ppxscal/embedding_network huggingface dataset:
Atom-Aid: Detecting and Surviving Atomicity Violations
Authors: Brandon Lucia, Joseph Devietti, Luis Ceze, Karin Strauss
Venue: IEEE Micro (2009)

Hardware can play a significant role in improving reliability of multithreaded software. Recent architectural proposals arbitrarily group consecutive dynamic memory operations into atomic blocks to enforce coarse-grained memory ordering, providing implicit atomicity. The authors of this article observe that implicit atomicity probabilistically hides atomicity violations by reducing the number of interleaving opportunities between memory operations. They propose Atom-Aid, which creates implicit atomic blocks intelligently instead of arbitrarily, dramatically reducing the probability that atomicity violations will manifest themselves.
Is Cloud Computing Really Ready for Prime Time?
Authors: Neal Leavitt
Venue: Computer (2009)

Even though the technology faces several significant challenges, many vendors and industry observers predict a bright future for cloud computing.
Fast Shape Matching Using a Hybrid Model
Authors: Gang Xu, Wenxian Yang
Venue: Proceedings of the 2009 International Conference on Computer Engineering and Technology - Volume 01 (2009)

A hybrid model is proposed to finish image shape matching from coarse to fine, which is composed of three parts: rough matching, accurate matching, and optimum matching search. According to the partial Hausdorff distance, a fast strategy for rough matching and a new improved partial Hausdorff distance for accurate matching are presented as the measures of the degree of shape similarity between the template and images. At the same time, a new genetic algorithm based on fuzzy logic, which can adaptively regulate the probabilities of crossover and mutation, is used to search the optimum shape matching quickly. The experimental results show that the model achieves the shape matching with higher speed and precision compared with the traditional matching algorithms and can be used in real-time image matching and pattern recognition.
Teammate Selection Using Multi-dimensional Trust and Attitude Models
Authors: Jaesuk Ahn, David Deangelis, K. Suzanne Barber
Venue: Trust in Agent Societies: 11th International Workshop, TRUST 2008, Estoril, Portugal, May 12 -13, 2008. Revised Selected and Invited Papers (2008)

Multi-dimensional trustworthiness assessments have been shown significantly beneficial to agents when selecting appropriate teammates to achieve a given goal. Reliability, quality, availability, and timeliness define the behavioral constraints of the proposed multi-dimensional trust (MDT) model. Given the multi-dimensional trust model in this research, an agent learns to identify the most beneficial teammates given different situations by prioritizing each dimension differently. An agent's attitudes towards rewards, risks and urgency are used to drive an agent's prioritization of dimensions in a MDT model. Each agent is equipped with a reinforcement learning mechanism with clustering technique to identify its optimal set of attitudes and change its attitudes when the environment changes. Experimental results show that changing attitudes to give preferences for respective dimensions in the MDT, and consequently, teammate selection based on the situation offer a superior means of finding the best teammates for goal achievement.
Audio-Visual Content Analysis for Content-Based Video Indexing
Authors: Sofia Tsekeridou, Ioannis Pitas
Venue: Proceedings of the IEEE International Conference on Multimedia Computing and Systems - Volume 2 (1999)

An audio-visual content analysis method is presented, which analyzes both auditory and visual information sources and accounts for their inter-relations and coincidence to extract high-level semantic information. Both shot-based and object-based access to the visual information is employed. Due to the temporal nature of video, time has to be accounted for. Thus, time-constrained video labelling functions are generated. Audio source parsing leads to the extraction of a speaker identity mapping function over time. Visual source parsing results in the extraction of a talking face shot mapping function over time. Integration of the audio and visual mappings constrained by interaction rules leads to more detailed video content descriptions and even partial detection of its context.
Adaptive OFDM Vs Single Carrier Modulation with Frequency Domain Equalization
Authors: Inderjeet Kaur, Kamal Thakur, M. Kulkarni, Daya Gupta, Prabhjyot Arora
Venue: Proceedings of the 2009 International Conference on Computer Engineering and Technology - Volume 01 (2009)

