OpenRaiser/Intern-Atlas huggingface dataset:
Social media as political party campaign in Indonesia
Venue: arXiv cs.CY 2014
Year: 2014

Social media as a trend in the Internet is now used as a medium for political campaigns. Author explores the advantages and social media implementation of any political party in Indonesia legislative elections 2014. Author visited and analyzed social media used by the contestants, such as: Facebook, and Twitter. Author collected data from social media until the end of April 2014. This article discusses the use of social media by political parties and their features. The results of this study indicate that social media are: 1) effective tool for current and future political campaigns, 2) reach the voters and supporters instantly, 3) used by Political parties to show their logo/icon, and 4) last but not least quick count results also show that political parties which using social media as part of their campaigns won the legislative elections.
Beautiful Structures: An Appreciation of the Contributions of Alan Selman
Venue: arXiv cs.CC 2014
Year: 2014

Professor Alan Selman has been a giant in the field of computational complexity for the past forty years. This article is an appreciation, on the occasion of his retirement, of some of the most lovely concepts and results that Alan has contributed to the field.
Joint Cooperative Spectrum Sensing and MAC Protocol Design for Multi-channel Cognitive Radio Networks
Venue: arXiv cs.NI 2014
Year: 2014

In this paper, we propose a semi-distributed cooperative spectrum sen sing (SDCSS) and channel access framework for multi-channel cognitive radio networks (CRNs). In particular, we c onsider a SDCSS scheme where secondary users (SUs) perform sensing and exchange sensing outcomes with ea ch other to locate spectrum holes. In addition, we devise the p -persistent CSMA-based cognitive MAC protocol integrating the SDCSS to enable efficient spectrum sharing among SUs. We then perform throughput analysis and develop an algorithm to determine the spectrum sensing and access parameters to maximize the throughput for a given allocation of channel sensing sets. Moreover, we consider the spectrum sensing set optimization problem for SUs to maxim ize the overall system throughput. We present both exhaustive search and low-complexity greedy algorithms to determine the sensing sets for SUs and analyze their complexity. We also show how our design and analysis can be extended to consider reporting errors. Finally, extensive numerical results are presented to demonstrate the sig nificant performance gain of our optimized design framework with respect to non-optimized designs as well as the imp acts of different protocol parameters on the throughput performance.
Construction of non-convex polynomial loss functions for training a binary classifier with quantum annealing
Venue: arXiv cs.LG 2014
Year: 2014

Quantum annealing is a heuristic quantum algorithm which exploits quantum resources to minimize an objective function embedded as the energy levels of a programmable physical system. To take advantage of a potential quantum advantage, one needs to be able to map the problem of interest to the native hardware with reasonably low overhead. Because experimental considerations constrain our objective function to take the form of a low degree PUBO (polynomial unconstrained binary optimization), we employ non-convex loss functions which are polynomial functions of the margin. We show that these loss functions are robust to label noise and provide a clear advantage over convex methods. These loss functions may also be useful for classical approaches as they compile to regularized risk expressions which can be evaluated in constant time with respect to the number of training examples.
Mapping the Economic Crisis: Some Preliminary Investigations
Venue: arXiv cs.CL 2014
Year: 2014

In this paper we describe our contribution to the PoliInformatics 2014 Challenge on the 2007-2008 financial crisis. We propose a state of the art technique to extract information from texts and provide different representations, giving first a static overview of the domain and then a dynamic representation of its main evolutions. We show that this strategy provides a practical solution to some recent theories in social sciences that are facing a lack of methods and tools to automatically extract information from natural language texts.
Towards a theory of granular sets
Venue: arXiv cs.AI 2014
Year: 2014

Motivated by the application problem of sensor fusion the author introduced the concept of graded set. It is reasoned that in classification problem arising in an information system (represented by information table), a novel set called Granular set naturally arises. It is realized that in any hierarchical classification problem, Granular set naturally arises. Also when the target set of objects forms a graded set the lower and upper approximations of target sets form a graded set. This generalizes the concept of rough set. It is hoped that a detailed theory of granular/ graded sets finds several applications.
Stable Recovery of Sparse Signals via $l_p-$Minimization
Venue: arXiv cs.IT 2014
Year: 2014

