OpenRaiser/Intern-Atlas huggingface dataset:
Balancing Communication for Multi-party Interactive Coding
Venue: arXiv cs.DS 2015
Year: 2015

We consider interactive coding in a setting where $n$ parties wish to compute a joint function of their inputs via an interactive protocol over imperfect channels. We assume that adversarial errors can comprise a $\mathcal{O}(\frac{1}{n})$ fraction of the total communication, occurring anywhere on the communication network. Our goal is to maintain a constant multiplicative overhead in the total communication required, as compared to the error-free setting, and also to balance the workload over the different parties. We build upon the prior protocol of Jain, Kalai, and Lewko, but while that protocol relies on a single coordinator to shoulder a heavy burden throughout the protocol, we design a mechanism to pass the coordination duties from party to party, resulting in a more even distribution of communication over the course of the computation.
Predictive model of the human muscle fatigue: application to repetitive push-pull tasks with light external load
Venue: arXiv cs.RO 2015
Year: 2015

Repetitive tasks in industrial works may contribute to health problems among operators, such as musculo-skeletal disorders, in part due to insufficient control of muscle fatigue. In this paper, a predictive model of fatigue is proposed for repetitive push/pull operations. Assumptions generally accepted in the literature are first explicitly set in this framework. Then, an earlier static fatigue model is recalled and extended to quasi-static situations. Specifically, the maximal torque that can be generated at a joint is not considered as constant, but instead varies over time accordingly to the operator's changing posture. The fatigue model is implemented with this new consideration and evaluated in a simulation of push/pull operation. Reference to this paper should be made as follows: Sakka, S., Chablat, D., Ma, R. and Bennis, F. (2015) 'Predictive model of the human muscle fatigue: application to repetitive push-pull tasks with light external load', Int.
Automated Linear Function Submission-based Double Auction as Bottom-up Real-Time Pricing in a Regional Prosumers' Electricity Network
Venue: arXiv cs.SY 2015
Year: 2015

A linear function submission-based double-auction (LFS-DA) mechanism for a regional electricity network is proposed in this paper. Each agent in the network is equipped with a battery and a generator. Each agent simultaneously becomes a producer and consumer of electricity, i.e., a prosumer and trades electricity in the regional market at a variable price. In the LFS-DA, each agent uses linear demand and supply functions when they submit bids and asks to an auctioneer in the regional market.The LFS-DA can achieve an exact balance between electricity demand and supply for each time slot throughout the learning phase and was shown capable of solving the primal problem of maximizing the social welfare of the network without any central price setter, e.g., a utility or a large electricity company, in contrast with conventional real-time pricing (RTP). This paper presents a clarification of the relationship between the RTP algorithm derived on the basis of a dual decomposition framework and LFS-DA. Specifically, we proved that the changes in the price profile of the LFS-DA mechanism are equal to those achieved by the RTP mechanism derived from the dual decomposition framework except for a constant factor.
Mining MOOC Clickstreams: On the Relationship Between Learner Behavior and Performance
Venue: arXiv cs.SI 2015
Year: 2015

We study student behavior and performance in two Massive Open Online Courses (MOOCs). In doing so, we present two frameworks by which video-watching clickstreams can be represented: one based on the sequence of events created, and another on the sequence of positions visited. With the event-based framework, we extract recurring subsequences of student behavior, which contain fundamental characteris- tics such as reflecting (i.e., repeatedly playing and pausing) and revising (i.e., plays and skip backs). We find that some of these behaviors are significantly associated with whether a user will be Correct on First Attempt (CFA) or not in answering quiz questions. With the position-based framework, we then devise models for performance. In evaluating these through CFA prediction, we find that three of them can substantially improve prediction quality in terms of accuracy and F1, which underlines the ability to relate behavior to performance. Since our prediction considers videos individually, these benefits also suggest that our models are useful in situations where there is limited training data, e.g., for early detection or in short courses.
Algorithm for Back-up and Authentication of Data Stored on Cloud
Venue: arXiv cs.DC 2015
Year: 2015

