OpenRaiser/Intern-Atlas huggingface dataset:
First-Pass Large Vocabulary Continuous Speech Recognition using Bi-Directional Recurrent DNNs
Venue: arXiv cs.CL 2014
Year: 2014

We present a method to perform first-pass large vocabulary continuous speech recognition using only a neural network and language model. Deep neural network acoustic models are now commonplace in HMM-based speech recognition systems, but building such systems is a complex, domain-specific task. Recent work demonstrated the feasibility of discarding the HMM sequence modeling framework by directly predicting transcript text from audio. This paper extends this approach in two ways. First, we demonstrate that a straightforward recurrent neural network architecture can achieve a high level of accuracy. Second, we propose and evaluate a modified prefix-search decoding algorithm. This approach to decoding enables first-pass speech recognition with a language model, completely unaided by the cumbersome infrastructure of HMM-based systems. Experiments on the Wall Street Journal corpus demonstrate fairly competitive word error rates, and the importance of bi-directional network recurrence.
DE-LEACH: Distance and Energy Aware LEACH
Venue: arXiv cs.NI 2014
Year: 2014

Wireless sensor network consists of large number of tiny sensor nodes which are usually deployed in a harsh environment. Self configuration and infrastructure less are the two fundamental properties of sensor networks. Sensor nodes are highly energy constrained devices because they are battery operated devices and due to harsh environment deployment it is impossible to change or recharge their battery. Energy conservation and prolonging the network life are two major challenges in a sensor network. Communication consumes the large portion of WSN energy. Several protocols have been proposed to realize power- efficient communication in a wireless sensor network. Cluster based routing protocols are best known for increasing energy efficiency, stability and network lifetime of WSNs. Low Energy Adaptive Clustering Hierarchy (LEACH) is an important protocol in this class. One of the disadvantages of LEACH is that it does not consider the nodes energy and distance for the election of cluster head. This paper proposes a new energy efficient clustering protocol DE-LEACH for homogeneous wireless sensor network which is an extension of LEACH. DE-LEACH elects cluster head on the basis of distance and residual energy of the nodes. Proposed protocol increases the network life, stability and throughput of sensor network and simulations result shows that DE-LEACH is better than LEACH.
Analysis of Heterogeneous Cellular Networks under Frequency Diversity and Interference Correlation
Venue: arXiv cs.IT 2014
Year: 2014

Analyzing heterogeneous cellular networks (HCNs) has become increasingly complex, particularly due to irregular base station locations, massive deployment of small cells, and flexible resource allocation. The latter is usually not captured by existing stochastic models for analytical tractability. In this work, we develop a more realistic stochastic model for a generic $K$-tier HCN, where users are served in the downlink under frequency diversity. We derive the rate coverage probability for this case, taking into account the interference correlation across different parts of the allocated spectrum. Our results indicate that ignoring this type of correlation may considerably overestimate the offered date rate. Furthermore, the gain of spectrum diversification is significant, particularly at low to moderate target data rates.
An Extended Stable Marriage Problem Algorithm for Clone Detection
Venue: arXiv cs.SE 2014
Year: 2014

Code cloning negatively affects industrial software and threatens intellectual property. This paper presents a novel approach to detecting cloned software by using a bijective matching technique. The proposed approach focuses on increasing the range of similarity measures and thus enhancing the precision of the detection. This is achieved by extending a well-known stable-marriage problem (SMP) and demonstrating how matches between code fragments of different files can be expressed. A prototype of the proposed approach is provided using a proper scenario, which shows a noticeable improvement in several features of clone detection such as scalability and accuracy.
A Network Architecture for Distributed Event Based Systems in an Ubiquitous Sensing Scenario
Venue: arXiv cs.DC 2014
Year: 2014