In this paper, we show that, under the assumption that $\|\e\|_2\leq ε$, every $k-$sparse signal $\x\in \mathbb{R}^n$ can be stably ($ε\neq0$) or exactly recovered ($ε=0$) from $\y=\A\x+\e$ via $l_p-$mnimization with $p\in(0, \bar{p}]$, where \beqnn \bar{p}= \begin{cases} \frac{50}{31}(1-δ_{2k}), &δ_{2k}\in[\frac{\sqrt{2}}{2}, 0.7183)\cr 0.4541, &δ_{2k}\in[0.7183,0.7729)\cr 2(1-δ_{2k}), &δ_{2k}\in[0.7729,1) \end{cases}, \eeqnn even if the restricted isometry constant of $\A$ satisfies $δ_{2k}\in[\frac{\sqrt{2}}{2}, 1)$. Furthermore, under the assumption that $n\leq 4k$, we show that the range of $p$ can be further improved to $p\in(0,\frac{3+2\sqrt{2}}{2}(1-δ_{2k})]$. This not only extends some discussions of only the noiseless recovery (Lai et al. and Wu et al.) to the noise recovery, but also greatly improves the best existing results where $p\in(0,\min\{1, 1.0873(1-δ_{2k}) \})$ (Wu et al.).
The dark side of Open Access in Google and Google Scholar: the case of Latin-American repositories
Venue: arXiv cs.DL 2014
Year: 2014

Since repositories are a key tool in making scholarly knowledge open access, determining their presence and impact on the Web is essential, particularly in Google (search engine par excellence) and Google Scholar (a tool increasingly used by researchers to search for academic information). The few studies conducted so far have been limited to very specific geographic areas (USA), which makes it necessary to find out what is happening in other regions that are not part of mainstream academia, and where repositories play a decisive role in the visibility of scholarly production. The main objective of this study is to ascertain the presence and visibility of Latin American repositories in Google and Google Scholar through the application of page count and visibility indicators. For a sample of 137 repositories, the results indicate that the indexing ratio is low in Google, and virtually nonexistent in Google Scholar; they also indicate a complete lack of correspondence between the repository records and the data produced by these two search tools. These results are mainly attributable to limitations arising from the use of description schemas that are incompatible with Google Scholar (repository design) and the reliability of web indicators (search engines). We conclude that neither Google nor Google Scholar accurately represent the actual size of open access content published by Latin American repositories; this may indicate a non-indexed, hidden side to open access, which could be limiting the dissemination and consumption of open access scholarly literature.
Improved Bounds on the Restricted Isometry Constant for Orthogonal Matching Pursuit
Venue: arXiv cs.IT 2014
Year: 2014

In this letter, we first construct a counter example to show that for any given positive integer $K\geq 2$ and for any $\frac{1}{\sqrt{K+1}}\leq t<1$, there always exist a $K-$sparse $\x$ and a matrix $\A$ with the restricted isometry constant $δ_{K+1}=t$ such that the OMP algorithm fails in $K$ iterations. Secondly, we show that even when $δ_{K+1}=\frac{1}{\sqrt{K}+1}$, the OMP algorithm can also perfectly recover every $K-$sparse vector $\x$ from $\y=\A\x$ in $K$ iteration. This improves the best existing results which were independently given by Mo et al. and Wang et al.
Authorship Attribution through Function Word Adjacency Networks
Venue: arXiv cs.CL 2014
Year: 2014

