OpenRaiser/Intern-Atlas huggingface dataset:
Learning Reputation in an Authorship Network
Venue: arXiv cs.SI 2013
Year: 2013

The problem of searching for experts in a given academic field is hugely important in both industry and academia. We study exactly this issue with respect to a database of authors and their publications. The idea is to use Latent Semantic Indexing (LSI) and Latent Dirichlet Allocation (LDA) to perform topic modelling in order to find authors who have worked in a query field. We then construct a coauthorship graph and motivate the use of influence maximisation and a variety of graph centrality measures to obtain a ranked list of experts. The ranked lists are further improved using a Markov Chain-based rank aggregation approach. The complete method is readily scalable to large datasets. To demonstrate the efficacy of the approach we report on an extensive set of computational simulations using the Arnetminer dataset. An improvement in mean average precision is demonstrated over the baseline case of simply using the order of authors found by the topic models.
A Comprehensive Approach to Universal Piecewise Nonlinear Regression Based on Trees
Venue: arXiv cs.LG 2013
Year: 2013

In this paper, we investigate adaptive nonlinear regression and introduce tree based piecewise linear regression algorithms that are highly efficient and provide significantly improved performance with guaranteed upper bounds in an individual sequence manner. We use a tree notion in order to partition the space of regressors in a nested structure. The introduced algorithms adapt not only their regression functions but also the complete tree structure while achieving the performance of the "best" linear mixture of a doubly exponential number of partitions, with a computational complexity only polynomial in the number of nodes of the tree. While constructing these algorithms, we also avoid using any artificial "weighting" of models (with highly data dependent parameters) and, instead, directly minimize the final regression error, which is the ultimate performance goal. The introduced methods are generic such that they can readily incorporate different tree construction methods such as random trees in their framework and can use different regressor or partitioning functions as demonstrated in the paper.
Synchronous Context-Free Grammars and Optimal Linear Parsing Strategies
Venue: arXiv cs.FL 2013
Year: 2013

Synchronous Context-Free Grammars (SCFGs), also known as syntax-directed translation schemata, are unlike context-free grammars in that they do not have a binary normal form. In general, parsing with SCFGs takes space and time polynomial in the length of the input strings, but with the degree of the polynomial depending on the permutations of the SCFG rules. We consider linear parsing strategies, which add one nonterminal at a time. We show that for a given input permutation, the problems of finding the linear parsing strategy with the minimum space and time complexity are both NP-hard.
Performance Evaluation of Multiterminal Backhaul Compression for Cloud Radio Access Networks
Venue: arXiv cs.IT 2013
Year: 2013

In cloud radio access networks (C-RANs), the baseband processing of the available macro- or pico/femto-base stations (BSs) is migrated to control units, each of which manages a subset of BS antennas. The centralized information processing at the control units enables effective interference management. The main roadblock to the implementation of C-RANs hinges on the effective integration of the radio units, i.e., the BSs, with the backhaul network. This work first reviews in a unified way recent results on the application of advanced multiterminal, as opposed to standard point-to-point, backhaul compression techniques. The gains provided by multiterminal backhaul compression are then confirmed via extensive simulations based on standard cellular models. As an example, it is observed that multiterminal compression strategies provide performance gains of more than 60% for both the uplink and the downlink in terms of the cell-edge throughput.
Higher-order semantics for quantum programming languages with classical control
Venue: arXiv cs.LO 2013
Year: 2013

This thesis studies the categorical formalisation of quantum computing, through the prism of type theory, in a three-tier process. The first stage of our investigation involves the creation of the dagger lambda calculus, a lambda calculus for dagger compact categories. Our second contribution lifts the expressive power of the dagger lambda calculus, to that of a quantum programming language, by adding classical control in the form of complementary classical structures and dualisers. Finally, our third contribution demonstrates how our lambda calculus can be applied to various well known problems in quantum computation.
$K$-anonymous Signaling Scheme
Venue: arXiv cs.GT 2013
Year: 2013

