OpenRaiser/Intern-Atlas huggingface dataset:
Constrained Codes for Rank Modulation
Venue: arXiv cs.IT 2014
Year: 2014

Motivated by the rank modulation scheme, a recent work by Sala and Dolecek explored the study of constraint codes for permutations. The constraint studied by them is inherited by the inter-cell interference phenomenon in flash memories, where high-level cells can inadvertently increase the level of low-level cells. In this paper, the model studied by Sala and Dolecek is extended into two constraints. A permutation $σ\in S_n$ satisfies the \emph{two-neighbor $k$-constraint} if for all $2 \leq i \leq n-1$ either $|σ(i-1)-σ(i)|\leq k$ or $|σ(i)-σ(i+1)|\leq k$, and it satisfies the \emph{asymmetric two-neighbor $k$-constraint} if for all $2 \leq i \leq n-1$, either $σ(i-1)-σ(i) < k$ or $σ(i+1)-σ(i) < k$. We show that the capacity of the first constraint is $(1+ε)/2$ in case that $k=Θ(n^ε)$ and the capacity of the second constraint is 1 regardless to the value of $k$. We also extend our results and study the capacity of these two constraints combined with error-correction codes in the Kendall's $τ$ metric.
On the Potential of Twitter for Understanding the Tunisia of the Post-Arab Spring
Venue: arXiv cs.SI 2014
Year: 2014

Micro-blogging through Twitter has made information short and to the point, and more importantly systematically searchable. This work is the first of a series in which quotidian observations about Tunisia are obtained using the micro-blogging site Twitter. Data was extracted using the open source Twitter API v1.1. Specific tweets were obtained using functional search operators in particular thematic hash tags, geo-location, date, time and language. The presence of Tunisia in the international tweet stream, the language of communication of Tunisian residents through Twitter as well as Twitter usage across Tunisia are the center of attention of this article.
miRNA and Gene Expression based Cancer Classification using Self- Learning and Co-Training Approaches
Venue: arXiv cs.CE 2014
Year: 2014

miRNA and gene expression profiles have been proved useful for classifying cancer samples. Efficient classifiers have been recently sought and developed. A number of attempts to classify cancer samples using miRNA/gene expression profiles are known in literature. However, the use of semi-supervised learning models have been used recently in bioinformatics, to exploit the huge corpuses of publicly available sets. Using both labeled and unlabeled sets to train sample classifiers, have not been previously considered when gene and miRNA expression sets are used. Moreover, there is a motivation to integrate both miRNA and gene expression for a semi-supervised cancer classification as that provides more information on the characteristics of cancer samples. In this paper, two semi-supervised machine learning approaches, namely self-learning and co-training, are adapted to enhance the quality of cancer sample classification. These approaches exploit the huge public corpuses to enrich the training data. In self-learning, miRNA and gene based classifiers are enhanced independently. While in co-training, both miRNA and gene expression profiles are used simultaneously to provide different views of cancer samples. To our knowledge, it is the first attempt to apply these learning approaches to cancer classification. The approaches were evaluated using breast cancer, hepatocellular carcinoma (HCC) and lung cancer expression sets. Results show up to 20% improvement in F1-measure over Random Forests and SVM classifiers. Co-Training also outperforms Low Density Separation (LDS) approach by around 25% improvement in F1-measure in breast cancer.
Location-Based Reasoning about Complex Multi-Agent Behavior
Venue: arXiv cs.MA 2014
Year: 2014

Recent research has shown that surprisingly rich models of human activity can be learned from GPS (positional) data. However, most effort to date has concentrated on modeling single individuals or statistical properties of groups of people. Moreover, prior work focused solely on modeling actual successful executions (and not failed or attempted executions) of the activities of interest. We, in contrast, take on the task of understanding human interactions, attempted interactions, and intentions from noisy sensor data in a fully relational multi-agent setting. We use a real-world game of capture the flag to illustrate our approach in a well-defined domain that involves many distinct cooperative and competitive joint activities. We model the domain using Markov logic, a statistical-relational language, and learn a theory that jointly denoises the data and infers occurrences of high-level activities, such as a player capturing an enemy. Our unified model combines constraints imposed by the geometry of the game area, the motion model of the players, and by the rules and dynamics of the game in a probabilistically and logically sound fashion. We show that while it may be impossible to directly detect a multi-agent activity due to sensor noise or malfunction, the occurrence of the activity can still be inferred by considering both its impact on the future behaviors of the people involved as well as the events that could have preceded it. Further, we show that given a model of successfully performed multi-agent activities, along with a set of examples of failed attempts at the same activities, our system automatically learns an augmented model that is capable of recognizing success and failure, as well as goals of peoples actions with high accuracy. We compare our approach with other alternatives and show that our unified model, which takes into account not only relationships among individual players, but also relationships among activities over the entire length of a game, although more computationally costly, is significantly more accurate. Finally, we demonstrate that explicitly modeling unsuccessful attempts boosts performance on other important recognition tasks.
Power Control Factor Selection in Uplink OFDMA Cellular Networks
Venue: arXiv cs.IT 2014
Year: 2014