The aim of the present paper is to compare multicarrier and single carrier modulation schemes for wireless communication systems. In both cases the fast Fourier transform (FFT) and its inverse are utilized. In case of OFDM(orthogonal frequency division multiplexing), the inverse FFT transforms the complex amplitudes of the individual subcarriers at the transmitter into time domain. At the receiver the inverse operation is carried out. In case of single carrier modulation, the FFT and its inverse are used at the input and output of the frequency domain equalizer in the receiver. Different single carrier and multi-carrier transmission systems are simulated with time-variant transfer functions measured with a wideband channel sounder. In case of OFDM, the individual sub-carriers are modulated with fixed and adaptive signal alphabets. Furthermore, a frequency-independent as well as the optimum power distribution are used. Single carrier modulation uses a single carrier, instead of the hundreds or thousands typically used in OFDM, so the peak-to-average transmitted power ratio for single carrier modulated signals is smaller. This in turn means that a SC system requires a smaller linear range to support a given average power. This enables the use of cheaper power amplifier as compared to OFDM system.
Multiple Scaling Extrinsic Soft Information for Improved Min-Sum Iterative Decoding of LDPC Codes
Authors: Cheon Ho Lee, Young Chai Ko, Jun Heo
Venue: IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences (2008)

This paper presents an improved min-sum iterative decoding scheme for regular and irregular LDPC codes. The proposed decoding scheme scales the extrinsic soft information from variable nodes to check. Different scaling factors are applied for iterations and the scaling factors are obtained by a simplified vector optimization method.
Application of an Improved Particle Swarm Optimization for Fault Diagnosis
Authors: Chu-Jiao Wang, Shi-Xiong Xia
Venue: Proceedings of the 2009 Second International Workshop on Knowledge Discovery and Data Mining (2009)

In this paper, the feasibility of using probabilistic causal-effect model is studied and we apply it in particle swarm optimization algorithm (PSO) to classify the faults of mine hoist. In order to enhance the PSO performance, we propose the probability function to nonlinearly map the data into a feature space in probabilistic causal-effect model, and with it, fault diagnosis is simplified into optimization problem from the original complex feature set. The proposed approach is applied to fault diagnosis, and our implementation has the advantages of being general, robust, and scalable. The raw datasets obtained from mine hoist system are preprocessed and used to generate networks fault diagnosis for the system. We studied the performance of the improved PSO algorithm and generated a Probabilistic Causal-effect network that can detect faults in the test data successfully. It can get >90% minimal diagnosis with cardinal number of fault symptom sets greater than 25.
An Efficient Distributed Flooding Scheme Using One-Hop Information in Heterogeneous Ad Hoc Networks
Authors: Ruilong Huo, Deying Li, Qinghua Zhu, Huiqiang Yang
Venue: Proceedings of the 2009 WRI International Conference on Communications and Mobile Computing - Volume 01 (2009)

Flooding is one of the most fundamental operations in wireless ad hoc networks. In this paper, we study the sufficient and necessary condition of efficient flooding schemes based on only one-hop out-neighbors' information in heterogeneous ad hoc networks. We further propose an efficient flooding algorithm using one-hop out-neighbors' information that achieves the local optimality: (1) the number of forwarding nodes in each step is minimal; (2) the time complexity of computing forwarding nodes is the lowest, which is, where n is the number of out-neighbors of a node. Extensive simulations have shown the good performance of our algorithm.
Robust Adaptive Control of Nonlinear Systems with Time-Varying Parameters and Its Application to Chua's Circuit
Authors: Hamid R. Koofigar, Saeed Hosseinnia, Farid Sheikholeslam
Venue: IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences (2008)

