computation algorithm of a stationary probability of a Markov chain.
Identifying reasoning patterns in games
Venue: arXiv cs.GT 2012
Year: 2012

We present an algorithm that identifies the reasoning patterns of agents in a game, by iteratively examining the graph structure of its Multi-Agent Influence Diagram (MAID) representation. If the decision of an agent participates in no reasoning patterns, then we can effectively ignore that decision for the purpose of calculating a Nash equilibrium for the game. In some cases, this can lead to exponential time savings in the process of equilibrium calculation. Moreover, our algorithm can be used to enumerate the reasoning patterns in a game, which can be useful for constructing more effective computerized agents interacting with humans.
Knowledge Combination in Graphical Multiagent Model
Venue: arXiv cs.AI 2012
Year: 2012

A graphical multiagent model (GMM) represents a joint distribution over the behavior of a set of agents. One source of knowledge about agents' behavior may come from gametheoretic analysis, as captured by several graphical game representations developed in recent years. GMMs generalize this approach to express arbitrary distributions, based on game descriptions or other sources of knowledge bearing on beliefs about agent behavior. To illustrate the flexibility of GMMs, we exhibit game-derived models that allow probabilistic deviation from equilibrium, as well as models based on heuristic action choice. We investigate three different methods of integrating these models into a single model representing the combined knowledge sources. To evaluate the predictive performance of the combined model, we treat as actual outcome the behavior produced by a reinforcement learning process. We find that combining the two knowledge sources, using any of the methods, provides better predictions than either source alone. Among the combination methods, mixing data outperforms the opinion pool and direct update methods investigated in this empirical trial.
Estimation and Clustering with Infinite Rankings
Venue: arXiv cs.LG 2012
Year: 2012

This paper presents a natural extension of stagewise ranking to the the case of infinitely many items. We introduce the infinite generalized Mallows model (IGM), describe its properties and give procedures to estimate it from data. For estimation of multimodal distributions we introduce the Exponential-Blurring-Mean-Shift nonparametric clustering algorithm. The experiments highlight the properties of the new model and demonstrate that infinite models can be simple, elegant and practical.
Discovering Cyclic Causal Models by Independent Components Analysis
Venue: arXiv cs.AI 2012
Year: 2012

We generalize Shimizu et al's (2006) ICA-based approach for discovering linear non-Gaussian acyclic (LiNGAM) Structural Equation Models (SEMs) from causally sufficient, continuous-valued observational data. By relaxing the assumption that the generating SEM's graph is acyclic, we solve the more general problem of linear non-Gaussian (LiNG) SEM discovery. LiNG discovery algorithms output the distribution equivalence class of SEMs which, in the large sample limit, represents the population distribution. We apply a LiNG discovery algorithm to simulated data. Finally, we give sufficient conditions under which only one of the SEMs in the output class is 'stable'.
Explanation Trees for Causal Bayesian Networks
Venue: arXiv cs.AI 2012
Year: 2012

Bayesian networks can be used to extract explanations about the observed state of a subset of variables. In this paper, we explicate the desiderata of an explanation and confront them with the concept of explanation proposed by existing methods. The necessity of taking into account causal approaches when a causal graph is available is discussed. We then introduce causal explanation trees, based on the construction of explanation trees using the measure of causal information ow (Ay and Polani, 2006). This approach is compared to several other methods on known networks.
A Polynomial-time Nash Equilibrium Algorithm for Repeated Stochastic Games
Venue: arXiv cs.GT 2012
Year: 2012

We present a polynomial-time algorithm that always finds an (approximate) Nash equilibrium for repeated two-player stochastic games. The algorithm exploits the folk theorem to derive a strategy profile that forms an equilibrium by buttressing mutually beneficial behavior with threats, where possible. One component of our algorithm efficiently searches for an approximation of the egalitarian point, the fairest pareto-efficient solution. The paper concludes by applying the algorithm to a set of grid games to illustrate typical solutions the algorithm finds. These solutions compare very favorably to those found by competing algorithms, resulting in strategies with higher social welfare, as well as guaranteed computational efficiency.
Topic Models Conditioned on Arbitrary Features with Dirichlet-multinomial Regression
Venue: arXiv cs.IR 2012
Year: 2012

Although fully generative models have been successfully used to model the contents of text documents, they are often awkward to apply to combinations of text data and document metadata. In this paper we propose a Dirichlet-multinomial regression (DMR) topic model that includes a log-linear prior on document-topic distributions that is a function of observed features of the document, such as author, publication venue, references, and dates. We show that by selecting appropriate features, DMR topic models can meet or exceed the performance of several previously published topic models designed for specific data.
Bounding Search Space Size via (Hyper)tree Decompositions
Venue: arXiv cs.AI 2012
Year: 2012

