OpenRaiser/Intern-Atlas huggingface dataset:
Incremental Redundancy, Fountain Codes and Advanced Topics
Venue: arXiv cs.IT 2014
Year: 2014

This document is written in order to establish a common base ground on which the majority of the relevant research about linear fountain codes can be analyzed and compared. As far as I am concerned, there is no unified approach that outlines and compares most of the published linear fountain codes in a single and self-contained framework. This written document has not only resulted in the review of theoretical fundamentals of efficient coding techniques for incremental redundancy and linear fountain coding, but also helped me have a comprehensive reference document and hopefully for many other graduate students who would like to have some background to pursue a research career regarding fountain codes and their various applications. Some background in information, coding, graph and probability theory is expected. Although various aspects of this topic and many other relevant research are deliberately left out, I still hope that this document shall serve researchers' need well. I have also included several exercises to warm up. The presentation style is usually informal and the presented material is not necessarily rigorous. There are many spots in the text that are product of my coauthors and myself, although some of which have not been published yet.
Algorithms for multi-armed bandit problems
Venue: arXiv cs.AI 2014
Year: 2014

Although many algorithms for the multi-armed bandit problem are well-understood theoretically, empirical confirmation of their effectiveness is generally scarce. This paper presents a thorough empirical study of the most popular multi-armed bandit algorithms. Three important observations can be made from our results. Firstly, simple heuristics such as epsilon-greedy and Boltzmann exploration outperform theoretically sound algorithms on most settings by a significant margin. Secondly, the performance of most algorithms varies dramatically with the parameters of the bandit problem. Our study identifies for each algorithm the settings where it performs well, and the settings where it performs poorly. Thirdly, the algorithms' performance relative each to other is affected only by the number of bandit arms and the variance of the rewards. This finding may guide the design of subsequent empirical evaluations. In the second part of the paper, we turn our attention to an important area of application of bandit algorithms: clinical trials. Although the design of clinical trials has been one of the principal practical problems motivating research on multi-armed bandits, bandit algorithms have never been evaluated as potential treatment allocation strategies. Using data from a real study, we simulate the outcome that a 2001-2002 clinical trial would have had if bandit algorithms had been used to allocate patients to treatments. We find that an adaptive trial would have successfully treated at least 50% more patients, while significantly reducing the number of adverse effects and increasing patient retention. At the end of the trial, the best treatment could have still been identified with a high level of statistical confidence. Our findings demonstrate that bandit algorithms are attractive alternatives to current adaptive treatment allocation strategies.
A DCT Approximation for Image Compression
Venue: arXiv cs.MM 2014
Year: 2014

An orthogonal approximation for the 8-point discrete cosine transform (DCT) is introduced. The proposed transformation matrix contains only zeros and ones; multiplications and bit-shift operations are absent. Close spectral behavior relative to the DCT was adopted as design criterion. The proposed algorithm is superior to the signed discrete cosine transform. It could also outperform state-of-the-art algorithms in low and high image compression scenarios, exhibiting at the same time a comparable computational complexity.
Generalized Nonlinear Robust Energy-to-Peak Filtering for Differential Algebraic Systems
Venue: arXiv cs.SY 2014
Year: 2014

The problem of robust nonlinear energy-to-peak filtering for nonlinear descriptor systems with model uncertainties is addressed. The system is assumed to have nonlinearities both in the state and output equations as well as norm-bounded time-varying uncertainties in the realization matrices. A generalized nonlinear dynamic filtering structure is proposed for such a class of systems with more degrees of freedom than the conventional static-gain and dynamic filtering structures. The L2-Linfty filter is synthesized through semidefinite programming and strict LMIs, in which the energy-to-peak filtering performance in optimized.
Inductive Logic Boosting
Venue: arXiv cs.LG 2014
Year: 2014

