OpenRaiser/Intern-Atlas huggingface dataset:
Information and Energy Cooperation in Cognitive Radio Networks
Venue: arXiv cs.IT 2014
Year: 2014

Cooperation between the primary and secondary systems can improve the spectrum efficiency in cognitive radio networks. The key idea is that the secondary system helps to boost the primary system's performance by relaying and in return the primary system provides more opportunities for the secondary system to access the spectrum. In contrast to most of existing works that only consider information cooperation, this paper studies joint information and energy cooperation between the two systems, i.e., the primary transmitter sends information for relaying and feeds the secondary system with energy as well. This is particularly useful when the secondary transmitter has good channel quality to the primary receiver but is energy constrained. We propose and study three schemes that enable this cooperation. Firstly, we assume there exists an ideal backhaul between the two systems for information and energy transfer. We then consider two wireless information and energy transfer schemes from the primary transmitter to the secondary transmitter using power splitting and time splitting energy harvesting techniques, respectively. For each scheme, the optimal and zero-forcing solutions are derived. Simulation results demonstrate promising performance gain for both systems due to the additional energy cooperation. It is also revealed that the power splitting scheme can achieve larger rate region than the time splitting scheme when the efficiency of the energy transfer is sufficiently large.
Cortex simulation system proposal using distributed computer network environments
Venue: arXiv cs.AI 2014
Year: 2014

In the dawn of computer science and the eve of neuroscience we participate in rebirth of neuroscience due to new technology that allows us to deeply and precisely explore whole new world that dwells in our brains.
Deficit Round-Robin-Based ISP Traffic Control Scheme Enabling Excess Bandwidth Allocation in Shared Access Networks
Venue: arXiv cs.NI 2014
Year: 2014

In shared access shaping subscriber traffic based on token bucket by ISPs wastes network resources when there are few active subscribers, because it cannot allocate excess bandwidth in the long term. To address it, traffic control schemes based on core-stateless fair queueing (CSFQ) and token bucket meters (TBMs) have been proposed, which can allocate excess bandwidth among active subscribers proportional to their token generation rates. Using FIFO queue for all packets, however, degrades the short-term performance of conformant traffic due to the presence of non-conformant packets already in the queue. Also, the rate estimation based on exponential averaging makes it difficult to react to rapid changes in traffic conditions. In this paper we propose a new traffic control scheme based on deficit round-robin (DRR) and TBMs to guarantee the quality of service of conformant packets in all time scales while allocating excess bandwidth among active subscribers proportional to their token generation rates, whose advantages over the CSFQ-based schemes are demonstrated through simulation results.
The Lyapunov Concept of Stability from the Standpoint of Poincare Approach: General Procedure of Utilization of Lyapunov Functions for Non-Linear Non-Autonomous Parametric Differential Inclusions
Venue: arXiv cs.SY 2014
Year: 2014

The objective of the research is to develop a general method of constructing Lyapunov functions for non-linear non-autonomous differential inclusions described by ordinary differential equations with parameters. The goal has been attained through the following ideas and tools. First, three-point Poincare strategy of the investigation of differential equations and manifolds has been used. Second, the geometric-topological structure of the non-linear non-autonomous parametric differential inclusions has been presented and analyzed in the framework of hierarchical fiber bundles. Third, a special canonizing transformation of the differential inclusions that allows to present them in special canonical form, for which certain standard forms of Lyapunov functions exist, has been found. The conditions establishing the relation between the local asymptotical stability of two corresponding particular integral curves of a given differential inclusion in its initial and canonical forms are ascertained. The global asymptotical stability of the entire free dynamical systems as some restrictions of a given parametric differential inclusion and the whole latter one per se has been investigated in terms of the classificational stability of the typical fiber of the meta-bundle. There have discussed the prospects of development and modifications of the Lyapunov second method in the light of the discovery of the new features of Lyapunov functions.
Step and Search Control Method to Track the Maximum Power in Wind Energy Conversion Systems A Study
Venue: arXiv cs.SY 2014
Year: 2014

A simple step and search control strategy for extracting maximum output power from grid connected Variable Speed Wind Energy Conversion System (VSWECS) is implemented in this work. This system consists of a variable speed wind turbine coupled to a Permanent Magnet Synchronous Generator (PMSG) through a gear box, a DC-DC boost converter and a hysteresis current controlled Voltage Source Converter (VSC). The Maximum Power Point Tracking (MPPT) extracts maximum power from the wind turbine from cut-into rated wind velocity by sensing only by DC link power. This system can be connected to a micro-grid. Also it can be used for supplying an isolated local load by means of converting the output of Permanent Magnet Synchronous Generator (PMSG) to DC and then convert to AC by means of hysteresis current controlled Voltage Source Converter (VSI).
Random Matrices and Erasure Robust Frames
Venue: arXiv cs.IT 2014
Year: 2014

