OpenRaiser/Intern-Atlas huggingface dataset:
New Symbolic Algorithms For Solving A General Bordered Tridiagonal Linear System
Venue: arXiv cs.SC 2013
Year: 2013

In this paper, the author present reliable symbolic algorithms for solving a general bordered tridiagonal linear system. The first algorithm is based on the LU decomposition of the coefficient matrix and the computational cost of it is O(n). The second is based on The Sherman-Morrison-Woodbury formula. The algorithms are implementable to the Computer Algebra System (CAS) such as MAPLE, MATLAB and MATHEMATICA. Three examples are presented for the sake of illustration.
Note on Undecidability of Bisimilarity for Second-Order Pushdown Processes
Venue: arXiv cs.LO 2013
Year: 2013

Broadbent and Göller (FSTTCS 2012) proved the undecidability of bisimulation equivalence for processes generated by epsilon-free second-order pushdown automata. We add a few remarks concerning the used proof technique, called Defender's forcing, and the related undecidability proof for first-order pushdown automata with epsilon-transitions (Jančar and Srba, JACM 2008).
Strategic Analysis of Trust Models for User-Centric Networks
Venue: arXiv cs.GT 2013
Year: 2013

We present a strategic analysis of a trust model that has recently been proposed for promoting cooperative behaviour in user-centric networks. The mechanism for cooperation is based on a combination of reputation and virtual currency schemes in which service providers reward paying customers and punish non-paying ones by adjusting their reputation, and hence the price they pay for services. We model and analyse this system using PRISM-games, a tool that performs automated verification and strategy synthesis for stochastic multi-player games using the probabilistic alternating-time temporal logic with rewards (rPATL). We construct optimal strategies for both service users and providers, which expose potential risks of the cooperation mechanism and which we use to devise improvements that counteract these risks.
Synthesizing Structured Reactive Programs via Deterministic Tree Automata
Venue: arXiv cs.LO 2013
Year: 2013

Existing approaches to the synthesis of reactive systems typically involve the construction of transition systems such as Mealy automata. However, in order to obtain a succinct representation of the desired system, structured programs can be a more suitable model. In 2011, Madhusudan proposed an algorithm to construct a structured reactive program for a given omega-regular specification without synthesizing a transition system first. His procedure is based on two-way alternating omega-automata on finite trees that recognize the set of "correct" programs. We present a more elementary and direct approach using only deterministic bottom-up tree automata that compute so-called signatures for a given program. In doing so, we extend Madhusudan's results to the wider class of programs with bounded delay, which may read several input symbols before producing an output symbol (or vice versa). As a formal foundation, we inductively define a semantics for such programs.
Towards the Usage of MBT at ETSI
Venue: arXiv cs.SE 2013
Year: 2013

In 2012 the Specialists Task Force (STF) 442 appointed by the European Telcommunication Standards Institute (ETSI) explored the possibilities of using Model Based Testing (MBT) for test development in standardization. STF 442 performed two case studies and developed an MBT-methodology for ETSI. The case studies were based on the ETSI-standards GeoNetworking protocol (ETSI TS 102 636) and the Diameter-based Rx protocol (ETSI TS 129 214). Models have been developed for parts of both standards and four different MBT-tools have been employed for generating test cases from the models. The case studies were successful in the sense that all the tools were able to produce the test suites having the same test adequacy as the corresponding manually developed conformance test suites. The MBT-methodology developed by STF 442 is based on the experiences with the case studies. It focusses on integrating MBT into the sophisticated standardization process at ETSI. This paper summarizes the results of the STF 442 work.
Testing Java implementations of algebraic specifications
Venue: arXiv cs.SE 2013
Year: 2013

In this paper we focus on exploiting a specification and the structures that satisfy it, to obtain a means of comparing implemented and expected behaviours and find the origin of faults in implementations. We present an approach to the creation of tests that are based on those specification-compliant structures, and to the interpretation of those tests' results leading to the discovery of the method responsible for an eventual test failure. Results of comparative experiments with a tool implementing this approach are presented.
On Large Deviation Property of Recurrence Times
Venue: arXiv cs.IT 2013
Year: 2013

We extend the study by Ornstein and Weiss on the asymptotic behavior of the normalized version of recurrence times and establish the large deviation property for a certain class of mixing processes. Further, an estimator for entropy based on recurrence times is proposed for which large deviation behavior is proved for stationary and ergodic sources satisfying similar mixing conditions.
A New Achievable Scheme for Interference Relay Channels
Venue: arXiv cs.IT 2013
Year: 2013

