OpenRaiser/Intern-Atlas huggingface dataset:
Scalable Analysis for Large Social Networks: the data-aware mean-field approach
Venue: arXiv cs.SI 2012
Year: 2012

Studies on social networks have proved that endogenous and exogenous factors influence dynamics. Two streams of modeling exist on explaining the dynamics of social networks: 1) models predicting links through network properties, and 2) models considering the effects of social attributes. In this interdisciplinary study we work to overcome a number of computational limitations within these current models. We employ a mean-field model which allows for the construction of a population-specific socially informed model for predicting links from both network and social properties in large social networks. The model is tested on a population of conference coauthorship behavior, considering a number of parameters from available Web data. We address how large social networks can be modeled preserving both network and social parameters. We prove that the mean-field model, using a data-aware approach, allows us to overcome computational burdens and thus scalability issues in modeling large social networks in terms of both network and social parameters. Additionally, we confirm that large social networks evolve through both network and social-selection decisions; asserting that the dynamics of networks cannot singly be studied from a single perspective but must consider effects of social parameters.
Decoding a Class of Affine Variety Codes with Fast DFT
Venue: arXiv cs.IT 2012
Year: 2012

An efficient procedure for error-value calculations based on fast discrete Fourier transforms (DFT) in conjunction with Berlekamp-Massey-Sakata algorithm for a class of affine variety codes is proposed. Our procedure is achieved by multidimensional DFT and linear recurrence relations from Grobner basis and is applied to erasure-and-error decoding and systematic encoding. The computational complexity of error-value calculations in our algorithm improves that in solving systems of linear equations from error correcting pairs in many cases. A motivating example of our algorithm in case of a Reed-Solomon code and a numerical example of our algorithm in case of a Hermitian code are also described.
A novel method for obtaining a better quality speech signal for cochlear Implants using kalman with drnl and ssb technique
Venue: arXiv cs.SD 2012
Year: 2012

Cochlear implant devices are known to exist since a long time. The purpose of the present work is to develop a speech algorithm for obtaining robust speech. In this paper, the technique of cochlear implant is first introduced, followed by discussions of some of the existing techniques available for obtaining speech. The next section introduces a new technique for obtaining robust speech. The key feature of this technique lies in the use of the advantages of an integrated approach involving the use of an estimation technique such as a kalman filter with non linear filter bank strategy, using Dual Resonance Non Linear(DRNL) and Single Side Band(SSB) Encoding method. A comparative study of the proposed method with the existing method indicates that the proposed method performs well compared to the existing method.
Lower Bounds on Revenue of Approximately Optimal Auctions
Venue: arXiv cs.GT 2012
Year: 2012

We obtain revenue guarantees for the simple pricing mechanism of a single posted price, in terms of a natural parameter of the distribution of buyers' valuations. Our revenue guarantee applies to the single item n buyers setting, with values drawn from an arbitrary joint distribution. Specifically, we show that a single price drawn from the distribution of the maximum valuation Vmax = max {V_1, V_2, ...,V_n} achieves a revenue of at least a 1/e fraction of the geometric expecation of Vmax. This generic bound is a measure of how revenue improves/degrades as a function of the concentration/spread of Vmax. We further show that in absence of buyers' valuation distributions, recruiting an additional set of identical bidders will yield a similar guarantee on revenue. Finally, our bound also gives a measure of the extent to which one can simultaneously approximate welfare and revenue in terms of the concentration/spread of Vmax.
Comparison of Speech Activity Detection Techniques for Speaker Recognition
Venue: arXiv cs.MM 2012
Year: 2012

Speech activity detection (SAD) is an essential component for a variety of speech processing applications. It has been observed that performances of various speech based tasks are very much dependent on the efficiency of the SAD. In this paper, we have systematically reviewed some popular SAD techniques and their applications in speaker recognition. Speaker verification system using different SAD technique are experimentally evaluated on NIST speech corpora using Gaussian mixture model- universal background model (GMM-UBM) based classifier for clean and noisy conditions. It has been found that two Gaussian modeling based SAD is comparatively better than other SAD techniques for different types of noises.
Band Selection and Classification of Hyperspectral Images using Mutual Information: An algorithm based on minimizing the error probability using the inequality of Fano
Venue: arXiv cs.CV 2012
Year: 2012

