OpenRaiser/Intern-Atlas huggingface dataset:
Fast Trace Generation of Many-Core Embedded Systems with Native Simulation
Venue: arXiv cs.DC 2014
Year: 2014

Embedded Software development and optimization are complex tasks. Late availably of hardware platforms, their usual low visibility and controllability, and their limiting resource constraints makes early performance estimation an attractive option instead of using the final execution platform. With early performance estimation, software development can progress although the real hardware is not yet available or it is too complex to interact with. In this paper, we present how the native simulation framework SCoPE is extended to generate OTF trace files. Those trace files can be later visualized with trace visualization tools, which recently were only used to optimize HPC workloads in order to iterate in the development process.
Automat Parsing of Audio Recordings. Testing Children with Dyslalia. Theoretical Background
Venue: arXiv cs.CY 2014
Year: 2014

In this paper we present our researches regarding automat parsing of audio recordings. These recordings are obtained from children with dyslalia and are necessary for an accurate identification of speech problems. We develop the ADM algorithm and we analyze the complexity of this solution. We utilize a digital voice recorder in High Quality mode and with VCVA (Variable Control Voice Actuator) activated. The record format is IMA-ADPCM, 16KHz and 4bits (16 bits PCM). A microphone was placed at 10 cm from mouth in order to minimize environment noise. A software set of classes (C#) was created for handling audio stream (read, conversion between different format, write). We also propose an original solution for placing markers in audio stream. These markers are needed for a correct parsing af full recoding.
Architecture of a Fuzzy Expert System Used for Dyslalic Children Therapy
Venue: arXiv cs.AI 2014
Year: 2014

In this paper we present architecture of a fuzzy expert system used for therapy of dyslalic children. With fuzzy approach we can create a better model for speech therapist decisions. A software interface was developed for validation of the system. The main objectives of this task are: personalized therapy (the therapy must be in according with child's problems level, context and possibilities), speech therapist assistant (the expert system offer some suggestion regarding what exercises are better for a specific moment and from a specific child), (self) teaching (when system's conclusion is different that speech therapist's conclusion the last one must have the knowledge base change possibility).
Inner Product Similarity Search using Compositional Codes
Venue: arXiv cs.CV 2014
Year: 2014

This paper addresses the nearest neighbor search problem under inner product similarity and introduces a compact code-based approach. The idea is to approximate a vector using the composition of several elements selected from a source dictionary and to represent this vector by a short code composed of the indices of the selected elements. The inner product between a query vector and a database vector is efficiently estimated from the query vector and the short code of the database vector. We show the superior performance of the proposed group $M$-selection algorithm that selects $M$ elements from $M$ source dictionaries for vector approximation in terms of search accuracy and efficiency for compact codes of the same length via theoretical and empirical analysis. Experimental results on large-scale datasets ($1M$ and $1B$ SIFT features, $1M$ linear models and Netflix) demonstrate the superiority of the proposed approach.
Academic Cloud Computing Research: Five Pitfalls and Five Opportunities
Venue: arXiv cs.DC 2014
Year: 2014

This discussion paper argues that there are five fundamental pitfalls, which can restrict academics from conducting cloud computing research at the infrastructure level, which is currently where the vast majority of academic research lies. Instead academics should be conducting higher risk research, in order to gain understanding and open up entirely new areas. We call for a renewed mindset and argue that academic research should focus less upon physical infrastructure and embrace the abstractions provided by clouds through five opportunities: user driven research, new programming models, PaaS environments, and improved tools to support elasticity and large-scale debugging. The objective of this paper is to foster discussion, and to define a roadmap forward, which will allow academia to make longer-term impacts to the cloud computing community.
Why are images smooth?
Venue: arXiv cs.CV 2014
Year: 2014

It is a well observed phenomenon that natural images are smooth, in the sense that nearby pixels tend to have similar values. We describe a mathematical model of images that makes no assumptions on the nature of the environment that images depict. It only assumes that images can be taken at different scales (zoom levels). We provide quantitative bounds on the smoothness of a typical image in our model, as a function of the number of available scales. These bounds can serve as a baseline against which to compare the observed smoothness of natural images.
The State of Open Data in Latvia: 2014
Venue: arXiv cs.CY 2014
Year: 2014

This paper examines the state of Open Data in Latvia at the middle of 2014. The study is divided into two parts: (i) a survey of open data situation and (ii) an overview of available open data sets. The first part examines the general open data climate in Latvia according to the guidelines of the OKFN Open Data Index making the results comparable to those of other participants of this index. The second part examines datasets made available on the Latvia Open Data community catalogue, the only open data catalogue available in Latvia at the moment. We conclude that Latvia public sector open data mostly fulfil the basic criteria (e.g., data is available) of the Open Data Index but fail on more advanced criteria: the majority of data considered in the study are not published in machine-readable form, are not available for bulk download and none of the data sources have open license statements.
Study on Downlink Spectral Efficiency in Orthogonal Frequency Division Multiple Access Systems
Venue: arXiv cs.IT 2014
Year: 2014