A method for authorship attribution based on function word adjacency networks (WANs) is introduced. Function words are parts of speech that express grammatical relationships between other words but do not carry lexical meaning on their own. In the WANs in this paper, nodes are function words and directed edges stand in for the likelihood of finding the sink word in the ordered vicinity of the source word. WANs of different authors can be interpreted as transition probabilities of a Markov chain and are therefore compared in terms of their relative entropies. Optimal selection of WAN parameters is studied and attribution accuracy is benchmarked across a diverse pool of authors and varying text lengths. This analysis shows that, since function words are independent of content, their use tends to be specific to an author and that the relational data captured by function WANs is a good summary of stylometric fingerprints. Attribution accuracy is observed to exceed the one achieved by methods that rely on word frequencies alone. Further combining WANs with methods that rely on word frequencies alone, results in larger attribution accuracy, indicating that both sources of information encode different aspects of authorial styles.
Notes on hierarchical ensemble methods for DAG-structured taxonomies
Venue: arXiv cs.AI 2014
Year: 2014

Several real problems ranging from text classification to computational biology are characterized by hierarchical multi-label classification tasks. Most of the methods presented in literature focused on tree-structured taxonomies, but only few on taxonomies structured according to a Directed Acyclic Graph (DAG). In this contribution novel classification ensemble algorithms for DAG-structured taxonomies are introduced. In particular Hierarchical Top-Down (HTD-DAG) and True Path Rule (TPR-DAG) for DAGs are presented and discussed.
Block matching algorithm based on Harmony Search optimization for motion estimation
Venue: arXiv cs.CV 2014
Year: 2014

Motion estimation is one of the major problems in developing video coding applications. Among all motion estimation approaches, Block-matching (BM) algorithms are the most popular methods due to their effectiveness and simplicity for both software and hardware implementations. A BM approach assumes that the movement of pixels within a defined region of the current frame can be modeled as a translation of pixels contained in the previous frame. In this procedure, the motion vector is obtained by minimizing a certain matching metric that is produced for the current frame over a determined search window from the previous frame. Unfortunately, the evaluation of such matching measurement is computationally expensive and represents the most consuming operation in the BM process. Therefore, BM motion estimation can be viewed as an optimization problem whose goal is to find the best-matching block within a search space. The simplest available BM method is the Full Search Algorithm (FSA) which finds the most accurate motion vector through an exhaustive computation of all the elements of the search space. Recently, several fast BM algorithms have been proposed to reduce the search positions by calculating only a fixed subset of motion vectors despite lowering its accuracy. On the other hand, the Harmony Search (HS) algorithm is a population-based optimization method that is inspired by the music improvisation process in which a musician searches for harmony and continues to polish the pitches to obtain a better harmony. In this paper, a new BM algorithm that combines HS with a fitness approximation model is proposed. The approach uses motion vectors belonging to the search window as potential solutions. A fitness function evaluates the matching quality of each motion vector candidate.
Quasi-optimal grouping for broadcast systems with hierarchical modulation
Venue: arXiv cs.NI 2014
Year: 2014

Recently, we proposed to combine time sharing with hierarchical modulation to increase the transmission rate of broadcast systems. Our proposal involves to group the receivers in pairs in order to transmit with hierarchical modulation. We introduced several grouping strategies but the optimal matching remained an open question. In this letter, we show that the optimal grouping is the solution of an assignment problem, for which efficient algorithms exist such as the Hungarian method. Based on this algorithm, we study the performance of the optimal grouping in terms of spectrum efficiency for a DVB-S2 system.
Classification of Attacks in Wireless Sensor Networks
Venue: arXiv cs.CR 2014
Year: 2014

In wireless sensor networks (WSNs), security has a vital importance. Recently, there was a huge interest to propose security solutions in WSNs because of their applications in both civilian and military domains. Adversaries can launch different types of attacks, and cryptography is used to countering these attacks. This paper presents challenges of security and a classification of the different possible attacks in WSNs. The problems of security in each layer of the network's OSI model are discussed.
Implementing an Optimal Rate Allocation Tuned to the User Quality of Experience
Venue: arXiv cs.NI 2014
Year: 2014

