OpenRaiser/Intern-Atlas huggingface dataset:
A Coordinated MDP Approach to Multi-Agent Planning for Resource Allocation, with Applications to Healthcare
Venue: arXiv cs.AI 2014
Year: 2014

This paper considers a novel approach to scalable multiagent resource allocation in dynamic settings. We propose an approximate solution in which each resource consumer is represented by an independent MDP-based agent that models expected utility using an average model of its expected access to resources given only limited information about all other agents. A global auction-based mechanism is proposed for allocations based on expected regret. We assume truthful bidding and a cooperative coordination mechanism, as we are considering healthcare scenarios. We illustrate the performance of our coordinated MDP approach against a Monte-Carlo based planning algorithm intended for large-scale applications, as well as other approaches suitable for allocating medical resources. The evaluations show that the global utility value across all consumer agents is closer to optimal when using our algorithms under certain time constraints, with low computational cost. As such, we offer a promising approach for addressing complex resource allocation problems that arise in healthcare settings.
Bounds and algorithms for limited packings in graphs
Venue: arXiv cs.DM 2014
Year: 2014

We consider (closed neighbourhood) packings and their generalization in graphs called limited packings. A vertex set X in a graph G is a k-limited packing if for any vertex $v\in V(G)$, $\left|N[v] \cap X\right| \le k$, where $N[v]$ is the closed neighbourhood of $v$. The k-limited packing number $L_k(G)$ is the largest size of a k-limited packing in G. Limited packing problems can be considered as secure facility location problems in networks. We develop probabilistic and greedy approaches to limited packings in graphs, providing lower bounds for the k-limited packing number, and randomized and greedy algorithms to find k-limited packings satisfying the bounds. Some upper bounds for $L_k(G)$ are given as well. The problem of finding a maximum size k-limited packing is known to be NP-complete even in split or bipartite graphs.
Decomposed Interval Type-2 Fuzzy Systems with Application to Inverted Pendulum
Venue: arXiv cs.SY 2014
Year: 2014

This article introduces the idea of decomposition of interval Type-2 fuzzy logic system into two parallel type-1 fuzzy systems. This decomposition avoids the problems associated with type-reduction techniques normally needed in type-2 fuzzy systems. Next, we compare the performance of a decomposed type-2 controller to the performance of a type-1 controller in stabilizing an inverted pendulum.
Recommending Learning Algorithms and Their Associated Hyperparameters
Venue: arXiv cs.LG 2014
Year: 2014

The success of machine learning on a given task dependson, among other things, which learning algorithm is selected and its associated hyperparameters. Selecting an appropriate learning algorithm and setting its hyperparameters for a given data set can be a challenging task, especially for users who are not experts in machine learning. Previous work has examined using meta-features to predict which learning algorithm and hyperparameters should be used. However, choosing a set of meta-features that are predictive of algorithm performance is difficult. Here, we propose to apply collaborative filtering techniques to learning algorithm and hyperparameter selection, and find that doing so avoids determining which meta-features to use and outperforms traditional meta-learning approaches in many cases.
On Termination of Integer Linear Loops
Venue: arXiv cs.CC 2014
Year: 2014

A fundamental problem in program verification concerns the termination of simple linear loops of the form x := u ; while Bx >= b do {x := Ax + a} where x is a vector of variables, u, a, and c are integer vectors, and A and B are integer matrices. Assuming the matrix A is diagonalisable, we give a decision procedure for the problem of whether, for all initial integer vectors u, such a loop terminates. The correctness of our algorithm relies on sophisticated tools from algebraic and analytic number theory, Diophantine geometry, and real algebraic geometry. To the best of our knowledge, this is the first substantial advance on a 10-year-old open problem of Tiwari (2004) and Braverman (2006).
Lexpresso: a Controlled Natural Language
Venue: arXiv cs.CL 2014
Year: 2014

This paper presents an overview of `Lexpresso', a Controlled Natural Language developed at the Defence Science & Technology Organisation as a bidirectional natural language interface to a high-level information fusion system. The paper describes Lexpresso's main features including lexical coverage, expressiveness and range of linguistic syntactic and semantic structures. It also touches on its tight integration with a formal semantic formalism and tentatively classifies it against the PENS system.
soCloud: A service-oriented component-based PaaS for managing portability, provisioning, elasticity, and high availability across multiple clouds
Venue: arXiv cs.SE 2014
Year: 2014

