OpenRaiser/Intern-Atlas huggingface dataset:
Pseudorandomness and Fourier Growth Bounds for Width 3 Branching Programs
Venue: arXiv cs.CC 2014
Year: 2014

We present an explicit pseudorandom generator for oblivious, read-once, width-$3$ branching programs, which can read their input bits in any order. The generator has seed length $\tilde{O}( \log^3 n ).$ The previously best known seed length for this model is $n^{1/2+o(1)}$ due to Impagliazzo, Meka, and Zuckerman (FOCS '12). Our work generalizes a recent result of Reingold, Steinke, and Vadhan (RANDOM '13) for \textit{permutation} branching programs. The main technical novelty underlying our generator is a new bound on the Fourier growth of width-3, oblivious, read-once branching programs. Specifically, we show that for any $f:\{0,1\}^n\rightarrow \{0,1\}$ computed by such a branching program, and $k\in [n],$ $$\sum_{s\subseteq [n]: |s|=k} \left| \hat{f}[s] \right| \leq n^2 \cdot (O(\log n))^k,$$ where $\widehat{f}[s] = \mathbb{E}\left[f[U] \cdot (-1)^{s \cdot U}\right]$ is the standard Fourier transform over $\mathbb{Z}_2^n$. The base $O(\log n)$ of the Fourier growth is tight up to a factor of $\log \log n$.
Održavanje računarskih sistema
Venue: arXiv cs.OH 2014
Year: 2014

Computer hardware and software are resources without which the modern business of any organization, from manufacturing to services, is impossible. Not enough attention is being payed to maintenance of computer systems as an aspect of business. This paper gives some recommendations for the selection of the computer systems maintenance approach, based on many years of experience maintaining these systems at the University of Zenica.
The PeerRank Method for Peer Assessment
Venue: arXiv cs.AI 2014
Year: 2014

We propose the PeerRank method for peer assessment. This constructs a grade for an agent based on the grades proposed by the agents evaluating the agent. Since the grade of an agent is a measure of their ability to grade correctly, the PeerRank method weights grades by the grades of the grading agent. The PeerRank method also provides an incentive for agents to grade correctly. As the grades of an agent depend on the grades of the grading agents, and as these grades themselves depend on the grades of other agents, we define the PeerRank method by a fixed point equation similar to the PageRank method for ranking web-pages. We identify some formal properties of the PeerRank method (for example, it satisfies axioms of unanimity, no dummy, no discrimination and symmetry), discuss some examples, compare with related work and evaluate the performance on some synthetic data. Our results show considerable promise, reducing the error in grade predictions by a factor of 2 or more in many cases over the natural baseline of averaging peer grades.
PUC-Logic embedding of Lewis' Deontic Logics
Venue: arXiv cs.LO 2014
Year: 2014

We present a embedding of Lewis Deontic logics in PUC-Logic. We achieve this by representing the vary basic $\boldsymbol{CO}$ logic and showing its relative completeness.
PyRDM: A Python-based library for automating the management and online publication of scientific software and data
Venue: arXiv cs.CE 2014
Year: 2014

The recomputability and reproducibility of results from scientific software requires access to both the source code and all associated input and output data. However, the full collection of these resources often does not accompany the key findings published in journal articles, thereby making it difficult or impossible for the wider scientific community to verify the correctness of a result or to build further research on it. This paper presents a new Python-based library, PyRDM, whose functionality aims to automate the process of sharing the software and data via online, citable repositories such as Figshare. The library is integrated into the workflow of an open-source computational fluid dynamics package, Fluidity, to demonstrate an example of its usage.
Radial basis function process neural network training based on generalized frechet distance and GA-SA hybrid strategy
Venue: arXiv cs.NE 2014
Year: 2014

