OpenRaiser/Intern-Atlas huggingface dataset:
Nonuniform Dynamic Discretization in Hybrid Networks
Venue: arXiv cs.AI 2013
Year: 2013

We consider probabilistic inference in general hybrid networks, which include continuous and discrete variables in an arbitrary topology. We reexamine the question of variable discretization in a hybrid network aiming at minimizing the information loss induced by the discretization. We show that a nonuniform partition across all variables as opposed to uniform partition of each variable separately reduces the size of the data structures needed to represent a continuous function. We also provide a simple but efficient procedure for nonuniform partition. To represent a nonuniform discretization in the computer memory, we introduce a new data structure, which we call a Binary Split Partition (BSP) tree. We show that BSP trees can be an exponential factor smaller than the data structures in the standard uniform discretization in multiple dimensions and show how the BSP trees can be used in the standard join tree algorithm. We show that the accuracy of the inference process can be significantly improved by adjusting discretization with evidence. We construct an iterative anytime algorithm that gradually improves the quality of the discretization and the accuracy of the answer on a query. We provide empirical evidence that the algorithm converges.
Computational Advantages of Relevance Reasoning in Bayesian Belief Networks
Venue: arXiv cs.AI 2013
Year: 2013

This paper introduces a computational framework for reasoning in Bayesian belief networks that derives significant advantages from focused inference and relevance reasoning. This framework is based on d -separation and other simple and computationally efficient techniques for pruning irrelevant parts of a network. Our main contribution is a technique that we call relevance-based decomposition. Relevance-based decomposition approaches belief updating in large networks by focusing on their parts and decomposing them into partially overlapping subnetworks. This makes reasoning in some intractable networks possible and, in addition, often results in significant speedup, as the total time taken to update all subnetworks is in practice often considerably less than the time taken to update the network as a whole. We report results of empirical tests that demonstrate practical significance of our approach.
On Stable Multi-Agent Behavior in Face of Uncertainty
Venue: arXiv cs.AI 2013
Year: 2013

A stable joint plan should guarantee the achievement of a designer's goal in a multi-agent environment, while ensuring that deviations from the prescribed plan would be detected. We present a computational framework where stable joint plans can be studied, as well as several basic results about the representation, verification and synthesis of stable joint plans.
Joint Scalable Coding and Routing for 60 GHz Real-Time Live HD Video Streaming Applications
Venue: arXiv cs.NI 2013
Year: 2013

Transmission of high-definition (HD) video is a promising application for 60 GHz wireless links, since very high transmission rates (up to several Gbit/s) are possible. In particular we consider a sports stadium broadcasting system where signals from multiple cameras are transmitted to a central location. Due to the high pathloss of 60 GHz radiation over the large distances encountered in this scenario, the use of relays might be required. The current paper analyzes the joint selection of the routes (relays) and the compression rates from the various sources for maximization of the overall video quality. We consider three different scenarios: (i) each source transmits only to one relay and the relay can receive only one data stream, and (ii) each source can transmit only to a single relay, but relays can aggregate streams from different sources and forward to the destination, and (iii) the source can split its data stream into parallel streams, which can be transmitted via different relays to the destination. For each scenario, we derive the mathematical formulations of the optimization problem and re-formulate them as convex mixed-integer programming, which can guarantee optimal solutions. Extensive simulations demonstrate that high-quality transmission is possible for at least ten cameras over distances of 300 m. Furthermore, optimization of the video quality gives results that can significantly outperform algorithms that maximize data rates.
Possible and Necessary Winner Problem in Social Polls
Venue: arXiv cs.GT 2013
Year: 2013

Social networks are increasingly being used to conduct polls. We introduce a simple model of such social polling. We suppose agents vote sequentially, but the order in which agents choose to vote is not necessarily fixed. We also suppose that an agent's vote is influenced by the votes of their friends who have already voted. Despite its simplicity, this model provides useful insights into a number of areas including social polling, sequential voting, and manipulation. We prove that the number of candidates and the network structure affect the computational complexity of computing which candidate necessarily or possibly can win in such a social poll. For social networks with bounded treewidth and a bounded number of candidates, we provide polynomial algorithms for both problems. In other cases, we prove that computing which candidates necessarily or possibly win are computationally intractable.
An ANN-based Method for Detecting Vocal Fold Pathology
Venue: arXiv cs.LG 2013
Year: 2013

There are different algorithms for vocal fold pathology diagnosis. These algorithms usually have three stages which are Feature Extraction, Feature Reduction and Classification. While the third stage implies a choice of a variety of machine learning methods, the first and second stages play a critical role in performance and accuracy of the classification system. In this paper we present initial study of feature extraction and feature reduction in the task of vocal fold pathology diagnosis. A new type of feature vector, based on wavelet packet decomposition and Mel-Frequency-Cepstral-Coefficients (MFCCs), is proposed. Also Principal Component Analysis (PCA) is used for feature reduction. An Artificial Neural Network is used as a classifier for evaluating the performance of our proposed method.
Acceleration detection of large (probably) prime numbers
Venue: arXiv cs.CR 2013
Year: 2013

