OpenRaiser/Intern-Atlas huggingface dataset:
Mining and Exploiting Domain-Specific Corpora in the PANACEA Platform
Venue: arXiv cs.CL 2013
Year: 2013

The objective of the PANACEA ICT-2007.2.2 EU project is to build a platform that automates the stages involved in the acquisition, production, updating and maintenance of the large language resources required by, among others, MT systems. The development of a Corpus Acquisition Component (CAC) for extracting monolingual and bilingual data from the web is one of the most innovative building blocks of PANACEA. The CAC, which is the first stage in the PANACEA pipeline for building Language Resources, adopts an efficient and distributed methodology to crawl for web documents with rich textual content in specific languages and predefined domains. The CAC includes modules that can acquire parallel data from sites with in-domain content available in more than one language. In order to extrinsically evaluate the CAC methodology, we have conducted several experiments that used crawled parallel corpora for the identification and extraction of parallel sentences using sentence alignment. The corpora were then successfully used for domain adaptation of Machine Translation Systems.
The Value of User-Visible Internet Cryptography
Venue: arXiv cs.CR 2013
Year: 2013

Cryptographic mechanisms are used in a wide range of applications, including email clients, web browsers, document and asset management systems, where typical users are not cryptography experts. A number of empirical studies have demonstrated that explicit, user-visible cryptographic mechanisms are not widely used by non-expert users, and as a result arguments have been made that cryptographic mechanisms need to be better hidden or embedded in end-user processes and tools. Other mechanisms, such as HTTPS, have cryptography built-in and only become visible to the user when a dialogue appears due to a (potential) problem. This paper surveys deployed and potential technologies in use, examines the social and legal context of broad classes of users, and from there, assesses the value and issues for those users.
Arithmetic Branching Programs with Memory
Venue: arXiv cs.CC 2013
Year: 2013

We extend the well known characterization of $\vpws$ as the class of polynomials computed by polynomial size arithmetic branching programs to other complexity classes. In order to do so we add additional memory to the computation of branching programs to make them more expressive. We show that allowing different types of memory in branching programs increases the computational power even for constant width programs. In particular, this leads to very natural and robust characterizations of $\vp$ and $\vnp$ by branching programs with memory.
Automatic Equivalence Proofs for Non-deterministic Coalgebras
Venue: arXiv cs.LO 2013
Year: 2013

A notion of generalized regular expressions for a large class of systems modeled as coalgebras, and an analogue of Kleene's theorem and Kleene algebra, were recently proposed by a subset of the authors of this paper. Examples of the systems covered include infinite streams, deterministic automata, Mealy machines and labelled transition systems. In this paper, we present a novel algorithm to decide whether two expressions are bisimilar or not. The procedure is implemented in the automatic theorem prover CIRC, by reducing coinduction to an entailment relation between an algebraic specification and an appropriate set of equations. We illustrate the generality of the tool with three examples: infinite streams of real numbers, Mealy machines and labelled transition systems.
Weak morphisms of higher dimensional automata
Venue: arXiv cs.FL 2013
Year: 2013

We introduce weak morphisms of higher dimensional automata and use them to define preorder relations for HDAs, among which homeomorphic abstraction and trace equivalent abstraction. It is shown that homeomorphic abstraction is essentially always stronger than trace equivalent abstraction. We also define the trace language of an HDA and show that, for a large class of HDAs, it is invariant under trace equivalent abstraction.
Possible Directions for Improving Dependency Versioning in R
Venue: arXiv cs.SE 2013
Year: 2013

One of the most powerful features of R is its infrastructure for contributed code. The built-in package manager and complementary repositories provide a great system for development and exchange of code, and have played an important role in the growth of the platform towards the de-facto standard in statistical computing that it is today. However, the number of packages on CRAN and other repositories has increased beyond what might have been foreseen, and is revealing some limitations of the current design. One such problem is the general lack of dependency versioning in the infrastructure. This paper explores this problem in greater detail, and suggests approaches taken by other open source communities that might work for R as well. Three use cases are defined that exemplify the issue, and illustrate how improving this aspect of package management could increase reliability while supporting further growth of the R community.
Information Transfer in the Agricultural Sector in Spain
Venue: arXiv cs.DL 2013
Year: 2013

This article examines the structures of information transfer to the agricultural (production) and agro-alimentary (transformation and commercialization of the products) sector within Spain. An historical perspective is provided to better illustrate the reality and complexity of Spain with regard to the systems of agrarian extension, agricultural research, the resources provided by Spain's central administration, and the use of information by related enterprises. The Service of Agrarian Extension appeared in Spain in the 1950s, and new political-administrative structures (agribusiness associations, cooperatives) were founded when Spain became a democratic nation in the late 1970s and with the arrival of electronic information, largely in the 1990s. We describe the research and technological centers supporting innovation in the agro-alimentary sector and the communication media dedicated to the agricultural sector. The article illustrates that the systems of agricultural information in Spain have been largely derived from initiatives of the Public Administration, with few private initiatives.
khmer: Working with Big Data in Bioinformatics
Venue: arXiv cs.CE 2013
Year: 2013