Recent years have seen a surge of interest in Probabilistic Logic Programming (PLP) and Statistical Relational Learning (SRL) models that combine logic with probabilities. Structure learning of these systems is an intersection area of Inductive Logic Programming (ILP) and statistical learning (SL). However, ILP cannot deal with probabilities, SL cannot model relational hypothesis. The biggest challenge of integrating these two machine learning frameworks is how to estimate the probability of a logic clause only from the observation of grounded logic atoms. Many current methods models a joint probability by representing clause as graphical model and literals as vertices in it. This model is still too complicate and only can be approximate by pseudo-likelihood. We propose Inductive Logic Boosting framework to transform the relational dataset into a feature-based dataset, induces logic rules by boosting Problog Rule Trees and relaxes the independence constraint of pseudo-likelihood. Experimental evaluation on benchmark datasets demonstrates that the AUC-PR and AUC-ROC value of ILP learned rules are higher than current state-of-the-art SRL methods.
Widely-Linear Digital Self-Interference Cancellation in Direct-Conversion Full-Duplex Transceiver
Venue: arXiv cs.IT 2014
Year: 2014

This article addresses the modeling and cancellation of self-interference in full-duplex direct-conversion radio transceivers, operating under practical imperfect radio frequency (RF) components. Firstly, detailed self-interference signal modeling is carried out, taking into account the most important RF imperfections, namely transmitter power amplifier nonlinear distortion as well as transmitter and receiver IQ mixer amplitude and phase imbalances. The analysis shows that after realistic antenna isolation and RF cancellation, the dominant self-interference waveform at receiver digital baseband can be modeled through a widely-linear transformation of the original transmit data, opposed to classical purely linear models. Such widely-linear self-interference waveform is physically stemming from the transmitter and receiver IQ imaging, and cannot be efficiently suppressed by classical linear digital cancellation. Motivated by this, novel widely-linear digital self-interference cancellation processing is then proposed and formulated, combined with efficient parameter estimation methods. Extensive simulation results demonstrate that the proposed widely-linear cancellation processing clearly outperforms the existing linear solutions, hence enabling the use of practical low-cost RF front-ends utilizing IQ mixing in full-duplex transceivers.
Explaining Snapshots of Network Diffusions: Structural and Hardness Results
Venue: arXiv cs.SI 2014
Year: 2014

Much research has been done on studying the diffusion of ideas or technologies on social networks including the \textit{Influence Maximization} problem and many of its variations. Here, we investigate a type of inverse problem. Given a snapshot of the diffusion process, we seek to understand if the snapshot is feasible for a given dynamic, i.e., whether there is a limited number of nodes whose initial adoption can result in the snapshot in finite time. While similar questions have been considered for epidemic dynamics, here, we consider this problem for variations of the deterministic Linear Threshold Model, which is more appropriate for modeling strategic agents. Specifically, we consider both sequential and simultaneous dynamics when deactivations are allowed and when they are not. Even though we show hardness results for all variations we consider, we show that the case of sequential dynamics with deactivations allowed is significantly harder than all others. In contrast, sequential dynamics make the problem trivial on cliques even though it's complexity for simultaneous dynamics is unknown. We complement our hardness results with structural insights that can help better understand diffusions of social networks under various dynamics.
On coalgebras with internal moves
Venue: arXiv cs.LO 2014
Year: 2014

In the first part of the paper we recall the coalgebraic approach to handling the so-called invisible transitions that appear in different state-based systems semantics. We claim that these transitions are always part of the unit of a certain monad. Hence, coalgebras with internal moves are exactly coalgebras over a monadic type. The rest of the paper is devoted to supporting our claim by studying two important behavioural equivalences for state-based systems with internal moves, namely: weak bisimulation and trace semantics. We continue our research on weak bisimulations for coalgebras over order enriched monads. The key notions used in this paper and proposed by us in our previous work are the notions of an order saturation monad and a saturator. A saturator operator can be intuitively understood as a reflexive, transitive closure operator. There are two approaches towards defining saturators for coalgebras with internal moves. Here, we give necessary conditions for them to yield the same notion of weak bisimulation. Finally, we propose a definition of trace semantics for coalgebras with silent moves via a uniform fixed point operator. We compare strong and weak bisimilation together with trace semantics for coalgebras with internal steps.
Journal topic citation potential and between-field comparisons: The topic normalized impact factor
Venue: arXiv cs.DL 2014
Year: 2014