Ubiquitous sensing devices frequently disseminate their data between them. The use of a distributed event-based system that decouples publishers of subscribers arises as an ideal candidate to implement the dissemination process. In this paper, we present a network architecture which merges the network and overlay layers of typical structured event-based systems. Directional Random Walks (DRWs) are used for the construction of this merged layer. Our first results show that DRWs are suitable to balance the load using a few nodes in the network to construct the dissemination path. As future work, we propose to study the properties of this new layer and to work on the design of Bloom filters to manage broker nodes.
Maximally Permissive Controlled System Synthesis for Modal Logic
Venue: arXiv cs.FL 2014
Year: 2014

We propose a new method for controlled system synthesis on non-deterministic automata, which includes the synthesis for deadlock-freeness, as well as invariant and reachability expressions. Our technique restricts the behavior of a Kripke-structure with labeled transitions, representing the uncontrolled system, such that it adheres to a given requirement specification in an expressive modal logic. while all non-invalidating behavior is retained. This induces maximal permissiveness in the context of supervisory control. Research presented in this paper allows a system model to be constrained according to a broad set of liveness, safety and fairness specifications of desired behavior, and embraces most concepts from Ramadge-Wonham supervisory control, including controllability and marker-state reachability. Synthesis is defined in this paper as a formal construction, which allowed a careful validation of its correctness using the Coq proof assistant.
Regularized Harmonic Surface Deformation
Venue: arXiv cs.GR 2014
Year: 2014

Harmonic surface deformation is a well-known geometric modeling method that creates plausible deformations in an interactive manner. However, this method is susceptible to artifacts, in particular close to the deformation handles. These artifacts often correlate with strong gradients of the deformation energy.In this work, we propose a novel formulation of harmonic surface deformation, which incorporates a regularization of the deformation energy. To do so, we build on and extend a recently introduced generic linear regularization approach. It can be expressed as a change of norm for the linear optimization problem, i.e., the regularization is baked into the optimization. This minimizes the implementation complexity and has only a small impact on runtime. Our results show that a moderate use of regularization suppresses many deformation artifacts common to the well-known harmonic surface deformation method, without introducing new artifacts.
Universal Computation with Arbitrary Polyomino Tiles in Non-Cooperative Self-Assembly
Venue: arXiv cs.ET 2014
Year: 2014

In this paper we explore the power of geometry to overcome the limitations of non-cooperative self-assembly. We define a generalization of the abstract Tile Assembly Model (aTAM), such that a tile system consists of a collection of polyomino tiles, the Polyomino Tile Assembly Model (polyTAM), and investigate the computational powers of polyTAM systems at temperature 1, where attachment among tiles occurs without glue cooperation. Systems composed of the unit-square tiles of the aTAM at temperature 1 are believed to be incapable of Turing universal computation (while cooperative systems, with temperature > 1, are able). As our main result, we prove that for any polyomino $P$ of size 3 or greater, there exists a temperature-1 polyTAM system containing only shape-$P$ tiles that is computationally universal. Our proof leverages the geometric properties of these larger (relative to the aTAM) tiles and their abilities to effectively utilize geometric blocking of particular growth paths of assemblies, while allowing others to complete. To round out our main result, we provide strong evidence that size-1 (i.e. aTAM tiles) and size-2 polyomino systems are unlikely to be computationally universal by showing that such systems are incapable of geometric bit-reading, which is a technique common to all currently known temperature-1 computationally universal systems. We further show that larger polyominoes with a limited number of binding positions are unlikely to be computationally universal, as they are only as powerful as temperature-1 aTAM systems. Finally, we connect our work with other work on domino self-assembly to show that temperature-1 assembly with at least 2 distinct shapes, regardless of the shapes or their sizes, allows for universal computation.
Improved Iterative Hard- and Soft-Reliability Based Majority-Logic Decoding Algorithms for Non-Binary Low-Density Parity-Check Codes
Venue: arXiv cs.IT 2014
Year: 2014