This paper develops a measure for bounding the performance of AND/OR search algorithms for solving a variety of queries over graphical models. We show how drawing a connection to the recent notion of hypertree decompositions allows to exploit determinism in the problem specification and produce tighter bounds. We demonstrate on a variety of practical problem instances that we are often able to improve upon existing bounds by several orders of magnitude.
New Techniques for Algorithm Portfolio Design
Venue: arXiv cs.AI 2012
Year: 2012

We present and evaluate new techniques for designing algorithm portfolios. In our view, the problem has both a scheduling aspect and a machine learning aspect. Prior work has largely addressed one of the two aspects in isolation. Building on recent work on the scheduling aspect of the problem, we present a technique that addresses both aspects simultaneously and has attractive theoretical guarantees. Experimentally, we show that this technique can be used to improve the performance of state-of-the-art algorithms for Boolean satisfiability, zero-one integer programming, and A.I. planning.
Efficient inference in persistent Dynamic Bayesian Networks
Venue: arXiv cs.AI 2012
Year: 2012

Numerous temporal inference tasks such as fault monitoring and anomaly detection exhibit a persistence property: for example, if something breaks, it stays broken until an intervention. When modeled as a Dynamic Bayesian Network, persistence adds dependencies between adjacent time slices, often making exact inference over time intractable using standard inference algorithms. However, we show that persistence implies a regular structure that can be exploited for efficient inference. We present three successively more general classes of models: persistent causal chains (PCCs), persistent causal trees (PCTs) and persistent polytrees (PPTs), and the corresponding exact inference algorithms that exploit persistence. We show that analytic asymptotic bounds for our algorithms compare favorably to junction tree inference; and we demonstrate empirically that we can perform exact smoothing on the order of 100 times faster than the approximate Boyen-Koller method on randomly generated instances of persistent tree models. We also show how to handle non-persistent variables and how persistence can be exploited effectively for approximate filtering.
Physical-Layer Network Coding for VPN in TDM-PON
Venue: arXiv cs.NI 2012
Year: 2012

We experimentally demonstrate a novel optical physical-layer network coding (PNC) scheme over time-division multiplexing (TDM) passive optical network (PON). Full-duplex error-free communications between optical network units (ONUs) at 2.5 Gb/s are shown for all-optical virtual private network (VPN) applications. Compared to the conventional half-duplex communications set-up, our scheme can increase the capacity by 100% with power penalty smaller than 3 dB. Synchronization of two ONUs is not required for the proposed VPN scheme
Robust and Efficient Distributed Compression for Cloud Radio Access Networks
Venue: arXiv cs.IT 2012
Year: 2012

This work studies distributed compression for the uplink of a cloud radio access network where multiple multi-antenna base stations (BSs) are connected to a central unit, also referred to as cloud decoder, via capacity-constrained backhaul links. Since the signals received at different BSs are correlated, distributed source coding strategies are potentially beneficial, and can be implemented via sequential source coding with side information. For the problem of compression with side information, available compression strategies based on the criteria of maximizing the achievable rate or minimizing the mean square error are reviewed first. It is observed that, in either case, each BS requires information about a specific covariance matrix in order to realize the advantage of distributed source coding. Since this covariance matrix depends on the channel realizations corresponding to other BSs, a robust compression method is proposed for a practical scenario in which the information about the covariance available at each BS is imperfect. The problem is formulated using a deterministic worst-case approach, and an algorithm is proposed that achieves a stationary point for the problem. Then, BS selection is addressed with the aim of reducing the number of active BSs, thus enhancing the energy efficiency of the network. An optimization problem is formulated in which compression and BS selection are performed jointly by introducing a sparsity-inducing term into the objective function. An iterative algorithm is proposed that is shown to converge to a locally optimal point. From numerical results, it is observed that the proposed robust compression scheme compensates for a large fraction of the performance loss induced by the imperfect statistical information. Moreover, the proposed BS selection algorithm is seen to perform close to the more complex exhaustive search solution.
Approximation Algorithm for Non-Boolean MAX k-CSP
Venue: arXiv cs.DS 2012
Year: 2012

In this paper, we present a randomized polynomial-time approximation algorithm for k-CSPd. In k-CSPd, we are given a set of predicates of arity k over an alphabet of size d. Our goal is to find an assignment that maximizes the number of satisfied constraints. Our algorithm has approximation factor Omega(kd/d^k) (when k > Ω(log d)). This bound is asymptotically optimal assuming the Unique Games Conjecture. The best previously known algorithm has approximation factor Omega(k log d/d^k). We also give an approximation algorithm for the boolean MAX k-CSP2 problem with a slightly improved approximation guarantee.
An Infinite Class of Sparse-Yao Spanners
Venue: arXiv cs.CG 2012
Year: 2012

We show that, for any integer k > 5, the Sparse-Yao graph YY_{6k} (also known as Yao-Yao) is a spanner with stretch factor 11.67. The stretch factor drops down to 4.75 for k > 7.
Balls into Bins: strict Capacities and Edge Weights
Venue: arXiv cs.DS 2012
Year: 2012