The journal impact factor is not comparable among fields of science and social science because of systematic differences in publication and citation behaviour across disciplines. In this work, a source normalization of the journal impact factor is proposed. We use the aggregate impact factor of the citing journals as a measure of the citation potential in the journal topic, and we employ this citation potential in the normalization of the journal impact factor to make it comparable between scientific fields. An empirical application comparing some impact indicators with our topic normalized impact factor in a set of 224 journals from four different fields shows that our normalization, using the citation potential in the journal topic, reduces the between-group variance with respect to the within-group variance in a higher proportion than the rest of indicators analysed. The effect of journal self-citations over the normalization process is also studied.
LSSVM-ABC Algorithm for Stock Price prediction
Venue: arXiv cs.CE 2014
Year: 2014

In this paper, Artificial Bee Colony (ABC) algorithm which inspired from the behavior of honey bees swarm is presented. ABC is a stochastic population-based evolutionary algorithm for problem solving. ABC algorithm, which is considered one of the most recently swarm intelligent techniques, is proposed to optimize least square support vector machine (LSSVM) to predict the daily stock prices. The proposed model is based on the study of stocks historical data, technical indicators and optimizing LSSVM with ABC algorithm. ABC selects best free parameters combination for LSSVM to avoid over-fitting and local minima problems and improve prediction accuracy. LSSVM optimized by Particle swarm optimization (PSO) algorithm, LSSVM, and ANN techniques are used for comparison with proposed model. Proposed model tested with twenty datasets representing different sectors in S&P 500 stock market. Results presented in this paper show that the proposed model has fast convergence speed, and it also achieves better accuracy than compared techniques in most cases.
Clustering Multidimensional Data with PSO based Algorithm
Venue: arXiv cs.NE 2014
Year: 2014

Data clustering is a recognized data analysis method in data mining whereas K-Means is the well known partitional clustering method, possessing pleasant features. We observed that, K-Means and other partitional clustering techniques suffer from several limitations such as initial cluster centre selection, preknowledge of number of clusters, dead unit problem, multiple cluster membership and premature convergence to local optima. Several optimization methods are proposed in the literature in order to solve clustering limitations, but Swarm Intelligence (SI) has achieved its remarkable position in the concerned area. Particle Swarm Optimization (PSO) is the most popular SI technique and one of the favorite areas of researchers. In this paper, we present a brief overview of PSO and applicability of its variants to solve clustering challenges. Also, we propose an advanced PSO algorithm named as Subtractive Clustering based Boundary Restricted Adaptive Particle Swarm Optimization (SC-BR-APSO) algorithm for clustering multidimensional data. For comparison purpose, we have studied and analyzed various algorithms such as K-Means, PSO, K-Means-PSO, Hybrid Subtractive + PSO, BRAPSO, and proposed algorithm on nine different datasets. The motivation behind proposing SC-BR-APSO algorithm is to deal with multidimensional data clustering, with minimum error rate and maximum convergence rate.
Emerging Technologies and Research Challenges for 5G Wireless Networks
Venue: arXiv cs.NI 2014
Year: 2014

As the take-up of Long Term Evolution (LTE)/4G cellular accelerates, there is increasing interest in technologies that will define the next generation (5G) telecommunication standard. This paper identifies several emerging technologies which will change and define the future generations of telecommunication standards. Some of these technologies are already making their way into standards such as 3GPP LTE, while others are still in development. Additionally, we will look at some of the research problems that these new technologies pose.
Solving MaxSAT and #SAT on structured CNF formulas
Venue: arXiv cs.DS 2014
Year: 2014