Uplink power control plays a key role on the performance of uplink cellular network. In this work, the power control factor ($\in[0,1]$) is evaluated based on three parameters namely: average transmit power, coverage probability and average rate. In other words, we evaluate power control factor such that average transmit power should be low, coverage probability of cell-edge users should be high and also average rate over all the uplink users should be high. We show through numerical studies that the power control factor should be close to $0.5$ in order to achieve an acceptable trade-off between these three parameters.
Construction of Partial MDS (PMDS) and Sector-Disk (SD) Codes with Two Global Parity Symbols
Venue: arXiv cs.IT 2014
Year: 2014

Partial MDS (PMDS) codes are erasure codes combining local (row) correction with global additional correction of entries, while Sector-Disk (SD) codes are erasure codes that address the mixed failure mode of current RAID systems. It has been an open problem to construct general codes that have the PMDS and the SD properties, and previous work has relied on Monte-Carlo searches. In this paper, we present a general construction that addresses the case of any number of failed disks and in addition, two erased sectors. The construction requires a modest field size. This result generalizes previous constructions extending RAID~5 and RAID~6.
Energy Harvesting in M2M and WSN Space
Venue: arXiv cs.NI 2014
Year: 2014

Energy harvesting or power harvesting or energy scavenging is a process where energy is derived from external sources (e.g. solar power, thermal energy, wind energy, salinity gradients, kinetic energy etc.), captured, and stored for small, wireless autonomous devices, like those used in wearable electronics and wireless sensor networks. This paper is focused to applications of Energy Harvesting in Wireless Sensor Networks. This is going to help the ever growing M2M (Machine to Machine) field where there is an exponential growth of intelligent devices and automatic control of these is of paramount importance.
How Much Frequency Can Be Reused in 5G Cellular Networks---A Matrix Graph Model
Venue: arXiv cs.IT 2014
Year: 2014

The 5th Generation cellular network may have the key feature of smaller cell size and denser resource employment, resulted from diminishing resource and increasing communication demands. However, small cell may result in high interference between cells. Moreover, the random geographic patterns of small cell networks make them hard to analyze, at least excluding schemes in the well-accepted hexagonal grid model. In this paper, a new model---the matrix graph is proposed which takes advantage of the small cell size and high inter-cell interference to reduce computation complexity. This model can simulate real world networks accurately and offers convenience in frequency allocation problems which are usually NP-complete. An algorithm dealing with this model is also given, which asymptotically achieves the theoretical limit of frequency allocation, and has a complexity which decreases with cell size and grows linearly with the network size. This new model is specifically proposed to characterize the next-generation cellular networks.
Maintaining a Large Process Model Aligned with a Process Standard: An Industrial Example
Venue: arXiv cs.SE 2014
Year: 2014

An essential characteristic of mature software and system development organizations is the definition and use of explicit process models. For a number of reasons, it can be valuable to produce new process models by tailoring existing process standards (such as the V-Modell XT). Both process models and standards evolve over time in order to integrate improvements or adapt the process models to context changes. An important challenge for a process engineering team is to keep tailored process models aligned over time with the standards originally used to produce them. This article presents an approach that supports the alignment of process standards evolving in parallel to derived process models, using an actual industrial example to illustrate the problems and potential solutions. We present and discuss the results of a quantitative analysis done to determine whether a strongly tailored model can still be aligned with its parent standard and to assess the potential cost of such an alignment. We close the paper with conclusions and outlook.
Process Model Difference Analysis for Supporting Process Evolution
Venue: arXiv cs.SE 2014
Year: 2014

Software development processes are subject to variations in time and space, variations that can originate from learning effects, differences in application domains, or a number of other causes. Identifying and analyzing such differences is crucial for a variety of process activities, like defining and evolving process standards, or analyzing the compliance of process models to existing standards, among others. In this paper, we show why appropriately identifying, describing, and visualizing differences between process models in order to support such activities is a highly challenging task. We present scenarios that motivate the need for process model difference analysis, and describe the conceptual and technical challenges arising from them. In addition, we sketch an initial tool-based approach implementing difference analysis, and contrast it with similar existing approaches. The results from this paper constitute the requirements for our ongoing development effort, whose objectives we also describe briefly.
Managing Congestion Control in Mobile AD-HOC Network Using Mobile Agents
Venue: arXiv cs.NI 2014
Year: 2014

