OpenRaiser/Intern-Atlas huggingface dataset:
Concurrent learning-based online approximate feedback-Nash equilibrium solution of N-player nonzero-sum differential games
Venue: arXiv cs.SY 2013
Year: 2013

This paper presents a concurrent learning-based actor-critic-identifier architecture to obtain an approximate feedback-Nash equilibrium solution to an infinite horizon N-player nonzero-sum differential game online, without requiring persistence of excitation (PE), for a nonlinear control-affine system. Under a condition milder than PE, uniformly ultimately bounded convergence of the developed control policies to the feedback-Nash equilibrium policies is established.
On Association Cells in Random Heterogeneous Networks
Venue: arXiv cs.IT 2013
Year: 2013

Characterizing user to access point (AP) association strategies in heterogeneous cellular networks (HetNets) is critical for their performance analysis, as it directly influences the load across the network. In this letter, we introduce and analyze a class of association strategies, which we term stationary association, and the resulting association cells. For random HetNets, where APs are distributed according to a stationary point process, the area of the resulting association cells are shown to be the marks of the corresponding point process. Addressing the need of quantifying the load experienced by a typical user, a "Feller-paradox" like relationship is established between the area of the association cell containing origin and that of a typical association cell. For the specific case of Poisson point process and max power/SINR association, the mean association area of each tier is derived and shown to increase with channel gain variance and decrease in the path loss exponents of the corresponding tier.
A State of the Art of Word Sense Induction: A Way Towards Word Sense Disambiguation for Under-Resourced Languages
Venue: arXiv cs.CL 2013
Year: 2013

Word Sense Disambiguation (WSD), the process of automatically identifying the meaning of a polysemous word in a sentence, is a fundamental task in Natural Language Processing (NLP). Progress in this approach to WSD opens up many promising developments in the field of NLP and its applications. Indeed, improvement over current performance levels could allow us to take a first step towards natural language understanding. Due to the lack of lexical resources it is sometimes difficult to perform WSD for under-resourced languages. This paper is an investigation on how to initiate research in WSD for under-resourced languages by applying Word Sense Induction (WSI) and suggests some interesting topics to focus on.
Local Feature or Mel Frequency Cepstral Coefficients - Which One is Better for MLN-Based Bangla Speech Recognition?
Venue: arXiv cs.CL 2013
Year: 2013

This paper discusses the dominancy of local features (LFs), as input to the multilayer neural network (MLN), extracted from a Bangla input speech over mel frequency cepstral coefficients (MFCCs). Here, LF-based method comprises three stages: (i) LF extraction from input speech, (ii) phoneme probabilities extraction using MLN from LF and (iii) the hidden Markov model (HMM) based classifier to obtain more accurate phoneme strings. In the experiments on Bangla speech corpus prepared by us, it is observed that the LFbased automatic speech recognition (ASR) system provides higher phoneme correct rate than the MFCC-based system. Moreover, the proposed system requires fewer mixture components in the HMMs.
Deeper Into the Folksonomy Graph: FolkRank Adaptations and Extensions for Improved Tag Recommendations
Venue: arXiv cs.IR 2013
Year: 2013

The information contained in social tagging systems is often modelled as a graph of connections between users, items and tags. Recommendation algorithms such as FolkRank, have the potential to leverage complex relationships in the data, corresponding to multiple hops in the graph. We present an in-depth analysis and evaluation of graph models for social tagging data and propose novel adaptations and extensions of FolkRank to improve tag recommendations. We highlight implicit assumptions made by the widely used folksonomy model, and propose an alternative and more accurate graph-representation of the data. Our extensions of FolkRank address the new item problem by incorporating content data into the algorithm, and significantly improve prediction results on unpruned datasets. Our adaptations address issues in the iterative weight spreading calculation that potentially hinder FolkRank's ability to leverage the deep graph as an information source. Moreover, we evaluate the benefit of considering each deeper level of the graph, and present important insights regarding the characteristics of social tagging data in general. Our results suggest that the base assumption made by conventional weight propagation methods, that closeness in the graph always implies a positive relationship, does not hold for the social tagging domain.
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
Venue: arXiv cs.CV 2013
Year: 2013

