OpenRaiser/Intern-Atlas huggingface dataset:
Collection security management at university libraries: assessment of its implementation status
Venue: arXiv cs.DL 2013
Year: 2013

This study examines the literature on library security and collection security to identify factors to be considered to develop a collection security management assessment instrument for university libraries. A "house" model was proposed consisting of five factors; collection security governance, operations and processes, people issues, physical and technical issues and the security culture in libraries. An assessment instrument listing items covering the five factors was pilot tested on 61 samples comprising chief librarians, deputy librarians, departmental, sectional heads and professional staff working in four university libraries in Nigeria. The level of security implementation is assessed on a scale of 1=not-implemented, 2=planning stage, 3=partial implementation, 4=close to completion, and 5=full implementation. The instrument was also tested for reliability. Reliability tests indicate that all five factors are reliable with Cronbach's alpha values between 0.7 and 0.9, indicating that the instrument can be used for wider distribution to explore and assess the level of collection security implementation in university libraries from a holistic perspective.
Design of Compandor Based on Approximate the First-Degree Spline Function
Venue: arXiv cs.IT 2013
Year: 2013

In this paper, the approximation of the optimal compressor function using spline function of the first-degree is done. For the companding quantizer designed on the basis of the approximative spline function of the first-degree, the support region is numerically optimized to provide the minimum of the total distortion for the last segment. It is shown that the companding quantizer with the optimized support region threshold provides the signal to quantization noise ratio that is very close to the one of the optimal companding quantizer having an equal number of levels.
On Gaussian Half-Duplex Relay Networks
Venue: arXiv cs.IT 2013
Year: 2013

This paper considers Gaussian relay networks where a source transmits a message to a sink terminal with the help of one or more relay nodes. The relays work in half-duplex mode, in the sense that they can not transmit and receive at the same time. For the case of one relay, the generalized Degrees-of-Freedom is characterized first and then it is shown that capacity can be achieved to within a constant gap regardless of the actual value of the channel parameters. Different achievable schemes are presented with either deterministic or random switch for the relay node. It is shown that random switch in general achieves higher rates than deterministic switch. For the case of K relays, it is shown that the generalized Degrees-of-Freedom can be obtained by solving a linear program and that capacity can be achieved to within a constant gap of K/2log(4K). This gap may be further decreased by considering more structured networks such as, for example, the diamond network.
Multi-Class Detection and Segmentation of Objects in Depth
Venue: arXiv cs.CV 2013
Year: 2013

The quality of life of many people could be improved by autonomous humanoid robots in the home. To function in the human world, a humanoid household robot must be able to locate itself and perceive the environment like a human; scene perception, object detection and segmentation, and object spatial localization in 3D are fundamental capabilities for such humanoid robots. This paper presents a 3D multi-class object detection and segmentation method. The contributions are twofold. Firstly, we present a multi-class detection method, where a minimal joint codebook is learned in a principled manner. Secondly, we incorporate depth information using RGB-D imagery, which increases the robustness of the method and gives the 3D location of objects -- necessary since the robot reasons in 3D space. Experiments show that the multi-class extension improves the detection efficiency with respect to the number of classes and the depth extension improves the detection robustness and give sufficient natural 3D location of the objects.
Measuring the Significance of the Geographic Flow of Music
Venue: arXiv cs.SI 2013
Year: 2013

In previous work, our results suggested that some cities tend to be ahead of others in their musical preferences. We concluded that work by noting that to properly test this claim, we would try to exploit the leader-follower relationships that we identified to make predictions. Here we present the results of our predictive evaluation. We find that information on the past musical preferences in other cities allows a linear model to improve its predictions by approx. 5% over a simple baseline. This suggests that at best, previously found leader-follower relationships are rather weak.
A discrete analysis of metal-v belt drive
Venue: arXiv cs.CE 2013
Year: 2013

The metal-V belt drive includes a large number of parts which interact between them to transmit power from the input to the output pulleys. A compression belt composed of a great number of struts is maintained by a tension flat belt. Power is them shared into the two belts that moves generally in opposite directions. Due to the particular geometry of the elements and to the great number of parts, a numerical approach achieves the global equilibrium of the mechanism from the elementary part equilibrium. Sliding arc on each pulley can be thus defined both for the compression and tension belts. Finally, power sharing can be calculated as differential motion between the belts, is defined. The first part of the paper will present the different steps of the quasi-static mechanical analysis and their numerical implementations. Load distributions, speed profiles and sliding angle values will be discussed. The second part of the paper will deal to a systematic use of the computer software. Speed ratio, transmitted torque, strut geometry and friction coefficients effect will be analysed with the output parameter variations. Finally, the effect pulley deformable flanges will be discussed.
Transfer Topic Modeling with Ease and Scalability
Venue: arXiv cs.CL 2013
Year: 2013