We incorporate signaling scheme into Ad Auction setting, to achieve better welfare and revenue while protect users' privacy. We propose a new \emph{$K$-anonymous signaling scheme setting}, prove the hardness of the corresponding welfare/revenue maximization problem, and finally propose the algorithms to approximate the optimal revenue or welfare.
A Framework for Semi-automated Web Service Composition in Semantic Web
Venue: arXiv cs.AI 2013
Year: 2013

Number of web services available on Internet and its usage are increasing very fast. In many cases, one service is not enough to complete the business requirement; composition of web services is carried out. Autonomous composition of web services to achieve new functionality is generating considerable attention in semantic web domain. Development time and effort for new applications can be reduced with service composition. Various approaches to carry out automated composition of web services are discussed in literature. Web service composition using ontologies is one of the effective approaches. In this paper we demonstrate how the ontology based composition can be made faster for each customer. We propose a framework to provide precomposed web services to fulfil user requirements. We detail how ontology merging can be used for composition which expedites the whole process. We discuss how framework provides customer specific ontology merging and repository. We also elaborate on how merging of ontologies is carried out.
Numerical Investigations on Quasi Steady-State Model for Voltage Stability: Limitations and Nonlinear Analysis
Venue: arXiv cs.SY 2013
Year: 2013

In this paper, several numerical examples to illustrate limitations of Quasi Steady-State (QSS) model in long-term voltage stability analysis are presented. In those cases, the QSS model provided incorrect stability assessment. Causes of failure of the QSS model are explained and analyzed in nonlinear system framework. Sufficient conditions of the QSS model for correct approximation are suggested.
Hilditchs Algorithm Based Tamil Character Recognition
Venue: arXiv cs.CV 2013
Year: 2013

Character identification plays a vital role in the contemporary world of Image processing. It can solve many composite problems and makes humans work easier. An instance is Handwritten Character detection. Handwritten recognition is not a novel expertise, but it has not gained community notice until Now. The eventual aim of designing Handwritten Character recognition structure with an accurateness rate of 100% is pretty illusionary. Tamil Handwritten Character recognition system uses the Neural Networks to distinguish them. Neural Network and structural characteristics are used to instruct and recognize written characters. After training and testing the exactness rate reached 99%. This correctness rate is extremely high. In this paper we are exploring image processing through the Hilditch algorithm foundation and structural characteristics of a character in the image. And we recognized some character of the Tamil language, and we are trying to identify all the character of Tamil In our future works.
A Novel Family of Adaptive Filtering Algorithms Based on The Logarithmic Cost
Venue: arXiv cs.LG 2013
Year: 2013

We introduce a novel family of adaptive filtering algorithms based on a relative logarithmic cost. The new family intrinsically combines the higher and lower order measures of the error into a single continuous update based on the error amount. We introduce important members of this family of algorithms such as the least mean logarithmic square (LMLS) and least logarithmic absolute difference (LLAD) algorithms that improve the convergence performance of the conventional algorithms. However, our approach and analysis are generic such that they cover other well-known cost functions as described in the paper. The LMLS algorithm achieves comparable convergence performance with the least mean fourth (LMF) algorithm and extends the stability bound on the step size. The LLAD and least mean square (LMS) algorithms demonstrate similar convergence performance in impulse-free noise environments while the LLAD algorithm is robust against impulsive interferences and outperforms the sign algorithm (SA). We analyze the transient, steady state and tracking performance of the introduced algorithms and demonstrate the match of the theoretical analyzes and simulation results. We show the extended stability bound of the LMLS algorithm and analyze the robustness of the LLAD algorithm against impulsive interferences. Finally, we demonstrate the performance of our algorithms in different scenarios through numerical examples.
Analysis and synthesis of nonlinear reversible cellular automata in linear time
Venue: arXiv cs.FL 2013
Year: 2013