Everyday a huge amount of data is generated in Cloud Computing. The maintenance of this electronic data needs some extremely efficient services. There is a need to properly collect this data, check for its authenticity and develop proper backups is needed. The Objective of this paper is to provide Response Server, some solution for the backup of data and its restoration, using the Cloud. Thecollection of the data is to be done from the client and then the data should be sent to a central location. This process is a platform independent one. The data can then be used as required. The Remote Backup Server facilitates the collection of information from any remote location and provides services to recover the data in case of loss. The authentication of the user is done by using the Asymmetric key algorithm which will in turn leads to the authentication of the data.
Estimation-Throughput Tradeoff for Underlay Cognitive Radio Systems
Venue: arXiv cs.IT 2015
Year: 2015

Understanding the performance of cognitive radio systems is of great interest. To perform dynamic spectrum access, different paradigms are conceptualized in the literature. Of these, Underlay System (US) has caught much attention in the recent past. According to US, a power control mechanism is employed at the Secondary Transmitter (ST) to constrain the interference at the Primary Receiver (PR) below a certain threshold. However, it requires the knowledge of channel towards PR at the ST. This knowledge can be obtained by estimating the received power, assuming a beacon or a pilot channel transmission by the PR. This estimation is never perfect, hence the induced error may distort the true performance of the US. Motivated by this fact, we propose a novel model that captures the effect of channel estimation errors on the performance of the system. More specifically, we characterize the performance of the US in terms of the estimation-throughput tradeoff. Furthermore, we determine the maximum achievable throughput for the secondary link. Based on numerical analysis, it is shown that the conventional model overestimates the performance of the US.
Robust Inference for State-Space Models with Skewed Measurement Noise
Venue: arXiv cs.SY 2015
Year: 2015

Filtering and smoothing algorithms for linear discrete-time state-space models with skewed and heavy-tailed measurement noise are presented. The algorithms use a variational Bayes approximation of the posterior distribution of models that have normal prior and skew-t-distributed measurement noise. The proposed filter and smoother are compared with conventional low-complexity alternatives in a simulated pseudorange positioning scenario. In the simulations the proposed methods achieve better accuracy than the alternative methods, the computational complexity of the filter being roughly 5 to 10 times that of the Kalman filter.
Metadata in SDN API
Venue: arXiv cs.NI 2015
Year: 2015

This paper discusses the system aspects of development of applied programming interfaces in Software-Defined Networking (SDN). Almost all existing SDN interfaces use so-called Representational State Transfer (REST) services as a basic model. This model is simple and straightforward for developers, but often does not support the information (metadata) necessary for programming automation. In this article, we cover the issues of representation of metadata in the SDN API.
Superpixelizing Binary MRF for Image Labeling Problems
Venue: arXiv cs.CV 2015
Year: 2015

Superpixels have become prevalent in computer vision. They have been used to achieve satisfactory performance at a significantly smaller computational cost for various tasks. People have also combined superpixels with Markov random field (MRF) models. However, it often takes additional effort to formulate MRF on superpixel-level, and to the best of our knowledge there exists no principled approach to obtain this formulation. In this paper, we show how generic pixel-level binary MRF model can be solved in the superpixel space. As the main contribution of this paper, we show that a superpixel-level MRF can be derived from the pixel-level MRF by substituting the superpixel representation of the pixelwise label into the original pixel-level MRF energy. The resultant superpixel-level MRF energy also remains submodular for a submodular pixel-level MRF. The derived formula hence gives us a handy way to formulate MRF energy in superpixel-level. In the experiments, we demonstrate the efficacy of our approach on several computer vision problems.
Evolutionary Dynamics of Complex Networks: Theory, Methods and Applications
Venue: arXiv cs.SI 2015
Year: 2015