We establish an achievable rate region for discrete memoryless interference relay channels that consist of two source-destination pairs and one or more relays. We develop an achievable scheme combining Han-Kobayashi and noisy network coding schemes. We apply our achievability to two cases. First, we characterize the capacity region of a class of discrete memoryless interference relay channels. This class naturally generalizes the injective deterministic discrete memoryless interference channel by El Gamal and Costa and the deterministic discrete memoryless relay channel with orthogonal receiver components by Kim. Moreover, for the Gaussian interference relay channel with orthogonal receiver components, we show that our scheme achieves a better sum rate than that of noisy network coding.
On Match Lengths and the Asymptotic Behavior of Sliding Window Lempel-Ziv Algorithm for Zero Entropy Sequences
Venue: arXiv cs.IT 2013
Year: 2013

The Sliding Window Lempel-Ziv (SWLZ) algorithm has been studied from various perspectives in information theory literature. In this paper, we provide a general law which defines the asymptotics of match length for stationary and ergodic zero entropy processes. Moreover, we use this law to choose the match length $L_o$ in the almost sure optimality proof of Fixed Shift Variant of Lempel-Ziv (FSLZ) and SWLZ algorithms given in literature. First, through an example of stationary and ergodic processes generated by irrational rotation we establish that for a window size of $n_w$ a compression ratio given by $O(\frac{\log n_w}{{n_w}^a})$ where $a$ is arbitrarily close to 1 and $0 < a < 1$, is obtained under the application of FSLZ and SWLZ algorithms. Further, we give a general expression for the compression ratio for a class of stationary and totally ergodic processes with zero entropy.
Recursive Sparse Recovery in Large but Structured Noise - Part 2
Venue: arXiv cs.IT 2013
Year: 2013

We study the problem of recursively recovering a time sequence of sparse vectors, St, from measurements Mt := St + Lt that are corrupted by structured noise Lt which is dense and can have large magnitude. The structure that we require is that Lt should lie in a low dimensional subspace that is either fixed or changes "slowly enough"; and the eigenvalues of its covariance matrix are "clustered". We do not assume any model on the sequence of sparse vectors. Their support sets and their nonzero element values may be either independent or correlated over time (usually in many applications they are correlated). The only thing required is that there be some support change every so often. We introduce a novel solution approach called Recursive Projected Compressive Sensing with cluster-PCA (ReProCS-cPCA) that addresses some of the limitations of earlier work. Under mild assumptions, we show that, with high probability, ReProCS-cPCA can exactly recover the support set of St at all times; and the reconstruction errors of both St and Lt are upper bounded by a time-invariant and small value.
Sample-Optimal Average-Case Sparse Fourier Transform in Two Dimensions
Venue: arXiv cs.DS 2013
Year: 2013

We present the first sample-optimal sublinear time algorithms for the sparse Discrete Fourier Transform over a two-dimensional sqrt{n} x sqrt{n} grid. Our algorithms are analyzed for /average case/ signals. For signals whose spectrum is exactly sparse, our algorithms use O(k) samples and run in O(k log k) time, where k is the expected sparsity of the signal. For signals whose spectrum is approximately sparse, our algorithm uses O(k log n) samples and runs in O(k log^2 n) time; the latter algorithm works for k=Theta(sqrt{n}). The number of samples used by our algorithms matches the known lower bounds for the respective signal models. By a known reduction, our algorithms give similar results for the one-dimensional sparse Discrete Fourier Transform when n is a power of a small composite number (e.g., n = 6^t).
GPU accelerated maximum cardinality matching algorithms for bipartite graphs
Venue: arXiv cs.DC 2013
Year: 2013

We design, implement, and evaluate GPU-based algorithms for the maximum cardinality matching problem in bipartite graphs. Such algorithms have a variety of applications in computer science, scientific computing, bioinformatics, and other areas. To the best of our knowledge, ours is the first study which focuses on GPU implementation of the maximum cardinality matching algorithms. We compare the proposed algorithms with serial and multicore implementations from the literature on a large set of real-life problems where in majority of the cases one of our GPU-accelerated algorithms is demonstrated to be faster than both the sequential and multicore implementations.
Minimal Assumption Distribution Propagation in Belief Networks
Venue: arXiv cs.AI 2013
Year: 2013

As belief networks are used to model increasingly complex situations, the need to automatically construct them from large databases will become paramount. This paper concentrates on solving a part of the belief network induction problem: that of learning the quantitative structure (the conditional probabilities), given the qualitative structure. In particular, a theory is presented that shows how to propagate inference distributions in a belief network, with the only assumption being that the given qualitative structure is correct. Most inference algorithms must make at least this assumption. The theory is based on four network transformations that are sufficient for any inference in a belief network. Furthermore, the claim is made that contrary to popular belief, error will not necessarily grow as the inference chain grows. Instead, for QBN belief nets induced from large enough samples, the error is more likely to decrease as the size of the inference chain increases.
A Construction of Bayesian Networks from Databases Based on an MDL Principle
Venue: arXiv cs.AI 2013
Year: 2013