Cellular automata (CA) have been found as an attractive modeling tool for various applications, such as, pattern recognition, image processing, data compression, encryption, and specially for VLSI design & test. For such applications, mostly a special class of CA, called as linear/additive CA, have been utilized. Since linear/additive CA refer a limited number of candidate CA, while searching for solution to a problem, the best result may not be expected. The nonlinear CA can be a better alternative to linear/additive CA for achieving desired solutions in different applications. However, the nonlinear CA are yet to be characterized to fit the design for modeling an application. This work targets characterization of the nonlinear CA to utilize the huge search space of nonlinear CA while developing applications in VLSI domain. An analytical framework is developed to explore the properties of CA rules. The characterization is directed to deal with the reversibility, as the reversible CA are primarily targeted for VLSI applications. The reported characterization enables us to design two algorithms of linear time complexities -- one for identification and nother for synthesis of nonlinear reversible CA. Finally, the CA rules are classified into 6 classes for developing further efficient synthesis algorithm.
A Constraint Programming Approach for Mining Sequential Patterns in a Sequence Database
Venue: arXiv cs.AI 2013
Year: 2013

Constraint-based pattern discovery is at the core of numerous data mining tasks. Patterns are extracted with respect to a given set of constraints (frequency, closedness, size, etc). In the context of sequential pattern mining, a large number of devoted techniques have been developed for solving particular classes of constraints. The aim of this paper is to investigate the use of Constraint Programming (CP) to model and mine sequential patterns in a sequence database. Our CP approach offers a natural way to simultaneously combine in a same framework a large set of constraints coming from various origins. Experiments show the feasibility and the interest of our approach.
Model-based clock synchronization protocol for wireless sensor networks
Venue: arXiv cs.NI 2013
Year: 2013

In a wireless sensor network, nodes communicate time-stamped packets in order to synchronize their clocks, i.e., estimate each other's time display. We introduce and analyze a parametrized stochastic model for clocks and use it to calculate, at any given time, the Maximum-Likelihood (ML) estimate of the relative skew/offset between two communicating nodes. For network synchronization, event-based Kalman-Bucy filtering gives rise to a centralized scheme, and we propose an efficient distributed suboptimal algorithm. We study the performance both analytically and experimentally and provide provable guarantees. We summarize our findings into defining a new distributed model-based clock synchronization protocol (MBCSP), and present a comparative simulation study of its accuracy versus prior art to showcase improvements.
Illustrating the Mezzo programming language
Venue: arXiv cs.PL 2013
Year: 2013

When programmers want to prove strong program invariants, they are usually faced with a choice between using theorem provers and using traditional programming languages. The former requires them to provide program proofs, which, for many applications, is considered a heavy burden. The latter provides less guarantees and the programmer usually has to write run-time assertions to compensate for the lack of suitable invariants expressible in the type system. We introduce Mezzo, a programming language in the tradition of ML, in which the usual concept of a type is replaced by a more precise notion of a permission. Programs written in Mezzo usually enjoy stronger guarantees than programs written in pure ML. However, because Mezzo is based on a type system, the reasoning requires no user input. In this paper, we illustrate the key concepts of Mezzo, highlighting the static guarantees our language provides.
Attaining High-performing Software Teams with Agile and Lean Practices: An Empirical Case Study
Venue: arXiv cs.SE 2013
Year: 2013

This paper presents an empirical study on how self- organized software teams could attain high performance using agile and lean practices. In particular, the paper qualitatively examines characteristics of high performance and self- organization in one project team. The case under study is a customer-driven student project, carried out to develop an alpha-version prototype. The paper also studies how certain agile software practices aid in initialising self-organization in the team. The main results indicate that self-organization as supported by certain Agile and Lean practices helps teams in achieving higher performance.
Sparse Linear Dynamical System with Its Application in Multivariate Clinical Time Series
Venue: arXiv cs.AI 2013
Year: 2013