Hyperspectral image is a substitution of more than a hundred images, called bands, of the same region. They are taken at juxtaposed frequencies. The reference image of the region is called Ground Truth map (GT). the problematic is how to find the good bands to classify the pixels of regions; because the bands can be not only redundant, but a source of confusion, and decreasing so the accuracy of classification. Some methods use Mutual Information (MI) and threshold, to select relevant bands. Recently there's an algorithm selection based on mutual information, using bandwidth rejection and a threshold to control and eliminate redundancy. The band top ranking the MI is selected, and if its neighbors have sensibly the same MI with the GT, they will be considered redundant and so discarded. This is the most inconvenient of this method, because this avoids the advantage of hyperspectral images: some precious information can be discarded. In this paper we'll make difference between useful and useless redundancy. A band contains useful redundancy if it contributes to decreasing error probability. According to this scheme, we introduce new algorithm using also mutual information, but it retains only the bands minimizing the error probability of classification. To control redundancy, we introduce a complementary threshold. So the good band candidate must contribute to decrease the last error probability augmented by the threshold. This process is a wrapper strategy; it gets high performance of classification accuracy but it is expensive than filter strategy.
On Multiplicative Linear Logic, Modality and Quantum Circuits
Venue: arXiv cs.LO 2012
Year: 2012

A logical system derived from linear logic and called QMLL is introduced and shown able to capture all unitary quantum circuits. Conversely, any proof is shown to compute, through a concrete GoI interpretation, some quantum circuits. The system QMLL, which enjoys cut-elimination, is obtained by endowing multiplicative linear logic with a quantum modality.
Analysis of a Quantum Error Correcting Code using Quantum Process Calculus
Venue: arXiv cs.LO 2012
Year: 2012

We describe the use of quantum process calculus to describe and analyze quantum communication protocols, following the successful field of formal methods from classical computer science. The key idea is to define two systems, one modelling a protocol and one expressing a specification, and prove that they are behaviourally equivalent. We summarize the necessary theory in the process calculus CQP, including the crucial result that equivalence is a congruence, meaning that it is preserved by embedding in any context. We illustrate the approach by analyzing two versions of a quantum error correction system.
Triadic Consensus: A Randomized Algorithm for Voting in a Crowd
Venue: arXiv cs.GT 2012
Year: 2012

Typical voting rules do not work well in settings with many candidates. If there are just several hundred candidates, then even a simple task such as choosing a top candidate becomes impractical. Motivated by the hope of developing group consensus mechanisms over the internet, where the numbers of candidates could easily number in the thousands, we study an urn-based voting rule where each participant acts as a voter and a candidate. We prove that when participants lie in a one-dimensional space, this voting protocol finds a $(1-ε/sqrt{n})$ approximation of the Condorcet winner with high probability while only requiring an expected $O(\frac{1}{ε^2}\log^2 \frac{n}{ε^2})$ comparisons on average per voter. Moreover, this voting protocol is shown to have a quasi-truthful Nash equilibrium: namely, a Nash equilibrium exists which may not be truthful, but produces a winner with the same probability distribution as that of the truthful strategy.
Optimality of Received Energy in Decision Fusion over Rayleigh Fading Diversity MAC with Non-Identical Sensors
Venue: arXiv cs.IT 2012
Year: 2012

Received-energy test for non-coherent decision fusion over a Rayleigh fading multiple access channel (MAC) without diversity was recently shown to be optimum in the case of conditionally mutually independent and identically distributed (i.i.d.) sensor decisions under specific conditions [1], [2]. Here, we provide a twofold generalization, allowing sensors to be non identical on one hand and introducing diversity on the other hand. Along with the derivation, we provide also a general tool to verify optimality of the the received energy test in scenarios with correlated sensor decisions. Finally, we derive an analytical expression of the effect of the diversity on the large-system performances, under both individual and total power constraints.
A Comparative Analysis of Data Mining Tools in Agent Based Systems
Venue: arXiv cs.DB 2012
Year: 2012