Data erasure can often occur in communication. Guarding against erasures involves redundancy in data representation. Mathematically this may be achieved by redundancy through the use of frames. One way to measure the robustness of a frame against erasures is to examine the worst case condition number of the frame with a certain number of vectors erased from the frame. The term {\em numerically erasure-robust frames (NERFs)} was introduced in \cite{FicMix12} to give a more precise characterization of erasure robustness of frames. In the paper the authors established that random frames whose entries are drawn independently from the standard normal distribution can be robust against up to approximately 15\% erasures, and asked whether there exist frames that are robust against erasures of more than 50\%. In this paper we show that with very high probability random frames are, independent of the dimension, robust against any amount of erasures as long as the number of remaining vectors is at least $1+δ$ times the dimension for some $δ_0>0$. This is the best possible result, and it also implies that the proportion of erasures can arbitrarily close to 1 while still maintaining robustness. Our result depends crucially on a new estimate for the smallest singular value of a rectangular random matrix with independent standard normal entries.
Brain Tumor Detection Based On Mathematical Analysis and Symmetry Information
Venue: arXiv cs.CV 2014
Year: 2014

Image segmentation some of the challenging issues on brain magnetic resonance image tumor segmentation caused by the weak correlation between magnetic resonance imaging intensity and anatomical meaning.With the objective of utilizing more meaningful information to improve brain tumor segmentation,an approach which employs bilateral symmetry information as an additional feature for segmentation is proposed.This is motivated by potential performance improvement in the general automatic brain tumor segmentation systems which are important for many medical and scientific applications.Brain Magnetic Resonance Imaging segmentation is a complex problem in the field of medical imaging despite various presented methods.MR image of human brain can be divided into several sub-regions especially soft tissues such as gray matter,white matter and cerebra spinal fluid.Although edge information is the main clue in image segmentation,it cannot get a better result in analysis the content of images without combining other information.Our goal is to detect the position and boundary of tumors automatically.Experiments were conducted on real pictures,and the results show that the algorithm is flexible and convenient.
A Tablet Based Learning Environment
Venue: arXiv cs.CY 2014
Year: 2014

Pen computing tools such as Tablet PC, Tablet Monitors and its various supporting software tool offer another dimension to enhance our today's digitally integrated and connected classroom learning environment. This paper first reviews the various state-of-the-art pen-computing hardware and software that have been applied in the classroom setting to introduce student-centric learning, collaboration and making annotations and designing classroom activities easier. We then propose a new classroom environment which is fully equipped with Tablet devices and the supporting software tools for the goals of 1) easy electronic ink annotations with least constraints; 2) enhanced active learning with timely feedback; 3) enhanced student collaborations and 4) lecture recording. The classroom has been put into practical teaching and learning environment as a pilot project in our higher learning environment. After overcoming the initial learning curves, the environment received positive feedbacks from the teaching faculties as well as from the students.
Ensemble Detection of Single & Multiple Events at Sentence-Level
Venue: arXiv cs.CL 2014
Year: 2014

Event classification at sentence level is an important Information Extraction task with applications in several NLP, IR, and personalization systems. Multi-label binary relevance (BR) are the state-of-art methods. In this work, we explored new multi-label methods known for capturing relations between event types. These new methods, such as the ensemble Chain of Classifiers, improve the F1 on average across the 6 labels by 2.8% over the Binary Relevance. The low occurrence of multi-label sentences motivated the reduction of the hard imbalanced multi-label classification problem with low number of occurrences of multiple labels per instance to an more tractable imbalanced multiclass problem with better results (+ 4.6%). We report the results of adding new features, such as sentiment strength, rhetorical signals, domain-id (source-id and date), and key-phrases in both single-label and multi-label event classification scenarios.
Topologies of Stochastic Markov Models: Computational Aspects
Venue: arXiv cs.FL 2014
Year: 2014

In this paper we propose two behavioral distances that support approximate reasoning on Stochastic Markov Models (SMMs), that are continuous-time stochastic transition systems where the residence time on each state is described by a generic probability measure on the positive real line. In particular, we study the problem of measuring the behavioral dissimilarity of two SMMs against linear real-time specifications expressed as Metric Temporal Logic (MTL) formulas or Deterministic Timed-Automata (DTA). The most natural choice for such a distance is the one that measures the maximal difference that can be observed comparing two SMMs with respect to their probability of satisfying an arbitrary specification. We show that computing this metric is NP-hard. In addition, we show that any algorithm that approximates the distance within a certain absolute error, depending on the size of the SMMs, is NP-hard. Nevertheless, we introduce an alternative distance, based on the Kantorovich metric, that is an over-approximation of the former and we show that, under mild assumptions on the residence time distributions, it can be computed in polynomial time.
Decentralized Primary Frequency Control in Power Networks
Venue: arXiv cs.SY 2014
Year: 2014