We explore a novel theoretical model for studying the performance of distributed storage management systems where the data-centers have limited capacities (as compared to storage space requested by the users). Prior schemes such as Balls-into-bins (used for load balancing) neither consider bin (consumer) capacities (multiple balls into a bin) nor the future performance of the system after, balls (producer requests) are allocated to bins and restrict number of balls as a function of the number of bins. Our problem consists of finding an optimal assignment of the online producer requests to consumers (via weighted edges) in a complete bipartite graph while ensuring that the total size of request assigned on a consumer is limited by its capacity. The metric used to measure the performance in this model is the (minimization of) weighted sum of the requests assigned on the edges (loads) and their corresponding weights. We first explore the optimal offline algorithms followed by competitive analysis of different online techniques. Using oblivious adversary. LP and Primal-Dual algorithms are used for calculating the optimal offline solution in O(r*n) time (where r and n are the number of requests and consumers respectively) while randomized algorithms are used for the online case. For the simplified model with equal consumer capacities an average-case competitive ratio of AVG(d) / MIN(d) (where d is the edge weight / distance) is achieved using an algorithm that has equal probability for selecting any of the available edges with a running time of $O(r)$. In the extending the model to arbitrary consumer capacities we show an average case competitive ratio of AVG(d*c) / (AVG(c) *MIN(d)).
Green and Gold Open Access Percentages and Growth, by Discipline
Venue: arXiv cs.DL 2012
Year: 2012

Most refereed journal articles today are published in subscription journals, accessible only to subscribing institutions, hence losing considerable research impact. Making articles freely accessible online ("Open Access," OA) maximizes their impact. Articles can be made OA in two ways: by self-archiving them on the web ("Green OA") or by publishing them in OA journals ("Gold OA"). We compared the percent and growth rate of Green and Gold OA for 14 disciplines in two random samples of 1300 articles per discipline out of the 12,500 journals indexed by Thomson-Reuters-ISI using a robot that trawled the web for OA full-texts. We sampled in 2009 and 2011 for publication year ranges 1998-2006 and 2005-2010, respectively. Green OA (21.4%) exceeds Gold OA (2.4%) in proportion and growth rate in all but the biomedical disciplines, probably because it can be provided for all journals articles and does not require paying extra Gold OA publication fees. The spontaneous overall OA growth rate is still very slow (about 1% per year). If institutions make Green OA self-archiving mandatory, however, it triples percent Green OA as well as accelerating its growth rate.
Information Retrieval in Intelligent Systems: Current Scenario & Issues
Venue: arXiv cs.IR 2012
Year: 2012

Web space is the huge repository of data. Everyday lots of new information get added to this web space. The more the information, more is demand for tools to access that information. Answering users' queries about the online information intelligently is one of the great challenges in information retrieval in intelligent systems. In this paper, we will start with the brief introduction on information retrieval and intelligent systems and explain how swoogle, the semantic search engine, uses its algorithms and techniques to search for the desired contents in the web. We then continue with the clustering technique that is used to group the similar things together and discuss the machine learning technique called Self-organizing maps [6] or SOM, which is a data visualization technique that reduces the dimensions of data through the use of self-organizing neural networks. We then discuss how SOM is used to visualize the contents of the data, by following some lines of algorithm, in the form of maps. So, we could say that websites or machines can be used to retrieve the information that what exactly users want from them.
Security Measures for Black Hole Attack in MANET: An Approach
Venue: arXiv cs.NI 2012
Year: 2012

A Mobile Ad-Hoc Network is a collection of mobile nodes that are dynamically and arbitrarily located in such a manner that the interconnections between nodes are capable of changing on continual basis. Due to security vulnerabilities of the routing protocols, wireless ad-hoc networks are unprotected to attacks of the malicious nodes. One of these attacks is the Black Hole Attack. In this paper, we give an algorithmic approach to focus on analyzing and improving the security of AODV, which is one of the popular routing protocols for MANET. Our aim is on ensuring the security against Black hole attack. The proposed solution is capable of detecting & removing Black hole node(s) in the MANET at the beginning. Also the objective of this paper is to provide a simulation study that illustrates the effects of Black hole attack on network performance.
ConeRANK: Ranking as Learning Generalized Inequalities
Venue: arXiv cs.LG 2012
Year: 2012

We propose a new data mining approach in ranking documents based on the concept of cone-based generalized inequalities between vectors. A partial ordering between two vectors is made with respect to a proper cone and thus learning the preferences is formulated as learning proper cones. A pairwise learning-to-rank algorithm (ConeRank) is proposed to learn a non-negative subspace, formulated as a polyhedral cone, over document-pair differences. The algorithm is regularized by controlling the `volume' of the cone. The experimental studies on the latest and largest ranking dataset LETOR 4.0 shows that ConeRank is competitive against other recent ranking approaches.