OpenRaiser/Intern-Atlas huggingface dataset:
Interference-Based Optimal Power-Efficient Access Scheme for Cognitive Radio Networks
Venue: arXiv cs.IT 2014
Year: 2014

In this paper, we propose a new optimization-based access strategy of multipacket reception (MPR) channel for multiple secondary users (SUs) accessing the primary user (PU) spectrum opportunistically. We devise an analytical model that realizes the multipacket access strategy of SUs that maximizes the throughput of individual backlogged SUs subject to queue stability of the PU. All the network receiving nodes have MPR capability. We aim at maximizing the throughput of the individual SUs such that the PU's queue is maintained stable. Moreover, we are interested in providing an energy-efficient cognitive scheme. Therefore, we include energy constraints on the PU and SU average transmitted energy to the optimization problem. Each SU accesses the medium with certain probability that depends on the PU's activity, i.e., active or inactive. The numerical results show the advantage in terms of SU throughput of the proposed scheme over the conventional access scheme, where the SUs access the channel randomly with fixed power when the PU is sensed to be idle.
A Task Allocation Schema Based on Response Time Optimization in Cloud Computing
Venue: arXiv cs.DC 2014
Year: 2014

Cloud computing is a newly emerging distributed computing which is evolved from Grid computing. Task scheduling is the core research of cloud computing which studies how to allocate the tasks among the physical nodes so that the tasks can get a balanced allocation or each task's execution cost decreases to the minimum or the overall system performance is optimal. Unlike the previous task slices' sequential execution of an independent task in the model of which the target is processing time, we build a model that targets at the response time, in which the task slices are executed in parallel. Then we give its solution with a method based on an improved adjusting entropy function. At last, we design a new task scheduling algorithm. Experimental results show that the response time of our proposed algorithm is much lower than the game-theoretic algorithm and balanced scheduling algorithm and compared with the balanced scheduling algorithm, game-theoretic algorithm is not necessarily superior in parallel although its objective function value is better.
How well developed are altmetrics? A cross-disciplinary analysis of the presence of 'alternative metrics' in scientific publications
Venue: arXiv cs.DL 2014
Year: 2014

In this paper an analysis of the presence and possibilities of altmetrics for bibliometric and performance analysis is carried out. Using the web based tool Impact Story, we collected metrics for 20,000 random publications from the Web of Science. We studied both the presence and distribution of altmetrics in the set of publications, across fields, document types and over publication years, as well as the extent to which altmetrics correlate with citation indicators. The main result of the study is that the altmetrics source that provides the most metrics is Mendeley, with metrics on readerships for 62.6% of all the publications studied, other sources only provide marginal information. In terms of relation with citations, a moderate spearman correlation (r=0.49) has been found between Mendeley readership counts and citation indicators. Other possibilities and limitations of these indicators are discussed and future research lines are outlined.
Structure of Belarusian educational and research web portal of nuclear knowledge
Venue: arXiv cs.CY 2014
Year: 2014

The main objectives and instruments to develop Belarusian educational and research web portal of nuclear knowledge are discussed. Draft structure of portal is presented.
Asymptotic Behavior of Ultra-Dense Cellular Networks and Its Economic Impact
Venue: arXiv cs.IT 2014
Year: 2014

This paper investigates the relationship between base station (BS) density and average spectral efficiency (SE) in the downlink of a cellular network. This relationship has been well known for sparse deployment, i.e. when the number of BSs is small compared to the number of users. In this case the SE is independent of BS density. As BS density grows, on the other hand, it has previously been shown that increasing the BS density increases the SE, but no tractable form for the SE-BS density relationship has yet been derived. In this paper we derive such a closed-form result that reveals the SE is asymptotically a logarithmic function of BS density as the density grows. Further, we study the impact of this result on the network operator's profit when user demand varies, and derive the profit maximizing BS density and the optimal amount of spectrum to be utilized in closed forms. In addition, we provide deployment planning guidelines that will aid the operator in his decision if he should invest in densifying his network or in acquiring more spectrum.
Fast Sequential Summation Algorithms Using Augmented Data Structures
Venue: arXiv cs.DS 2014
Year: 2014