We introduce design and optimization considerations for the 'khmer' package.
Zero-point attracting projection algorithm for sequential compressive sensing
Venue: arXiv cs.IT 2013
Year: 2013

Sequential Compressive Sensing, which may be widely used in sensing devices, is a popular topic of recent research. This paper proposes an online recovery algorithm for sparse approximation of sequential compressive sensing. Several techniques including warm start, fast iteration, and variable step size are adopted in the proposed algorithm to improve its online performance. Finally, numerical simulations demonstrate its better performance than the relative art.
A stochastic gradient approach on compressive sensing signal reconstruction based on adaptive filtering framework
Venue: arXiv cs.IT 2013
Year: 2013

Based on the methodological similarity between sparse signal reconstruction and system identification, a new approach for sparse signal reconstruction in compressive sensing (CS) is proposed in this paper. This approach employs a stochastic gradient-based adaptive filtering framework, which is commonly used in system identification, to solve the sparse signal reconstruction problem. Two typical algorithms for this problem: $l_0$-least mean square ($l_0$-LMS) algorithm and $l_0$-exponentially forgetting window LMS ($l_0$-EFWLMS) algorithm are hence introduced here. Both the algorithms utilize a zero attraction method, which has been implemented by minimizing a continuous approximation of $l_0$ norm of the studied signal. To improve the performances of these proposed algorithms, an $l_0$-zero attraction projection ($l_0$-ZAP) algorithm is also adopted, which has effectively accelerated their convergence rates, making them much faster than the other existing algorithms for this problem. Advantages of the proposed approach, such as its robustness against noise etc., are demonstrated by numerical experiments.
l_0 Norm Constraint LMS Algorithm for Sparse System Identification
Venue: arXiv cs.IT 2013
Year: 2013

In order to improve the performance of Least Mean Square (LMS) based system identification of sparse systems, a new adaptive algorithm is proposed which utilizes the sparsity property of such systems. A general approximating approach on $l_0$ norm -- a typical metric of system sparsity, is proposed and integrated into the cost function of the LMS algorithm. This integration is equivalent to add a zero attractor in the iterations, by which the convergence rate of small coefficients, that dominate the sparse system, can be effectively improved. Moreover, using partial updating method, the computational complexity is reduced. The simulations demonstrate that the proposed algorithm can effectively improve the performance of LMS-based identification algorithms on sparse system.
Is Learning to Rank Worth It? A Statistical Analysis of Learning to Rank Methods
Venue: arXiv cs.IR 2013
Year: 2013

The Learning to Rank (L2R) research field has experienced a fast paced growth over the last few years, with a wide variety of benchmark datasets and baselines available for experimentation. We here investigate the main assumption behind this field, which is that, the use of sophisticated L2R algorithms and models, produce significant gains over more traditional and simple information retrieval approaches. Our experimental results surprisingly indicate that many L2R algorithms, when put up against the best individual features of each dataset, may not produce statistically significant differences, even if the absolute gains may seem large. We also find that most of the reported baselines are statistically tied, with no clear winner.
Comprehensive Analysis on the Vulnerability and Efficiency of P2P Networks under Static Failures and Targeted Attacks
Venue: arXiv cs.NI 2013
Year: 2013

Peer to peer systems are the networks consisting of a group of nodes possible to be as wide as the Internet. These networks are required of evaluation mechanisms and distributed control and configurations, so each peer will be able to communicate with other peers. Resilience to faults, failures and attacks, are the main requirements of most communication systems and networks today. Thus, since P2P networks can be individually used as an infrastructure and an alternative for many other communication networks, they have to be more reliable, and resilient to the faults, failures and attacks compared to the client and server approach. In this work, we present a detailed study on the behavior of various P2P networks toward faults and failures, and focus on fault-tolerance subject. We consider two different static failure scenarios: a)a random strategy in which nodes or edges of the network will be removed with an equal probability and without any knowledge of the networks infrastructure, b)a targeted strategy that uses some information about the nodes, and in which the nodes with the highest degree have the most priority to be attacked. By static faults, we mean a situation where the nodes or components encounter some faults before the network starts to work or through its operation, and will remain faulty to the end of the work session. Our goal is to introduce various measures to analyzing P2P networks evaluating their vulnerability rate. The presented criteria can be used for evaluating the reliability and vulnerability of P2P networks toward both random and targeted failures. There is no limit to the number and types of failures, the presented measures are able to be used for different types of failures and even a wide range of networks.
Trajectory Based Optimal Segment Computation in Road Network Databases
Venue: arXiv cs.DB 2013
Year: 2013