World wide technological advancement has brought in a widespread change in adoption and utilization of open source tools. Since, most of the organizations across the globe deal with a large amount of data to be updated online and transactions are made every second, managing, mining and processing this dynamic data is very complex. Successful implementation of the data mining technique requires a careful assessment of the various tools and algorithms available to mining experts. This paper provides a comparative study of open source data mining tools available to the professionals. Parameters influencing the choice of apt tools in addition to the real time challenges are discussed. However, it is well proven that agents aid in improving the performance of data mining tools. This paper provides information on an agent-based framework for data preprocessing with implementation details for the development of better tool in the market. An integration of open source data mining tools with agent simulation enable one to implement an effective data pre processing architecture thereby providing robust capabilities of the application which can be upgraded using a minimum of pre planning requirement from the application developer.
Feature Subset Selection for Software Cost Modelling and Estimation
Venue: arXiv cs.SE 2012
Year: 2012

Feature selection has been recently used in the area of software engineering for improving the accuracy and robustness of software cost models. The idea behind selecting the most informative subset of features from a pool of available cost drivers stems from the hypothesis that reducing the dimensionality of datasets will significantly minimise the complexity and time required to reach to an estimation using a particular modelling technique. This work investigates the appropriateness of attributes, obtained from empirical project databases and aims to reduce the cost drivers used while preserving performance. Finding suitable subset selections that may cater improved predictions may be considered as a pre-processing step of a particular technique employed for cost estimation (filter or wrapper) or an internal (embedded) step to minimise the fitting error. This paper compares nine relatively popular feature selection methods and uses the empirical values of selected attributes recorded in the ISBSG and Desharnais datasets to estimate software development effort.
A Pseudorandom Generator for Polynomial Threshold Functions of Gaussian with Subpolynomial Seed Length
Venue: arXiv cs.CC 2012
Year: 2012

We develop a pseudorandom generator that fools degree-$d$ polynomial threshold functions in $n$ variables with respect to the Gaussian distribution and has seed length $O_{c,d}(\log(n) ε^{-c})$.
Wireless Network Coding via Modified 802.11 MAC/PHY: Design and Implementation on SDR
Venue: arXiv cs.IT 2012
Year: 2012

Network coding (NC), in principle, is a Layer-3 innovation that improves network throughput in wired networks for multicast/broadcast scenarios. Due to the fundamental differences between wired and wireless networks, extending NC to wireless networks generates several new and significant practical challenges. Two-way information exchange (both symmetric and asymmetric). Network coding (NC), in principle, is a Layer-3 innovation that improves network throughput in wired networks for multicast/broadcast scenarios. Due to the fundamental differences between wired and wireless networks, extending NC to wireless networks generates several new and significant practical challenges. Two-way information exchange (both symmetric and asymmetric) between a pair of 802.11 sources/sinks using an intermediate relay node is a canonical scenario for evaluating the effectiveness of Wireless Network Coding (WNC) in a practical setting. Our primary objective in this work is to suggest pragmatic and novel modifications at the MAC and PHY layers of the 802.11 protocol stack on a Software Radio (SORA) platform to support WNC and obtain achievable throughput estimates via lab-scale experiments. Our results show that network coding (at the MAC or PHY layer) increases system throughput-typically by 20-30%%.
The Distributed Computing Model Based on The Capabilities of The Internet
Venue: arXiv cs.NI 2012
Year: 2012

Paper describes the theoretical and practical aspects of the proposed model that uses distributed computing to a global network of Internet communication. Distributed computing are widely used in modern solutions such as research, where the requirement is very high processing power, which can not be placed in one centralized point. The presented solution is based on open technologies and computers to perform calculations provided mainly by Internet users who are volunteers.
Intelligent Search Heuristics for Cost Based Scheduling
Venue: arXiv cs.AI 2012
Year: 2012