Multi-cloud computing is a promising paradigm to support very large scale world wide distributed applications. Multi-cloud computing is the usage of multiple, independent cloud environments, which assumed no priori agreement between cloud providers or third party. However, multi-cloud computing has to face several key challenges such as portability, provisioning, elasticity, and high availability. Developers will not only have to deploy applications to a specific cloud, but will also have to consider application portability from one cloud to another, and to deploy distributed applications spanning multiple clouds. This article presents soCloud a service-oriented component-based Platform as a Service (PaaS) for managing portability, elasticity, provisioning, and high availability across multiple clouds. soCloud is based on the OASIS Service Component Architecture (SCA) standard in order to address portability. soCloud provides services for managing provisioning, elasticity, and high availability across multiple clouds. soCloud has been deployed and evaluated on top of ten existing cloud providers: Windows Azure, DELL KACE, Amazon EC2, CloudBees, OpenShift, dotCloud, Jelastic, Heroku, Appfog, and an Eucalyptus private cloud.
Hidden and Uncontrolled - On the Emergence of Network Steganographic Threats
Venue: arXiv cs.CR 2014
Year: 2014

Network steganography is the art of hiding secret information within innocent network transmissions. Recent findings indicate that novel malware is increasingly using network steganography. Similarly, other malicious activities can profit from network steganography, such as data leakage or the exchange of pedophile data. This paper provides an introduction to network steganography and highlights its potential application for harmful purposes. We discuss the issues related to countering network steganography in practice and provide an outlook on further research directions and problems.
A Polynomial Delay Algorithm for Enumerating Minimal Dominating Sets in Chordal Graphs
Venue: arXiv cs.DS 2014
Year: 2014

An output-polynomial algorithm for the listing of minimal dominating sets in graphs is a challenging open problem and is known to be equivalent to the well-known Transversal problem which asks for an output-polynomial algorithm for listing the set of minimal hitting sets in hypergraphs. We give a polynomial delay algorithm to list the set of minimal dominating sets in chordal graphs, an important and well-studied graph class where such an algorithm was open for a while.
Which of the world's institutions employ the most highly cited researchers? An analysis of the data from highlycited.com
Venue: arXiv cs.DL 2014
Year: 2014

A few weeks ago, Thomson Reuters published a list of the highly cited researchers worldwide (highlycited.com). Since the data is freely available for downloading and includes the names of the researchers' institutions, we produced a ranking of the institutions on the basis of the number of highly cited researchers per institution. This ranking is intended to be a helpful amendment of other available institutional rankings.
MCA: Multiresolution Correlation Analysis, a graphical tool for subpopulation identification in single-cell gene expression data
Venue: arXiv cs.GR 2014
Year: 2014

Background: Biological data often originate from samples containing mixtures of subpopulations, corresponding e.g. to distinct cellular phenotypes. However, identification of distinct subpopulations may be difficult if biological measurements yield distributions that are not easily separable. Results: We present Multiresolution Correlation Analysis (MCA), a method for visually identifying subpopulations based on the local pairwise correlation between covariates, without needing to define an a priori interaction scale. We demonstrate that MCA facilitates the identification of differentially regulated subpopulations in simulated data from a small gene regulatory network, followed by application to previously published single-cell qPCR data from mouse embryonic stem cells. We show that MCA recovers previously identified subpopulations, provides additional insight into the underlying correlation structure, reveals potentially spurious compartmentalizations, and provides insight into novel subpopulations. Conclusions: MCA is a useful method for the identification of subpopulations in low-dimensional expression data, as emerging from qPCR or FACS measurements. With MCA it is possible to investigate the robustness of covariate correlations with respect subpopulations, graphically identify outliers, and identify factors contributing to differential regulation between pairs of covariates. MCA thus provides a framework for investigation of expression correlations for genes of interests and biological hypothesis generation.
Link Prediction and the Role of Stronger Ties in Networks of Face-to-Face Proximity
Venue: arXiv cs.SI 2014
Year: 2014

Understanding the structures why links are formed is an important and prominent research topic. In this paper, we therefore consider the link prediction problem in face-to-face contact networks, and analyze the predictability of new and recurring links. Furthermore, we study additional influence factors, and the role of stronger ties in these networks. Specifically, we compare neighborhood-based and path-based network proximity measures in a threshold-based analysis for capturing temporal dynamics. The results and insights of the analysis are a first step onto predictability applications for human contact networks, for example, for improving recommendations.
TLS hardening
Venue: arXiv cs.CR 2014
Year: 2014

This document presents TLS and how to make it secure enough as of 2014 Spring. Of course all the information given here will rot with time. Protocols known as secure will be cracked and will be replaced with better versions. Fortunately we will see that there are ways to assess the current security of your setup, but this explains why you may have to read further from this document to get the up to date knowledge on TLS security. We will first introduce the TLS protocol and its underlying components: X.509 certificates, ciphers, and protocol versions. Next we will have a look at TLS hardening for web servers, and how to plug various vulnerabilities: CRIME, BREACH, BEAST, session renegotiation, Heartbleed, and others. We will finally see how the know-how acquired on hardening web servers can be used for other protocols and tools such as Dovecot, Sendmail, SquirrelMail, RoundCube, and OpenVPN. We assume you already maintain services that use TLS, and have basic TCP/IP network knowledge. Some information will also be useful for the application developer.
A Wavelet Based Algorithm for the Identification of Oscillatory Event-Related Potential Components
Venue: arXiv cs.OH 2014
Year: 2014