This paper provides an introduction to the design of augmented data structures that offer an efficient representation of a mathematical sequence and fast sequential summation algorithms, which guarantee both logarithmic running time and logarithmic computational cost in terms of the total number of operations. In parallel summation algorithms, logarithmic running time is achieved by high linear computational cost with a linear number of processors. The important practical advantage of the fast sequential summation algorithms is that they do not require supercomputers and can run on the cheapest single processor systems.
Utility Optimal Scheduling in Degree-Limited Satellite Networks
Venue: arXiv cs.NI 2014
Year: 2014

In this paper, we consider the problem of flow control together with power allocation to antennas on satellite with arbitrary link states, so as to maximize the utility function while stabilizing the network. Inspired by Lyapunov optimization method, a Degree-Limited Scheduling Algorithm (DLSA) is proposed with a control parameter V, which requires no stochastic knowledge of link state. Discussion about implementation is carried out about the complexity of DLSA and several approximation methods to reduce complexity. Analyze shows DLSA stabilizes the network and the gap between utility function under DLSA and the optimal value is arbitrarily close to zero on the order of O(1/V). Simulation results verify DLSA on a simple network.
Analysis of Capacity Region of Delay-Tolerant Hybrid Mobile Ad Hoc Networks
Venue: arXiv cs.NI 2014
Year: 2014

Network capacity region is an important character of mobile ad hoc networks. Using cell-partitioned model, an expression of upper bound of delay-tolerant hybrid mobile ad hoc network is deduced regardless of coverage of base stations, types of mobile process, scheduling and routing algorithms. The limitation of the upper bound is derived, Analysis of the limitation of upper bound is carried out when the steady-state follows even-distribution law. The relationship among limitation of capacity, the node density and coverage of base station is analyzed.
Distributed MAC Protocol for Cognitive Radio Networks: Design, Analysis,and Optimization
Venue: arXiv cs.IT 2014
Year: 2014

In this paper, we investigate the joint optimal sensing and distributed Medium Access Control (MAC) protocol design problem for cognitive radio (CR) networks. We consider both scenarios with single and multiple channels. For each scenario, we design a synchronized MAC protocol for dynamic spectrum sharing among multiple secondary users (SUs), which incorporates spectrum sensing for protecting active primary users (PUs). We perform saturation throughput analysis for the corresponding proposed MAC protocols that explicitly capture the spectrum-sensing performance. Then, we find their optimal configuration by formulating throughput maximization problems subject to detection probability constraints for PUs. In particular, the optimal solution of the optimization problem returns the required sensing time for PUs' protection and optimal contention window to maximize the total throughput of the secondary network. Finally, numerical results are presented to illustrate developed theoretical findings in this paper and significant performance gains of the optimal sensing and protocol configuration.
Collective Innovation in Open Source Hardware
Venue: arXiv cs.CY 2014
Year: 2014

A growing community that shares digital 3D designs has created an opportunity to study, encourage and stimulate innovation. This remix community allows people not only to prototype at a minimal cost but also to work on projects they are genuinely interested in. Participants free of the limitations typically imposed by formal organizations develop products driven by their own interest.
Evaluation of Labeling Strategies for Rotating Maps
Venue: arXiv cs.CG 2014
Year: 2014

We consider the following problem of labeling points in a dynamic map that allows rotation. We are given a set of points in the plane labeled by a set of mutually disjoint labels, where each label is an axis-aligned rectangle attached with one corner to its respective point. We require that each label remains horizontally aligned during the map rotation and our goal is to find a set of mutually non-overlapping active labels for every rotation angle $α\in [0, 2π)$ so that the number of active labels over a full map rotation of 2$π$ is maximized. We discuss and experimentally evaluate several labeling models that define additional consistency constraints on label activities in order to reduce flickering effects during monotone map rotation. We introduce three heuristic algorithms and compare them experimentally to an existing approximation algorithm and exact solutions obtained from an integer linear program. Our results show that on the one hand low flickering can be achieved at the expense of only a small reduction in the objective value, and that on the other hand the proposed heuristics achieve a high labeling quality significantly faster than the other methods.
Plug and Play! A Simple, Universal Model for Energy Disaggregation
Venue: arXiv cs.AI 2014
Year: 2014