We augment existing generator-side primary frequency control with load-side control that are local, ubiquitous, and continuous. The mechanisms on both the generator and the load sides are decentralized in that their control decisions are functions of locally measurable frequency deviations. These local algorithms interact over the network through nonlinear power flows. We design the local frequency feedback control so that any equilibrium point of the closed-loop system is the solution to an optimization problem that minimizes the total generation cost and user disutility subject to power balance across entire network. With Lyapunov method we derive a sufficient condition ensuring an equilibrium point of the closed-loop system is asymptotically stable. Simulation demonstrates improvement in both the transient and steady-state performance over the traditional control only on the generators, even when the total control capacity remains the same.
Why Do You Spread This Message? Understanding Users Sentiment in Social Media Campaigns
Venue: arXiv cs.SI 2014
Year: 2014

Twitter has been increasingly used for spreading messages about campaigns. Such campaigns try to gain followers through their Twitter accounts, influence the followers and spread messages through them. In this paper, we explore the relationship between followers sentiment towards the campaign topic and their rate of retweeting of messages generated by the campaign. Our analysis with followers of multiple social-media campaigns found statistical significant correlations between such sentiment and retweeting rate. Based on our analysis, we have conducted an online intervention study among the followers of different social-media campaigns. Our study shows that targeting followers based on their sentiment towards the campaign can give higher retweet rate than a number of other baseline approaches.
Pumping lemma and Ogden lemma for displacement context-free grammars
Venue: arXiv cs.FL 2014
Year: 2014

The pumping lemma and Ogden lemma offer a powerful method to prove that a particular language is not context-free. In 2008 Kanazawa proved an analogue of pumping lemma for well-nested multiple-context free languages. However, the statement of lemma is too weak for practical usage. We prove a stronger variant of pumping lemma and an analogue of Ogden lemma for this language family. We also use these statements to prove that some natural context-sensitive languages cannot be generated by tree-adjoining grammars.
A Theory of Complexity, Condition and Roundoff
Venue: arXiv cs.CC 2014
Year: 2014

We develop a theory of complexity for numerical computations that takes into account the condition of the input data and allows for roundoff in the computations. We follow the lines of the theory developed by Blum, Shub, and Smale for computations over R (which in turn followed those of the classical, discrete, complexity theory as laid down by Cook, Karp, and Levin among others). In particular, we focus on complexity classes of decision problems and paramount among them, on appropriate versions of the classes P, NP and EXP of polynomial, nondeterministic polynomial, and exponential time, respectively. We prove some basic relationships between these complexity classes and exhibit natural NP-complete problems.
Classroom Video Assessment and Retrieval via Multiple Instance Learning
Venue: arXiv cs.IR 2014
Year: 2014

We propose a multiple instance learning approach to content-based retrieval of classroom video for the purpose of supporting human assessing the learning environment. The key element of our approach is a mapping between the semantic concepts of the assessment system and features of the video that can be measured using techniques from the fields of computer vision and speech analysis. We report on a formative experiment in content-based video retrieval involving trained experts in the Classroom Assessment Scoring System, a widely used framework for assessment and improvement of learning environments. The results of this experiment suggest that our approach has potential application to productivity enhancement in assessment and to broader retrieval tasks.
Arguments for Nested Patterns in Neural Ensembles
Venue: arXiv cs.NE 2014
Year: 2014

This paper describes a relatively simple way of allowing a brain model to self-organise its concept patterns through nested structures. Time is a key element and a simulator would be able to show how patterns may form and then fire in sequence, as part of a search or thought process. It uses a very simple equation to show how the inhibitors in particular, can switch off certain areas, to allow other areas to become the prominent ones and thereby define the current brain state. This allows for a small amount of control over what appears to be a chaotic structure inside of the brain. It is attractive because it is still mostly mechanical and therefore can be added as an automatic process, or the modelling of that. The paper also describes how the nested pattern structure can be used as a basic counting mechanism.
A Tiered Move-making Algorithm for General Non-submodular Pairwise Energies
Venue: arXiv cs.CV 2014
Year: 2014

