OpenRaiser/Intern-Atlas huggingface dataset:
Contracts and Behavioral Patterns for SoS: The EU IP DANSE approach
Venue: arXiv cs.SE 2013
Year: 2013

This paper presents some of the results of the first year of DANSE, one of the first EU IP projects dedicated to SoS. Concretely, we offer a tool chain that allows to specify SoS and SoS requirements at high level, and analyse them using powerful toolsets coming from the formal verification area. At the high level, we use UPDM, the system model provided by the british army as well as a new type of contract based on behavioral patterns. At low level, we rely on a powerful simulation toolset combined with recent advances from the area of statistical model checking. The approach has been applied to a case study developed at EADS Innovation Works.
Online Coded Caching
Venue: arXiv cs.IT 2013
Year: 2013

We consider a basic content distribution scenario consisting of a single origin server connected through a shared bottleneck link to a number of users each equipped with a cache of finite memory. The users issue a sequence of content requests from a set of popular files, and the goal is to operate the caches as well as the server such that these requests are satisfied with the minimum number of bits sent over the shared link. Assuming a basic Markov model for renewing the set of popular files, we characterize approximately the optimal long-term average rate of the shared link. We further prove that the optimal online scheme has approximately the same performance as the optimal offline scheme, in which the cache contents can be updated based on the entire set of popular files before each new request. To support these theoretical results, we propose an online coded caching scheme termed coded least-recently sent (LRS) and simulate it for a demand time series derived from the dataset made available by Netflix for the Netflix Prize. For this time series, we show that the proposed coded LRS algorithm significantly outperforms the popular least-recently used (LRU) caching algorithm.
Hierarchical Model of Human Guidance Performance Based on Interaction Patterns in Behavior
Venue: arXiv cs.HC 2013
Year: 2013

This paper describes a framework for the investigation and modeling of human spatial guidance behavior in complex environments. The model is derived from the concept of interaction patterns, which represent the invariances or symmetries inherent in the interactions between an agent and its environment. These patterns provide the basic elements needed for the formalization of spatial behavior and determine a natural hierarchy that can be unified under a hierarchical hidden Markov model.
Calculating risk in functional programming
Venue: arXiv cs.LO 2013
Year: 2013

In the trend towards tolerating hardware unreliability, accuracy is exchanged for cost savings. Running on less reliable machines, "functionally correct" code becomes risky and one needs to know how risk propagates so as to mitigate it. Risk estimation, however, seems to live outside the average programmer's technical competence and core practice. In this paper we propose that risk be constructively handled in functional programming by (a) writing programs which may choose between expected and faulty behaviour, and by (b) reasoning about them in a linear algebra extension to standard, a la Bird-Moor algebra of programming. In particular, the propagation of faults across standard program transformation techniques known as tupling and fusion is calculated, enabling the fault of the whole to be expressed in terms of the faults of its parts.
Non-Convex Compressed Sensing Using Partial Support Information
Venue: arXiv cs.IT 2013
Year: 2013

In this paper we address the recovery conditions of weighted $\ell_p$ minimization for signal reconstruction from compressed sensing measurements when partial support information is available. We show that weighted $\ell_p$ minimization with $0<p<1$ is stable and robust under weaker sufficient conditions compared to weighted $\ell_1$ minimization. Moreover, the sufficient recovery conditions of weighted $\ell_p$ are weaker than those of regular $\ell_p$ minimization if at least $50%$ of the support estimate is accurate. We also review some algorithms which exist to solve the non-convex $\ell_p$ problem and illustrate our results with numerical experiments.
Applying Network Coding To Neighbour Topology Based Broadcasting Techniques in MANETs
Venue: arXiv cs.NI 2013
Year: 2013

In Mobile Ad-Hoc networks, broadcasting is a fundamental operation in the network layer. A node transmits a rebroadcast message to any or all other nodes whenever it receives for the first time. It will generate several redundant transmissions and it ends up in a significant downside Broadcast Storm problem. Within the literature, researchers have proposed 2-Hop Neighbour based protocol like DP, TDP, PDP and APDP to reduce broadcast storm in MANETs by choosing the minimum number of forwarding nodes from 1-Hop nodes to cover all 2-Hop nodes.
On the automorphism groups of binary linear codes
Venue: arXiv cs.IT 2013
Year: 2013

Let C be a binary linear code and suppose that its automorphism group contains a non trivial subgroup G. What can we say about C knowing G? In this paper we collect some answers to this question in the cases G=C_p, G=C_2p and G=D_2p (p an odd prime), with a particular regard to the case in which C is self-dual. Furthermore we generalize some methods used in other papers on this subject. Finally we give a short survey on the problem of determining the automorphism group of a putative self-dual [72,36,16] code, in order to show where these methods can be applied.
Inferring Multilateral Relations from Dynamic Pairwise Interactions
Venue: arXiv cs.AI 2013
Year: 2013