For learning problem of Radial Basis Function Process Neural Network (RBF-PNN), an optimization training method based on GA combined with SA is proposed in this paper. Through building generalized Fréchet distance to measure similarity between time-varying function samples, the learning problem of radial basis centre functions and connection weights is converted into the training on corresponding discrete sequence coefficients. Network training objective function is constructed according to the least square error criterion, and global optimization solving of network parameters is implemented in feasible solution space by use of global optimization feature of GA and probabilistic jumping property of SA . The experiment results illustrate that the training algorithm improves the network training efficiency and stability.
Diffusion of two brands in competition: cross-brand effect
Venue: arXiv cs.SI 2014
Year: 2014

We study the equilibrium points of a system of equations corresponding to a Bass based model that describes the diffusion of two brands in competition. To increase the understanding of the effects of the cross-brand parameters, we perform a sensitivity analysis. Finally, we show a comparison with an agent-based model inspired in the Potts model. Conclusions include that both models give the same diffusion curves only when the cross coeficients are not null.
An HMM Based Named Entity Recognition System for Indian Languages: The JU System at ICON 2013
Venue: arXiv cs.CL 2014
Year: 2014

This paper reports about our work in the ICON 2013 NLP TOOLS CONTEST on Named Entity Recognition. We submitted runs for Bengali, English, Hindi, Marathi, Punjabi, Tamil and Telugu. A statistical HMM (Hidden Markov Models) based model has been used to implement our system. The system has been trained and tested on the NLP TOOLS CONTEST: ICON 2013 datasets. Our system obtains F-measures of 0.8599, 0.7704, 0.7520, 0.4289, 0.5455, 0.4466, and 0.4003 for Bengali, English, Hindi, Marathi, Punjabi, Tamil and Telugu respectively.
Energy-Efficient Flow Scheduling and Routing with Hard Deadlines in Data Center Networks
Venue: arXiv cs.NI 2014
Year: 2014

The power consumption of enormous network devices in data centers has emerged as a big concern to data center operators. Despite many traffic-engineering-based solutions, very little attention has been paid on performance-guaranteed energy saving schemes. In this paper, we propose a novel energy-saving model for data center networks by scheduling and routing "deadline-constrained flows" where the transmission of every flow has to be accomplished before a rigorous deadline, being the most critical requirement in production data center networks. Based on speed scaling and power-down energy saving strategies for network devices, we aim to explore the most energy efficient way of scheduling and routing flows on the network, as well as determining the transmission speed for every flow. We consider two general versions of the problem. For the version of only flow scheduling where routes of flows are pre-given, we show that it can be solved polynomially and we develop an optimal combinatorial algorithm for it. For the version of joint flow scheduling and routing, we prove that it is strongly NP-hard and cannot have a Fully Polynomial-Time Approximation Scheme (FPTAS) unless P=NP. Based on a relaxation and randomized rounding technique, we provide an efficient approximation algorithm which can guarantee a provable performance ratio with respect to a polynomial of the total number of flows.
Training a Multilingual Sportscaster: Using Perceptual Context to Learn Language
Venue: arXiv cs.CL 2014
Year: 2014

We present a novel framework for learning to interpret and generate language using only perceptual context as supervision. We demonstrate its capabilities by developing a system that learns to sportscast simulated robot soccer games in both English and Korean without any language-specific prior knowledge. Training employs only ambiguous supervision consisting of a stream of descriptive textual comments and a sequence of events extracted from the simulation trace. The system simultaneously establishes correspondences between individual comments and the events that they describe while building a translation model that supports both parsing and generation. We also present a novel algorithm for learning which events are worth describing. Human evaluations of the generated commentaries indicate they are of reasonable quality and in some cases even on par with those produced by humans for our limited domain.
The Computational Impact of Partial Votes on Strategic Voting
Venue: arXiv cs.GT 2014
Year: 2014