A large number of problems in computer vision can be modelled as energy minimization problems in a Markov Random Field (MRF) or Conditional Random Field (CRF) framework. Graph-cuts based $α$-expansion is a standard move-making method to minimize the energy functions with sub-modular pairwise terms. However, certain problems require more complex pairwise terms where the $α$-expansion method is generally not applicable. In this paper, we propose an iterative {\em tiered move making algorithm} which is able to handle general pairwise terms. Each move to the next configuration is based on the current labeling and an optimal tiered move, where each tiered move requires one application of the dynamic programming based tiered labeling method introduced in Felzenszwalb et. al. \cite{tiered_cvpr_felzenszwalbV10}. The algorithm converges to a local minimum for any general pairwise potential, and we give a theoretical analysis of the properties of the algorithm, characterizing the situations in which we can expect good performance. We first evaluate our method on an object-class segmentation problem using the Pascal VOC-11 segmentation dataset where we learn general pairwise terms. Further we evaluate the algorithm on many other benchmark labeling problems such as stereo, image segmentation, image stitching and image denoising. Our method consistently gets better accuracy and energy values than alpha-expansion, loopy belief propagation (LBP), quadratic pseudo-boolean optimization (QPBO), and is competitive with TRWS.
Do the Fix Ingredients Already Exist? An Empirical Inquiry into the Redundancy Assumptions of Program Repair Approaches
Venue: arXiv cs.SE 2014
Year: 2014

Much initial research on automatic program repair has focused on experimental results to probe their potential to find patches and reduce development effort. Relatively less effort has been put into understanding the hows and whys of such approaches. For example, a critical assumption of the GenProg technique is that certain bugs can be fixed by copying and re-arranging existing code. In other words, GenProg assumes that the fix ingredients already exist elsewhere in the code. In this paper, we formalize these assumptions around the concept of ''temporal redundancy''. A temporally redundant commit is only composed of what has already existed in previous commits. Our experiments show that a large proportion of commits that add existing code are temporally redundant. This validates the fundamental redundancy assumption of GenProg.
An efficiency dependency parser using hybrid approach for tamil language
Venue: arXiv cs.CL 2014
Year: 2014

Natural language processing is a prompt research area across the country. Parsing is one of the very crucial tool in language analysis system which aims to forecast the structural relationship among the words in a given sentence. Many researchers have already developed so many language tools but the accuracy is not meet out the human expectation level, thus the research is still exists. Machine translation is one of the major application area under Natural Language Processing. While translation between one language to another language, the structure identification of a sentence play a key role. This paper introduces the hybrid way to solve the identification of relationship among the given words in a sentence. In existing system is implemented using rule based approach, which is not suited in huge amount of data. The machine learning approaches is suitable for handle larger amount of data and also to get better accuracy via learning and training the system. The proposed approach takes a Tamil sentence as an input and produce the result of a dependency relation as a tree like structure using hybrid approach. This proposed tool is very helpful for researchers and act as an odd-on improve the quality of existing approaches.
Recoloring graphs via tree decompositions
Venue: arXiv cs.DM 2014
Year: 2014

Let $k$ be an integer. Two vertex $k$-colorings of a graph are \emph{adjacent} if they differ on exactly one vertex. A graph is \emph{$k$-mixing} if any proper $k$-coloring can be transformed into any other through a sequence of adjacent proper $k$-colorings. Jerrum proved that any graph is $k$-mixing if $k$ is at least the maximum degree plus two. We first improve Jerrum's bound using the grundy number, which is the worst number of colors in a greedy coloring. Any graph is $(tw+2)$-mixing, where $tw$ is the treewidth of the graph (Cereceda 2006). We prove that the shortest sequence between any two $(tw+2)$-colorings is at most quadratic (which is optimal up to a constant factor), a problem left open in Bonamy et al. (2012). We also prove that given any two $(χ(G)+1)$-colorings of a cograph (resp. distance-hereditary graph) $G$, we can find a linear (resp. quadratic) sequence between them. In both cases, the bounds cannot be improved by more than a constant factor for a fixed $χ(G)$. The graph classes are also optimal in some sense: one of the smallest interesting superclass of distance-hereditary graphs corresponds to comparability graphs, for which no such property holds (even when relaxing the constraint on the length of the sequence). As for cographs, they are equivalently the graphs with no induced $P_4$, and there exist $P_5$-free graphs that admit no sequence between two of their $(χ(G)+1)$-colorings. All the proofs are constructivist and lead to polynomial-time recoloring algorithm
Implementation of an Automatic Sign Language Lexical Annotation Framework based on Propositional Dynamic Logic
Venue: arXiv cs.CL 2014
Year: 2014

In this paper, we present the implementation of an automatic Sign Language (SL) sign annotation framework based on a formal logic, the Propositional Dynamic Logic (PDL). Our system relies heavily on the use of a specific variant of PDL, the Propositional Dynamic Logic for Sign Language (PDLSL), which lets us describe SL signs as formulae and corpora videos as labeled transition systems (LTSs). Here, we intend to show how a generic annotation system can be constructed upon these underlying theoretical principles, regardless of the tracking technologies available or the input format of corpora. With this in mind, we generated a development framework that adapts the system to specific use cases. Furthermore, we present some results obtained by our application when adapted to one distinct case, 2D corpora analysis with pre-processed tracking information. We also present some insights on how such a technology can be used to analyze 3D real-time data, captured with a depth device.