Correlations between anomalous activity patterns can yield pertinent information about complex social processes: a significant deviation from normal behavior, exhibited simultaneously by multiple pairs of actors, provides evidence for some underlying relationship involving those pairs---i.e., a multilateral relation. We introduce a new nonparametric Bayesian latent variable model that explicitly captures correlations between anomalous interaction counts and uses these shared deviations from normal activity patterns to identify and characterize multilateral relations. We showcase our model's capabilities using the newly curated Global Database of Events, Location, and Tone, a dataset that has seen considerable interest in the social sciences and the popular press, but which has is largely unexplored by the machine learning community. We provide a detailed analysis of the latent structure inferred by our model and show that the multilateral relations correspond to major international events and long-term international relationships. These findings lead us to recommend our model for any data-driven analysis of interaction networks where dynamic interactions over the edges provide evidence for latent social structure.
Big Data and Cross-Document Coreference Resolution: Current State and Future Opportunities
Venue: arXiv cs.CL 2013
Year: 2013

Information Extraction (IE) is the task of automatically extracting structured information from unstructured/semi-structured machine-readable documents. Among various IE tasks, extracting actionable intelligence from ever-increasing amount of data depends critically upon Cross-Document Coreference Resolution (CDCR) - the task of identifying entity mentions across multiple documents that refer to the same underlying entity. Recently, document datasets of the order of peta-/tera-bytes has raised many challenges for performing effective CDCR such as scaling to large numbers of mentions and limited representational power. The problem of analysing such datasets is called "big data". The aim of this paper is to provide readers with an understanding of the central concepts, subtasks, and the current state-of-the-art in CDCR process. We provide assessment of existing tools/techniques for CDCR subtasks and highlight big data challenges in each of them to help readers identify important and outstanding issues for further investigation. Finally, we provide concluding remarks and discuss possible directions for future work.
Methods for Integrating Knowledge with the Three-Weight Optimization Algorithm for Hybrid Cognitive Processing
Venue: arXiv cs.AI 2013
Year: 2013

In this paper we consider optimization as an approach for quickly and flexibly developing hybrid cognitive capabilities that are efficient, scalable, and can exploit knowledge to improve solution speed and quality. In this context, we focus on the Three-Weight Algorithm, which aims to solve general optimization problems. We propose novel methods by which to integrate knowledge with this algorithm to improve expressiveness, efficiency, and scaling, and demonstrate these techniques on two example problems (Sudoku and circle packing).
Sound and Precise Malware Analysis for Android via Pushdown Reachability and Entry-Point Saturation
Venue: arXiv cs.PL 2013
Year: 2013

We present Anadroid, a static malware analysis framework for Android apps. Anadroid exploits two techniques to soundly raise precision: (1) it uses a pushdown system to precisely model dynamically dispatched interprocedural and exception-driven control-flow; (2) it uses Entry-Point Saturation (EPS) to soundly approximate all possible interleavings of asynchronous entry points in Android applications. (It also integrates static taint-flow analysis and least permissions analysis to expand the class of malicious behaviors which it can catch.) Anadroid provides rich user interface support for human analysts which must ultimately rule on the "maliciousness" of a behavior. To demonstrate the effectiveness of Anadroid's malware analysis, we had teams of analysts analyze a challenge suite of 52 Android applications released as part of the Auto- mated Program Analysis for Cybersecurity (APAC) DARPA program. The first team analyzed the apps using a ver- sion of Anadroid that uses traditional (finite-state-machine-based) control-flow-analysis found in existing malware analysis tools; the second team analyzed the apps using a version of Anadroid that uses our enhanced pushdown-based control-flow-analysis. We measured machine analysis time, human analyst time, and their accuracy in flagging malicious applications. With pushdown analysis, we found statistically significant (p < 0.05) decreases in time: from 85 minutes per app to 35 minutes per app in human plus machine analysis time; and statistically significant (p < 0.05) increases in accuracy with the pushdown-driven analyzer: from 71% correct identification to 95% correct identification.
Optimal Foresighted Multi-User Wireless Video
Venue: arXiv cs.MM 2013
Year: 2013