In order to avoid unnecessary applications of Miller-Rabin algorithm to the number in question, we resort to trial division by a few initial prime numbers, since such a division take less time. How far we should go with such a division is the that we are trying to answer in this paper?For the theory of the matter is fully resolved. However, that in practice we do not have much use. Therefore, we present a solution that is probably irrelevant to theorists, but it is very useful to people who have spent many nights to produce large (probably) prime numbers using its own software.
Embedding agents in business applications using enterprise integration patterns
Venue: arXiv cs.MA 2013
Year: 2013

This paper addresses the issue of integrating agents with a variety of external resources and services, as found in enterprise computing environments. We propose an approach for interfacing agents and existing message routing and mediation engines based on the endpoint concept from the enterprise integration patterns of Hohpe and Woolf. A design for agent endpoints is presented, and an architecture for connecting the Jason agent platform to the Apache Camel enterprise integration framework using this type of endpoint is described. The approach is illustrated by means of a business process use case, and a number of Camel routes are presented. These demonstrate the benefits of interfacing agents to external services via a specialised message routing tool that supports enterprise integration patterns.
A fuzzy similarity based approach for intelligent web based e-learning
Venue: arXiv cs.CY 2013
Year: 2013

In this paper, an intelligent system for web based e-Learning is proposed which analyzes students knowledge capacity by applying clustering technique. This system uses fuzzy logic and k-means clustering algorithm to arrange the documents according to the level of their performance. Moreover, a new domain ontology alignment technique is proposed that uses contextual information of the knowledge sources from the e-Learning domain for effective decision making. The proposed ontology alignment method has been empirically tested in an e-Learning environment and the experimental results show that the proposed method performs better than the existing methods in terms of precision and recall. The salient contributions of this paper are the use of Jaccard Similarity, fuzzy approach for ontology alignment and K-Means clustering algorithm for decision making using decision rules for providing intelligent e-Learning.
Cryptanalysis and Improvement of Akleylek et al.'s cryptosystem
Venue: arXiv cs.CR 2013
Year: 2013

Akleylek et al. [S. Akleylek, L. Emmungil and U. Nuriyev, A mod ified algorithm for peer-to-peer security, journal of Appl. Comput. Math., vol. 6(2), pp.258-264, 2007.], introduced a modified public-key encryption scheme with steganographic approach for security in peer-to-peer (P2P) networks. In this cryptosystem, Akleylek et al. attempt to increase security of the P2P networks by mixing ElGamal cryptosystem with knapsack problem. In this paper, we present a ciphertext-only attack against their system to recover message. In addition, we show that for their scheme completeness property is not holds, and therefore, the receiver cannot uniquely decrypts messages. Furthermore, we also show that this system is not chosen-ciphertext secure, thus the proposed scheme is vulnerable to man-in-the-middle-attack, one of the most pernicious attacks against P2P networks. Therefore, this scheme is not suitable to implement in the P2P networks. We modify this cryptosystem in order to increase its security and efficiency. Our construction is the efficient CCA2-secure variant of the Akleylek et al.'s encryption scheme in the standard model, the de facto security notion for public-key encryption schemes.
Belief Semantics of Authorization Logic
Venue: arXiv cs.LO 2013
Year: 2013

Authorization logics have been used in the theory of computer security to reason about access control decisions. In this work, a formal belief semantics for authorization logics is given. The belief semantics is proved to subsume a standard Kripke semantics. The belief semantics yields a direct representation of principals' beliefs, without resorting to the technical machinery used in Kripke semantics. A proof system is given for the logic; that system is proved sound with respect to the belief and Kripke semantics. The soundness proof for the belief semantics, and for a variant of the Kripke semantics, is mechanized in Coq.
On the Tradeoff between Stability and Fit
Venue: arXiv cs.DS 2013
Year: 2013

In computing, as in many aspects of life, changes incur cost. Many optimization problems are formulated as a one-time instance starting from scratch. However, a common case that arises is when we already have a set of prior assignments, and must decide how to respond to a new set of constraints, given that each change from the current assignment comes at a price. That is, we would like to maximize the fitness or efficiency of our system, but we need to balance it with the changeout cost from the previous state. We provide a precise formulation for this tradeoff and analyze the resulting {\em stable extensions} of some fundamental problems in measurement and analytics. Our main technical contribution is a stable extension of PPS (probability proportional to size) weighted random sampling, with applications to monitoring and anomaly detection problems. We also provide a general framework that applies to top-$k$, minimum spanning tree, and assignment. In both cases, we are able to provide exact solutions, and discuss efficient incremental algorithms that can find new solutions as the input changes.
Computer-Aided Derivation of Multi-scale Models: A Rewriting Framework
Venue: arXiv cs.SC 2013
Year: 2013