Optimal resource allocation elegantly kaizens bandwidth utilization in present-day communications systems carrying distinctive traffic types with specific quality of service (QoS) requirements, whose fulfillment may elevate users' quality of Experience (QoE). This paper investigates the QoE of users running real-life real-time and delay-tolerant applications by implementing an Internet-connected real-world mobile network which hosts a node with a centralized convex resource allocation optimization algorithm to calculate and enforce an optimal bandwidth distribution. The experiments show that leveraging the rate assignment approach escalates the real-life network traffic QoE through a fine-grained temporal resource allocation pattern which plummets the total bandwidth consumption and the cost of employing the services.
Gröbner Bases for Linearized Polynomials
Venue: arXiv cs.SC 2014
Year: 2014

In this work we develop the theory of Gröbner bases for modules over the ring of univariate linearized polynomials with coefficients from a finite field.
On the Achievable Rates of Pairwise Multiway Relay Channels
Venue: arXiv cs.IT 2014
Year: 2014

In this paper, we study the effect of users' transmission ordering on the common rate and sum rate of pairwise multiway relay channels (MWRCs) with functional-decode-forward strategy. To this end, we first develop a graphical model for the data transmission in a pairwise MWRC. Using this model, we then find the optimal orderings that achieve the maximum common rate and sum rate of the system. The achieved maximum common and sum rate are also found. Moreover, we show that the performance gap between optimal orderings and a random ordering vanishes when SNR increases. Computer simulations are presented for better illustration of the results.
Multi-Objective Design Optimization of the Leg Mechanism for a Piping Inspection Robot
Venue: arXiv cs.RO 2014
Year: 2014

This paper addresses the dimensional synthesis of an adaptive mechanism of contact points ie a leg mechanism of a piping inspection robot operating in an irradiated area as a nuclear power plant. This studied mechanism is the leading part of the robot sub-system responsible of the locomotion. Firstly, three architectures are chosen from the literature and their properties are described. Then, a method using a multi-objective optimization is proposed to determine the best architecture and the optimal geometric parameters of a leg taking into account environmental and design constraints. In this context, the objective functions are the minimization of the mechanism size and the maximization of the transmission force factor. Representations of the Pareto front versus the objective functions and the design parameters are given. Finally, the CAD model of several solutions located on the Pareto front are presented and discussed.
A Sober Look at Spectral Learning
Venue: arXiv cs.LG 2014
Year: 2014

Spectral learning recently generated lots of excitement in machine learning, largely because it is the first known method to produce consistent estimates (under suitable conditions) for several latent variable models. In contrast, maximum likelihood estimates may get trapped in local optima due to the non-convex nature of the likelihood function of latent variable models. In this paper, we do an empirical evaluation of spectral learning (SL) and expectation maximization (EM), which reveals an important gap between the theory and the practice. First, SL often leads to negative probabilities. Second, EM often yields better estimates than spectral learning and it does not seem to get stuck in local optima. We discuss how the rank of the model parameters and the amount of training data can yield negative probabilities. We also question the common belief that maximum likelihood estimators are necessarily inconsistent.
Classifying and Propagating Parity Constraints (extended version)
Venue: arXiv cs.LO 2014
Year: 2014

Parity constraints, common in application domains such as circuit verification, bounded model checking, and logical cryptanalysis, are not necessarily most efficiently solved if translated into conjunctive normal form. Thus, specialized parity reasoning techniques have been developed in the past for propagating parity constraints. This paper studies the questions of deciding whether unit propagation or equivalence reasoning is enough to achieve full propagation in a given parity constraint set. Efficient approximating tests for answering these questions are developed. It is also shown that equivalence reasoning can be simulated by unit propagation by adding a polynomial amount of redundant parity constraints to the problem. It is proven that without using additional variables, an exponential number of new parity constraints would be needed in the worst case. The presented classification and propagation methods are evaluated experimentally.
Graph Isomorphism Parameterized by Elimination Distance to Bounded Degree
Venue: arXiv cs.DS 2014
Year: 2014