In many real world elections, agents are not required to rank all candidates. We study three of the most common methods used to modify voting rules to deal with such partial votes. These methods modify scoring rules (like the Borda count), elimination style rules (like single transferable vote) and rules based on the tournament graph (like Copeland) respectively. We argue that with an elimination style voting rule like single transferable vote, partial voting does not change the situations where strategic voting is possible. However, with scoring rules and rules based on the tournament graph, partial voting can increase the situations where strategic voting is possible. As a consequence, the computational complexity of computing a strategic vote can change. For example, with Borda count, the complexity of computing a strategic vote can decrease or stay the same depending on how we score partial votes.
Online and Adaptive Pseudoinverse Solutions for ELM Weights
Venue: arXiv cs.NE 2014
Year: 2014

The ELM method has become widely used for classification and regressions problems as a result of its accuracy, simplicity and ease of use. The solution of the hidden layer weights by means of a matrix pseudoinverse operation is a significant contributor to the utility of the method; however, the conventional calculation of the pseudoinverse by means of a singular value decomposition (SVD) is not always practical for large data sets or for online updates to the solution. In this paper we discuss incremental methods for solving the pseudoinverse which are suitable for ELM. We show that careful choice of methods allows us to optimize for accuracy, ease of computation, or adaptability of the solution.
Very narrow quantum OBDDs and width hierarchies for classical OBDDs
Venue: arXiv cs.CC 2014
Year: 2014

We present several results on comparative complexity for different variants of OBDD models. - We present some results on comparative complexity of classical and quantum OBDDs. We consider a partial function depending on parameter k such that for any k > 0 this function is computed by an exact quantum OBDD of width 2 but any classical OBDD (deterministic or stable bounded error probabilistic) needs width 2k+1. - We consider quantum and classical nondeterminism. We show that quantum nondeterminism can be more efficient than classical one. In particular, an explicit function is presented which is computed by a quantum nondeterministic OBDD with constant width but any classical nondeterministic OBDD for this function needs non-constant width. - We also present new hierarchies on widths of deterministic and non-deterministic OBDDs. We focus both on small and large widths.
Neutrosophic soft sets with applications in decision making
Venue: arXiv cs.AI 2014
Year: 2014

We firstly present definitions and properties in study of Maji \cite{maji-2013} on neutrosophic soft sets. We then give a few notes on his study. Next, based on Çağman \cite{cagman-2014}, we redefine the notion of neutrosophic soft set and neutrosophic soft set operations to make more functional. By using these new definitions we construct a decision making method and a group decision making method which selects a set of optimum elements from the alternatives. We finally present examples which shows that the methods can be successfully applied to many problems that contain uncertainties.
Multi-layered graph-based multi-document summarization model
Venue: arXiv cs.IR 2014
Year: 2014

Multi-document summarization is a process of automatic generation of a compressed version of the given collection of documents. Recently, the graph-based models and ranking algorithms have been actively investigated by the extractive document summarization community. While most work to date focuses on homogeneous connecteness of sentences and heterogeneous connecteness of documents and sentences (e.g. sentence similarity weighted by document importance), in this paper we present a novel 3-layered graph model that emphasizes not only sentence and document level relations but also the influence of under sentence level relations (e.g. a part of sentence similarity).
Towards a Multiagent Decision Support System for crisis Management
Venue: arXiv cs.AI 2014
Year: 2014

Crisis management is a complex problem raised by the scientific community currently. Decision support systems are a suitable solution for such issues, they are indeed able to help emergency managers to prevent and to manage crisis in emergency situations. However, they should be enough flexible and adaptive in order to be reliable to solve complex problems that are plunged in dynamic and unpredictable environments. The approach we propose in this paper addresses this challenge. We expose here a modelling of information for an emergency environment and an architecture of a multiagent decision support system that deals with these information in order to prevent and to manage the occur of a crisis in emergency situations. We focus on the first level of the system mechanism which intends to perceive and to reflect the evolution of the current situation. The general approach and experimentations are provided here.
Bridging the gap between Legal Practitioners and Knowledge Engineers using semi-formal KR
Venue: arXiv cs.CL 2014
Year: 2014