We evaluate whether features extracted from the activation of a deep convolutional network trained in a fully supervised fashion on a large, fixed set of object recognition tasks can be re-purposed to novel generic tasks. Our generic tasks may differ significantly from the originally trained tasks and there may be insufficient labeled or unlabeled data to conventionally train or adapt a deep architecture to the new tasks. We investigate and visualize the semantic clustering of deep convolutional features with respect to a variety of such tasks, including scene recognition, domain adaptation, and fine-grained recognition challenges. We compare the efficacy of relying on various network levels to define a fixed feature, and report novel results that significantly outperform the state-of-the-art on several important vision challenges. We are releasing DeCAF, an open-source implementation of these deep convolutional activation features, along with all associated network parameters to enable vision researchers to be able to conduct experimentation with deep representations across a range of visual concept learning paradigms.
Impacting the bioscience progress by backporting software for Bio-Linux
Venue: arXiv cs.OS 2013
Year: 2013

In year 2006 Bio-Linux with the work of Tim Booth and team gives its rising and provide an operating system that was and still specialized in providing a bioinformatic specific software environment for the working needs in this corner of bioscience. It is shown that Bio-Linux is affected by a 2 year release cycle and with this the final releases of Bio-Linux will not have the latest bioinformatic software on board. The paper shows how to get around this huge time gap and bring new software for Bio-Linux on board through a process that is called backporting. A summary of within the work to this paper just backported bioinformatic tools is given. A describtion of a workflow for continuously integration of the newest bioinformatic tools gives an outlook to further concrete planned developments and the influence of speeding up scientific progress.
Evolution of the Modern Phase of Written Bangla: A Statistical Study
Venue: arXiv cs.CL 2013
Year: 2013

Active languages such as Bangla (or Bengali) evolve over time due to a variety of social, cultural, economic, and political issues. In this paper, we analyze the change in the written form of the modern phase of Bangla quantitatively in terms of character-level, syllable-level, morpheme-level and word-level features. We collect three different types of corpora---classical, newspapers and blogs---and test whether the differences in their features are statistically significant. Results suggest that there are significant changes in the length of a word when measured in terms of characters, but there is not much difference in usage of different characters, syllables and morphemes in a word or of different words in a sentence. To the best of our knowledge, this is the first work on Bangla of this kind.
Cross-lingual Pseudo-Projected Expectation Regularization for Weakly Supervised Learning
Venue: arXiv cs.CL 2013
Year: 2013

We consider a multilingual weakly supervised learning scenario where knowledge from annotated corpora in a resource-rich language is transferred via bitext to guide the learning in other languages. Past approaches project labels across bitext and use them as features or gold labels for training. We propose a new method that projects model expectations rather than labels, which facilities transfer of model uncertainty across language boundaries. We encode expectations as constraints and train a discriminative CRF model using Generalized Expectation Criteria (Mann and McCallum, 2010). Evaluated on standard Chinese-English and German-English NER datasets, our method demonstrates F1 scores of 64% and 60% when no labeled data is used. Attaining the same accuracy with supervised CRFs requires 12k and 1.5k labeled sentences. Furthermore, when combined with labeled examples, our method yields significant improvements over state-of-the-art supervised methods, achieving best reported numbers to date on Chinese OntoNotes and German CoNLL-03 datasets.
Non-idempotent intersection types and strong normalisation
Venue: arXiv cs.LO 2013
Year: 2013