In previous studies on the capacity of orthogonal frequency division multiple access (OFDMA) systems, it is usually assumed that co-channel interference (CCI) from adjacent cells is a Gaussian-distributed random variable. However, very-little work shows that the Gaussian assumption does not hold true in OFDMA systems. In this paper, the statistical property of CCI in downlink OFDMA systems is studied, and spectral efficiency of downlink OFDMA system is analyzed based on the derived statistical model. First, the probability density function (PDF) of CCI in downlink OFDMA cellular systems is studied with the considerations of path loss, multipath fading and Gaussian-like transmit signals. Moreover, some closed-form expressions of the PDF are obtained for special cases. The derived results show that the PDFs of CCI are with a heavy tail, and significantly deviate from the Gaussian distribution. Then, based on the derived statistical properties of CCI, the downlink spectral efficiency is derived. Numerical and simulation results justify the derived statistical CCI model and spectral efficiency.
Fast Support Vector Machines Using Parallel Adaptive Shrinking on Distributed Systems
Venue: arXiv cs.DC 2014
Year: 2014

Support Vector Machines (SVM), a popular machine learning technique, has been applied to a wide range of domains such as science, finance, and social networks for supervised learning. Whether it is identifying high-risk patients by health-care professionals, or potential high-school students to enroll in college by school districts, SVMs can play a major role for social good. This paper undertakes the challenge of designing a scalable parallel SVM training algorithm for large scale systems, which includes commodity multi-core machines, tightly connected supercomputers and cloud computing systems. Intuitive techniques for improving the time-space complexity including adaptive elimination of samples for faster convergence and sparse format representation are proposed. Under sample elimination, several heuristics for {\em earliest possible} to {\em lazy} elimination of non-contributing samples are proposed. In several cases, where an early sample elimination might result in a false positive, low overhead mechanisms for reconstruction of key data structures are proposed. The algorithm and heuristics are implemented and evaluated on various publicly available datasets. Empirical evaluation shows up to 26x speed improvement on some datasets against the sequential baseline, when evaluated on multiple compute nodes, and an improvement in execution time up to 30-60\% is readily observed on a number of other datasets against our parallel baseline.
A Scala Prototype to Generate Multigrid Solver Implementations for Different Problems and Target Multi-Core Platforms
Venue: arXiv cs.MS 2014
Year: 2014

Many problems in computational science and engineering involve partial differential equations and thus require the numerical solution of large, sparse (non)linear systems of equations. Multigrid is known to be one of the most efficient methods for this purpose. However, the concrete multigrid algorithm and its implementation highly depend on the underlying problem and hardware. Therefore, changes in the code or many different variants are necessary to cover all relevant cases. In this article we provide a prototype implementation in Scala for a framework that allows abstract descriptions of PDEs, their discretization, and their numerical solution via multigrid algorithms. From these, one is able to generate data structures and implementations of multigrid components required to solve elliptic PDEs on structured grids. Two different test problems showcase our proposed automatic generation of multigrid solvers for both CPU and GPU target platforms.
Proper Hamiltonian Paths in Edge-Coloured Multigraphs
Venue: arXiv cs.DM 2014
Year: 2014

Given a $c$-edge-coloured multigraph, a proper Hamiltonian path is a path that contains all the vertices of the multigraph such that no two adjacent edges have the same colour. In this work we establish sufficient conditions for an edge-coloured multigraph to guarantee the existence of a proper Hamiltonian path, involving various parameters as the number of edges, the number of colours, the rainbow degree and the connectivity.
A Framework to Quantify the Benefits of Network Functions Virtualization in Cellular Networks
Venue: arXiv cs.NI 2014
Year: 2014

Network functions virtualization (NFV) is an appealing vision that promises to dramatically reduce capital and operating expenses for cellular providers. However, existing efforts in this space leave open broad issues about how NFV deployments should be instantiated or how they should be provisioned. In this paper, we present an initial attempt at a framework that will help network operators systematically evaluate the potential benefits that different points in the NFV design space can offer.
Parallel Algorithms for Counting Triangles in Networks with Large Degrees
Venue: arXiv cs.DC 2014
Year: 2014