The problem of designing a robust adaptive control for nonlinear systems with uncertain time-varying parameters is addressed. The upper bound of uncertain parameters, considered even in control coefficients, are not required to be known. An adaptive tracking controller is presented and, using the Lyapunov theory, the closed-loop stability and tracking error convergence is shown. In order to improve the performance of the method, a robust mechanism is incorporated into the adaptive controller yielding a robust adaptive algorithm. The proposed controller guarantees the boundedness of all closed-loop signals and robust convergence of tracking error in spite of time-varying parameter uncertainties with unknown bounds. The parametric uncertain systems under consideration describes a wide class of nonlinear circuits and systems. As an application, a novel parametric model is derived for nonlinear Chua's circuit and then, the proposed method is used for its control. The effectiveness of the method is demonstrated by some simulation results.
Grey Prediction Model and Multivariate Statistical Techniques Forecasting Electrical Energy Consumption in Wenzhou, China
Authors: Qi Wang
Venue: Proceedings of the 2009 Second International Symposium on Intelligent Information Technology and Security Informatics (2009)

Electricity consumption has always been one of the critical economic issues in Wenzhou. This paper presents a combination method of grey prediction models and multivariate statistical techniques to forecast the trend of electrical energy consumption in Wenzhou. Hierarchical cluster analysis and discriminant analysis grouped 18 sampling years into three clusters, i.e., relatively less electrical energy consumption phase (LEECP), medium electrical energy consumption phase (MEECP) and highly electrical energy consumption phase (HEECP). The two grey prediction models established are the first-degree. Mean absolute percentage error (MAPE) criteria are more suitable than traditional accuracy and error test to evaluate grey models accuracy. Grey prediction model has been tested with high precision in a short-term. Using the grey prediction model, electrical energy consumption of Wenzhou will be 44.4719 billion kilowatt hour in 2010.
Plenary lecture I: computational intelligence solutions for biometrics
Authors: Victor-Emil Neagoe
Venue: Proceedings of the 12th WSEAS international conference on Computers (2008)

The word biometrics is a combination of the Greek words bio and metric. When combined, it means "life measurement." Biometrics concerns the study of automated methods for identifying an individual by measuring one or more physical or behavioral features of him. Certain physical human features or behaviors are characteristics that are specific and can be uniquely associated to one person. Common physiological biometric traits include: fingerprints, hand geometry, retina, iris, DNA and facial images. Whereas, common behavioral biometric traits include: handwriting, voice print, gait, and keystroke rhythms. Nowadays biometrics is rapidly evolving; it becomes more and more attractive and effective in critical applications, such as to create safe personal IDs, to control the access to personal information or physical areas, to recognize terrorists or criminals, to study the movements of people, and to monitor the human behavior. Several governments are now using or will soon be using biometric technology. The U.S. INSPASS immigration card and the Hong Kong ID card, for example, both store biometric features for authentication. Computational intelligence (CI) is a fastmoving research field with approaches primarily based on neural networks, machine learning, fuzzy logic, genetic algorithms and evolutionary computing. Computational intelligence (CI) technologies are robust, can be successfully applied to complex problems, are efficiently adaptive, and usually have a parallel computational architecture. For those reasons they have been proved to be effective and efficient in biometric feature extraction and biometric matching tasks, sometimes used in combination with traditional methods. In this lecture we survey two kinds of major applications of CI in biometric technologies: CI-based feature extraction and CI-based biometric matching. We also present the original contribution of the author regarding some CI solutions for facial image recognition and iris identification.
Decision support for determining veracity via linguistic-based cues
Authors: Christie M. Fuller, David P. Biros, Rick L. Wilson
Venue: Decision Support Systems (2009)

Deception detection is an essential skill in careers such as law enforcement and must be accomplished accurately. However, humans are not very competent at determining veracity without aid. This study examined automated text-based deception detection which attempts to overcome the shortcomings of previous credibility assessment methods. A real-world, high-stakes sample of statements was collected and analyzed. Several different sets of linguistic-based cues were used as inputs for classification models. Overall accuracy rates of up to 74% were achieved, suggesting that automated deception detection systems can be an invaluable tool for those who must assess the credibility of text.
Model reduction via projection onto nonlinear manifolds, with applications to analog circuits and biochemical systems
Authors: Chenjie Gu, Jaijeet Roychowdhury
Venue: Proceedings of the 2008 IEEE/ACM International Conference on Computer-Aided Design (2008)