Linear Dynamical System (LDS) is an elegant mathematical framework for modeling and learning multivariate time series. However, in general, it is difficult to set the dimension of its hidden state space. A small number of hidden states may not be able to model the complexities of a time series, while a large number of hidden states can lead to overfitting. In this paper, we study methods that impose an $\ell_1$ regularization on the transition matrix of an LDS model to alleviate the problem of choosing the optimal number of hidden states. We incorporate a generalized gradient descent method into the Maximum a Posteriori (MAP) framework and use Expectation Maximization (EM) to iteratively achieve sparsity on the transition matrix of an LDS model. We show that our Sparse Linear Dynamical System (SLDS) improves the predictive performance when compared to ordinary LDS on a multivariate clinical time series dataset.
Efficient deterministic approximate counting for low-degree polynomial threshold functions
Venue: arXiv cs.CC 2013
Year: 2013

We give a deterministic algorithm for approximately counting satisfying assignments of a degree-$d$ polynomial threshold function (PTF). Given a degree-$d$ input polynomial $p(x_1,\dots,x_n)$ over $R^n$ and a parameter $ε> 0$, our algorithm approximates $\Pr_{x \sim \{-1,1\}^n}[p(x) \geq 0]$ to within an additive $\pm ε$ in time $O_{d,ε}(1)\cdot \mathop{poly}(n^d)$. (Any sort of efficient multiplicative approximation is impossible even for randomized algorithms assuming $NP\not=RP$.) Note that the running time of our algorithm (as a function of $n^d$, the number of coefficients of a degree-$d$ PTF) is a \emph{fixed} polynomial. The fastest previous algorithm for this problem (due to Kane), based on constructions of unconditional pseudorandom generators for degree-$d$ PTFs, runs in time $n^{O_{d,c}(1) \cdot ε^{-c}}$ for all $c > 0$. The key novel contributions of this work are: A new multivariate central limit theorem, proved using tools from Malliavin calculus and Stein's Method. This new CLT shows that any collection of Gaussian polynomials with small eigenvalues must have a joint distribution which is very close to a multidimensional Gaussian distribution. A new decomposition of low-degree multilinear polynomials over Gaussian inputs. Roughly speaking we show that (up to some small error) any such polynomial can be decomposed into a bounded number of multilinear polynomials all of which have extremely small eigenvalues. We use these new ingredients to give a deterministic algorithm for a Gaussian-space version of the approximate counting problem, and then employ standard techniques for working with low-degree PTFs (invariance principles and regularity lemmas) to reduce the original approximate counting problem over the Boolean hypercube to the Gaussian version.
Integrating RDF into Hypergraph-Graph (HG(2)) Data Structure
Venue: arXiv cs.DS 2013
Year: 2013

Current paper discusses the methodologies involved in integrating Resource Description Framework into a HyperGraph Graph HG 2 data structure in order to preserve the semantics of the information contained in RDF document for dealing future cross platform information portability issues. The entire semantic web is mostly dominated by few information frameworks like RDF, Topic Map, OWL etc. Hence semantic web currently faces the problem of non existence of common information meta-model which can integrate them all for ex-panded semantic search. On the background of development of Hyper Graph Graph HG 2 data structure, an RDF document if integrated to it, maintains the original semantics and exposes some critical semantic and object mapping lift as well which could further be exploited for semantic search and information transitional problems. The focus of the paper is to present the mapping constructs between RDF elements and HyperGraph Graph HG 2 elements.
Unobtrusive Low Cost Pupil Size Measurements using Web cameras
Venue: arXiv cs.CV 2013
Year: 2013

Unobtrusive every day health monitoring can be of important use for the elderly population. In particular, pupil size may be a valuable source of information, since, apart from pathological cases, it can reveal the emotional state, the fatigue and the ageing. To allow for unobtrusive monitoring to gain acceptance, one should seek for efficient methods of monitoring using com- mon low-cost hardware. This paper describes a method for monitoring pupil sizes using a common web camera in real time. Our method works by first detecting the face and the eyes area. Subsequently, optimal iris and sclera location and radius, modelled as ellipses, are found using efficient filtering. Finally, the pupil center and radius is estimated by optimal filtering within the area of the iris. Experimental result show both the efficiency and the effectiveness of our approach.
LiteLab: Efficient Large-scale Network Experiments
Venue: arXiv cs.DC 2013
Year: 2013