Finding the number of triangles in a network is an important problem in the analysis of complex networks. The number of triangles also has important applications in data mining. Existing distributed memory parallel algorithms for counting triangles are either Map-Reduce based or message passing interface (MPI) based and work with overlapping partitions of the given network. These algorithms are designed for very sparse networks and do not work well when the degrees of the nodes are relatively larger. For networks with larger degrees, Map-Reduce based algorithm generates prohibitively large intermediate data, and in MPI based algorithms with overlapping partitions, each partition can grow as large as the original network, wiping out the benefit of partitioning the network. In this paper, we present two efficient MPI-based parallel algorithms for counting triangles in massive networks with large degrees. The first algorithm is a space-efficient algorithm for networks that do not fit in the main memory of a single compute node. This algorithm divides the network into non-overlapping partitions. The second algorithm is for the case where the main memory of each node is large enough to contain the entire network. We observe that for such a case, computation load can be balanced dynamically and present a dynamic load balancing scheme which improves the performance significantly. Both of our algorithms scale well to large networks and to a large number of processors.
The price of fairness for a small number of indivisible items
Venue: arXiv cs.GT 2014
Year: 2014

Incorporating fairness criteria in optimization problems comes at a certain cost, which is measured by the so-called price of fairness. Here we consider the allocation of indivisible goods. For envy-freeness as fairness criterion it is known from literature that the price of fairness can increase linearly in terms of the number of agents. For the constructive lower bound a quadratic number of items was used. In practice this might be inadequately large. So we introduce the price of fairness in terms of both the number of agents and items, i.e., key parameters which generally may be considered as common and available knowledge. It turned out that the price of fairness increases sublinear if the number of items is not too much larger than the number of agents. For the special case of coincide of both counts exact asymptotics could be determined. Additionally an efficient integer programming formulation is given.
Secrecy Outage and Diversity Analysis of Cognitive Radio Systems
Venue: arXiv cs.IT 2014
Year: 2014

In this paper, we investigate the physical-layer security of a multi-user multi-eavesdropper cognitive radio system, which is composed of multiple cognitive users (CUs) transmitting to a common cognitive base station (CBS), while multiple eavesdroppers may collaborate with each other or perform independently in intercepting the CUs-CBS transmissions, which are called the coordinated and uncoordinated eavesdroppers, respectively. Considering multiple CUs available, we propose the round-robin scheduling as well as the optimal and suboptimal user scheduling schemes for improving the security of CUs-CBS transmissions against eavesdropping attacks. Specifically, the optimal user scheduling is designed by assuming that the channel state information (CSI) of all links from CUs to CBS, to primary user (PU) and to eavesdroppers are available. By contrast, the suboptimal user scheduling only requires the CSI of CUs-CBS links without the PU's and eavesdroppers' CSI. We derive closed-form expressions of the secrecy outage probability of these three scheduling schemes in the presence of the coordinated and uncoordinated eavesdroppers. We also carry out the secrecy diversity analysis and show that the round-robin scheduling achieves the diversity order of only one, whereas the optimal and suboptimal scheduling schemes obtain the full secrecy diversity, no matter whether the eavesdroppers collaborate or not. In addition, numerical secrecy outage results demonstrate that for both the coordinated and uncoordinated eavesdroppers, the optimal user scheduling achieves the best security performance and the round-robin scheduling performs the worst. Finally, upon increasing the number of CUs, the secrecy outage probabilities of the optimal and suboptimal user scheduling schemes both improve significantly.
An Experimental Study of Load Balancing of OpenNebula Open-Source Cloud Computing Platform
Venue: arXiv cs.DC 2014
Year: 2014

Cloud Computing is becoming a viable computing solution for services oriented computing. Several open-source cloud solutions are available to these supports. Open-source software stacks offer a huge amount of customizability without huge licensing fees. As a result, open source software are widely used for designing cloud, and private clouds are being built increasingly in the open source way. Numerous contributions have been made by the open-source community related to private-IaaS-cloud. OpenNebula - a cloud platform is one of the popular private cloud management software. However, little has been done to systematically investigate the performance evaluation of this open-source cloud solution in the existing literature. The performance evaluation aids new and existing research, industry and international projects when selecting OpenNebula software to their work. The objective of this paper is to evaluate the load-balancing performance of the OpenNebula cloud management software. For the performance evaluation, the OpenNebula cloud management software is installed and configured as a prototype implementation and tested on the DIU Cloud Lab. In this paper, two set of experiments are conducted to identify the load balancing performance of the OpenNebula cloud management platform- (1) Delete and Add Virtual Machine (VM) from OpenNebula cloud platform; (2) Mapping Physical Hosts to Virtual Machines (VMs) in the OpenNebula cloud platform.
A Low Cost Two-Tier Architecture Model For High Availability Clusters Application Load Balancing
Venue: arXiv cs.NI 2014
Year: 2014