We present a typing system with non-idempotent intersection types, typing a term syntax covering three different calculi: the pure λ-calculus, the calculus with explicit substitutions λS, and the calculus with explicit substitutions, contractions and weakenings λlxr. In each of the three calculi, a term is typable if and only if it is strongly normalising, as it is the case in (many) systems with idempotent intersections. Non-idempotency brings extra information into typing trees, such as simple bounds on the longest reduction sequence reducing a term to its normal form. Strong normalisation follows, without requiring reducibility techniques. Using this, we revisit models of the λ-calculus based on filters of intersection types, and extend them to λS and λlxr. Non-idempotency simplifies a methodology, based on such filter models, that produces modular proofs of strong normalisation for well-known typing systems (e.g. System F). We also present a filter model by means of orthogonality techniques, i.e. as an instance of an abstract notion of orthogonality model formalised in this paper and inspired by classical realisability. Compared to other instances based on terms (one of which rephrases a now standard proof of strong normalisation for the λ-calculus), the instance based on filters is shown to be better at proving strong normalisation results for λS and λlxr. Finally, the bounds on the longest reduction sequence, read off our typing trees, are refined into an exact measure, read off a specific typing tree (called principal); in each of the three calculi, a specific reduction sequence of such length is identified. In the case of the λ-calculus, this complexity result is, for longest reduction sequences, the counterpart of de Carvalho's result for linear head-reduction sequences.
Soft metrics and their Performance Analysis for Optimal Data Detection in the Presence of Strong Oscillator Phase Noise
Venue: arXiv cs.IT 2013
Year: 2013

In this paper, we address the classical problem of maximum-likelihood (ML) detection of data in the presence of random phase noise. We consider a system, where the random phase noise affecting the received signal is first compensated by a tracker/estimator. Then the phase error and its statistics are used for deriving the ML detector. Specifically, we derive an ML detector based on a Gaussian assumption for the phase error probability density function (PDF). Further without making any assumptions on the phase error PDF, we show that the actual ML detector can be reformulated as a weighted sum of central moments of the phase error PDF. We present a simple approximation of this new ML rule assuming that the phase error distribution is unknown. The ML detectors derived are also the aposteriori probabilities of the transmitted symbols, and are referred to as soft metrics. Then, using the detector developed based on Gaussian phase error assumption, we derive the symbol error probability (SEP) performance and error floor analytically for arbitrary constellations. Finally we compare SEP performance of the various detectors/metrics in this work and those from literature for different signal constellations, phase noise scenarios and SNR values.
Four-connected triangulations of planar point sets
Venue: arXiv cs.CG 2013
Year: 2013

In this paper, we consider the problem of determining in polynomial time whether a given planar point set $P$ of $n$ points admits 4-connected triangulation. We propose a necessary and sufficient condition for recognizing $P$, and present an $O(n^3)$ algorithm of constructing a 4-connected triangulation of $P$. Thus, our algorithm solves a longstanding open problem in computational geometry and geometric graph theory. We also provide a simple method for constructing a noncomplex triangulation of $P$ which requires $O(n^2)$ steps. This method provides a new insight to the structure of 4-connected triangulation of point sets.
Offline and Online Incentive Mechanism Design for Smart-phone Crowd-sourcing
Venue: arXiv cs.CY 2013
Year: 2013

In this paper, we consider the problem of incentive mechanism design for smart-phone crowd-sourcing. Each user participating in crowd-sourcing submits a set of tasks it can accomplish and its corresponding bid. The platform then selects the users and their payments to maximize its utility while ensuring truthfulness, individual rationality, profitability, and polynomial algorithm complexity. Both the offline and the online scenarios are considered, where in the offline case, all users submit their profiles simultaneously, while in the online case they do it sequentially, and the decision whether to accept or reject each user is done instantaneously with no revocation. The proposed algorithms for both the offline and the online case are shown to satisfy all the four desired properties of an efficient auction. Through extensive simulation, the performance of the offline and the online algorithm is also compared.
VCG Auction Mechanism Cost Expectations and Variances
Venue: arXiv cs.GT 2013
Year: 2013