Nurse scheduling is a difficult optimization problem with multiple constraints. There is extensive research in the literature solving the problem using meta-heuristics approaches. In this paper, we will investigate an intelligent search heuristics that handles cost based scheduling problem. The heuristics demonstrated superior performances compared to the original algorithms used to solve the problems described in Li et. Al. (2003) and Ozkarahan (1989) in terms of time needed to establish a feasible solution. Both problems can be formulated as a cost problem. The search heuristic consists of several phrases of search and input based on the cost of each assignment and how the assignment will interact with the cost of the resources.
Variable-length Hill Cipher with MDS Key Matrix
Venue: arXiv cs.CR 2012
Year: 2012

The Hill Cipher is a classical symmetric cipher which breaks plaintext into blocks of size m and then multiplies each block by an m by m key matrix to yield ciphertext. However, it is well known that the Hill cipher succumbs to cryptanalysis relatively easily. As a result, there have been efforts to strengthen the cipher through the use of various techniques e.g. permuting rows and columns of the key matrix to encrypt each plaintext vector with a new key matrix. In this paper, we strengthen the security of the Hill cipher against a known-plaintext attack by encrypting each plaintext matrix by a variable-length key matrix obtained from a Maximum Distance Separable (MDS) master key matrix.
TCP-UB: A New congestion aware transmission control protocolvariant
Venue: arXiv cs.NI 2012
Year: 2012

Transmission control protocol (TCP) is a connection oriented protocol for several types of distributed applications. TCP is reliable particularly for traditional fixed networks. With emergence of faster wireless networks, TCP has been performing poorly in its original format. The performance of TCP is affected due to assorted factors including congestion window, maximum packet size, retry limit, recovery mechanism, backup mechanism and mobility. To overcome deficiency of original TCP, Several modifications have been introduced to improve network quality. The mobility is a major hurdle in degrading the performance of mobile wireless networks. In this paper, we introduce and implement new TCP variant University of Bridgeport (UB) that combines the features of TCP Westwood and Vegas. We examine the performance of TCP-UB, Vegas and Westwood using different realistic scenarios. NS2 simulator demonstrates the stability of TCP-UB as compared with TCP Vegas and Westwood in highly congested networks from the mobility point of view.
Lists that are smaller than their parts: A coding approach to tunable secrecy
Venue: arXiv cs.IT 2012
Year: 2012

We present a new information-theoretic definition and associated results, based on list decoding in a source coding setting. We begin by presenting list-source codes, which naturally map a key length (entropy) to list size. We then show that such codes can be analyzed in the context of a novel information-theoretic metric, ε-symbol secrecy, that encompasses both the one-time pad and traditional rate-based asymptotic metrics, but, like most cryptographic constructs, can be applied in non-asymptotic settings. We derive fundamental bounds for ε-symbol secrecy and demonstrate how these bounds can be achieved with MDS codes when the source is uniformly distributed. We discuss applications and implementation issues of our codes.
Amplitude Space Sharing among the Macro-Cell and Small-Cell Users
Venue: arXiv cs.IT 2012
Year: 2012

The crushing demand for wireless data services will soon exceed the capability of the current homogeneous cellular architecture. An emerging solution is to overlay small-cell networks with the macro-cell networks. In this paper, we propose an amplitude space sharing (ASS) method among the macro-cell user and small-cell users. By transmit layer design and data-rate optimization, the signals and interferences are promised to be separable at each receiver and the network sum-rate is maximized. The Han-Koboyashi coding is employed and optimal power allocation is derived for the one small-cell scenario, and a simple ASS transmission scheme is developed for the multiple small-cells scenarios. Simulation results show great superiority over other interference management schemes.
Structured Dispersion Matrices from Space-Time Block Codes for Space-Time Shift Keying
Venue: arXiv cs.IT 2012
Year: 2012