Finding a location for a new facility such that the facility attracts the maximal number of customers is a challenging problem. Existing studies either model customers as static sites and thus do not consider customer movement, or they focus on theoretical aspects and do not provide solutions that are shown empirically to be scalable. Given a road network, a set of existing facilities, and a collection of customer route traversals, an optimal segment query returns the optimal road network segment(s) for a new facility. We propose a practical framework for computing this query, where each route traversal is assigned a score that is distributed among the road segments covered by the route according to a score distribution model. The query returns the road segment(s) with the highest score. To achieve low latency, it is essential to prune the very large search space. We propose two algorithms that adopt different approaches to computing the query. Algorithm AUG uses graph augmentation, and ITE uses iterative road-network partitioning. Empirical studies with real data sets demonstrate that the algorithms are capable of offering high performance in realistic settings.
Image compression using anti-forensics method
Venue: arXiv cs.MM 2013
Year: 2013

A large number of image forensics methods are available which are capable of identifying image tampering. But these techniques are not capable of addressing the anti-forensics method which is able to hide the trace of image tampering. In this paper anti-forensics method for digital image compression has been proposed. This anti-forensics method is capable of removing the traces of image compression. Additionally, technique is also able to remove the traces of blocking artifact that are left by image compression algorithms that divide an image into segments during compression process. This method is targeted to remove the compression fingerprints of JPEG compression.
Maximin Analysis of Message Passing Algorithms for Recovering Block Sparse Signals
Venue: arXiv cs.IT 2013
Year: 2013

We consider the problem of recovering a block (or group) sparse signal from an underdetermined set of random linear measurements, which appear in compressed sensing applications such as radar and imaging. Recent results of Donoho, Johnstone, and Montanari have shown that approximate message passing (AMP) in combination with Stein's shrinkage outperforms group LASSO for large block sizes. In this paper, we prove that, for a fixed block size and in the strong undersampling regime (i.e., having very few measurements compared to the ambient dimension), AMP cannot improve upon group LASSO, thereby complementing the results of Donoho et al.
Voxel-wise Weighted MR Image Enhancement using an Extended Neighborhood Filter
Venue: arXiv cs.CV 2013
Year: 2013

We present an edge preserving and denoising filter for enhancing the features in images, which contain an ROI having a narrow spatial extent. Typical examples include angiograms, or ROI spatially distributed in multiple locations and contained within an outlying region, such as in multiple-sclerosis. The filtering involves determination of multiplicative weights in the spatial domain using an extended set of neighborhood directions. Equivalently, the filtering operation may be interpreted as a combination of directional filters in the frequency domain, with selective weighting for spatial frequencies contained within each direction. The advantages of the proposed filter in comparison to specialized non-linear filters, which operate on diffusion principle, are illustrated using numerical phantom data. The performance evaluation is carried out on simulated images from BrainWeb database for multiple-sclerosis, acute ischemic stroke using clinically acquired FLAIR images and MR angiograms.
Monte-Carlo utility estimates for Bayesian reinforcement learning
Venue: arXiv cs.LG 2013
Year: 2013

This paper introduces a set of algorithms for Monte-Carlo Bayesian reinforcement learning. Firstly, Monte-Carlo estimation of upper bounds on the Bayes-optimal value function is employed to construct an optimistic policy. Secondly, gradient-based algorithms for approximate upper and lower bounds are introduced. Finally, we introduce a new class of gradient algorithms for Bayesian Bellman error minimisation. We theoretically show that the gradient methods are sound. Experimentally, we demonstrate the superiority of the upper bound method in terms of reward obtained. However, we also show that the Bayesian Bellman error method is a close second, despite its significant computational simplicity.
On the Complexity of Maximum Clique Algorithms: usage of coloring heuristics leads to the 2^(n\5) algorithm running time lower bound
Venue: arXiv cs.DS 2013
Year: 2013

Maximum Clique Problem(MCP) is one of the 21 original NP--complete problems enumerated by Karp in 1972. In recent years a large number of exact methods to solve MCP have been appeared(Babel, Wood, Kumlander, Fahle, Li, Tomita and etc). Most of them are branch and bound algorithms that use branching rule introduced by Balas and Yu and based on coloring heuristics to establish an upper bound on the clique number. They differ from each other primarily in vertex preordering and vertex coloring methods. Current methods of worst case running time analysis for branch and bound algorithms do not allow to provide tight upper bounds. This motivates the study of lower bounds for such algorithms. We prove 2^(n\5) lower bound for group of MCP algorithms based on usage of coloring heuristics.
On a family of binary completely transitive codes with growing covering radius
Venue: arXiv cs.IT 2013
Year: 2013