The use of Structured English as a computation independent knowledge representation format for non-technical users in business rules representation has been proposed in OMGs Semantics and Business Vocabulary Representation (SBVR). In the legal domain we face a similar problem. Formal representation languages, such as OASIS LegalRuleML and legal ontologies (LKIF, legal OWL2 ontologies etc.) support the technical knowledge engineer and the automated reasoning. But, they can be hardly used directly by the legal domain experts who do not have a computer science background. In this paper we adapt the SBVR Structured English approach for the legal domain and implement a proof-of-concept, called KR4IPLaw, which enables legal domain experts to represent their knowledge in Structured English in a computational independent and hence, for them, more usable way. The benefit of this approach is that the underlying pre-defined semantics of the Structured English approach makes transformations into formal languages such as OASIS LegalRuleML and OWL2 ontologies possible. We exemplify our approach in the domain of patent law.
Error Control Codes: A Novel Solution for Secret Key Generation and Key Refreshment Problem
Venue: arXiv cs.CR 2014
Year: 2014

Cryptography is the science of encrypting the information so that it is rendered unreadable for an intruder. Cryptographic techniques are of utmost importance in today's world as the information to be sent might be of invaluable importance to both the sender and the receiver. Various cryptographic techniques ensure that even if an intruder intercepts the sent information, he is not able to decipher it thus render ending it useless for the intruder. Cryptography can be grouped into two types, that is Symmetric key cryptography and Asymmetric key cryptography. Symmetric key cryptography uses the same key for encryption as well as decryption thus making it faster compared to Asymmetric Key cryptography which uses different keys for encryption and decryption. Generation of dynamic keys for Symmetric key cryptography is an interesting field and in this we have tapped this field so as to generate dynamic keys for symmetric key cryptography. In this work, we have devised an algorithm for generating dynamic keys for sending messages over a communication channel and also solving key refreshment problem.
Seeing the Big Picture: Deep Embedding with Contextual Evidences
Venue: arXiv cs.CV 2014
Year: 2014

In the Bag-of-Words (BoW) model based image retrieval task, the precision of visual matching plays a critical role in improving retrieval performance. Conventionally, local cues of a keypoint are employed. However, such strategy does not consider the contextual evidences of a keypoint, a problem which would lead to the prevalence of false matches. To address this problem, this paper defines "true match" as a pair of keypoints which are similar on three levels, i.e., local, regional, and global. Then, a principled probabilistic framework is established, which is capable of implicitly integrating discriminative cues from all these feature levels. Specifically, the Convolutional Neural Network (CNN) is employed to extract features from regional and global patches, leading to the so-called "Deep Embedding" framework. CNN has been shown to produce excellent performance on a dozen computer vision tasks such as image classification and detection, but few works have been done on BoW based image retrieval. In this paper, firstly we show that proper pre-processing techniques are necessary for effective usage of CNN feature. Then, in the attempt to fit it into our model, a novel indexing structure called "Deep Indexing" is introduced, which dramatically reduces memory usage. Extensive experiments on three benchmark datasets demonstrate that, the proposed Deep Embedding method greatly promotes the retrieval accuracy when CNN feature is integrated. We show that our method is efficient in terms of both memory and time cost, and compares favorably with the state-of-the-art methods.
Evolutionary Search in the Space of Rules for Creation of New Two-Player Board Games
Venue: arXiv cs.NE 2014
Year: 2014