Coherent Space-Time Shift Keying (CSTSK) is a recently developed generalized shift-keying framework for Multiple-Input Multiple-Output systems, which uses a set of Space-Time matrices termed as Dispersion Matrices (DM). CSTSK may be combined with a classic signaling set (eg. QAM, PSK) in order to strike a flexible tradeoff between the achievable diversity and multiplexing gain. One of the key benefits of the CSTSK scheme is its Inter-Channel Interference (ICI) free system that makes single-stream Maximum Likelihood detection possible at low-complexity. In the existing CSTSK scheme, DMs are chosen by maximizing the mutual information over a large set of complex valued, Gaussian random matrices through numerical simulations. We refer to them as Capacity-Optimized (CO) DMs. In this contribution we establish a connection between the STSK scheme as well as the Space-Time Block Codes (STBC) and show that a class of STBCs termed as Decomposable Dispersion Codes (DDC) enjoy all the benefits that are specific to the STSK scheme. Two STBCs belonging to this class are proposed, a rate-one code from Field Extensions and a full-rate code from Cyclic Division Algebras, that offer structured DMs with desirable properties such as full-diversity, and a high coding gain. We show that the DMs derived from these codes are capable of achieving a performance than CO-DMs, and emphasize the importance of DMs having a higher coding gain than CO-DMs in scenarios having realistic, imperfect channel state information at the receiver.
SMAT: An Input Adaptive Sparse Matrix-Vector Multiplication Auto-Tuner
Venue: arXiv cs.MS 2012
Year: 2012

Sparse matrix vector multiplication (SpMV) is an important kernel in scientific and engineering applications. The previous optimizations are sparse matrix format specific and expose the choice of the best format to application programmers. In this work we develop an auto-tuning framework to bridge gap between the specific optimized kernels and their general-purpose use. We propose an SpMV auto-tuner (SMAT) that provides an unified interface based on compressed sparse row (CSR) to programmers by implicitly choosing the best format and the fastest implementation of any input sparse matrix in runtime. SMAT leverage a data mining model, which is formulated based on a set of performance parameters extracted from 2373 matrices in UF sparse matrix collection, to fast search the best combination. The experiments show that SMAT achieves the maximum performance of 75 GFLOP/s in single-precision and 33 GFLOP/s in double-precision on Intel, and 41 GFLOP/s in single-precision and 34 GFLOP/s in double-precision on AMD. Compared with the sparse functions in MKL library, SMAT runs faster by more than 3 times.
Sudoku as a special transportation problem
Venue: arXiv cs.DS 2012
Year: 2012

Sudoku is a popular combinatorial puzzle. A new method of solving Sudoku is presented, which involves formulating a puzzle as a special type of transportation problem. This model allows one to solve puzzles with more than one solution, keeping the constraints of the problem fixed, and simply changing a cost matrix between solutions.
A General Methodology for the Determination of 2D Bodies Elastic Deformation Invariants. Application to the Automatic Identification of Parasites
Venue: arXiv cs.CV 2012
Year: 2012

A novel methodology is introduced here that exploits 2D images of arbitrary elastic body deformation instances, so as to quantify mechano-elastic characteristics that are deformation invariant. Determination of such characteristics allows for developing methods offering an image of the undeformed body. General assumptions about the mechano-elastic properties of the bodies are stated, which lead to two different approaches for obtaining bodies' deformation invariants. One was developed to spot deformed body's neutral line and its cross sections, while the other solves deformation PDEs by performing a set of equivalent image operations on the deformed body images. Both these processes may furnish a body undeformed version from its deformed image. This was confirmed by obtaining the undeformed shape of deformed parasites, cells (protozoa), fibers and human lips. In addition, the method has been applied to the important problem of parasite automatic classification from their microscopic images. To achieve this, we first apply the previous method to straighten the highly deformed parasites and then we apply a dedicated curve classification method to the straightened parasite contours. It is demonstrated that essentially different deformations of the same parasite give rise to practically the same undeformed shape, thus confirming the consistency of the introduced methodology. Finally, the developed pattern recognition method classifies the unwrapped parasites into 6 families, with an accuracy rate of 97.6 %.