A new family of binary linear completely transitive (and, therefore, completely regular) codes is constructed. The covering radius of these codes is growing with the length of the code. In particular, for any integer r > 1, there exist two codes with d=3, covering radius r and length 2r(4r-1) and (2r+1)(4r+1), respectively. These new completely transitive codes induce, as coset graphs, a family of distance-transitive graphs of growing diameter.
Optimized Transmission with Improper Gaussian Signaling in the K-User MISO Interference Channel
Venue: arXiv cs.IT 2013
Year: 2013

This paper studies the achievable rate region of the K-user Gaussian multiple-input single-output interference channel (MISO-IC) with the interference treated as noise, when improper or circularly asymmetric complex Gaussian signaling is applied. The transmit optimization with improper Gaussian signaling involves not only the signal covariance matrix as in the conventional proper or circularly symmetric Gaussian signaling, but also the signal pseudo-covariance matrix, which is conventionally set to zero in proper Gaussian signaling. By exploiting the separable rate expression with improper Gaussian signaling, we propose a separate transmit covariance and pseudo-covariance optimization algorithm, which is guaranteed to improve the users' achievable rates over the conventional proper Gaussian signaling. In particular, for the pseudo-covariance optimization, we establish the optimality of rank-1 pseudo-covariance matrices, given the optimal rank-1 transmit covariance matrices for achieving the Pareto boundary of the rate region. Based on this result, we are able to greatly reduce the number of variables in the pseudo-covariance optimization problem and thereby develop an efficient solution by applying the celebrated semidefinite relaxation (SDR) technique. Finally, we extend the result to the Gaussian MISO broadcast channel (MISO-BC) with improper Gaussian signaling or so-called widely linear transmit precoding.
Combined Learning of Salient Local Descriptors and Distance Metrics for Image Set Face Verification
Venue: arXiv cs.CV 2013
Year: 2013

In contrast to comparing faces via single exemplars, matching sets of face images increases robustness and discrimination performance. Recent image set matching approaches typically measure similarities between subspaces or manifolds, while representing faces in a rigid and holistic manner. Such representations are easily affected by variations in terms of alignment, illumination, pose and expression. While local feature based representations are considerably more robust to such variations, they have received little attention within the image set matching area. We propose a novel image set matching technique, comprised of three aspects: (i) robust descriptors of face regions based on local features, partly inspired by the hierarchy in the human visual system, (ii) use of several subspace and exemplar metrics to compare corresponding face regions, (iii) jointly learning which regions are the most discriminative while finding the optimal mixing weights for combining metrics. Face recognition experiments on LFW, PIE and MOBIO face datasets show that the proposed algorithm obtains considerably better performance than several recent state-of-the-art techniques, such as Local Principal Angle and the Kernel Affine Hull Method.
Modeling Basic Aspects of Cyber-Physical Systems
Venue: arXiv cs.RO 2013
Year: 2013

Designing novel cyber-physical systems entails significant, costly physical experimentation. Simulation tools can enable the virtualization of experiments. Unfortunately, current tools have shortcomings that limit their utility for virtual experimentation. Language research can be especially helpful in addressing many of these problems. As a first step in this direction, we consider the question of determining what language features are needed to model cyber-physical systems. Using a series of elementary examples of cyber-physical systems, we reflect on the extent to which a small, experimental domain-specific formalism called Acumen suffices for this purpose.
Asynchronous Distributed Optimization using a Randomized Alternating Direction Method of Multipliers
Venue: arXiv cs.DC 2013
Year: 2013

Consider a set of networked agents endowed with private cost functions and seeking to find a consensus on the minimizer of the aggregate cost. A new class of random asynchronous distributed optimization methods is introduced. The methods generalize the standard Alternating Direction Method of Multipliers (ADMM) to an asynchronous setting where isolated components of the network are activated in an uncoordinated fashion. The algorithms rely on the introduction of randomized Gauss-Seidel iterations of a Douglas-Rachford operator for finding zeros of a sum of two monotone operators. Convergence to the sought minimizers is provided under mild connectivity conditions. Numerical results sustain our claims.
Integration of SOA and Cloud Computing in RM-ODP
Venue: arXiv cs.SE 2013
Year: 2013

The objective of ODP is according to ITU-T Recommendation X.901 stated as follows: The objective of ODP standardization is the development of standards that allow the benefits of distributing information processing services to be realized in an environment of heterogeneous IT resources and multiple organizational domains. These standards address constraints on system specification and the provision of a system infrastructure that accommodate difficulties inherent in the design and programming of distributed systems. This objective seems to cover cloud computing systems. Therefore, we in this paper discuss the concepts of cloud, and discuss the use of RM-ODP for specifying the solution. We indicate that the current RM-ODP may be too abstract for the purpose, and indicate how to adapt RM-ODP to fit the purpose