The increasing volume of short texts generated on social media sites, such as Twitter or Facebook, creates a great demand for effective and efficient topic modeling approaches. While latent Dirichlet allocation (LDA) can be applied, it is not optimal due to its weakness in handling short texts with fast-changing topics and scalability concerns. In this paper, we propose a transfer learning approach that utilizes abundant labeled documents from other domains (such as Yahoo! News or Wikipedia) to improve topic modeling, with better model fitting and result interpretation. Specifically, we develop Transfer Hierarchical LDA (thLDA) model, which incorporates the label information from other domains via informative priors. In addition, we develop a parallel implementation of our model for large-scale applications. We demonstrate the effectiveness of our thLDA model on both a microblogging dataset and standard text collections including AP and RCV1 datasets.
Outage Probability of Wireless Ad Hoc Networks with Cooperative Relaying
Venue: arXiv cs.IT 2013
Year: 2013

In this paper, we analyze the performance of cooperative transmissions in wireless ad hoc networks with random node locations. According to a contention probability for message transmission, each source node can either transmits its own message signal or acts as a potential relay for others. Hence, each destination node can potentially receive two copies of the message signal, one from the direct link and the other from the relay link. Taking the random node locations and interference into account, we derive closed-form expressions for the outage probability with different combining schemes at the destination nodes. In particular, the outage performance of optimal combining, maximum ratio combining, and selection combining strategies are studied and quantified.
A Potential Theory of General Spatially-Coupled Systems via a Continuum Approximation
Venue: arXiv cs.IT 2013
Year: 2013

This paper analyzes general spatially-coupled (SC) systems with multi-dimensional coupling. A continuum approximation is used to derive potential functions that characterize the performance of the SC systems. For any dimension of coupling, it is shown that, if the boundary of the SC systems is fixed to the unique stable solution that minimizes the potential over all stationary solutions, the systems can approach the optimal performance as the number of coupled systems tends to infinity.
Producing a Unified Graph Representation from Multiple Social Network Views
Venue: arXiv cs.SI 2013
Year: 2013

In many social networks, several different link relations will exist between the same set of users. Additionally, attribute or textual information will be associated with those users, such as demographic details or user-generated content. For many data analysis tasks, such as community finding and data visualisation, the provision of multiple heterogeneous types of user data makes the analysis process more complex. We propose an unsupervised method for integrating multiple data views to produce a single unified graph representation, based on the combination of the k-nearest neighbour sets for users derived from each view. These views can be either relation-based or feature-based. The proposed method is evaluated on a number of annotated multi-view Twitter datasets, where it is shown to support the discovery of the underlying community structure in the data.
Ranking Games that have Competitiveness-based Strategies
Venue: arXiv cs.GT 2013
Year: 2013

An extensive literature in economics and social science addresses contests, in which players compete to outperform each other on some measurable criterion, often referred to as a player's score, or output. Players incur costs that are an increasing function of score, but receive prizes for obtaining higher score than their competitors. In this paper we study finite games that are discretized contests, and the problems of computing exact and approximate Nash equilibria. Our motivation is the worst-case hardness of Nash equilibrium computation, and the resulting interest in important classes of games that admit polynomial-time algorithms. For games that have a tie-breaking rule for players' scores, we present a polynomial-time algorithm for computing an exact equilibrium in the 2-player case, and for multiple players, a characterization of Nash equilibria that shows an interesting parallel between these games and unrestricted 2-player games in normal form. When ties are allowed, via a reduction from these games to a subclass of anonymous games, we give approximation schemes for two special cases: constant-sized set of strategies, and constant number of players.
How do you know your spreadsheet is right?
Venue: arXiv cs.SE 2013
Year: 2013

Principles, Techniques and Practice of Spreadsheet Style
Information Centric Networking over SDN and OpenFlow: Architectural Aspects and Experiments on the OFELIA Testbed
Venue: arXiv cs.NI 2013
Year: 2013

Information Centric Networking (ICN) has been proposed as a new networking paradigm in which the network provides users with content instead communication channels between hosts. The Software Defined Networking (SDN) approach promises to be a solution to enable the continuous evolution of networking architectures. In this paper we propose and discuss solutions to support ICN using SDN concepts. We focus on an ICN framework called CONET, which grounds its roots in the CCN/NDN architecture. We face the problem in two complementary ways. First we discuss a general and long term solution based on SDN concepts without taking into account specific limitations of SDN standards and equipment. Then we focus on an experiment to support ICN functionality over a large scale SDN testbed based on OpenFlow, developed in the context of the OFELIA European research project. The current OFELIA testbed is based on OpenFlow 1.0 equipment from a variety of vendors, therefore we had to design the experiment taking into account the features that are currently available on off-the-shelf OpenFlow equipment.
Improving the lifecycle of robotics components using Domain-Specific Languages
Venue: arXiv cs.RO 2013
Year: 2013