A commonly studied means of parameterizing graph problems is the deletion distance from triviality (Guo et al. 2004), which counts vertices that need to be deleted from a graph to place it in some class for which efficient algorithms are known. In the context of graph isomorphism, we define triviality to mean a graph with maximum degree bounded by a constant, as such graph classes admit polynomial-time isomorphism tests. We generalise deletion distance to a measure we call elimination distance to triviality, based on elimination trees or tree-depth decompositions. We establish that graph canonisation, and thus graph isomorphism, is FPT when parameterized by elimination distance to bounded degree, extending results of Bouland et al. (2012).
An Experimental Evaluation of Nearest Neighbour Time Series Classification
Venue: arXiv cs.LG 2014
Year: 2014

Data mining research into time series classification (TSC) has focussed on alternative distance measures for nearest neighbour classifiers. It is standard practice to use 1-NN with Euclidean or dynamic time warping (DTW) distance as a straw man for comparison. As part of a wider investigation into elastic distance measures for TSC~\cite{lines14elastic}, we perform a series of experiments to test whether this standard practice is valid. Specifically, we compare 1-NN classifiers with Euclidean and DTW distance to standard classifiers, examine whether the performance of 1-NN Euclidean approaches that of 1-NN DTW as the number of cases increases, assess whether there is any benefit of setting $k$ for $k$-NN through cross validation whether it is worth setting the warping path for DTW through cross validation and finally is it better to use a window or weighting for DTW. Based on experiments on 77 problems, we conclude that 1-NN with Euclidean distance is fairly easy to beat but 1-NN with DTW is not, if window size is set through cross validation.
Reorganization of Links to Improve User Navigation
Venue: arXiv cs.HC 2014
Year: 2014

Website can be easily design but to efficient user navigation is not a easy task since user behavior is keep changing and developer view is quite different from what user wants, so to improve navigation one way is reorganization of website structure. For reorganization here proposed strategy is farthest first traversal clustering algorithm perform clustering on two numeric parameters and for finding frequent traversal path of user Apriori algorithm is used. Our aim is to perform reorganization with fewer changes in website structure.
A Computational Study of Feasible Repackings in the FCC Incentive Auctions
Venue: arXiv cs.GT 2014
Year: 2014

We report the results of a computational study of repacking in the FCC Incentive Auctions. Our interest lies in the structure and constraints of the solution space of feasible repackings. Our analyses are "mechanism-free", in the sense that they identify constraints that must hold regardless of the reverse auction mechanism chosen or the prices offered for broadcaster clearing. We examine topics such as the amount of spectrum that can be cleared nationwide, the geographic distribution of broadcaster clearings required to reach a clearing target, and the likelihood of reaching clearing targets under various models for broadcaster participation. Our study uses FCC interference data and a satisfiability-checking approach, and elucidates both the unavoidable mathematical constraints on solutions imposed by interference, as well as additional constraints imposed by assumptions on the participation decisions of broadcasters.
A New Web Based Student Annual Review Information System (SARIS) With Student Success Prediction
Venue: arXiv cs.CY 2014
Year: 2014

In this paper, we are proposing new web based Student Annual Review Information System (SARIS) and prediction method for the success of scholar students to China Scholarship Council(CSC). The main objective of developing this system is to save the cost of paper, to reduce the risk of data loss, to decrease the processing time, to reduce the delay in finding for the successful students. The proposed system and prediction method is intended to be used by China Scholarship Council; however SARIS and prediction method are quite generic and can be used by other scholarship agencies.
Knowledge Base of an Expert System Used for Dyslalic Children Therapy
Venue: arXiv cs.AI 2014
Year: 2014

In order to improve children speech therapy, we develop a Fuzzy Expert System based on a speech therapy guide. This guide, write in natural language, was formalized using fuzzy logic paradigm. In this manner we obtain a knowledge base with over 150 rules and 19 linguistic variables. All these researches, including expert system validation, are part of TERAPERS project.