This paper addresses learning stochastic rules especially on an inter-attribute relation based on a Minimum Description Length (MDL) principle with a finite number of examples, assuming an application to the design of intelligent relational database systems. The stochastic rule in this paper consists of a model giving the structure like the dependencies of a Bayesian Belief Network (BBN) and some stochastic parameters each indicating a conditional probability of an attribute value given the state determined by the other attributes' values in the same record. Especially, we propose the extended version of the algorithm of Chow and Liu in that our learning algorithm selects the model in the range where the dependencies among the attributes are represented by some general plural number of trees.
Incremental Probabilistic Inference
Venue: arXiv cs.AI 2013
Year: 2013

Propositional representation services such as truth maintenance systems offer powerful support for incremental, interleaved, problem-model construction and evaluation. Probabilistic inference systems, in contrast, have lagged behind in supporting this incrementality typically demanded by problem solvers. The problem, we argue, is that the basic task of probabilistic inference is typically formulated at too large a grain-size. We show how a system built around a smaller grain-size inference task can have the desired incrementality and serve as the basis for a low-level (propositional) probabilistic representation service.
A Bayesian Variant of Shafer's Commonalities For Modelling Unforeseen Events
Venue: arXiv cs.AI 2013
Year: 2013

Shafer's theory of belief and the Bayesian theory of probability are two alternative and mutually inconsistent approaches toward modelling uncertainty in artificial intelligence. To help reduce the conflict between these two approaches, this paper reexamines expected utility theory-from which Bayesian probability theory is derived. Expected utility theory requires the decision maker to assign a utility to each decision conditioned on every possible event that might occur. But frequently the decision maker cannot foresee all the events that might occur, i.e., one of the possible events is the occurrence of an unforeseen event. So once we acknowledge the existence of unforeseen events, we need to develop some way of assigning utilities to decisions conditioned on unforeseen events. The commonsensical solution to this problem is to assign similar utilities to events which are similar. Implementing this commonsensical solution is equivalent to replacing Bayesian subjective probabilities over the space of foreseen and unforeseen events by random set theory probabilities over the space of foreseen events. This leads to an expected utility principle in which normalized variants of Shafer's commonalities play the role of subjective probabilities. Hence allowing for unforeseen events in decision analysis causes Bayesian probability theory to become much more similar to Shaferian theory.
Computing Similarity between a Pair of Trajectories
Venue: arXiv cs.CG 2013
Year: 2013

With recent advances in sensing and tracking technology, trajectory data is becoming increasingly pervasive and analysis of trajectory data is becoming exceedingly important. A fundamental problem in analyzing trajectory data is that of identifying common patterns between pairs or among groups of trajectories. In this paper, we consider the problem of identifying similar portions between a pair of trajectories, each observed as a sequence of points sampled from it. We present new measures of trajectory similarity --- both local and global --- between a pair of trajectories to distinguish between similar and dissimilar portions. Our model is robust under noise and outliers, it does not make any assumptions on the sampling rates on either trajectory, and it works even if they are partially observed. Additionally, the model also yields a scalar similarity score which can be used to rank multiple pairs of trajectories according to similarity, e.g. in clustering applications. We also present efficient algorithms for computing the similarity under our measures; the worst-case running time is quadratic in the number of sample points. Finally, we present an extensive experimental study evaluating the effectiveness of our approach on real datasets, comparing with it with earlier approaches, and illustrating many issues that arise in trajectory data. Our experiments show that our approach is highly accurate in distinguishing similar and dissimilar portions as compared to earlier methods even with sparse sampling.
Concurrent Cyber Physical Systems:Tensor State Space Representation
Venue: arXiv cs.NI 2013
Year: 2013

In this research paper, state space representation of concurrent, linearly coupled dynamical systems is discussed. It is reasoned that the Tensor State Space Representation (TSSR) proposed in [Rama1] is directly applicable in such a problem. Also some discussion on linearly coupled, concurrent systems evolving on multiple time scales is included. Briefly new ideas related to distributed signal processing in cyber physical systems are included.
Secure Video Streaming Plug-In
Venue: arXiv cs.MM 2013
Year: 2013

Video sharing sites like YouTube, Metacafe, Dailymotion, Vimeo, etc. provide a platform for media content sharing among its users. Some of these videos are copyright protected and restricted from being downloaded and saved. But users can use various download managers or application programs to download and save these videos. This affects the incoming traffic on these websites reducing their hit rate and consequently reducing their revenue. Adobe Flash Player is the most commonly used player for watching online videos. It uses RTMP (Real Time Messaging Protocol) to stream audio, video and data over the Internet, between a Flash Player and Adobe Flash Media Server.Here, we propose a plug-in that enables the site owner control over downloading of videos from such website. The plug-in will be installed at the client side with the consent of the user. When the video is being played this plug-in will send unique keys to the media server. The server will continue streaming the video after verifying the keys. Download managers or application programs will not be able to download the videos as they wont be able to create the unique keys that need to be sent to the server.
Static and dynamic semantics of NoSQL languages
Venue: arXiv cs.PL 2013
Year: 2013