Recent years have seen an explosion in wireless video communication systems. Optimization in such systems is crucial - but most existing methods intended to optimize the performance of multi-user wireless video transmission are inefficient. Some works (e.g. Network Utility Maximization (NUM)) are myopic: they choose actions to maximize instantaneous video quality while ignoring the future impact of these actions. Such myopic solutions are known to be inferior to foresighted solutions that optimize the long-term video quality. Alternatively, foresighted solutions such as rate-distortion optimized packet scheduling focus on single-user wireless video transmission, while ignoring the resource allocation among the users. In this paper, we propose an optimal solution for performing joint foresighted resource allocation and packet scheduling among multiple users transmitting video over a shared wireless network. A key challenge in developing foresighted solutions for multiple video users is that the users' decisions are coupled. To decouple the users' decisions, we adopt a novel dual decomposition approach, which differs from the conventional optimization solutions such as NUM, and determines foresighted policies. Specifically, we propose an informationally-decentralized algorithm in which the network manager updates resource "prices" (i.e. the dual variables associated with the resource constraints), and the users make individual video packet scheduling decisions based on these prices. Because a priori knowledge of the system dynamics is almost never available at run-time, the proposed solution can learn online, concurrently with performing the foresighted optimization. Simulation results show 7 dB and 3 dB improvements in Peak Signal-to-Noise Ratio (PSNR) over myopic solutions and existing foresighted solutions, respectively.
Simulating Parity Reasoning (extended version)
Venue: arXiv cs.LO 2013
Year: 2013

Propositional satisfiability (SAT) solvers, which typically operate using conjunctive normal form (CNF), have been successfully applied in many domains. However, in some application areas such as circuit verification, bounded model checking, and logical cryptanalysis, instances can have many parity (xor) constraints which may not be handled efficiently if translated to CNF. Thus, extensions to the CNF-driven search with various parity reasoning engines ranging from equivalence reasoning to incremental Gaussian elimination have been proposed. This paper studies how stronger parity reasoning techniques in the DPLL(XOR) framework can be simulated by simpler systems: resolution, unit propagation, and parity explanations. Such simulations are interesting, for example, for developing the next generation SAT solvers capable of handling parity constraints efficiently.
Checking Race Freedom of Clocked X10 Programs
Venue: arXiv cs.DC 2013
Year: 2013

One of many approaches to better take advantage of parallelism, which has now become mainstream, is the introduction of parallel programming languages. However, parallelism is by nature non-deterministic, and not all parallel bugs can be avoided by language design. This paper proposes a method for guaranteeing absence of data races in the polyhedral subset of clocked X10 programs. Clocks in X10 are similar to barriers, but are more dynamic; the subset of processes that participate in the synchronization can dynamically change at runtime. We construct the happens-before relation for clocked X10 programs, and show that the problem of race detection is undecidable. However, in many practical cases, modern tools are able to find solutions or disprove their existence. We present a set of benchmarks for which the analysis is possible and has an acceptable running time.
Distributed Widely Linear Complex Kalman Filtering
Venue: arXiv cs.SY 2013
Year: 2013

We introduce cooperative sequential state space estimation in the domain of augmented complex statistics, whereby nodes in a network collaborate locally to estimate noncircular complex signals. For rigour, a distributed augmented (widely linear) complex Kalman filter (D-ACKF) suited to the generality of complex signals is introduced, allowing for unified treatment of both proper (rotation invariant) and improper (rotation dependent) signal distributions. Its duality with the bivariate real-valued distributed Kalman filter, along with several issues of implementation are also illuminated. The analysis and simulations show that unlike existing distributed Kalman filter solutions, the D-ACKF caters for both the improper data and the correlations between nodal observation noises, thus providing enhanced performance in real-world scenarios.
Understanding Visualization: A Formal Approach using Category Theory and Semiotics
Venue: arXiv cs.LO 2013
Year: 2013

This article combines the vocabulary of semiotics and category theory to provide a formal analysis of visualization. It shows how familiar processes of visualization fit the semiotic frameworks of both Saussure and Peirce, and extends these structures using the tools of category theory to provide a general framework for understanding visualization in practice, including: relationships between systems, data collected from those systems, renderings of those data in the form of representations, the reading of those representations to create visualizations, and the use of those visualizations to create knowledge and understanding of the system under inspection. The resulting framework is validated by demonstrating how familiar information visualization concepts (such as literalness, sensitivity, redundancy, ambiguity, generalizability, and chart junk) arise naturally from it and can be defined formally and precisely. This article generalizes previous work on the formal characterization of visualization by, inter alia, Ziemkiewicz and Kosara and allows us to formally distinguish properties of the visualization process that previous work does not.
Discriminative Density-ratio Estimation
Venue: arXiv cs.LG 2013
Year: 2013