This article proposes a design and implementation of a low cost two-tier architecture model for high availability cluster combined with load-balancing and shared storage technology to achieve desired scale of three-tier architecture for application load balancing e.g. web servers. The research work proposes a design that physically omits Network File System (NFS) server nodes and implements NFS server functionalities within the cluster nodes, through Red Hat Cluster Suite (RHCS) with High Availability (HA) proxy load balancing technologies. In order to achieve a low-cost implementation in terms of investment in hardware and computing solutions, the proposed architecture will be beneficial. This system intends to provide steady service despite any system components fails due to uncertainly such as network system, storage and applications.
Approximating the Maximum Overlap of Polygons under Translation
Venue: arXiv cs.CG 2014
Year: 2014

Let $P$ and $Q$ be two simple polygons in the plane of total complexity $n$, each of which can be decomposed into at most $k$ convex parts. We present an $(1-\varepsilon)$-approximation algorithm, for finding the translation of $Q$, which maximizes its area of overlap with $P$. Our algorithm runs in $O(c n)$ time, where $c$ is a constant that depends only on $k$ and $\varepsilon$. This suggest that for polygons that are "close" to being convex, the problem can be solved (approximately), in near linear time.
Queued cross-bar network models for replication and coded storage systems
Venue: arXiv cs.IT 2014
Year: 2014

Coding techniques may be useful for data center data survivability as well as for reducing traffic congestion. We present a queued cross-bar network (QCN) method that can be used for traffic analysis of both replication/uncoded and coded storage systems. We develop a framework for generating QCN rate regions (RRs) by analyzing their conflict graph stable set polytopes (SSPs). In doing so, we apply recent results from graph theory on the characterization of particular graph SSPs. We characterize the SSP of QCN conflict graphs under a variety of traffic patterns, allowing for their efficient RR computation. For uncoded systems, we show how to compute RRs and find rate optimal scheduling algorithms. For coded storage, we develop a RR upper bound, for which we provide an intuitive interpretation. We show that the coded storage RR upper bound is achievable in certain coded systems in which drives store sufficient coded information, as well in certain dynamic coding systems. Numerical illustrations show that coded storage can result in gains in RR volume of approximately 50%, averaged across traffic patterns.
VideoSET: Video Summary Evaluation through Text
Venue: arXiv cs.CV 2014
Year: 2014

In this paper we present VideoSET, a method for Video Summary Evaluation through Text that can evaluate how well a video summary is able to retain the semantic information contained in its original video. We observe that semantics is most easily expressed in words, and develop a text-based approach for the evaluation. Given a video summary, a text representation of the video summary is first generated, and an NLP-based metric is then used to measure its semantic distance to ground-truth text summaries written by humans. We show that our technique has higher agreement with human judgment than pixel-based distance metrics. We also release text annotations and ground-truth text summaries for a number of publicly available video datasets, for use by the computer vision community.
A Simple Lower Bound on the Noncoherent Capacity of Highly Underspread Fading Channels
Venue: arXiv cs.IT 2014
Year: 2014

Communication channels are said to be underspread if their coherence time is greater than their delay spread. In such cases it can be shown that in the infinite bandwidth limit the information capacity tends to that of a channel with perfect receiver Channel State Information (CSI). This paper presents a lower bound on the capacity of a channel with finite bandwidth, expressed in a form which is mathematically elegant, and computationally simple. The bounding method exploits the fact that most actual channels are highly underspread; and that typically more is known about their impulse response than the channel time variation. The capacity is lower bounded by finding an achievable rate for individual time blocks which are shorter than the channel coherence time, in an orthogonal frequency division multiplexing system model. A highly underspread channel of particular interest is the invehicle channel, and a numerical example is given to verify that the capacity is indeed approximately that of a channel with perfect receiver CSI. The resulting lower bound is shown to be tighter than those previously derived.
Image Completion for View Synthesis Using Markov Random Fields and Efficient Belief Propagation
Venue: arXiv cs.CV 2014
Year: 2014

View synthesis is a process for generating novel views from a scene which has been recorded with a 3-D camera setup. It has important applications in 3-D post-production and 2-D to 3-D conversion. However, a central problem in the generation of novel views lies in the handling of disocclusions. Background content, which was occluded in the original view, may become unveiled in the synthesized view. This leads to missing information in the generated view which has to be filled in a visually plausible manner. We present an inpainting algorithm for disocclusion filling in synthesized views based on Markov random fields and efficient belief propagation. We compare the result to two state-of-the-art algorithms and demonstrate a significant improvement in image quality.