Event Related Potentials (ERPs) are very feeble alterations in the ongoing Electroencephalogram (EEG) and their detection is a challenging problem. Based on the unique time-based parameters derived from wavelet coefficients and the asymmetry property of wavelets a novel algorithm to separate ERP components in single-trial EEG data is described. Though illustrated as a specific application to N170 ERP detection, the algorithm is a generalized approach that can be easily adapted to isolate different kinds of ERP components. The algorithm detected the N170 ERP component with a high level of accuracy. We demonstrate that the asymmetry method is more accurate than the matching wavelet algorithm and t-CWT method by 48.67 and 8.03 percent respectively. This paper provides an off-line demonstration of the algorithm and considers issues related to the extension of the algorithm to real-time applications.
Synthetic Minority Over-sampling TEchnique(SMOTE) for Predicting Software Build Outcomes
Venue: arXiv cs.SE 2014
Year: 2014

In this research we use a data stream approach to mining data and construct Decision Tree models that predict software build outcomes in terms of software metrics that are derived from source code used in the software construction process. The rationale for using the data stream approach was to track the evolution of the prediction model over time as builds are incrementally constructed from previous versions either to remedy errors or to enhance functionality. As the volume of data available for mining from the software repository that we used was limited, we synthesized new data instances through the application of the SMOTE oversampling algorithm. The results indicate that a small number of the available metrics have significance for prediction software build outcomes. It is observed that classification accuracy steadily improves after approximately 900 instances of builds have been fed to the classifier. At the end of the data streaming process classification accuracies of 80% were achieved, though some bias arises due to the distribution of data across the two classes over time.
How COBIT Can Complement ITIL TO Achieve BIT
Venue: arXiv cs.CY 2014
Year: 2014

Strategic alignment is a conviction that is considered extremely important in understanding how organizations can apply their arrangement of information technology (IT) into substantial boosts in achievement. To attain alignment advantage, Information Technology Infrastructure Library (ITIL) prepares a framework of best practice approach for IT Service Management in all countries and Control Objectives for Information and Related Technology (COBIT) is an IT governance framework and aiding toolset that permits managers to stretch the gap between control prerequisites, technical matters and business risks. The purpose of this paper is to recognize how COBIT can complement ITIL to attain Business-IT Alignment.
Blind Interference Alignment in General Heterogeneous Networks
Venue: arXiv cs.IT 2014
Year: 2014

Heterogeneous networks have a key role in the design of future mobile communication networks, since the employment of small cells around a macrocell enhances the network's efficiency and decreases complexity and power demand. Moreover, research on Blind Interference Alignment (BIA) has shown that optimal Degrees of Freedom (DoF) can be achieved in certain network architectures, with no requirement of Channel State Information (CSI) at the transmitters. Our contribution is a generalised model of BIA in a heterogeneous network with one macrocell with K users and K femtocells each with one user, by using Kronecker (Tensor) Product representation. We introduce a solution on how to vary beamforming vectors under power constraints to maximize the sum rate of the network and how optimal DoF can be achieved over K+1 time slots.
Multi-Dimensional Wireless Tomography with Tensor-Based Compressed Sensing
Venue: arXiv cs.IT 2014
Year: 2014

Wireless tomography is a technique for inferring a physical environment within a monitored region by analyzing RF signals traversed across the region. In this paper, we consider wireless tomography in a two and higher dimensionally structured monitored region, and propose a multi-dimensional wireless tomography scheme based on compressed sensing to estimate a spatial distribution of shadowing loss in the monitored region. In order to estimate the spatial distribution, we consider two compressed sensing frameworks: vector-based compressed sensing and tensor-based compressed sensing. When the shadowing loss has a high spatial correlation in the monitored region, the spatial distribution has a sparsity in its frequency domain. Existing wireless tomography schemes are based on the vector-based compressed sensing and estimates the distribution by utilizing the sparsity. On the other hand, the proposed scheme is based on the tensor-based compressed sensing, which estimates the distribution by utilizing its low-rank property. We reveal that the tensor-based compressed sensing has a potential for highly accurate estimation as compared with the vector-based compressed sensing.
Improved Estimation of the Spectral Efficiency Versus Energy-Per-Bit Tradeoff in the Wideband Regime
Venue: arXiv cs.IT 2014
Year: 2014

In this paper, a new lower bound on spectral efficiency in the low-power wideband regime is derived and utilized to develop an improved estimate of the behavior of spectral efficiency above but in the vicinity of the Shannon limit.
Mining Software Metrics from Jazz
Venue: arXiv cs.SE 2014
Year: 2014