We consider Vickrey-Clarke-Groves (VCG) auctions for a very general combinatorial structure, in an average-case setting where item costs are independent, identically distributed uniform random variables. We prove that the expected VCG cost is at least double the expected nominal cost, and exactly double when the desired structure is a basis of a bridgeless matroid. In the matroid case we further show that, conditioned upon the VCG cost, the expectation of the nominal cost is exactly half the VCG cost, and we show several results on variances and covariances among the nominal cost, the VCG cost, and related quantities. As an application, we find the asymptotic variance of the VCG cost of the minimum spanning tree in a complete graph with random edge costs.
Parallel coordinate descent for the Adaboost problem
Venue: arXiv cs.LG 2013
Year: 2013

We design a randomised parallel version of Adaboost based on previous studies on parallel coordinate descent. The algorithm uses the fact that the logarithm of the exponential loss is a function with coordinate-wise Lipschitz continuous gradient, in order to define the step lengths. We provide the proof of convergence for this randomised Adaboost algorithm and a theoretical parallelisation speedup factor. We finally provide numerical examples on learning problems of various sizes that show that the algorithm is competitive with concurrent approaches, especially for large scale problems.
Isogeometric finite element analysis of functionally graded plates using a refined plate theory
Venue: arXiv cs.NA 2013
Year: 2013

We propose in this paper a novel inverse tangent transverse shear deformation formulation for functionally graded material (FGM) plates. The isogeometric finite element analysis (IGA) of static, free vibration and buckling problems of FGM plates is then addressed using a refined plate theory (RPT). The RPT enables us to describe the non-linear distribution of shear stresses through the plate thickness without any requirement of shear correction factors (SCF). IGA utilizes basis functions, namely B-splines or non-uniform rational B-splines (NURBS), which achieve easily the smoothness of any arbitrary order. It hence satisfies the C1 requirement of the RPT model. The present method approximates the displacement field of four degrees of freedom per each control point and retains the computational efficiency while ensuring the high accuracy in solution.
Every list-decodable code for high noise has abundant near-optimal rate puncturings
Venue: arXiv cs.IT 2013
Year: 2013

We show that any q-ary code with sufficiently good distance can be randomly punctured to obtain, with high probability, a code that is list decodable up to radius $1 - 1/q - ε$ with near-optimal rate and list sizes. Our results imply that "most" Reed-Solomon codes are list decodable beyond the Johnson bound, settling the long-standing open question of whether any Reed Solomon codes meet this criterion. More precisely, we show that a Reed-Solomon code with random evaluation points is, with high probability, list decodable up to radius $1 - ε$ with list sizes $O(1/ε)$ and rate $Ω(ε)$. As a second corollary of our argument, we obtain improved bounds on the list decodability of random linear codes over large fields. Our approach exploits techniques from high dimensional probability. Previous work used similar tools to obtain bounds on the list decodability of random linear codes, but the bounds did not scale with the size of the alphabet. In this paper, we use a chaining argument to deal with large alphabet sizes.
ARKref: a rule-based coreference resolution system
Venue: arXiv cs.CL 2013
Year: 2013

ARKref is a tool for noun phrase coreference. It is a deterministic, rule-based system that uses syntactic information from a constituent parser, and semantic information from an entity recognition component. Its architecture is based on the work of Haghighi and Klein (2009). ARKref was originally written in 2009. At the time of writing, the last released version was in March 2011. This document describes that version, which is open-source and publicly available at: http://www.ark.cs.cmu.edu/ARKref
A de Bruijn identity for symmetric stable laws
Venue: arXiv cs.IT 2013
Year: 2013

We show how some attractive information--theoretic properties of Gaussians pass over to more general families of stable densities. We define a new score function for symmetric stable laws, and use it to give a stable version of the heat equation. Using this, we derive a version of the de Bruijn identity, allowing us to write the derivative of relative entropy as an inner product of score functions. We discuss maximum entropy properties of symmetric stable densities.
Predicting Students' Performance Using ID3 And C4.5 Classification Algorithms
Venue: arXiv cs.CY 2013
Year: 2013