Non-binary low-density parity-check (LDPC) codes have some advantages over their binary counterparts, but unfortunately their decoding complexity is a significant challenge. The iterative hard- and soft-reliability based majority-logic decoding algorithms are attractive for non-binary LDPC codes, since they involve only finite field additions and multiplications as well as integer operations and hence have significantly lower complexity than other algorithms. In this paper, we propose two improvements to the majority-logic decoding algorithms. Instead of the accumulation of reliability information in the existing majority-logic decoding algorithms, our first improvement is a new reliability information update. The new update not only results in better error performance and fewer iterations on average, but also further reduces computational complexity. Since existing majority-logic decoding algorithms tend to have a high error floor for codes whose parity check matrices have low column weights, our second improvement is a re-selection scheme, which leads to much lower error floors, at the expense of more finite field operations and integer operations, by identifying periodic points, re-selecting intermediate hard decisions, and changing reliability information.
Linear Contour Learning: A Method for Supervised Dimension Reduction
Venue: arXiv cs.LG 2014
Year: 2014

We propose a novel approach to sufficient dimension reduction in regression, based on estimating contour directions of negligible variation for the response surface. These directions span the orthogonal complement of the minimal space relevant for the regression, and can be extracted according to a measure of the variation in the response, leading to General Contour Regression(GCR). In comparison to exiisting sufficient dimension reduction techniques, this sontour-based mothology guarantees exhaustive estimation of the central space under ellipticity of the predictoor distribution and very mild additional assumptions, while maintaining vn-consisytency and somputational ease. Moreover, it proves to be robust to departures from ellipticity. We also establish some useful population properties for GCR. Simulations to compare performance with that of standard techniques such as ordinary least squares, sliced inverse regression, principal hessian directions, and sliced average variance estimation confirm the advntages anticipated by theoretical analyses. We also demonstrate the use of contour-based methods on a data set concerning grades of students from Massachusetts colleges.
Likely to stop? Predicting Stopout in Massive Open Online Courses
Venue: arXiv cs.CY 2014
Year: 2014

Understanding why students stopout will help in understanding how students learn in MOOCs. In this report, part of a 3 unit compendium, we describe how we build accurate predictive models of MOOC student stopout. We document a scalable, stopout prediction methodology, end to end, from raw source data to model analysis. We attempted to predict stopout for the Fall 2012 offering of 6.002x. This involved the meticulous and crowd-sourced engineering of over 25 predictive features extracted for thousands of students, the creation of temporal and non-temporal data representations for use in predictive modeling, the derivation of over 10 thousand models with a variety of state-of-the-art machine learning techniques and the analysis of feature importance by examining over 70000 models. We found that stop out prediction is a tractable problem. Our models achieved an AUC (receiver operating characteristic area-under-the-curve) as high as 0.95 (and generally 0.88) when predicting one week in advance. Even with more difficult prediction problems, such as predicting stop out at the end of the course with only one weeks' data, the models attained AUCs of 0.7.
A Family of Rank Similarity Measures based on Maximized Effectiveness Difference
Venue: arXiv cs.IR 2014
Year: 2014

Rank similarity measures provide a method for quantifying differences between search engine results without the need for relevance judgments. For example, the providers of a search service might use such measures to estimate the impact of a proposed algorithmic change across a large number of queries - perhaps millions - identifying those queries where the impact is greatest. In this paper, we propose and validate a family of rank similarity measures, each derived from an associated effectiveness measure. Each member of the family is based on the maximization of effectiveness difference under this associated measure. Computing this maximized effectiveness difference (MED) requires the solution of an optimization problem that varies in difficulty, depending on the associated measure. We present solutions for several standard effectiveness measures, including nDCG, MAP, and ERR. Through an experimental validation, we show that MED reveals meaningful differences between retrieval runs. Mathematically, MED is a metric, regardless of the associated measure. Prior work has established a number of other desiderata for rank similarity in the context of search, and we demonstrate that MED satisfies these requirements. Unlike previous proposals, MED allows us to directly translate assumptions about user behavior from any established effectiveness measure to create a corresponding rank similarity measure. In addition, MED cleanly accommodates partial relevance judgments, and if complete relevance information is available, it reduces to a simple difference between effectiveness values.
The "Game about Squares" is NP-hard
Venue: arXiv cs.CC 2014
Year: 2014