In mobile adhoc networks, congestion occurs with limited resources. The standard TCP congestion control mechanism is not able to handle the special properties of a shared wireless channel. TCP congestion control works very well on the Internet. But mobile adhoc networks exhibit some unique properties that greatly affect the design of appropriate protocols and protocol stacks in general, and of congestion control mechanism in particular. As it turned out, the vastly differing environment in a mobile adhoc network is highly problematic for standard TCP. Many approaches have been proposed to overcome these difficulties. Mobile agent based congestion control Technique is proposed to avoid congestion in adhoc network. When mobile agent travels through the network, it can select a less-loaded neighbor node as its next hop and update the routing table according to the node congestion status. With the aid of mobile agents, the nodes can get the dynamic network topology in time. In this paper, a mobile agent based congestion control mechanism is presented.
A Genetic Algorithm to Optimize a Tweet for Retweetability
Venue: arXiv cs.NE 2014
Year: 2014

Twitter is a popular microblogging platform. When users send out messages, other users have the ability to forward these messages to their own subgraph. Most research focuses on increasing retweetability from a node's perspective. Here, we center on improving message style to increase the chance of a message being forwarded. To this end, we simulate an artificial Twitter-like network with nodes deciding deterministically on retweeting a message or not. A genetic algorithm is used to optimize message composition, so that the reach of a message is increased. When analyzing the algorithm's runtime behavior across a set of different node types, we find that the algorithm consistently succeeds in significantly improving the retweetability of a message.
Iterative pre-distortion of the non-linear satellite channel
Venue: arXiv cs.IT 2014
Year: 2014

Digital Video Broadcasting - Satellite - Second Generation (DVB-S2) is the current European standard for satellite broadcast and broadband communications. It relies on high order modulations up to 32-amplitude/phase-shift-keying (APSK) in order to increase the system spectral efficiency. Unfortunately, as the modulation order increases, the receiver becomes more sensitive to physical layer impairments, and notably to the distortions induced by the power amplifier and the channelizing filters aboard the satellite. Pre-distortion of the non-linear satellite channel has been studied for many years. However, the performance of existing pre-distortion algorithms generally becomes poor when high-order modulations are used on a non-linear channel with a long memory. In this paper, we investigate a new iterative method that pre-distorts blocks of transmitted symbols so as to minimize the Euclidian distance between the transmitted and received symbols. We also propose approximations to relax the pre-distorter complexity while keeping its performance acceptable.
Light spanners for snowflake metrics
Venue: arXiv cs.CG 2014
Year: 2014

A classic result in the study of spanners is the existence of light low-stretch spanners for Euclidean spaces. These spanners ahve arbitrary low stretch, and weight only a constant factor greater than that of the minimum spanning tree of the points (with dependence on the stretch and Euclidean dimention). A central open problem in this field asks whether other spaces admit low weight spanners as well - for example metric space with low intrinsic dimension - yet only a handful of results of this type are known. In this paper, we consider snowflake metric spaces of low intrinsic dimension. The α-snowflake of a metric (X,δ) is the metric (X,$δ^α$) for 0<α<1. By utilizing an approach completely different than those used for Euclidean spaces, we demonstrate that snowflake metrics admit light spanners. Further, we show that the spanner is of diameter O($\log$n), a result not possible for Euclidean spaces. As an immediate corollary to our spanner, we obtain dramatic improvments in algorithms for the traveling salesman problem in this setting, achieving a polynomial-time approximation scheme with near-linear runtime. Along the way, we show that all ${\ell}_p$ spaces admit light spanners, a result of interest in its own right.
Symmetric Two-User Gaussian Interference Channel with Common Messages
Venue: arXiv cs.IT 2014
Year: 2014

We consider symmetric two-user Gaussian interference channel with common messages. We derive an upper bound on the sum capacity, and show that the upper bound is tight in the low interference regime, where the optimal transmission scheme is to send no common messages and each receiver treats interference as noise. Our result shows that although the availability of common messages provides a cooperation opportunity for transmitters, in the low interference regime the presence of common messages does not help increase the sum capacity.
Fully Online Grammar Compression in Constant Space
Venue: arXiv cs.DS 2014
Year: 2014