In this paper, we describe the extraction of source code metrics from the Jazz repository and the application of data mining techniques to identify the most useful of those metrics for predicting the success or failure of an attempt to construct a working instance of the software product. We present results from a systematic study using the J48 classification method. The results indicate that only a relatively small number of the available software metrics that we considered have any significance for predicting the outcome of a build. These significant metrics are discussed and implication of the results discussed, particularly the relative difficulty of being able to predict failed build attempts.
A Theorem on the Asymptotic Outage Behavior of Fixed-Gain Amplify-and-Forward Relay Systems
Venue: arXiv cs.IT 2014
Year: 2014

A theorem that describes the high signal-to-noise ratio (SNR) outage behavior of fixed-gain amplify-and-forward (FGAF) relay systems is given. Qualitatively, the theorem states that the outage probability decays according to a power law, where the power is dictated by the poles of the moments of the underlying per-hop fading distributions. The power law decay is dampened by a logarithmic factor when the leading pole (furthest to the right in the plane) is of order two or more. The theorem is easy to apply and several examples are presented to this effect.
Parallel MATLAB Techniques
Venue: arXiv cs.DC 2014
Year: 2014

In this chapter, we show why parallel MATLAB is useful, provide a comparison of the different parallel MATLAB choices, and describe a number of applications in Signal and Image Processing: Audio Signal Processing, Synthetic Aperture Radar (SAR) Processing and Superconducting Quantum Interference Filters (SQIFs). Each of these applications have been parallelized using different methods (Task parallel and Data parallel techniques). The applications presented may be considered representative of type of problems faced by signal and image processing researchers. This chapter will also strive to serve as a guide to new signal and image processing parallel programmers, by suggesting a parallelization strategy that can be employed when developing a general parallel algorithm. The objective of this chapter is to help signal and image processing algorithm developers understand the advantages of using parallel MATLAB to tackle larger problems while staying within the powerful environment of MATLAB.
A Real-Time Detecting Algorithm for Tracking Community Structure of Dynamic Networks
Venue: arXiv cs.SI 2014
Year: 2014

In this paper a simple but efficient real-time detecting algorithm is proposed for tracking community structure of dynamic networks. Community structure is intuitively characterized as divisions of network nodes into subgroups, within which nodes are densely connected while between which they are sparsely connected. To evaluate the quality of community structure of a network, a metric called modularity is proposed and many algorithms are developed on optimizing it. However, most of the modularity based algorithms deal with static networks and cannot be performed frequently, due to their high computing complexity. In order to track the community structure of dynamic networks in a fine-grained way, we propose a modularity based algorithm that is incremental and has very low computing complexity. In our algorithm we adopt a two-step approach. Firstly we apply the algorithm of Blondel et al for detecting static communities to obtain an initial community structure. Then, apply our incremental updating strategies to track the dynamic communities. The performance of our algorithm is measured in terms of the modularity. We test the algorithm on tracking community structure of Enron Email and three other real world datasets. The experimental results show that our algorithm can keep track of community structure in time and outperform the well known CNM algorithm in terms of modularity.
Offline handwritten signature identification using adaptive window positioning techniques
Venue: arXiv cs.CV 2014
Year: 2014

The paper presents to address this challenge, we have proposed the use of Adaptive Window Positioning technique which focuses on not just the meaning of the handwritten signature but also on the individuality of the writer. This innovative technique divides the handwritten signature into 13 small windows of size nxn(13x13).This size should be large enough to contain ample information about the style of the author and small enough to ensure a good identification performance.The process was tested with a GPDS data set containing 4870 signature samples from 90 different writers by comparing the robust features of the test signature with that of the user signature using an appropriate classifier. Experimental results reveal that adaptive window positioning technique proved to be the efficient and reliable method for accurate signature feature extraction for the identification of offline handwritten signatures.The contribution of this technique can be used to detect signatures signed under emotional duress.
When Things Matter: A Data-Centric View of the Internet of Things
Venue: arXiv cs.DB 2014
Year: 2014

With the recent advances in radio-frequency identification (RFID), low-cost wireless sensor devices, and Web technologies, the Internet of Things (IoT) approach has gained momentum in connecting everyday objects to the Internet and facilitating machine-to-human and machine-to-machine communication with the physical world. While IoT offers the capability to connect and integrate both digital and physical entities, enabling a whole new class of applications and services, several significant challenges need to be addressed before these applications and services can be fully realized. A fundamental challenge centers around managing IoT data, typically produced in dynamic and volatile environments, which is not only extremely large in scale and volume, but also noisy, and continuous. This article surveys the main techniques and state-of-the-art research efforts in IoT from data-centric perspectives, including data stream processing, data storage models, complex event processing, and searching in IoT. Open research issues for IoT data management are also discussed.