Previous model order reduction methods fit into the framework of identifying the low-order linear subspace and using the linear projection to project the full state space into the low-order subspace. Despite its simplicity, the macromodel might automatically include redundancies. In this paper, we present a model order reduction approach, named maniMOR, which extends the linear projection framework to a general nonlinear projection framework. The two key ideas of maniMOR are (1) it explicitly separates the construction of the low-order subspace and projection operation; (2) it constructs a nonlinear manifold which captures important system responses and defines the corresponding nonlinear projection operator. The low-order manifold subspace in maniMOR is identified by stitching together the low-order linear subspaces around a set of sample points on the manifold. After the manifold is determined, it is embedded into a global nonlinear coordinate system. The projection function is defined in a piece-wise linear manner, and the model evaluation is conducted directly in the manifold subspace using cheap matrix-vector product computations. As a result, a compact model is generated by precomputing all the functions and Jacobians and storing them in a look-up table. We apply maniMOR on two analog circuits and a bio-chemical system to validate its correctness. Extensive comparisons with the results of the full model and other macromodels are provided. Experimental results show that maniMOR manages to obtain a huge reduction -- e.g., from 52 to 5 for the I/O buffer circuit and from 304 to 30 for yeast pheromone pathway system. This is less than half of the size of the TPWL model with the same accuracy. With great promise to capture important system responses, maniMOR presents a novel and powerful paradigm for nonlinear model reduction, and casts inspirations for further researches.
Formal analysis tools for the synchronous aspect language Larissa
Authors: David Stauch
Venue: EURASIP Journal on Embedded Systems (2008)

We present two tools for the formal analysis of the aspect language Larissa, which extends the simple synchronous language Argos. The first tool concerns the combination of design-by-contract with Larissa aspects, and shows how we can apply an aspect not only to a program, but to a specification of programs in form of a contract, and obtain a new contract. The second concerns aspect interferences, that is, aspects that influence each other in an unintended way if they are applied to the same program. We present a way to weave aspects in a less conflict-prone manner, and a means to detect remaining conflicts statically. These tools are quite powerful, compared to those available for other aspect languages.
A Single-Step Characteristic-Curve Finite Element Scheme of Second Order in Time for the Incompressible Navier-Stokes Equations
Authors: Hirofumi Notsu, Masahisa Tabata
Venue: Journal of Scientific Computing (2009)

In this paper we present a new single-step characteristic-curve finite element scheme of second order in time for the nonstationary incompressible Navier-Stokes equations. After supplying correction terms in the variational formulation, we prove that the scheme is of second order in time. The convergence rate of the scheme is numerically recognized by computational results.
ACE-A FORTRAN subroutine for analytical computation of effective grid parameters for finite-difference seismic waveform modeling with standard Earth models
Authors: Genti Toyokuni, Hiroshi Takenaka
Venue: Computers Geosciences (2009)

Despite the broad use of the heterogeneous finite-difference (FD) method for seismic waveform modeling, accurate treatment of material discontinuities inside the grid cells has been a serious problem for many years. One possible way to solve this problem is to introduce effective grid elastic moduli and densities (effective parameters) calculated by the volume harmonic averaging of elastic moduli and volume arithmetic averaging of densities in grid cells. This scheme enables us to place a material discontinuity in an arbitrary position in the spatial grids. Standard Earth models have made a significant contribution to synthetic seismogram calculations with a variety of numerical procedures such as the FD method. For the FD computation of seismic waveform with these models, we must first ensure accurate treatment of material discontinuities in the radius (or depth). The present paper introduces a FORTRAN subroutine ACE which calculates effective parameters analytically for an arbitrary spatial region in either the radius or depth direction for four major standard Earth models, namely, the PREM, IASP91, SP6, and AK135. This program is intended for all FD users who are concerned with seismic wave simulation for these models.
A class of optimal linear codes of length one above the Griesmer bound
Authors: E. J. Cheon
Venue: Designs, Codes and Cryptography (2009)