In this paper we propose a structural parameter of CNF formulas and use it to identify instances of weighted MaxSAT and #SAT that can be solved in polynomial time. Given a CNF formula we say that a set of clauses is precisely satisfiable if there is some complete assignment satisfying these clauses only. Let the ps-value of the formula be the number of precisely satisfiable sets of clauses. Applying the notion of branch decompositions to CNF formulas and using ps-value as cut function, we define the ps-width of a formula. For a formula given with a decomposition of polynomial ps-width we show dynamic programming algorithms solving weighted MaxSAT and #SAT in polynomial time. Combining with results of 'Belmonte and Vatshelle, Graph classes with structured neighborhoods and algorithmic applications, Theor. Comput. Sci. 511: 54-65 (2013)' we get polynomial-time algorithms solving weighted MaxSAT and #SAT for some classes of structured CNF formulas. For example, we get $O(m^2(m + n)s)$ algorithms for formulas $F$ of $m$ clauses and $n$ variables and size $s$, if $F$ has a linear ordering of the variables and clauses such that for any variable $x$ occurring in clause $C$, if $x$ appears before $C$ then any variable between them also occurs in $C$, and if $C$ appears before $x$ then $x$ occurs also in any clause between them. Note that the class of incidence graphs of such formulas do not have bounded clique-width.
Further improvements of determinization methods for fuzzy finite automata
Venue: arXiv cs.FL 2014
Year: 2014

In this paper we combine determinization and state reduction methods into two-in-one algorithms that simultaneously perform determinization and state reduction. These algorithms perform better than all previous determinization algorithms for fuzzy finite automata, developed by Belohlavek [Inform Sciences 143 (2002) 205-209], Li and Pedrycz [Fuzzy Set Syst 156 (2005) 68-92], Ignjatović et al. [Inform Sciences 178 (2008) 164-180], and Jančić et al. [Inform Sciences 181 (2011) 1358-1368], in the sense that they produce smaller automata, while require the same computation time. The only exception is the Brzozowski type determinization algorithm developed recently by Jančić and Ćirić [Fuzzy Set Syst (2014), to appear], which produces a minimal crisp-deterministic fuzzy automaton, but the algorithms created here can also be used within the Brzozowski type algorithm and improve its performances.
Performance Analysis of 2*4 MIMO-MC-CDMA in Rayleigh Fading Channel Using ZF-Decoder
Venue: arXiv cs.IT 2014
Year: 2014

In this paper we analyze the performance of 2*4 MIMO-MC-CDMA system in MATLAB which highly reduces BER. In this paper we combine MIMO and MC-CDMA system to reduce bit error rate in which MC-CDMA is multi user and multiple access schemes which is used to increase the data rate of the system. MC-CDMA system is a single wideband frequency selective carrier which converts frequency selective to parallel narrowband flat fading multiple sub-carriers to enhance the performance of system. Now MC-CDMA system further improved by grouping with 2*4 MIMO system which uses ZF (Zero Forcing) decoder at the receiver to decrease BER with half rate convolutionally encoded Alamouti STBC block code is used as transmit diversity of MIMO through multiple transmit antenna. Importance of using MIMO-MC-CDMA using convolution code is firstly to reduce the complexity of system secondary to reduce BER and lastly to increase gain. In this paper we examine system performance in diverse modulation techniques like, 8-PSK, 16-QAM, QPSK, 32-QAM, 8-QAM and 64-QAM in Rayleigh fading channel using MATLAB.
Renewable Energy Prediction using Weather Forecasts for Optimal Scheduling in HPC Systems
Venue: arXiv cs.LG 2014
Year: 2014