We propose new direction to understanding evolutionary dynamics of complex networks using two different types of collaboration networks: academic collaboration networks; and, disaster collaboration networks. The results show that academic collaboration network has all properties of small-world networks and can be considered a real small-world network with the result of its structural analysis maybe being extendable for other real-networks who share the common grounds of small-world properties.
Yara Parser: A Fast and Accurate Dependency Parser
Venue: arXiv cs.CL 2015
Year: 2015

Dependency parsers are among the most crucial tools in natural language processing as they have many important applications in downstream tasks such as information retrieval, machine translation and knowledge acquisition. We introduce the Yara Parser, a fast and accurate open-source dependency parser based on the arc-eager algorithm and beam search. It achieves an unlabeled accuracy of 93.32 on the standard WSJ test set which ranks it among the top dependency parsers. At its fastest, Yara can parse about 4000 sentences per second when in greedy mode (1 beam). When optimizing for accuracy (using 64 beams and Brown cluster features), Yara can parse 45 sentences per second. The parser can be trained on any syntactic dependency treebank and different options are provided in order to make it more flexible and tunable for specific tasks. It is released with the Apache version 2.0 license and can be used for both commercial and academic purposes. The parser can be found at https://github.com/yahoo/YaraParser.
Approximate Joint MAP Detection of Co-Channel Signals
Venue: arXiv cs.IT 2015
Year: 2015

We consider joint detection of co-channel signals---specifically, signals which do not possess a natural separability due to, for example, the multiple access technique or the use of multiple antennas. Iterative joint detection and decoding is a well known approach for utilizing the error correction code to improve detection performance. However, the joint maximum a posteriori probability (MAP) detector may be prohibitively complex, especially in a multipath channel. In this paper, we present an approximation to the joint MAP detector motivated by a factor graph model of the received signal. The proposed algorithm is designed to approximate the joint MAP detector as closely as possible within the computational capability of the receiver.
Auction-based Incentive Mechanisms for Dynamic Mobile Ad-Hoc Crowd Service
Venue: arXiv cs.NI 2015
Year: 2015

We investigate a type of emerging user-assisted mobile applications or services, referred to as Dynamic Mobile Ad-hoc Crowd Service (DMACS), such as collaborative streaming via smartphones or location privacy protection through a crowd of smartphone users. Such services are provided and consumed by users carrying smart mobile devices (e.g., smartphones) who are in close proximity of each other (e.g., within Bluetooth range). Users in a DMACS system dynamically arrive and depart over time, and are divided into multiple possibly overlapping groups according to radio range constraints. Crucial to the success of such systems is a mechanism that incentivizes users' participation and ensures fair trading. In this paper, we design a multi-market, dynamic double auction mechanism, referred to as M-CHAIN, and show that it is truthful, feasible, individual-rational, no-deficit, and computationally efficient. The novelty and significance of M-CHAIN is that it addresses and solves the fair trading problem in a multi-group or multi-market dynamic double auction problem which naturally occurs in a mobile wireless environment. We demonstrate its efficiency via simulations based on generated user patterns (stochastic arrivals, random market clustering of users) and real-world traces.
Low latency protocol for transmission of measurement data from FPGA to Linux computer via 10 Gbps Ethernet link
Venue: arXiv cs.NI 2015
Year: 2015

This paper presents FADE-10G - an integrated solution for modern multichannel measurement systems. Its main aim is a low latency, reliable transmission of measurement data from FPGA-based front-end electronic boards (FEBs) to a computer-based node in the Data Acquisition System (DAQ), using a standard Ethernet 1 Gbps or 10 Gbps link. In addition to transmission of data, the system allows the user to send reliably simple control commands from DAQ to FEB and to receive responses. The aim of the work is to provide a possible simple base solution, which can be adapted by the end user to his or her particular needs. Therefore, the emphasis is put on the minimal consumption of FPGA resources in FEB and the minimal CPU load in the DAQ computer. The open source implementation of the FPGA IP core and the Linux kernel driver published under permissive license facilitates modifications and reuse of the solution. The system has been successfully tested in real hardware, both with 1 Gbps and 10 Gbps links.
A Fundamental Inequality for Lower-bounding the Error Probability for Classical and Quantum Multiple Access Channels and Its Applications
Venue: arXiv cs.IT 2015
Year: 2015

