OpenRaiser/Intern-Atlas huggingface dataset:
Empirically Driven Design of Software Development Processes for Wireless Internet Services
Venue: arXiv cs.SE 2014
Year: 2014

The development of software for wireless services on the Internet is a challenging task due to the extreme time-to-market pressure, the newness of the application domain, and the quick evolution of the technical infrastructure. Nevertheless, developing software of a predetermined quality in a predictable fashion can only be achieved with systematic development processes and the use of engineering principles. Thus, systematic development processes for this domain are needed urgently. This article presents a method for the design of an adaptable software development process based on existing practices from related domains, industrial piloting, and expert knowledge. First results of the application of the method for the wireless Internet services domain are described. The benefit for the reader is twofold: the article describes a validated method on how to gain process knowledge for an upcoming field fast and incrementally. Furthermore, first results of the process design for the wireless Internet services domain are given.
Optimal Control of Applications for Hybrid Cloud Services
Venue: arXiv cs.DC 2014
Year: 2014

Development of cloud computing enables to move Big Data in the hybrid cloud services. This requires research of all processing systems and data structures for provide QoS. Due to the fact that there are many bottlenecks requires monitoring and control system when performing a query. The models and optimization criteria for the design of systems in a hybrid cloud infrastructures are created. In this article suggested approaches and the results of this build.
When Learners Surpass their Sources: Mathematical Modeling of Learning from an Inconsistent Source
Venue: arXiv cs.CL 2014
Year: 2014

We present a new algorithm to model and investigate the learning process of a learner mastering a set of grammatical rules from an inconsistent source. The compelling interest of human language acquisition is that the learning succeeds in virtually every case, despite the fact that the input data are formally inadequate to explain the success of learning. Our model explains how a learner can successfully learn from or even surpass its imperfect source without possessing any additional biases or constraints about the types of patterns that exist in the language. We use the data collected by Singleton and Newport (2004) on the performance of a 7-year boy Simon, who mastered the American Sign Language (ASL) by learning it from his parents, both of whom were imperfect speakers of ASL. We show that the algorithm possesses a frequency-boosting property, whereby the frequency of the most common form of the source is increased by the learner. We also explain several key features of Simon's ASL.
On Cloud-based Oversubscription
Venue: arXiv cs.DC 2014
Year: 2014

Rising trends in the number of customers turning to the cloud for their computing needs has made effective resource allocation imperative for cloud service providers. In order to maximize profits and reduce waste, providers have started to explore the role of oversubscribing cloud resources. However, the benefits of cloud-based oversubscription are not without inherent risks. This paper attempts to unveil the incentives, risks, and techniques behind oversubscription in a cloud infrastructure. Additionally, an overview of the current research that has been completed on this highly relevant topic is reviewed, and suggestions are made regarding potential avenues for future work.
The Impact of Cost and Network Topology on Urban Mobility: A Study of Public Bicycle Usage in 2 U.S. Cities
Venue: arXiv cs.SI 2014
Year: 2014

Understanding the drivers of urban mobility is vital for epidemiology, urban planning, and communication networks. Human movements have so far been studied by observing people's positions in a given space and time, though most recent models only implicitly account for expected costs and returns for movements. This paper explores the explicit impact of cost and network topology on mobility dynamics, using data from 2 city-wide public bicycle share systems in the USA. User mobility is characterized through the distribution of trip durations, while network topology is characterized through the pairwise distances between stations and the popularity of stations and routes. Despite significant differences in station density and physical layout between the 2 cities, trip durations follow remarkably similar distributions that exhibit cost sensitive trends around pricing point boundaries, particularly with long-term users of the system. Based on the results, recommendations for dynamic pricing and incentive schemes are provided to positively influence mobility patterns and guide improved planning and management of public bicycle systems to increase uptake.
Formal Description of Components in Operating Systems
Venue: arXiv cs.OS 2014
Year: 2014