The objective of the GreenPAD project is to use green energy (wind, solar and biomass) for powering data-centers that are used to run HPC jobs. As a part of this it is important to predict the Renewable (Wind) energy for efficient scheduling (executing jobs that require higher energy when there is more green energy available and vice-versa). For predicting the wind energy we first analyze the historical data to find a statistical model that gives relation between wind energy and weather attributes. Then we use this model based on the weather forecast data to predict the green energy availability in the future. Using the green energy prediction obtained from the statistical model we are able to precompute job schedules for maximizing the green energy utilization in the future. We propose a model which uses live weather data in addition to machine learning techniques (which can predict future deviations in weather conditions based on current deviations from the forecast) to make on-the-fly changes to the precomputed schedule (based on green energy prediction). For this we first analyze the data using histograms and simple statistical tools such as correlation. In addition we build (correlation) regression model for finding the relation between wind energy availability and weather attributes (temperature, cloud cover, air pressure, wind speed / direction, precipitation and sunshine). We also analyze different algorithms and machine learning techniques for optimizing the job schedules for maximizing the green energy utilization.
Even more generic solution construction in Valuation-Based Systems
Venue: arXiv cs.AI 2014
Year: 2014

Valuation algebras abstract a large number of formalisms for automated reasoning and enable the definition of generic inference procedures. Many of these formalisms provide some notions of solutions. Typical examples are satisfying assignments in constraint systems, models in logics or solutions to linear equation systems. Recently, formal requirements for the presence of solutions and a generic algorithm for solution construction based on the results of a previously executed inference scheme have been proposed in the literature. Unfortunately, the formalization of Pouly and Kohlas relies on a theorem for which we provide a counter example. In spite of that, the mainline of the theory described is correct, although some of the necessary conditions to apply some of the algorithms have to be revised. To fix the theory, we generalize some of their definitions and provide correct sufficient conditions for the algorithms. As a result, we get a more general and corrected version of the already existing theory.
Scheduling data flow program in xkaapi: A new affinity based Algorithm for Heterogeneous Architectures
Venue: arXiv cs.DC 2014
Year: 2014

Efficient implementations of parallel applications on heterogeneous hybrid architectures require a careful balance between computations and communications with accelerator devices. Even if most of the communication time can be overlapped by computations, it is essential to reduce the total volume of communicated data. The literature therefore abounds with ad-hoc methods to reach that balance, but that are architecture and application dependent. We propose here a generic mechanism to automatically optimize the scheduling between CPUs and GPUs, and compare two strategies within this mechanism: the classical Heterogeneous Earliest Finish Time (HEFT) algorithm and our new, parametrized, Distributed Affinity Dual Approximation algorithm (DADA), which consists in grouping the tasks by affinity before running a fast dual approximation. We ran experiments on a heterogeneous parallel machine with six CPU cores and eight NVIDIA Fermi GPUs. Three standard dense linear algebra kernels from the PLASMA library have been ported on top of the Xkaapi runtime. We report their performances. It results that HEFT and DADA perform well for various experimental conditions, but that DADA performs better for larger systems and number of GPUs, and, in most cases, generates much lower data transfers than HEFT to achieve the same performance.
Recommendation System for Outfit Selection (RSOS)
Venue: arXiv cs.OH 2014
Year: 2014

We propose a system which will be able to recommend the user to choose appropriate outfits suits to their personality. The necessity of this system is to reduce the outfit selection and purchasing time; this will also help to create tailor made outfits as per the personality traits. The guidelines for selection of their respective outfits are based upon various bodily parameters that evolve with the learning of available labeled and unlabeled data. The system is based on two modules of processes; first one is to recognize the features for usage of outfits like traditional, western, functional, daytime or night etc, second is to calculate the body measurement parameters. The proposed system will have image capturing by using HAAR feature or input device for getting body parameters. We intend to classify and extract the best possible outfits from the system by using HIGEN MINER algorithm. The applications of outfit selection will be ranging from manual gender selection, image processing with body feature extractions, Value comparison with database by using different statistical techniques and data mining algorithms. After that it will recommend best outfits as per body parameters, inputs and availability
Lattice structures for bisimilar Probabilistic Automata
Venue: arXiv cs.FL 2014
Year: 2014

The paper shows that there is a deep structure on certain sets of bisimilar Probabilistic Automata (PA). The key prerequisite for these structures is a notion of compactness of PA. It is shown that compact bisimilar PA form lattices. These results are then used in order to establish normal forms not only for finite automata, but also for infinite automata, as long as they are compact.