In the "Game about Squares" the task is to push unit squares on an integer lattice onto corresponding dots. A square can only be moved into one given direction. When a square is pushed onto a lattice point with an arrow the direction of the square adopts the direction of the arrow. Moreover, squares can push other squares. In this paper we study the decision problem, whether all squares can be moved onto their corresponding dots by a finite number of pushes. We prove that this problem is NP-hard.
Robust Consensus in the Presence of Impulsive Channel Noise
Venue: arXiv cs.SY 2014
Year: 2014

A distributed average consensus algorithm robust to a wide range of impulsive channel noise distributions is proposed. This work is the first of its kind in the literature to propose a consensus algorithm which relaxes the requirement of finite moments on the communication noise. It is shown that the nodes reach consensus asymptotically to a finite random variable whose expectation is the desired sample average of the initial observations with a variance that depends on the step size of the algorithm and the receiver nonlinear function. The asymptotic performance is characterized by deriving the asymptotic covariance matrix using results from stochastic approximation theory. Simulations corroborate our analytical findings and highlight the robustness of the proposed algorithm.
Semantic Publishing Challenge -- Assessing the Quality of Scientific Output
Venue: arXiv cs.DL 2014
Year: 2014

Linked Open Datasets about scholarly publications enable the development and integration of sophisticated end-user services; however, richer datasets are still needed. The first goal of this Challenge was to investigate novel approaches to obtain such semantic data. In particular, we were seeking methods and tools to extract information from scholarly publications, to publish it as LOD, and to use queries over this LOD to assess quality. This year we focused on the quality of workshop proceedings, and of journal articles w.r.t. their citation network. A third, open task, asked to showcase how such semantic data could be exploited and how Semantic Web technologies could help in this emerging context.
Spanning Tree Enumeration in 2-trees: Sequential and Parallel Perspective
Venue: arXiv cs.DM 2014
Year: 2014

For a connected graph, a vertex separator is a set of vertices whose removal creates at least two components. A vertex separator $S$ is minimal if it contains no other separator as a strict subset and a minimum vertex separator is a minimal vertex separator of least cardinality. A {\em clique} is a set of mutually adjacent vertices. A 2-tree is a connected graph in which every maximal clique is of size three and every minimal vertex separator is of size two. A spanning tree of a graph $G$ is a connected and an acyclic subgraph of $G$. In this paper, we focus our attention on two enumeration problems, both from sequential and parallel perspective. In particular, we consider listing all possible spanning trees of a 2-tree and listing all perfect elimination orderings of a chordal graph. As far as enumeration of spanning trees is concerned, our approach is incremental in nature and towards this end, we work with the construction order of the 2-tree, i.e. enumeration of $n$-vertex trees are from $n-1$ vertex trees, $n \geq 4$. Further, we also present a parallel algorithm for spanning tree enumeration using $O(2^n)$ processors. To our knowledge, this paper makes the first attempt in designing a parallel algorithm for this problem. We conclude this paper by presenting a sequential and parallel algorithm for enumerating all Perfect Elimination Orderings of a chordal graph.
Self Organization Map based Texture Feature Extraction for Efficient Medical Image Categorization
Venue: arXiv cs.CV 2014
Year: 2014

Texture is one of the most important properties of visual surface that helps in discriminating one object from another or an object from background. The self-organizing map (SOM) is an excellent tool in exploratory phase of data mining. It projects its input space on prototypes of a low-dimensional regular grid that can be effectively utilized to visualize and explore properties of the data. This paper proposes an enhancement extraction method for accurate extracting features for efficient image representation it based on SOM neural network. In this approach, we apply three different partitioning approaches as a region of interested (ROI) selection methods for extracting different accurate textural features from medical image as a primary step of our extraction method. Fisherfaces feature selection is used, for selecting discriminated features form extracted textural features. Experimental result showed the high accuracy of medical image categorization with our proposed extraction method. Experiments held on Mammographic Image Analysis Society (MIAS) dataset.
The Hidden Cost of Accommodating Crowdfunder Privacy Preferences: A Randomized Field Experiment
Venue: arXiv cs.SI 2014
Year: 2014