The contemporary development of hardware components is a prerequisite for increasing the concentration of computing power. System software is developing at a much slower pace. To use available resources efficiently modeling is required. Formalization of elements, present in the material, provides the basis for modeling. Examples are presented to demonstrate the efficiency of the concept.
On the reconstruction of convex sets from random normal measurements
Venue: arXiv cs.CG 2014
Year: 2014

We study the problem of reconstructing a convex body using only a finite number of measurements of outer normal vectors. More precisely, we suppose that the normal vectors are measured at independent random locations uniformly distributed along the boundary of our convex set. Given a desired Hausdorff error eta, we provide an upper bounds on the number of probes that one has to perform in order to obtain an eta-approximation of this convex set with high probability. Our result rely on the stability theory related to Minkowski's theorem.
Exploiting Two-Dimensional Group Sparsity in 1-Bit Compressive Sensing
Venue: arXiv cs.CV 2014
Year: 2014

We propose a new approach, {\it two-dimensional fused binary compressive sensing} (2DFBCS) to recover 2D sparse piece-wise signals from 1-bit measurements, exploiting 2D group sparsity for 1-bit compressive sensing recovery. The proposed method is a modified 2D version of the previous {\it binary iterative hard thresholding} (2DBIHT) algorithm, where the objective function includes a 2D one-sided $\ell_1$ (or $\ell_2$) penalty function encouraging agreement with the observed data, an indicator function of $K$-sparsity, and a total variation (TV) or modified TV (MTV) constraint. The subgradient of the 2D one-sided $\ell_1$ (or $\ell_2$) penalty and the projection onto the $K$-sparsity and TV or MTV constraint can be computed efficiently, allowing the appliaction of algorithms of the {\it forward-backward splitting} (a.k.a. {\it iterative shrinkage-thresholding}) family. Experiments on the recovery of 2D sparse piece-wise smooth signals show that the proposed approach is able to take advantage of the piece-wise smoothness of the original signal, achieving more accurate recovery than 2DBIHT. More specifically, 2DFBCS with the MTV and the $\ell_2$ penalty performs best amongst the algorithms tested.
Symmetric QR Algorithm with Permutations
Venue: arXiv cs.NA 2014
Year: 2014

In this paper, we present the QR Algorithm with Permutations that shows an improved convergence rate compared to the classical QR algorithm. We determine a bound for performance based on best instantaneous convergence, and develop low complexity methods for computing the permutation matrices at every iteration. We use simulations to verify the improvement, and to compare the performance of proposed algorithms to the classical QR algorithm.
A Comparison and Evaluation of Map Construction Algorithms
Venue: arXiv cs.CG 2014
Year: 2014

Map construction methods automatically produce and/or update road network datasets using vehicle tracking data. Enabled by the ubiquitous generation of georeferenced tracking data, there has been a recent surge in map construction algorithms coming from different computer science domains. A cross-comparison of the various algorithms is still very rare, since (i) algorithms and constructed maps are generally not publicly available and (ii) there is no standard approach to assess the result quality, given the lack of benchmark data and quantitative evaluation methods. This work represents a first comprehensive attempt to benchmark map construction algorithms. We provide an evaluation and comparison of seven algorithms using four datasets and four different evaluation measures. In addition to this comprehensive comparison, we make our datasets, source code of map construction algorithms and evaluation measures publicly available on mapconstruction.org. This site has been established as a repository for map con- struction data and algorithms and we invite other researchers to contribute by uploading code and benchmark data supporting their contributions to map construction algorithms.
Synchronization-Free Delay Tomography Based on Compressed Sensing
Venue: arXiv cs.NI 2014
Year: 2014