In the study of the capacity problem for multiple access channels (MACs), a lower bound on the error probability obtained by Han plays a crucial role in the converse parts of several kinds of channel coding theorems in the information-spectrum framework. Recently, Yagi and Oohama showed a tighter bound than the Han bound by means of Polyanskiy's converse. In this paper, we give a new bound which generalizes and strengthens the Yagi-Oohama bound, and demonstrate that the bound plays a fundamental role in deriving extensions of several known bounds. In particular, the Yagi-Oohama bound is generalized to two different directions; i.e, to general input distributions and to general encoders. In addition we extend these bounds to the quantum MACs and apply them to the converse problems for several information-spectrum settings.
Multicast Multigroup Beamforming for Per-antenna Power Constrained Large-scale Arrays
Venue: arXiv cs.IT 2015
Year: 2015

Large in the number of transmit elements, multi-antenna arrays with per-element limitations are in the focus of the present work. In this context, physical layer multigroup multicasting under per-antenna power constrains, is investigated herein. To address this complex optimization problem low-complexity alternatives to semi-definite relaxation are proposed. The goal is to optimize the per-antenna power constrained transmitter in a maximum fairness sense, which is formulated as a non-convex quadratically constrained quadratic problem. Therefore, the recently developed tool of feasible point pursuit and successive convex approximation is extended to account for practical per-antenna power constraints. Interestingly, the novel iterative method exhibits not only superior performance in terms of approaching the relaxed upper bound but also a significant complexity reduction, as the dimensions of the optimization variables increase. Consequently, multicast multigroup beamforming for large-scale array transmitters with per-antenna dedicated amplifiers is rendered computationally efficient and accurate. A preliminary performance evaluation in large-scale systems for which the semi-definite relaxation constantly yields non rank-1 solutions is presented.
Challenges and Recommendations for Preparing HPC Applications for Exascale
Venue: arXiv cs.DC 2015
Year: 2015

While the HPC community is working towards the development of the first Exaflop computer (expected around 2020), after reaching the Petaflop milestone in 2008 still only few HPC applications are able to fully exploit the capabilities of Petaflop systems. In this paper we argue that efforts for preparing HPC applications for Exascale should start before such systems become available. We identify challenges that need to be addressed and recommend solutions in key areas of interest, including formal modeling, static analysis and optimization, runtime analysis and optimization, and autonomic computing. Furthermore, we outline a conceptual framework for porting HPC applications to future Exascale computing systems and propose steps for its implementation.
Output Feedback Control of Inhomogeneous Parabolic PDEs with Point Actuation and Point Measurement using SOS and Semi-Separable Kernels
Venue: arXiv cs.SY 2015
Year: 2015

In this paper we use SOS and SDP to design output feedback controllers for a class of one-dimensional parabolic partial differential equations with point measurements and point actuation. Our approach is based on the use of SOS to search for positive quadratic Lyapunov functions, controllers and observers. These Lyapunov functions, controllers and observers are parameterized by linear operators which are defined by SOS polynomials. The main result of the paper is the development of an improved class of observer-based controllers and evidence which indicates that when the system is controllable and observable, these methods will find a observer-based controller for sufficiently high polynomial degree (similar to well-known results from backstepping).
Interpolation of a spline developable surface between a curve and two rulings
Venue: arXiv cs.GR 2015
Year: 2015