We introduce a framework for computer-aided derivation of multi-scale models. It relies on a combination of an asymptotic method used in the field of partial differential equations with term rewriting techniques coming from computer science. In our approach, a multi-scale model derivation is characterized by the features taken into account in the asymptotic analysis. Its formulation consists in a derivation of a reference model associated to an elementary nominal model, and in a set of transformations to apply to this proof until it takes into account the wanted features. In addition to the reference model proof, the framework includes first order rewriting principles designed for asymptotic model derivations, and second order rewriting principles dedicated to transformations of model derivations. We apply the method to generate a family of homogenized models for second order elliptic equations with periodic coefficients that could be posed in multi-dimensional domains, with possibly multi-domains and/or thin domains.
On the list decodability of random linear codes with large error rates
Venue: arXiv cs.IT 2013
Year: 2013

It is well known that a random q-ary code of rate Ω(ε^2) is list decodable up to radius (1 - 1/q - ε) with list sizes on the order of 1/ε^2, with probability 1 - o(1). However, until recently, a similar statement about random linear codes has until remained elusive. In a recent paper, Cheraghchi, Guruswami, and Velingker show a connection between list decodability of random linear codes and the Restricted Isometry Property from compressed sensing, and use this connection to prove that a random linear code of rate Ω(ε^2 / log^3(1/ε)) achieves the list decoding properties above, with constant probability. We improve on their result to show that in fact we may take the rate to be Ω(ε^2), which is optimal, and further that the success probability is 1 - o(1), rather than constant. As an added benefit, our proof is relatively simple. Finally, we extend our methods to more general ensembles of linear codes. As an example, we show that randomly punctured Reed-Muller codes have the same list decoding properties as the original codes, even when the rate is improved to a constant.
Modeling Morphology of Social Network Cascades
Venue: arXiv cs.SI 2013
Year: 2013

Cascades represent an important phenomenon across various disciplines such as sociology, economy, psychology, political science, marketing, and epidemiology. An important property of cascades is their morphology, which encompasses the structure, shape, and size. However, cascade morphology has not been rigorously characterized and modeled in prior literature. In this paper, we propose a Multi-order Markov Model for the Morphology of Cascades ($M^4C$) that can represent and quantitatively characterize the morphology of cascades with arbitrary structures, shapes, and sizes. $M^4C$ can be used in a variety of applications to classify different types of cascades. To demonstrate this, we apply it to an unexplored but important problem in online social networks -- cascade size prediction. Our evaluations using real-world Twitter data show that $M^4C$ based cascade size prediction scheme outperforms the baseline scheme based on cascade graph features such as edge growth rate, degree distribution, clustering, and diameter. $M^4C$ based cascade size prediction scheme consistently achieves more than 90% classification accuracy under different experimental scenarios.
Byzantine Vector Consensus in Complete Graphs
Venue: arXiv cs.DC 2013
Year: 2013

Consider a network of n processes each of which has a d-dimensional vector of reals as its input. Each process can communicate directly with all the processes in the system; thus the communication network is a complete graph. All the communication channels are reliable and FIFO (first-in-first-out). The problem of Byzantine vector consensus (BVC) requires agreement on a d-dimensional vector that is in the convex hull of the d-dimensional input vectors at the non-faulty processes. We obtain the following results for Byzantine vector consensus in complete graphs while tolerating up to f Byzantine failures: * We prove that in a synchronous system, n >= max(3f+1, (d+1)f+1) is necessary and sufficient for achieving Byzantine vector consensus. * In an asynchronous system, it is known that exact consensus is impossible in presence of faulty processes. For an asynchronous system, we prove that n >= (d+2)f+1 is necessary and sufficient to achieve approximate Byzantine vector consensus. Our sufficiency proofs are constructive. We show sufficiency by providing explicit algorithms that solve exact BVC in synchronous systems, and approximate BVC in asynchronous systems. We also obtain tight bounds on the number of processes for achieving BVC using algorithms that are restricted to a simpler communication pattern.
Coded aperture compressive temporal imaging
Venue: arXiv cs.CV 2013
Year: 2013

We use mechanical translation of a coded aperture for code division multiple access compression of video. We present experimental results for reconstruction at 148 frames per coded snapshot.
The trace norm constrained matrix-variate Gaussian process for multitask bipartite ranking
Venue: arXiv cs.LG 2013
Year: 2013