Delay tomography has so far burdened source and receiver measurement nodes in a network with two requirements such as path establishment and clock synchronization between them. In this letter, we focus on the clock synchronization problem in delay tomography and propose a synchronization-free delay tomography scheme. The proposed scheme selects a path between source and receiver measurement nodes as a reference path, which results in a loss of equation in a conventional delay tomography problem. However, by utilizing compressed sensing, the proposed scheme becomes robust to the loss. Simulation experiments confirm that the proposed scheme works comparable to a conventional delay tomography scheme in networks with no clock synchronization between source and receiver measurement nodes.
Analysing Parallel and Passive Web Browsing Behavior and its Effects on Website Metrics
Venue: arXiv cs.HC 2014
Year: 2014

Getting deeper insights into the online browsing behavior of Web users has been a major research topic since the advent of the WWW. It provides useful information to optimize website design, Web browser design, search engines offerings, and online advertisement. We argue that new technologies and new services continue to have significant effects on the way how people browse the Web. For example, listening to music clips on YouTube or to a radio station on Last.fm does not require users to sit in front of their computer. Social media and networking sites like Facebook or micro-blogging sites like Twitter have attracted new types of users that previously were less inclined to go online. These changes in how people browse the Web feature new characteristics which are not well understood so far. In this paper, we provide novel and unique insights by presenting first results of DOBBS, our long-term effort to create a comprehensive and representative dataset capturing online user behavior. We firstly investigate the concepts of parallel browsing and passive browsing, showing that browsing the Web is no longer a dedicated task for many users. Based on these results, we then analyze their impact on the calculation of a user's dwell time -- i.e., the time the user spends on a webpage -- which has become an important metric to quantify the popularity of websites.
An Evolutionary approach for solving Shrödinger Equation
Venue: arXiv cs.NE 2014
Year: 2014

The purpose of this paper is to present a method of solving the Shrödinger Equation (SE) by Genetic Algorithms and Grammatical Evolution. The method forms generations of trial solutions expressed in an analytical form. We illustrate the effectiveness of this method providing, for example, the results of its application to a quantum system minimal energy, and we compare these results with those produced by traditional analytical methods
Characterizing and computing stable models of logic programs: The non-stratified case
Venue: arXiv cs.AI 2014
Year: 2014

Stable Logic Programming (SLP) is an emergent, alternative style of logic programming: each solution to a problem is represented by a stable model of a deductive database/function-free logic program encoding the problem itself. Several implementations now exist for stable logic programming, and their performance is rapidly improving. To make SLP generally applicable, it should be possible to check for consistency (i.e., existence of stable models) of the input program before attempting to answer queries. In the literature, only rather strong sufficient conditions have been proposed for consistency, e.g., stratification. This paper extends these results in several directions. First, the syntactic features of programs, viz. cyclic negative dependencies, affecting the existence of stable models are characterized, and their relevance is discussed. Next, a new graph representation of logic programs, the Extended Dependency Graph (EDG), is introduced, which conveys enough information for reasoning about stable models (while the traditional Dependency Graph does not). Finally, we show that the problem of the existence of stable models can be reformulated in terms of coloring of the EDG.
Predictive Comparative QSAR analysis of Sulfathiazole Analogues as Mycobacterium Tuberculosis H37RV Inhabitors
Venue: arXiv cs.CE 2014
Year: 2014

Antitubercular activity of Sulfathiazole Derivitives series were subjected to Quantitative Structure Activity Relationship (QSAR) Analysis with an attempt to derive and understand a correlation between the Biologically Activity as dependent variable and various descriptors as independent variables. QSAR models generated using 28 compounds. Several statistical regression expressions were obtained using Partial Least Squares (PLS) Regression, Multiple Linear Regression (MLR) and Principal Component Regression (PCR) methods. The among these methods, Partial Least Square Regression (PLS) method has shown very promising result as compare to other two methods. A QSAR model was generated by a training set of 18 molecules with correlation coefficient r (r square) of 0.9191, significant cross validated correlation coefficient (q square) of 0.8300, F test of 53.5783, r square for external test set pred_r square -3.6132, coefficient of correlation of predicted data set pred_r_se square 1.4859 and degree of freedom 14 by Partial Least Squares Regression Method.
Mobile Conductance and Gossip-based Information Spreading in Mobile Networks
Venue: arXiv cs.SI 2014
Year: 2014