In this paper we address the problem of interpolating a spline developable patch bounded by a given spline curve and the first and the last rulings of the developable surface. In order to complete the boundary of the patch a second spline curve is to be given. Up to now this interpolation problem could be solved, but without the possibility of choosing both endpoints for the rulings. We circumvent such difficulty here by resorting to degree elevation of the developable surface. This is useful not only to solve this problem, but also other problems dealing with triangular developable patches.
Analysis of Spectrum Occupancy Using Machine Learning Algorithms
Venue: arXiv cs.NI 2015
Year: 2015

In this paper, we analyze the spectrum occupancy using different machine learning techniques. Both supervised techniques (naive Bayesian classifier (NBC), decision trees (DT), support vector machine (SVM), linear regression (LR)) and unsupervised algorithm (hidden markov model (HMM)) are studied to find the best technique with the highest classification accuracy (CA). A detailed comparison of the supervised and unsupervised algorithms in terms of the computational time and classification accuracy is performed. The classified occupancy status is further utilized to evaluate the probability of secondary user outage for the future time slots, which can be used by system designers to define spectrum allocation and spectrum sharing policies. Numerical results show that SVM is the best algorithm among all the supervised and unsupervised classifiers. Based on this, we proposed a new SVM algorithm by combining it with fire fly algorithm (FFA), which is shown to outperform all other algorithms.
Comparing Asynchronous $l$-Complete Approximations and Quotient Based Abstractions
Venue: arXiv cs.SY 2015
Year: 2015

This paper is concerned with a detailed comparison of two different abstraction techniques for the construction of finite state symbolic models for controller synthesis of hybrid systems. Namely, we compare quotient based abstractions (QBA), with different realizations of strongest (asynchronous) $l$-complete approximations (SAlCA) Even though the idea behind their construction is very similar, we show that they are generally incomparable both in terms of behavioral inclusion and similarity relations. We therefore derive necessary and sufficient conditions for QBA to coincide with particular realizations of SAlCA. Depending on the original system, either QBA or SAlCA can be a tighter abstraction.
GraphMat: High performance graph analytics made productive
Venue: arXiv cs.PF 2015
Year: 2015

Given the growing importance of large-scale graph analytics, there is a need to improve the performance of graph analysis frameworks without compromising on productivity. GraphMat is our solution to bridge this gap between a user-friendly graph analytics framework and native, hand-optimized code. GraphMat functions by taking vertex programs and mapping them to high performance sparse matrix operations in the backend. We get the productivity benefits of a vertex programming framework without sacrificing performance. GraphMat is in C++, and we have been able to write a diverse set of graph algorithms in this framework with the same effort compared to other vertex programming frameworks. GraphMat performs 1.2-7X faster than high performance frameworks such as GraphLab, CombBLAS and Galois. It achieves better multicore scalability (13-15X on 24 cores) than other frameworks and is 1.2X off native, hand-optimized code on a variety of different graph algorithms. Since GraphMat performance depends mainly on a few scalable and well-understood sparse matrix operations, GraphMatcan naturally benefit from the trend of increasing parallelism on future hardware.
Using Latent Semantic Analysis to Identify Quality in Use (QU) Indicators from User Reviews
Venue: arXiv cs.CL 2015
Year: 2015

The paper describes a novel approach to categorize users' reviews according to the three Quality in Use (QU) indicators defined in ISO: effectiveness, efficiency and freedom from risk. With the tremendous amount of reviews published each day, there is a need to automatically summarize user reviews to inform us if any of the software able to meet requirement of a company according to the quality requirements. We implemented the method of Latent Semantic Analysis (LSA) and its subspace to predict QU indicators. We build a reduced dimensionality universal semantic space from Information System journals and Amazon reviews. Next, we projected set of indicators' measurement scales into the universal semantic space and represent them as subspace. In the subspace, we can map similar measurement scales to the unseen reviews and predict the QU indicators. Our preliminary study able to obtain the average of F-measure, 0.3627.