The covariate shift is a challenging problem in supervised learning that results from the discrepancy between the training and test distributions. An effective approach which recently drew a considerable attention in the research community is to reweight the training samples to minimize that discrepancy. In specific, many methods are based on developing Density-ratio (DR) estimation techniques that apply to both regression and classification problems. Although these methods work well for regression problems, their performance on classification problems is not satisfactory. This is due to a key observation that these methods focus on matching the sample marginal distributions without paying attention to preserving the separation between classes in the reweighted space. In this paper, we propose a novel method for Discriminative Density-ratio (DDR) estimation that addresses the aforementioned problem and aims at estimating the density-ratio of joint distributions in a class-wise manner. The proposed algorithm is an iterative procedure that alternates between estimating the class information for the test data and estimating new density ratio for each class. To incorporate the estimated class information of the test data, a soft matching technique is proposed. In addition, we employ an effective criterion which adopts mutual information as an indicator to stop the iterative procedure while resulting in a decision boundary that lies in a sparse region. Experiments on synthetic and benchmark datasets demonstrate the superiority of the proposed method in terms of both accuracy and robustness.
Achievable Rate Regions for Network Coding
Venue: arXiv cs.IT 2013
Year: 2013

Determining the achievable rate region for networks using routing, linear coding, or non-linear coding is thought to be a difficult task in general, and few are known. We describe the achievable rate regions for four interesting networks (completely for three and partially for the fourth). In addition to the known matrix-computation method for proving outer bounds for linear coding, we present a new method which yields actual characteristic-dependent linear rank inequalities from which the desired bounds follow immediately.
Sampling versus Random Binning for Multiple Descriptions of a Bandlimited Source
Venue: arXiv cs.IT 2013
Year: 2013

Random binning is an efficient, yet complex, coding technique for the symmetric L-description source coding problem. We propose an alternative approach, that uses the quantized samples of a bandlimited source as "descriptions". By the Nyquist condition, the source can be reconstructed if enough samples are received. We examine a coding scheme that combines sampling and noise-shaped quantization for a scenario in which only K < L descriptions or all L descriptions are received. Some of the received K-sets of descriptions correspond to uniform sampling while others to non-uniform sampling. This scheme achieves the optimum rate-distortion performance for uniform-sampling K-sets, but suffers noise amplification for nonuniform-sampling K-sets. We then show that by increasing the sampling rate and adding a random-binning stage, the optimal operation point is achieved for any K-set.
Query Interface Integrator For Domain Specific Hidden Web
Venue: arXiv cs.IR 2013
Year: 2013

Web is title admittance today mainly relies on search engines. A large amount of data is hidden in the databases behind the search interfaces referred to as Hidden web, which needs to be indexed so in order to serve user query. In this paper database and data mining techniques are used for query interface integration. The query interface must resemble the look and feel of local interface as much as possible despite being automatically generated without human support.This technique keeps the related documents in the same domain so that searching of documents becomes more efficient in terms of time complexity.
Distributed Painting by a Swarm of Robots with Unlimited Sensing Capabilities and Its Simulation
Venue: arXiv cs.DC 2013
Year: 2013

This paper presents a distributed painting algorithm for painting a priori known rectangular region by swarm of autonomous mobile robots. We assume that the region is obstacle free and of rectangular in shape. The basic approach is to divide the region into some cells, and to let each robot to paint one of these cells. Assignment of different cells to the robots is done by ranking the robots according to their relative positions. In this algorithm, the robots follow the basic Wait-Observe-Compute-Move model together with the synchronous timing model. This paper also presents a simulation of the proposed algorithm. The simulation is performed using the Player/Stage Robotic Simulator on Ubuntu 10.04 (Lucid Lynx) platform.
Android Based Emergency Alert Button
Venue: arXiv cs.CY 2013
Year: 2013

Android is a java based operating system which runs on the Linux kernel. It is lightweight and full featured. Android applications are developed using Java and can be ported to new platform easily thereby fostering huge number of useful mobile applications. This paper describes about a SOS application being developed and its successful implementation with tested results. The application has target users those sections of the people who surprisingly falls into a situation where instant communication of their whereabouts becomes indispensable to be informed to certain authorized persons at remote end
Home Energy Management in Smart Grid
Venue: arXiv cs.NI 2013
Year: 2013

A significant amount of research has been conducted in order to make home appliances more efficient in terms of energy usage. Various techniques have been designed and implemented in order to control the power demand and supply. This paper encompasses reviews of different research works on a wide range of energy management techniques for smart homes aimed at reducing energy consumption and minimizing energy wastage. The idea of smart home is elaborated followed by a review of existing energy management methods.
Challenges in computational lower bounds
Venue: arXiv cs.CC 2013
Year: 2013

We draw two incomplete, biased maps of challenges in computational complexity lower bounds.