Energy disaggregation is to discover the energy consumption of individual appliances from their aggregated energy values. To solve the problem, most existing approaches rely on either appliances' signatures or their state transition patterns, both hard to obtain in practice. Aiming at developing a simple, universal model that works without depending on sophisticated machine learning techniques or auxiliary equipments, we make use of easily accessible knowledge of appliances and the sparsity of the switching events to design a Sparse Switching Event Recovering (SSER) method. By minimizing the total variation (TV) of the (sparse) event matrix, SSER can effectively recover the individual energy consumption values from the aggregated ones. To speed up the process, a Parallel Local Optimization Algorithm (PLOA) is proposed to solve the problem in active epochs of appliance activities in parallel. Using real-world trace data, we compare the performance of our method with that of the state-of-the-art solutions, including Least Square Estimation (LSE) and iterative Hidden Markov Model (HMM). The results show that our approach has an overall higher detection accuracy and a smaller overhead.
Polish and English wordnets -- statistical analysis of interconnected networks
Venue: arXiv cs.CL 2014
Year: 2014

Wordnets are semantic networks containing nouns, verbs, adjectives, and adverbs organized according to linguistic principles, by means of semantic relations. In this work, we adopt a complex network perspective to perform a comparative analysis of the English and Polish wordnets. We determine their similarities and show that the networks exhibit some of the typical characteristics observed in other real-world networks. We analyse interlingual relations between both wordnets and deliberate over the problem of mapping the Polish lexicon onto the English one.
A fractal approach to the dark silicon problem: a comparison of 3D computer architectures -- standard slices versus fractal Menger sponge geometry
Venue: arXiv cs.ET 2014
Year: 2014

The dark silicon problem, which limits the power-growth of future computer generations, is interpreted as a heat energy transport problem when increasing the energy emitting surface area within a given volume. A comparison of two 3D-configuration models, namely a standard slicing and a fractal surface generation within the Menger sponge geometry is presented. It is shown, that for iteration orders $n>3$ the fractal model shows increasingly better thermal behavior. As a consequence cooling problems may be minimized by using a fractal architecture. Therefore the Menger sponge geometry is a good example for fractal architectures applicable not only in computer science, but also e.g. in chemistry when building chemical reactors, optimizing catalytic processes or in sensor construction technology building highly effective sensors for toxic gases or water analysis.
SELFISHMIGRATE: A Scalable Algorithm for Non-clairvoyantly Scheduling Heterogeneous Processors
Venue: arXiv cs.DS 2014
Year: 2014

We consider the classical problem of minimizing the total weighted flow-time for unrelated machines in the online \emph{non-clairvoyant} setting. In this problem, a set of jobs $J$ arrive over time to be scheduled on a set of $M$ machines. Each job $j$ has processing length $p_j$, weight $w_j$, and is processed at a rate of $\ell_{ij}$ when scheduled on machine $i$. The online scheduler knows the values of $w_j$ and $\ell_{ij}$ upon arrival of the job, but is not aware of the quantity $p_j$. We present the {\em first} online algorithm that is {\em scalable} ($(1+\eps)$-speed $O(\frac{1}{ε^2})$-competitive for any constant $\eps > 0$) for the total weighted flow-time objective. No non-trivial results were known for this setting, except for the most basic case of identical machines. Our result resolves a major open problem in online scheduling theory. Moreover, we also show that no job needs more than a logarithmic number of migrations. We further extend our result and give a scalable algorithm for the objective of minimizing total weighted flow-time plus energy cost for the case of unrelated machines and obtain a scalable algorithm. The key algorithmic idea is to let jobs migrate selfishly until they converge to an equilibrium. Towards this end, we define a game where each job's utility which is closely tied to the instantaneous increase in the objective the job is responsible for, and each machine declares a policy that assigns priorities to jobs based on when they migrate to it, and the execution speeds. This has a spirit similar to coordination mechanisms that attempt to achieve near optimum welfare in the presence of selfish agents (jobs). To the best our knowledge, this is the first work that demonstrates the usefulness of ideas from coordination mechanisms and Nash equilibria for designing and analyzing online algorithms.
Notes on Generalized Linear Models of Neurons
Venue: arXiv cs.NE 2014
Year: 2014