We propose a novel hierarchical model for multitask bipartite ranking. The proposed approach combines a matrix-variate Gaussian process with a generative model for task-wise bipartite ranking. In addition, we employ a novel trace constrained variational inference approach to impose low rank structure on the posterior matrix-variate Gaussian process. The resulting posterior covariance function is derived in closed form, and the posterior mean function is the solution to a matrix-variate regression with a novel spectral elastic net regularizer. Further, we show that variational inference for the trace constrained matrix-variate Gaussian process combined with maximum likelihood parameter estimation for the bipartite ranking model is jointly convex. Our motivating application is the prioritization of candidate disease genes. The goal of this task is to aid the identification of unobserved associations between human genes and diseases using a small set of observed associations as well as kernels induced by gene-gene interaction networks and disease ontologies. Our experimental results illustrate the performance of the proposed model on real world datasets. Moreover, we find that the resulting low rank solution improves the computational scalability of training and testing as compared to baseline models.
Assessing Semantic Quality of Web Directory Structure
Venue: arXiv cs.IR 2013
Year: 2013

The administration of a Web directory content and associated structure is a labor intensive task performed by human domain experts. Because of that there always exists a realistic risk of the structure becoming unbalanced, uneven and difficult to use to all except for a few users proficient in a particular Web directory. These problems emphasize the importance of generic and objective measures of Web directories structure quality. In this paper we demonstrate how to formally merge Web directories into the Semantic Web vision. We introduce a set of objective criterions for evaluation of a Web directory's structure quality. Some criteria functions are based on heuristics while others require the application of ontologies.
Enabling Secure Database as a Service using Fully Homomorphic Encryption: Challenges and Opportunities
Venue: arXiv cs.DB 2013
Year: 2013

The database community, at least for the last decade, has been grappling with querying encrypted data, which would enable secure database as a service solutions. A recent breakthrough in the cryptographic community (in 2009) related to fully homomorphic encryption (FHE) showed that arbitrary computation on encrypted data is possible. Successful adoption of FHE for query processing is, however, still a distant dream, and numerous challenges have to be addressed. One challenge is how to perform algebraic query processing of encrypted data, where we produce encrypted intermediate results and operations on encrypted data can be composed. In this paper, we describe our solution for algebraic query processing of encrypted data, and also outline several other challenges that need to be addressed, while also describing the lessons that can be learnt from a decade of work by the database community in querying encrypted data.
Metrics for Assessing The Design of Software Interfaces
Venue: arXiv cs.SE 2013
Year: 2013

Recent studies have largely investigated the detection of class design anomalies. They proposed a large set of metrics that help in detecting those anomalies and in predicting the quality of class design. While those studies and the proposed metrics are valuable, they do not address the particularities of software interfaces. Interfaces define the contracts that spell out how software modules and logic units interact with each other. This paper proposes a list of design defects related to interfaces: shared similarity between interfaces, interface clones and redundancy in interface hierarchy. We identify and describe those design defects through real examples, taken from well-known Java applications. Then we define three metrics that help in automatically estimating the interface design quality, regarding the proposed design anomalies, and identify refactoring candidates. We investigate our metrics and show their usefulness through an empirical study conducted on three large Java applications.
Effective factors in agile transformation process from change management perspective
Venue: arXiv cs.SE 2013
Year: 2013

After introducing agile approach in 2001, several agile methods were founded over the last decade. Agile values such as customer collaboration, embracing changes, iteration and frequent delivery, continuous integration, etc. motivate all software stakeholders to use these methods in their projects. The main issue is that for using these methods instead of traditional methods in software development, companies should change their approach from traditional to agile. This change is a fundamental and critical mutation. Several studies have been done for investigating of barriers, challenges and issues in agile movement process and also in how to use agile methods in companies. The main issue is altering attitude from traditional to agile approach. We believe that before managing agile transformation process, its related factors should be studied in deep. This study focuses on different dimensions of changing approach to agile from change management perspective. These factors are how to being agile, method selection and awareness of challenges and issues. These fundamental factors encompass many items for agile movement and adoption process. However these factors may change in different organization, but they should be studied in deep before any action plan for designing a change strategy. The main contribution of this paper is introducing and these factors and discuss on them deeply.
Combining non-stationary prediction, optimization and mixing for data compression
Venue: arXiv cs.IT 2013
Year: 2013

In this paper an approach to modelling nonstationary binary sequences, i.e., predicting the probability of upcoming symbols, is presented. After studying the prediction model we evaluate its performance in two non-artificial test cases. First the model is compared to the Laplace and Krichevsky-Trofimov estimators. Secondly a statistical ensemble model for compressing Burrows-Wheeler-Transform output is worked out and evaluated. A systematic approach to the parameter optimization of an individual model and the ensemble model is stated.