There is currently a large amount of robotics software using the component-oriented programming paradigm. However, the rapid growth in number and complexity of components may compromise the scalability and the whole lifecycle of robotics software systems. Model-Driven Engineering can be used to mitigate these problems. This paper describes how using Domain-Specific Languages to generate and describe critical parts of robotic systems helps developers to perform component managerial tasks such as component creation, modification, monitoring and deployment. Four different DSLs are proposed in this paper: i) CDSL for specifying the structure of the components, ii) IDSL for the description of their interfaces, iii) DDSL for describing the deployment process of component networks and iv) PDSL to define and configure component parameters. Their benefits have been demonstrated after their implementation in RoboComp, a general-purpose and component-based robotics framework. Examples of the usage of these DSLs are shown along with experiments that demonstrate the benefits they bring to the lifecycle of the components.
Solving the Shortest Vector Problem in Lattices Faster Using Quantum Search
Venue: arXiv cs.CR 2013
Year: 2013

By applying Grover's quantum search algorithm to the lattice algorithms of Micciancio and Voulgaris, Nguyen and Vidick, Wang et al., and Pujol and Stehlé, we obtain improved asymptotic quantum results for solving the shortest vector problem. With quantum computers we can provably find a shortest vector in time $2^{1.799n + o(n)}$, improving upon the classical time complexity of $2^{2.465n + o(n)}$ of Pujol and Stehlé and the $2^{2n + o(n)}$ of Micciancio and Voulgaris, while heuristically we expect to find a shortest vector in time $2^{0.312n + o(n)}$, improving upon the classical time complexity of $2^{0.384n + o(n)}$ of Wang et al. These quantum complexities will be an important guide for the selection of parameters for post-quantum cryptosystems based on the hardness of the shortest vector problem.
Tight is better: Performance Improvement of the Compressive Classifier Using Equi-Norm Tight Frames
Venue: arXiv cs.IT 2013
Year: 2013

Detecting or classifying already known sparse signals contaminated by Gaussian noise from compressive measurements is different from reconstructing sparse signals, as its objective is to minimize the error probability which describes performance of the detectors or classifiers. This paper is concerned about the performance improvement of a commonly used Compressive Classifier. We prove that when the arbitrary sensing matrices used to get the Compressive Measurements are transformed into Equi-Norm Tight Frames, i.e. the matrices that are row-orthogonal, The Compressive Classifier achieves better performance. Although there are other proofs that among all Equi-Norm Tight Frames the Equiangular tight Frames (ETFs) bring best worst-case performance, the existence and construction of ETFs on some dimensions is still an open problem. As the construction of Equi-Norm Tight Frames from any arbitrary matrices is very easy and practical compared with ETF matrices, the result of this paper can also provide a practical method to design an improved sensing matrix for Compressive Classification. We can conclude that: Tight is Better!
LA-LDA: A Limited Attention Topic Model for Social Recommendation
Venue: arXiv cs.SI 2013
Year: 2013

Social media users have finite attention which limits the number of incoming messages from friends they can process. Moreover, they pay more attention to opinions and recommendations of some friends more than others. In this paper, we propose LA-LDA, a latent topic model which incorporates limited, non-uniformly divided attention in the diffusion process by which opinions and information spread on the social network. We show that our proposed model is able to learn more accurate user models from users' social network and item adoption behavior than models which do not take limited attention into account. We analyze voting on news items on the social news aggregator Digg and show that our proposed model is better able to predict held out votes than alternative models. Our study demonstrates that psycho-socially motivated models have better ability to describe and predict observed behavior than models which only consider topics.
Fault-Tolerant Shortest Paths - Beyond the Uniform Failure Model
Venue: arXiv cs.DS 2013
Year: 2013

The overwhelming majority of survivable (fault-tolerant) network design models assume a uniform scenario set. Such a scenario set assumes that every subset of the network resources (edges or vertices) of a given cardinality $k$ comprises a scenario. While this approach yields problems with clean combinatorial structure and good algorithms, it often fails to capture the true nature of the scenario set coming from applications. One natural refinement of the uniform model is obtained by partitioning the set of resources into faulty and secure resources. The scenario set contains every subset of at most $k$ faulty resources. This work studies the Fault-Tolerant Path (FTP) problem, the counterpart of the Shortest Path problem in this failure model. We present complexity results alongside exact and approximation algorithms for FTP. We emphasize the vast increase in the complexity of the problem with respect to its uniform analogue, the Edge-Disjoint Paths problem.
Equitability Analysis of the Maximal Information Coefficient, with Comparisons
Venue: arXiv cs.LG 2013
Year: 2013