In this paper, we determine the smallest lengths of linear codes with some minimum distances. We construct a [g q (k, d) + 1, k, d] q code for sq k-1 - sq k-2 - q s - q 2 + 1 &le; d &le; sq k-1 - sq k-2 - q s with 3 &le; s &le; k - 2 and q &ge; s + 1. Then we get n q (k, d) = g q (k, d) + 1 for (k - 2)q k-1 - (k - 1)q k-2 - q 2 + 1 &le; d &le; (k - 2)q k-1 - (k - 1)q k-2, k &ge; 6, q &ge; 2k - 3; and sq k-1 - sq k-2 - q s - q + 1 &le; d &le; sq k-1 - sq k-2 - q s , s &ge; 2, k &ge; 2s + 1 and q &ge; 2s - 1.
Computers and Art, 2 edition
Authors: Stuart Mealing
Venue:  (2008)

Computers andArt provides insightful perspectives on the use of the computer as a tool for artists. The approaches taken vary from its historical, philosophical and practical implications to the use of computer technology in art practice. The contributors include an art critic, an educator, a practising artist and a researcher. Mealing looks at the potential for future developments in the field, looking at both the artistic and the computational aspects of the field.
Hybrid learning for interval type-2 fuzzy logic systems based on orthogonal least-squares and back-propagation methods
Authors: Gerardo M. Méndez, M. de los Angeles Hernandez
Venue: Information Sciences: an International Journal (2009)

This paper presents a novel learning methodology based on a hybrid algorithm for interval type-2 fuzzy logic systems. Since only the back-propagation method has been proposed in the literature for the tuning of both the antecedent and the consequent parameters of type-2 fuzzy logic systems, a hybrid learning algorithm has been developed. The hybrid method uses a recursive orthogonal least-squares method for tuning the consequent parameters and the back-propagation method for tuning the antecedent parameters. Systems were tested for three types of inputs: (a) interval singleton, (b) interval type-1 non-singleton, and (c) interval type-2 non-singleton. Experiments were carried out on the application of hybrid interval type-2 fuzzy logic systems for prediction of the scale breaker entry temperature in a real hot strip mill for three different types of coil. The results proved the feasibility of the systems developed here for scale breaker entry temperature prediction. Comparison with type-1 fuzzy logic systems shows that hybrid learning interval type-2 fuzzy logic systems provide improved performance under the conditions tested.
CLON: overlay network for clouds
Authors: Miguel Matos, António Sousa, José Pereira, Rui Oliveira
Venue: Proceedings of the Third Workshop on Dependable Distributed Data Management (2009)

Gossip-based protocols have been gaining an increasing interest from the research community due to the high resilience to node churn and high scalability, thus making them suitable to modern large-scale dynamic systems. Unfortunately, these properties come at the cost of redundant message transmissions to ensure bimodal delivery to all interested peers. In systems with high message throughput, those additional messages could pose a significant burden on the excess of required bandwidth. Furthermore, the overlays upon which message dissemination takes place are oblivious to the underlying network, or rely on posterior optimizations that bias the overlay to mimic the network topology. This contributes even more to the required bandwidth as 'undesirable' paths are chosen with equal probability among desired ones. In a Cloud Computing scenario, nodes tend to be aggregated in sub-nets inside a data-center or in multiple datacenters, which are connected by costlier, long-distance links. The goal of this work is, therefore, to build an overlay that approximates the structure of the physical network, while ensuring the connectivity properties desirable to ensure reliable dissemination. By having each node judiciously choose which nodes are in its dissemination list at construction time, i.e. by giving preference to local nodes, we are able to significantly reduce the number of messages traversing the long-distance links. In a later stage, this overlay shall be presented as a service upon which data dissemination and management protocols could be run.