We present novel variants of fully online LCA (FOLCA), a fully online grammar compression that builds a straight line program (SLP) and directly encodes it into a succinct representation in an online manner. FOLCA enables a direct encoding of an SLP into a succinct representation that is asymptotically equivalent to an information theoretic lower bound for representing an SLP (Maruyama et al., SPIRE'13). The compression of FOLCA takes linear time proportional to the length of an input text and its working space depends only on the size of the SLP, which enables us to apply FOLCA to large-scale repetitive texts. Recent repetitive texts, however, include some noise. For example, current sequencing technology has significant error rates, which embeds noise into genome sequences. For such noisy repetitive texts, FOLCA working in the SLP size consumes a large amount of memory. We present two variants of FOLCA working in constant space by leveraging the idea behind stream mining techniques. Experiments using 100 human genomes corresponding to about 300GB from the 1000 human genomes project revealed the applicability of our method to large-scale, noisy repetitive texts.
A Simple Software Application for Simulating Commercially Available Solar Panels
Venue: arXiv cs.CE 2014
Year: 2014

This article addresses the formulation and validation of a simple PC based software application developed for simulating commercially available solar panels. The important feature of this application is its capability to produce speedy results in the form of solar panel output characteristics at given environmental conditions by using minimal input data. Besides, it is able to deliver critical information about the maximum power point of the panel at a given environmental condition in quick succession. The application is based on a standard equation which governs solar panels and works by means of estimating unknown parameters in the equation to fit a given solar panel. The process of parameter estimation is described in detail with the aid of equations and data of a commercial solar panel. A validation of obtained results for commercial solar panels is also presented by comparing the panel manufacturers' results with the results generated by the application. In addition, implications of the obtained results are discussed along with possible improvements to the developed software application.
Supporting MOOC Instruction with Social Network Analysis
Venue: arXiv cs.SI 2014
Year: 2014

With an expansive and ubiquitously available gold mine of educational data, Massive Open Online courses (MOOCs) have become the an important foci of learning analytics research. In this paper, we investigate potential reasons as to why are these digitalized learning repositories being plagued with huge attrition rates. We analyze an ongoing online course offered in Coursera using a social network perspective, with an objective to identify students who are actively participating in course discussions and those who are potentially at a risk of dropping off. We additionally perform extensive forum analysis to visualize student's posting patterns longitudinally. Our results provide insights that can assist educational designers in establishing a pedagogical basis for decision-making while designing MOOCs. We infer prominent characteristics about the participation patterns of distinct groups of students in the networked learning community, and effectively discover important discussion threads. These methods can, despite the otherwise prohibitive number of students involved, allow an instructor to leverage forum behavior to identify opportunities for support.
Simulating Behaviours to face up an Emergency Evacuation
Venue: arXiv cs.OH 2014
Year: 2014

Computer based models describing pedestrian behavior in an emergency evacuation play a vital role in the development of active strategies that minimize the evacuation time when a closed area must be evacuated. The reference model has a hybrid structure where the dynamics of fire and smoke propagation are modeled by means of Cellular Automata and for simulating people's behavior we are using Intelligent Agents. The model consists of two sub-models, called environmental and pedestrian ones. As part of the pedestrian model, this paper concentrates in a methodology that is able to model some of the frequently observed human's behaviors in evacuation exercises. Each agent will perceive what is happening around, select the options that exist in that context and then it makes a decision that will reflect its ability to cope with an emergency evacuation, called in this work, behavior. We also developed simple exercises where the model is applied to the simulation of an evacuation due to a potential hazard, such as fire, smoke or some kind of collapse.
Optimal Intelligent Control for Wind Turbulence Rejection in WECS Using ANNs and Genetic Fuzzy Approach
Venue: arXiv cs.SY 2014
Year: 2014

One of the disadvantages in Connection of wind energy conversion systems (WECSs) to transmission networks is plentiful turbulence of wind speed. Therefore effects of this problem must be controlled. Nowadays, pitch-controlled WECSs are increasingly used for variable speed and pitch wind turbines. Megawatt class wind turbines generally turn at variable speed in wind farm. Thus turbine operation must be controlled in order to maximize the conversion efficiency below rated power and reduce loading on the drive-train. Due to random and non-linear nature of the wind turbulence and the ability of Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) Artificial Neural Networks (ANNs) in the modeling and control of this turbulence, in this study, widespread changes of wind have been perused using MLP and RBF artificial NNs. In addition in this study, a new genetic fuzzy system has been successfully applied to identify disturbance wind in turbine input. Thus output power has been regulated in optimal and nominal range by pitch angle regulation. Consequently, our proposed approaches have regulated output aerodynamic power and torque in the nominal rang.
Edge detection of binary images using the method of masks
Venue: arXiv cs.CV 2014
Year: 2014

In this work the method of masks, creating and using of inverted image masks, together with binary operation of image data are used in edge detection of binary images, monochrome images, which yields about 300 times faster than ordinary methods. The method is divided into three stages: Mask construction, Fundamental edge detection, and Edge Construction Comparison with an ordinary method and a fuzzy based method is carried out.