A measure of dependence is said to be equitable if it gives similar scores to equally noisy relationships of different types. Equitability is important in data exploration when the goal is to identify a relatively small set of strongest associations within a dataset as opposed to finding as many non-zero associations as possible, which often are too many to sift through. Thus an equitable statistic, such as the maximal information coefficient (MIC), can be useful for analyzing high-dimensional data sets. Here, we explore both equitability and the properties of MIC, and discuss several aspects of the theory and practice of MIC. We begin by presenting an intuition behind the equitability of MIC through the exploration of the maximization and normalization steps in its definition. We then examine the speed and optimality of the approximation algorithm used to compute MIC, and suggest some directions for improving both. Finally, we demonstrate in a range of noise models and sample sizes that MIC is more equitable than natural alternatives, such as mutual information estimation and distance correlation.
An improvement to k-nearest neighbor classifier
Venue: arXiv cs.CV 2013
Year: 2013

K-Nearest neighbor classifier (k-NNC) is simple to use and has little design time like finding k values in k-nearest neighbor classifier, hence these are suitable to work with dynamically varying data-sets. There exists some fundamental improvements over the basic k-NNC, like weighted k-nearest neighbors classifier (where weights to nearest neighbors are given based on linear interpolation), using artificially generated training set called bootstrapped training set, etc. These improvements are orthogonal to space reduction and classification time reduction techniques, hence can be coupled with any of them. The paper proposes another improvement to the basic k-NNC where the weights to nearest neighbors are given based on Gaussian distribution (instead of linear interpolation as done in weighted k-NNC) which is also independent of any space reduction and classification time reduction technique. We formally show that our proposed method is closely related to non-parametric density estimation using a Gaussian kernel. We experimentally demonstrate using various standard data-sets that the proposed method is better than the existing ones in most cases.
Brute force searching, the typical set and Guesswork
Venue: arXiv cs.IT 2013
Year: 2013

Consider the situation where a word is chosen probabilistically from a finite list. If an attacker knows the list and can inquire about each word in turn, then selecting the word via the uniform distribution maximizes the attacker's difficulty, its Guesswork, in identifying the chosen word. It is tempting to use this property in cryptanalysis of computationally secure ciphers by assuming coded words are drawn from a source's typical set and so, for all intents and purposes, uniformly distributed within it. By applying recent results on Guesswork, for i.i.d. sources it is this equipartition ansatz that we investigate here. In particular, we demonstrate that the expected Guesswork for a source conditioned to create words in the typical set grows, with word length, at a lower exponential rate than that of the uniform approximation, suggesting use of the approximation is ill-advised.
Scalar Quantize-and-Forward for Symmetric Half-duplex Two-Way Relay Channels
Venue: arXiv cs.IT 2013
Year: 2013

Scalar Quantize & Forward (QF) schemes are studied for the Two-Way Relay Channel. Different QF approaches are compared in terms of rates as well as relay and decoder complexity. A coding scheme not requiring Slepian-Wolf coding at the relay is proposed and properties of the corresponding sum-rate optimization problem are presented. A numerical scheme similar to the Blahut-Arimoto algorithm is derived that guides optimized quantizer design. The results are supported by simulations.
Joint Power Adjustment and Interference Mitigation Techniques for Cooperative Spread Spectrum Systems
Venue: arXiv cs.IT 2013
Year: 2013

This paper presents joint power allocation and interference mitigation techniques for the downlink of spread spectrum systems which employ multiple relays and the amplify and forward cooperation strategy. We propose a joint constrained optimization framework that considers the allocation of power levels across the relays subject to an individual power constraint and the design of linear receivers for interference suppression. We derive constrained minimum mean-squared error (MMSE) expressions for the parameter vectors that determine the optimal power levels across the relays and the linear receivers. In order to solve the proposed optimization problem efficiently, we develop joint adaptive power allocation and interference suppression algorithms that can be implemented in a distributed fashion. The proposed stochastic gradient (SG) and recursive least squares (RLS) algorithms mitigate the interference by adjusting the power levels across the relays and estimating the parameters of the linear receiver. SG and RLS channel estimation algorithms are also derived to determine the coefficients of the channels across the base station, the relays and the destination terminal. The results of simulations show that the proposed techniques obtain significant gains in performance and capacity over non-cooperative systems and cooperative schemes with equal power allocation.