Experimental neuroscience increasingly requires tractable models for analyzing and predicting the behavior of neurons and networks. The generalized linear model (GLM) is an increasingly popular statistical framework for analyzing neural data that is flexible, exhibits rich dynamic behavior and is computationally tractable (Paninski, 2004; Pillow et al., 2008; Truccolo et al., 2005). What follows is a brief summary of the primary equations governing the application of GLM's to spike trains with a few sentences linking this work to the larger statistical literature. Latter sections include extensions of a basic GLM to model spatio-temporal receptive fields as well as network activity in an arbitrary numbers of neurons.
Notes on Kullback-Leibler Divergence and Likelihood
Venue: arXiv cs.IT 2014
Year: 2014

The Kullback-Leibler (KL) divergence is a fundamental equation of information theory that quantifies the proximity of two probability distributions. Although difficult to understand by examining the equation, an intuition and understanding of the KL divergence arises from its intimate relationship with likelihood theory. We discuss how KL divergence arises from likelihood theory in an attempt to provide some intuition and reserve a rigorous (but rather simple) derivation for the appendix. Finally, we comment on recent applications of KL divergence in the neural coding literature and highlight its natural application.
Main Memory Adaptive Indexing for Multi-core Systems
Venue: arXiv cs.DB 2014
Year: 2014

Adaptive indexing is a concept that considers index creation in databases as a by-product of query processing; as opposed to traditional full index creation where the indexing effort is performed up front before answering any queries. Adaptive indexing has received a considerable amount of attention, and several algorithms have been proposed over the past few years; including a recent experimental study comparing a large number of existing methods. Until now, however, most adaptive indexing algorithms have been designed single-threaded, yet with multi-core systems already well established, the idea of designing parallel algorithms for adaptive indexing is very natural. In this regard only one parallel algorithm for adaptive indexing has recently appeared in the literature: The parallel version of standard cracking. In this paper we describe three alternative parallel algorithms for adaptive indexing, including a second variant of a parallel standard cracking algorithm. Additionally, we describe a hybrid parallel sorting algorithm, and a NUMA-aware method based on sorting. We then thoroughly compare all these algorithms experimentally; along a variant of a recently published parallel version of radix sort. Parallel sorting algorithms serve as a realistic baseline for multi-threaded adaptive indexing techniques. In total we experimentally compare seven parallel algorithms. Additionally, we extensively profile all considered algorithms. The initial set of experiments considered in this paper indicates that our parallel algorithms significantly improve over previously known ones. Our results suggest that, although adaptive indexing algorithms are a good design choice in single-threaded environments, the rules change considerably in the parallel case. That is, in future highly-parallel environments, sorting algorithms could be serious alternatives to adaptive indexing.
Cascades of Regression Tree Fields for Image Restoration
Venue: arXiv cs.CV 2014
Year: 2014