We present a calculus for processing semistructured data that spans differences of application area among several novel query languages, broadly categorized as "NoSQL". This calculus lets users define their own operators, capturing a wider range of data processing capabilities, whilst providing a typing precision so far typical only of primitive hard-coded operators. The type inference algorithm is based on semantic type checking, resulting in type information that is both precise, and flexible enough to handle structured and semistructured data. We illustrate the use of this calculus by encoding a large fragment of Jaql, including operations and iterators over JSON, embedded SQL expressions, and co-grouping, and show how the encoding directly yields a typing discipline for Jaql as it is, namely without the addition of any type definition or type annotation in the code.
Automated Identification and Classification of Stereochemistry: Chirality and Double Bond Stereoisomerism
Venue: arXiv cs.CE 2013
Year: 2013

Stereoisomers have the same molecular formula and the same atom connectivity and their existence can be related to the presence of different three-dimensional arrangements. Stereoisomerism is of great importance in many different fields since the molecular properties and biological effects of the stereoisomers are often significantly different. Most drugs for example, are often composed of a single stereoisomer of a compound, and while one of them may have therapeutic effects on the body, another may be toxic. A challenging task is the automatic detection of stereoisomers using line input specifications such as SMILES or InChI since it requires information about group theory (to distinguish stereoisomers using mathematical information about its symmetry), topology and geometry of the molecule. There are several software packages that include modules to handle stereochemistry, especially the ones to name a chemical structure and/or view, edit and generate chemical structure diagrams. However, there is a lack of software capable of automatically analyzing a molecule represented as a graph and generate a classification of the type of isomerism present in a given atom or bond. Considering the importance of stereoisomerism when comparing chemical structures, this report describes a computer program for analyzing and processing steric information contained in a chemical structure represented as a molecular graph and providing as output a binary classification of the isomer type based on the recommended conventions. Due to the complexity of the underlying issue, specification of stereochemical information is currently limited to explicit stereochemistry and to the two most common types of stereochemistry caused by asymmetry around carbon atoms: chiral atom and double bond. A Webtool to automatically identify and classify stereochemistry is available at http://nams.lasige.di.fc.ul.pt/tools.php
Multi-relational Learning Using Weighted Tensor Decomposition with Modular Loss
Venue: arXiv cs.LG 2013
Year: 2013

We propose a modular framework for multi-relational learning via tensor decomposition. In our learning setting, the training data contains multiple types of relationships among a set of objects, which we represent by a sparse three-mode tensor. The goal is to predict the values of the missing entries. To do so, we model each relationship as a function of a linear combination of latent factors. We learn this latent representation by computing a low-rank tensor decomposition, using quasi-Newton optimization of a weighted objective function. Sparsity in the observed data is captured by the weighted objective, leading to improved accuracy when training data is limited. Exploiting sparsity also improves efficiency, potentially up to an order of magnitude over unweighted approaches. In addition, our framework accommodates arbitrary combinations of smooth, task-specific loss functions, making it better suited for learning different types of relations. For the typical cases of real-valued functions and binary relations, we propose several loss functions and derive the associated parameter gradients. We evaluate our method on synthetic and real data, showing significant improvements in both accuracy and scalability over related factorization techniques.
Forensic Analysis of Phone Call Networks
Venue: arXiv cs.SI 2013
Year: 2013

In the context of preventing and fighting crime, the analysis of mobile phone traffic, among actors of a criminal network, is helpful in order to reconstruct illegal activities on the base of the relationships connecting those specific individuals. Thus, forensic analysts and investigators require new advanced tools and techniques which allow them to manage these data in a meaningful and efficient way. In this paper we present LogAnalysis, a tool we developed to provide visual data representation and filtering, statistical analysis features and the possibility of a temporal analysis of mobile phone activities. Its adoption may help in unveiling the structure of a criminal network and the roles and dynamics of communications among its components. By using LogAnalysis, forensic investigators could deeply understand hierarchies within criminal organizations, for example discovering central members that provide connections among different sub-groups, etc. Moreover, by analyzing the temporal evolution of the contacts among individuals, or by focusing on specific time windows they could acquire additional insights on the data they are analyzing. Finally, we put into evidence how the adoption of LogAnalysis may be crucial to solve real cases, providing as example a number of case studies inspired by real forensic investigations led by one of the authors.