In this paper, we propose a general analytical framework for information spreading in mobile networks based on a new performance metric, mobile conductance, which allows us to separate the details of mobility models from the study of mobile spreading time. We derive a general result for the information spreading time in mobile networks in terms of this new metric, and instantiate it through several popular mobility models. Large scale network simulation is conducted to verify our analysis.
Parallel Selective Algorithms for Big Data Optimization
Venue: arXiv cs.DC 2014
Year: 2014

We propose a decomposition framework for the parallel optimization of the sum of a differentiable (possibly nonconvex) function and a (block) separable nonsmooth, convex one. The latter term is usually employed to enforce structure in the solution, typically sparsity. Our framework is very flexible and includes both fully parallel Jacobi schemes and Gauss- Seidel (i.e., sequential) ones, as well as virtually all possibilities "in between" with only a subset of variables updated at each iteration. Our theoretical convergence results improve on existing ones, and numerical results on LASSO, logistic regression, and some nonconvex quadratic problems show that the new method consistently outperforms existing algorithms.
The Melbourne Shuffle: Improving Oblivious Storage in the Cloud
Venue: arXiv cs.CR 2014
Year: 2014

We present a simple, efficient, and secure data-oblivious randomized shuffle algorithm. This is the first secure data-oblivious shuffle that is not based on sorting. Our method can be used to improve previous oblivious storage solutions for network-based outsourcing of data.
To go deep or wide in learning?
Venue: arXiv cs.LG 2014
Year: 2014

To achieve acceptable performance for AI tasks, one can either use sophisticated feature extraction methods as the first layer in a two-layered supervised learning model, or learn the features directly using a deep (multi-layered) model. While the first approach is very problem-specific, the second approach has computational overheads in learning multiple layers and fine-tuning of the model. In this paper, we propose an approach called wide learning based on arc-cosine kernels, that learns a single layer of infinite width. We propose exact and inexact learning strategies for wide learning and show that wide learning with single layer outperforms single layer as well as deep architectures of finite width for some benchmark datasets.
Exemplar-based Linear Discriminant Analysis for Robust Object Tracking
Venue: arXiv cs.CV 2014
Year: 2014

Tracking-by-detection has become an attractive tracking technique, which treats tracking as a category detection problem. However, the task in tracking is to search for a specific object, rather than an object category as in detection. In this paper, we propose a novel tracking framework based on exemplar detector rather than category detector. The proposed tracker is an ensemble of exemplar-based linear discriminant analysis (ELDA) detectors. Each detector is quite specific and discriminative, because it is trained by a single object instance and massive negatives. To improve its adaptivity, we update both object and background models. Experimental results on several challenging video sequences demonstrate the effectiveness and robustness of our tracking algorithm.
The Cerebellum: New Computational Model that Reveals its Primary Function to Calculate Multibody Dynamics Conform to Lagrange-Euler Formulation
Venue: arXiv cs.NE 2014
Year: 2014

Cerebellum is part of the brain that occupies only 10% of the brain volume, but it contains about 80% of total number of brain neurons. New cerebellar function model is developed that sets cerebellar circuits in context of multibody dynamics model computations, as important step in controlling balance and movement coordination, functions performed by two oldest parts of the cerebellum. Model gives new functional interpretation for granule cells-Golgi cell circuit, including distinct function for upper and lower Golgi cell dendritc trees, and resolves issue of sharing Granule cells between Purkinje cells. Sets new function for basket cells, and for stellate cells according to position in molecular layer. New model enables easily and direct integration of sensory information from vestibular system and cutaneous mechanoreceptors, for balance, movement and interaction with environments. Model gives explanation of Purkinje cells convergence on deep-cerebellar nuclei.
Power Efficient and Secure Multiuser Communication Systems with Wireless Information and Power Transfer
Venue: arXiv cs.IT 2014
Year: 2014