An educational institution needs to have an approximate prior knowledge of enrolled students to predict their performance in future academics. This helps them to identify promising students and also provides them an opportunity to pay attention to and improve those who would probably get lower grades. As a solution, we have developed a system which can predict the performance of students from their previous performances using concepts of data mining techniques under Classification. We have analyzed the data set containing information about students, such as gender, marks scored in the board examinations of classes X and XII, marks and rank in entrance examinations and results in first year of the previous batch of students. By applying the ID3 (Iterative Dichotomiser 3) and C4.5 classification algorithms on this data, we have predicted the general and individual performance of freshly admitted students in future examinations.
A short note on the axiomatic requirements of uncertainty measure
Venue: arXiv cs.IT 2013
Year: 2013

In this note, we argue that the axiomatic requirement of range to the measure of aggregated total uncertainty (ATU) in Dempster-Shafer theory is not reasonable.
Group lifting structures for multirate filter banks II: Linear phase filter banks
Venue: arXiv cs.IT 2013
Year: 2013

The theory of group lifting structures is applied to linear phase lifting factorizations for the two nontrivial classes of two-channel linear phase perfect reconstruction filter banks, the whole- and half-sample symmetric classes. Group lifting structures defined for the reversible and irreversible classes of whole- and half-sample symmetric filter banks are shown to satisfy the hypotheses of the uniqueness theorem for group lifting structures. It follows that linear phase group lifting factorizations of whole- and half-sample symmetric filter banks are therefore independent of the factorization methods used to construct them. These results cover the specification of whole-sample symmetric filter banks in the ISO/IEC JPEG 2000 image coding standard.
The Unified Approach For Organizational Network Vulnerability Assessment
Venue: arXiv cs.SE 2013
Year: 2013

The present business network infrastructure is quickly varying with latest servers, services, connections, and ports added often, at times day by day, and with a uncontrollably inflow of laptops, storage media and wireless networks. With the increasing amount of vulnerabilities and exploits coupled with the recurrent evolution of IT infrastructure, organizations at present require more numerous vulnerability assessments. In this paper new approach the Unified process for Network vulnerability Assessments hereafter called as a unified NVA is proposed for network vulnerability assessment derived from Unified Software Development Process or Unified Process, it is a popular iterative and incremental software development process framework.
Laundering Money Online: a review of cybercriminals methods
Venue: arXiv cs.CY 2013
Year: 2013

Money laundering is a critical step in the cyber crime process which is experiencing some changes as hackers and their criminal colleagues continually alter and optimize payment mechanisms. Conducting quantitative research on underground laundering activity poses an inherent challenge: Bad guys and their banks do not share information on criminal pursuits. However, by analyzing forums, we have identified two growth areas in money laundering: online gaming and micro laundering.
Approximability of Connected Factors
Venue: arXiv cs.DS 2013
Year: 2013

Finding a d-regular spanning subgraph (or d-factor) of a graph is easy by Tutte's reduction to the matching problem. By the same reduction, it is easy to find a minimal or maximal d-factor of a graph. However, if we require that the d-factor is connected, these problems become NP-hard - finding a minimal connected 2-factor is just the traveling salesman problem (TSP). Given a complete graph with edge weights that satisfy the triangle inequality, we consider the problem of finding a minimal connected $d$-factor. We give a 3-approximation for all $d$ and improve this to an (r+1)-approximation for even d, where r is the approximation ratio of the TSP. This yields a 2.5-approximation for even d. The same algorithm yields an (r+1)-approximation for the directed version of the problem, where r is the approximation ratio of the asymmetric TSP. We also show that none of these minimization problems can be approximated better than the corresponding TSP. Finally, for the decision problem of deciding whether a given graph contains a connected d-factor, we extend known hardness results.