Conditional random fields (CRFs) are popular discriminative models for computer vision and have been successfully applied in the domain of image restoration, especially to image denoising. For image deblurring, however, discriminative approaches have been mostly lacking. We posit two reasons for this: First, the blur kernel is often only known at test time, requiring any discriminative approach to cope with considerable variability. Second, given this variability it is quite difficult to construct suitable features for discriminative prediction. To address these challenges we first show a connection between common half-quadratic inference for generative image priors and Gaussian CRFs. Based on this analysis, we then propose a cascade model for image restoration that consists of a Gaussian CRF at each stage. Each stage of our cascade is semi-parametric, i.e. it depends on the instance-specific parameters of the restoration problem, such as the blur kernel. We train our model by loss minimization with synthetically generated training data. Our experiments show that when applied to non-blind image deblurring, the proposed approach is efficient and yields state-of-the-art restoration quality on images corrupted with synthetic and real blur. Moreover, we demonstrate its suitability for image denoising, where we achieve competitive results for grayscale and color images.
Datacenter Changes vs. Employment Rates for Datacenter Managers In the Cloud Computing Era
Venue: arXiv cs.CY 2014
Year: 2014

Due to the evolving Cloud Computing paradigm, there is a prevailing concern that in the near future data center managers may be in short supply. Cloud computing, as a whole, is becoming more prevalent into today s computing world. In fact, cloud computing has become so popular that some are now referring to data centers as cloud centers. How does this interest in cloud computing translate into employment rates for data center managers? The popularity of the public and private cloud models are the prevailing force behind answering this question. Therefore, the skill set of the datacenter manager has evolved to harness the on demand self-services, broad network access, resource pooling, rapid elasticity, measured service, and multi tenacity characteristics of cloud computing. Using diverse sources ranging from the Bureau of Labor and Statistics to trade articles, this manuscript takes an in-depth look at these employment rates related to the cloud and the determining factors behind them. Based on the information available, datacenter manager employment rates in the cloud computing era will continue to increase well into 2016.
Leveraging VMware vCloud Director Virtual Applications (vApps) for Operational Expense (OpEx) Efficiency
Venue: arXiv cs.DC 2014
Year: 2014

Virtualization technology has provided many benefits to organizations, but it cannot provide automation. This causes operational expenditure (OpEx) inefficiencies, which are solved by cloud computing (vCloud Director vApps). Organizations have adopted virtualization technology to reduce IT costs and meet business needs. In addition to improved CapEx efficiency, virtualization has enabled organizations to respond to business needs faster. While virtualization has dramatically optimized core IT infrastructures, organizations struggle to reduce OpEx costs. Because virtualization only addresses server consolidation, administrators are faced with the manual and resource-intensive day-to-day tasks of managing the rest of the data center: networking, storage, user management. This manuscript presents details on how leverage vApps based on a virtualized platform to improve CapEx efficiency in today s data center. The combination of virtualization and cloud computing can transform the data center into a dynamic, scalable, and agile resource capable of achieving significant CapEx and OpEx cost savings.
Webpage Load Speed: ASP.NET vs. PHP
Venue: arXiv cs.PL 2014
Year: 2014

As data transmission speeds over the Internet continue to increase, it is necessary to research and identify technologies that can take advantage of the increased speeds by enhancing the loading speed of webpages. One area of consideration is found in the type of framework that is utilized for a website. There are numerous frameworks that can be chosen from to be used to support a website, each with their distinctive advantages. There are many different opinions that have been tested on which framework should be used to fully realize the optimization of page load speed. This manuscript examines and implements testing methods for two popular frameworks, Active Server Pages .net and PHP, to make a final determination of which framework is most beneficial for webpage load speeds.
Securing Virtualized Datacenters
Venue: arXiv cs.CR 2014
Year: 2014

Virtualization is a very popular solution to many problems in datacenter management. It offers increased utilization of existing system resources through effective consolidation, negating the need for more servers and additional rack space. Furthermore, it offers essential capabilities in terms of disaster recovery and potential savings on energy and maintenance costs. However, these benefits may be tempered by the increased complexities of securing virtual infrastructure. Do the benefits of virtualization outweigh the risks? In this study, the authors evaluated the functionalities of the basic components of virtual datacenters, identified the major risks to the data infrastructure, and present here several solutions for overcoming potential threats to virtual infrastructure.