Games have always been a popular test bed for artificial intelligence techniques. Game developers are always in constant search for techniques that can automatically create computer games minimizing the developer's task. In this work we present an evolutionary strategy based solution towards the automatic generation of two player board games. To guide the evolutionary process towards games, which are entertaining, we propose a set of metrics. These metrics are based upon different theories of entertainment in computer games. This work also compares the entertainment value of the evolved games with the existing popular board based games. Further to verify the entertainment value of the evolved games with the entertainment value of the human user a human user survey is conducted. In addition to the user survey we check the learnability of the evolved games using an artificial neural network based controller. The proposed metrics and the evolutionary process can be employed for generating new and entertaining board games, provided an initial search space is given to the evolutionary algorithm.
LODE: Linking Digital Humanities Content to the Web of Data
Venue: arXiv cs.DL 2014
Year: 2014

Numerous digital humanities projects maintain their data collections in the form of text, images, and metadata. While data may be stored in many formats, from plain text to XML to relational databases, the use of the resource description framework (RDF) as a standardized representation has gained considerable traction during the last five years. Almost every digital humanities meeting has at least one session concerned with the topic of digital humanities, RDF, and linked data. While most existing work in linked data has focused on improving algorithms for entity matching, the aim of the LinkedHumanities project is to build digital humanities tools that work "out of the box," enabling their use by humanities scholars, computer scientists, librarians, and information scientists alike. With this paper, we report on the Linked Open Data Enhancer (LODE) framework developed as part of the LinkedHumanities project. With LODE we support non-technical users to enrich a local RDF repository with high-quality data from the Linked Open Data cloud. LODE links and enhances the local RDF repository without compromising the quality of the data. In particular, LODE supports the user in the enhancement and linking process by providing intuitive user-interfaces and by suggesting high-quality linking candidates using tailored matching algorithms. We hope that the LODE framework will be useful to digital humanities scholars complementing other digital humanities tools.
The constitution of visual perceptual units in the functional architecture of V1
Venue: arXiv cs.CV 2014
Year: 2014

Scope of this paper is to consider a mean field neural model which takes into account the functional neurogeometry of the visual cortex modelled as a group of rotations and translations. The model generalizes well known results of Bressloff and Cowan which, in absence of input, accounts for hallucination patterns. The main result of our study consists in showing that in presence of a visual input, the eigenmodes of the linearized operator which become stable represent perceptual units present in the image. The result is strictly related to dimensionality reduction and clustering problems.
Interactive Simplifier Tracing and Debugging in Isabelle
Venue: arXiv cs.MS 2014
Year: 2014

The Isabelle proof assistant comes equipped with a very powerful tactic for term simplification. While tremendously useful, the results of simplifying a term do not always match the user's expectation: sometimes, the resulting term is not in the form the user expected, or the simplifier fails to apply a rule. We describe a new, interactive tracing facility which offers insight into the hierarchical structure of the simplification with user-defined filtering, memoization and search. The new simplifier trace is integrated into the Isabelle/jEdit Prover IDE.
Analysis of Security Threats in Wireless Sensor Network
Venue: arXiv cs.NI 2014
Year: 2014

Wireless Sensor Network(WSN) is an emerging technology and explored field of researchers worldwide in the past few years, so does the need for effective security mechanisms. The sensing technology combined with processing power and wireless communication makes it lucrative for being exploited in abundance in future. The inclusion of wireless communication technology also incurs various types of security threats due to unattended installation of sensor nodes as sensor networks may interact with sensitive data and /or operate in hostile unattended environments. These security concerns be addressed from the beginning of the system design. The intent of this paper is to investigate the security related issues in wireless sensor networks. In this paper we have explored general security threats in wireless sensor network with extensive study.
A Superposition Calculus for Abductive Reasoning
Venue: arXiv cs.LO 2014
Year: 2014

We present a modification of the superposition calculus that is meant to generate consequences of sets of first-order axioms. This approach is proven to be sound and deductive-complete in the presence of redundancy elimination rules, provided the considered consequences are built on a given finite set of ground terms, represented by constant symbols. In contrast to other approaches, most existing results about the termination of the superposition calculus can be carried over to our procedure. This ensures in particular that the calculus is terminating for many theories of interest to the SMT community.