Large-scale network experiments is a challenging problem. Simulations, emulations, and real-world testbeds all have their advantages and disadvantages. In this paper we present LiteLab, a light-weight platform specialized for large-scale networking experiments. We cover in detail its design, key features, and architecture. We also perform an extensive evaluation of LiteLab's performance and accuracy and show that it is able to both simulate network parameters with high accuracy, and also able to scale up to very large networks. LiteLab is flexible, easy to deploy, and allows researchers to perform large-scale network experiments with a short development cycle. We have used LiteLab for many different kinds of network experiments and are planning to make it available for others to use as well.
Power Penalty Due to First-order PMD in Optical OFDM/QAM and FBMC/OQAM Transmission System
Venue: arXiv cs.IT 2013
Year: 2013

Polarization mode dispersion (PMD) is a challenge for high-data-rate optical-communication systems. More researches are desirable for impairments that is induced by PMD in high-speed optical orthogonal frequency division multiplexing (OFDM) transmission system. In this paper, an approximately analytical method for evaluating the power penalty due to first-order PMD in optical OFDM with quadrature amplitude modulation (OFDM/QAM) and filter bank based multi-carrier with offset quadrature amplitude modulation (FBMC/OQAM) transmission system is presented. The simulation results show that, compared with the single carrier with quadrature phase shift keying(SC-QPSK), both the OFDM/QAM and the FBMC/OQAM can decrease the power penalty caused by PMD by half. Furthermore, the FBMC/OQAM shows better power penalty immunity than the OFDM/QAM under the influence of first order PMD.
Dynamic Interference Management
Venue: arXiv cs.IT 2013
Year: 2013

A linear interference network is considered. Long-term fluctuations (shadow fading) in the wireless channel can lead to any link being erased with probability p. Each receiver is interested in one unique message that can be available at M transmitters. In a cellular downlink scenario, the case where M=1 reflects the cell association problem, and the case where M>1 reflects the problem of setting up the backhaul links for Coordinated Multi-Point (CoMP) transmission. In both cases, we analyze Degrees of Freedom (DoF) optimal schemes for the case of no erasures, and propose new schemes with better average DoF performance at high probabilities of erasure. For M=1, we characterize the average per user DoF, and identify the optimal assignment of messages to transmitters at each value of p. For general values of M, we show that there is no strategy for assigning messages to transmitters in large networks that is optimal for all values of p.
Stochastic Optimization of Smooth Loss
Venue: arXiv cs.LG 2013
Year: 2013

In this paper, we first prove a high probability bound rather than an expectation bound for stochastic optimization with smooth loss. Furthermore, the existing analysis requires the knowledge of optimal classifier for tuning the step size in order to achieve the desired bound. However, this information is usually not accessible in advanced. We also propose a strategy to address the limitation.
Communication Through Collisions: Opportunistic Utilization of Past Receptions
Venue: arXiv cs.IT 2013
Year: 2013

When several wireless users are sharing the spectrum, packet collision is a simple, yet widely used model for interference. Under this model, when transmitters cause interference at any of the receivers, their collided packets are discarded and need to be retransmitted. However, in reality, that receiver can still store its analog received signal and utilize it for decoding the packets in the future (for example, by successive interference cancellation techniques). In this work, we propose a physical layer model for wireless packet networks that allows for such flexibility at the receivers. We assume that the transmitters will be aware of the state of the channel (i.e. when and where collisions occur, or an unintended receiver overhears the signal) with some delay, and propose several coding opportunities that can be utilized by the transmitters to exploit the available signal at the receivers for interference management (as opposed to discarding them). We analyze the achievable throughput of our strategy in a canonical interference channel with two transmitter-receiver pairs, and demonstrate the gain over conventional schemes. By deriving an outer-bound, we also prove the optimality of our scheme for the corresponding model.