In this paper, we study resource allocation algorithm design for power efficient secure communication with simultaneous wireless information and power transfer (WIPT) in multiuser communication systems. In particular, we focus on power splitting receivers which are able to harvest energy and decode information from the received signals. The considered problem is modeled as an optimization problem which takes into account a minimum required signal-to-interference-plus-noise ratio (SINR) at multiple desired receivers, a maximum tolerable data rate at multiple multi-antenna potential eavesdroppers, and a minimum required power delivered to the receivers. The proposed problem formulation facilitates the dual use of artificial noise in providing efficient energy transfer and guaranteeing secure communication. We aim at minimizing the total transmit power by jointly optimizing transmit beamforming vectors, power splitting ratios at the desired receivers, and the covariance of the artificial noise. The resulting non-convex optimization problem is transformed into a semidefinite programming (SDP) and solved by SDP relaxation. We show that the adopted SDP relaxation is tight and achieves the global optimum of the original problem. Simulation results illustrate the significant power saving obtained by the proposed optimal algorithm compared to suboptimal baseline schemes.
Secure Logical Schema and Decomposition Algorithm for Proactive Context Dependent Attribute Based Access Control
Venue: arXiv cs.DB 2014
Year: 2014

Traditional database access control mechanisms use role based methods, with generally row based and attribute based constraints for granularity, and privacy is achieved mainly by using views. However if only a set of views according to policy are made accessible to users, then this set should be checked against the policy for the whole probable query history. The aim of this work is to define a proactive decomposition algorithm according to the attribute based policy rules and build a secure logical schema in which relations are decomposed into several ones in order to inhibit joins or inferences that may violate predefined privacy constraints. The attributes whose association should not be inferred, are defined as having security dependency among them and they form a new kind of context dependent attribute based policy rule named as security dependent set. The decomposition algorithm works on a logical schema with given security dependent sets and aims to prohibit the inference of the association among the elements of these sets. It is also proven that the decomposition technique generates a secure logical schema that is in compliance with the given security dependent set constraints.
An Integrated e-science Analysis Base for Computation Neuroscience Experiments and Analysis
Venue: arXiv cs.SE 2014
Year: 2014

Recent developments in data management and imaging technologies have significantly affected diagnostic and extrapolative research in the understanding of neurodegenerative diseases. However, the impact of these new technologies is largely dependent on the speed and reliability with which the medical data can be visualised, analysed and interpreted. The EUs neuGRID for Users (N4U) is a follow-on project to neuGRID, which aims to provide an integrated environment to carry out computational neuroscience experiments. This paper reports on the design and development of the N4U Analysis Base and related Information Services, which addresses existing research and practical challenges by offering an integrated medical data analysis environment with the necessary building blocks for neuroscientists to optimally exploit neuroscience workflows, large image datasets and algorithms in order to conduct analyses. The N4U Analysis Base enables such analyses by indexing and interlinking the neuroimaging and clinical study datasets stored on the N4U Grid infrastructure, algorithms and scientific workflow definitions along with their associated provenance information.
A Novel Face Recognition Method using Nearest Line Projection
Venue: arXiv cs.CV 2014
Year: 2014

Face recognition is a popular application of pat- tern recognition methods, and it faces challenging problems including illumination, expression, and pose. The most popular way is to learn the subspaces of the face images so that it could be project to another discriminant space where images of different persons can be separated. In this paper, a nearest line projection algorithm is developed to represent the face images for face recognition. Instead of projecting an image to its nearest image, we try to project it to its nearest line spanned by two different face images. The subspaces are learned so that each face image to its nearest line is minimized. We evaluated the proposed algorithm on some benchmark face image database, and also compared it to some other image projection algorithms. The experiment results showed that the proposed algorithm outperforms other ones.