Online crowdfunding has received a great deal of attention from entrepreneurs and policymakers as a promising avenue to fostering entrepreneurship and innovation. A notable aspect of this shift from an offline to an online setting is that it brings increased visibility and traceability of transactions. Many crowdfunding platforms therefore provide mechanisms that enable a campaign contributor to conceal his or her identity or contribution amount from peers. We study the impact of these information (privacy) control mechanisms on crowdfunder behavior. Employing a randomized experiment at one of the largest online crowdfunding platforms, we find evidence of both positive (e.g., comfort) and negative (e.g., privacy priming) causal effects. We find that reducing access to information controls induces a net increase in fundraising, yet this outcome results from two competing influences: treatment increases willingness to engage with the platform (a 4.9% increase in the probability of contribution) and simultaneously decreases the average contribution (a $5.81 decline). This decline derives from a publicity effect, wherein contributors respond to a lack of privacy by tempering extreme contributions. We unravel the causal mechanisms that drive the results and discuss the implications of our findings for the design of online platforms.
Battery-Aware Relay Selection for Energy Harvesting Cooperative Networks
Venue: arXiv cs.IT 2014
Year: 2014

The use of energy harvesting (EH) nodes as cooperative relays is an emerging solution for enabling green wireless systems. In this paper, we consider multiple EH relay nodes harvesting energy from the radio frequency (RF) signal received from the source and use that harvested energy to forward the source information to the destination. Unlike conventional relays with fixed power supplies, EH relays may not be permanently available to assist the source transmission due to the limited energy conversion efficiency, the mismatch between the charging and discharging profiles, and the finite energy storage capacity. We propose the battery-aware relay selection (BARS) scheme, which jointly considers the channel condition and the battery status for relay selection. The outage probability of the proposed scheme is analyzed using a Markov chain model. Simulations are performed to validate the analysis accuracy. Through numerical results, we show that the proposed BARS scheme can achieve full diversity order equal to the number of relays without the need of fixed power cables.
The Structure of Optimal and Near Optimal Target Sets in Consensus Models
Venue: arXiv cs.DM 2014
Year: 2014

We consider the problem of identifying a subset of nodes in a network that will enable the fastest spread of information in a decentralized environment.In a model of communication based on a random walk on an undirected graph, the optimal set over all sets of the same or smaller cardinality minimizes the sum of the mean first arrival times to the set by walkers starting at nodes outside the set. The problem originates from the study of the spread of information or consensus in a network and was introduced in this form by V.Borkar et al. in 2010. More generally, the work of A. Clark et al. in 2012 showed that estimating the fastest rate of convergence to consensus of so-called leader follower systems leads to a consideration of the same optimization problem. The set function $F$ to be minimized is supermodular and therefore the greedy algorithm is commonly used to construct optimal sets or their approximations. In this paper, the problem is reformulated so that the search for solutions is restricted to optimal and near optimal subsets of the graph. We prove sufficient conditions for the existence of a greedoid structure that contains feasible optimal and near optimal sets. It is therefore possible we conjecture, to search for optimal or near optimal sets by local moves in a stepwise manner to obtain near optimal sets that are better approximations than the factor $(1-1/e)$ degree of optimality guaranteed by the use of the greedy algorithm. A simple example illustrates aspects of the method.
Undecidability of Finite Model Reasoning in DLFD
Venue: arXiv cs.DB 2014
Year: 2014

We resolve an open problem concerning finite logical implication for path functional dependencies (PFDs).