ppxscal/embedding_network huggingface dataset:
Donnybrook: enabling large-scale, high-speed, peer-to-peer games
Authors: Ashwin Bharambe, John R. Douceur, Jacob R. Lorch, Thomas Moscibroda, Jeffrey Pang, Srinivasan Seshan, Xinyu Zhuang
Venue: ACM SIGCOMM Computer Communication Review (2008)

Without well-provisioned dedicated servers, modern fast-paced action games limit the number of players who can interact simultaneously to 16-32. This is because interacting players must frequently exchange state updates, and high player counts would exceed the bandwidth available to participating machines. In this paper, we describe Donnybrook, a system that enables epic-scale battles without dedicated server resources, even in a fast-paced game with tight latency bounds. It achieves this scalability through two novel components. First, it reduces bandwidth demand by estimating what players are paying attention to, thereby enabling it to reduce the frequency of sending less important state updates. Second, it overcomes resource and interest heterogeneity by disseminating updates via a multicast system designed for the special requirements of games: that they have multiple sources, are latency-sensitive, and have frequent group membership changes. We present user study results using a prototype implementation based on Quake III that show our approach provides a desirable user experience. We also present simulation results that demonstrate Donnybrook's efficacy in enabling battles of up to 900 players.
Sentence Understanding and Learning of New Words with Large-Scale Neural Networks
Authors: Heiner Markert, Zöhre Kara Kayikci, Günther Palm
Venue: Proceedings of the 3rd IAPR workshop on Artificial Neural Networks in Pattern Recognition (2008)

We have implemented a speech command system which can understand simple command sentences like "Bot lift ball" or "Bot go table" using hidden Markov models (HMMs) and associative memories with sparse distributed representations. The system is composed of three modules: (1) A set of HMMs is used on phoneme level to get a phonetic transcription of the spoken sentence, (2) a network of associative memories is used to determine the word belonging to the phonetic transcription and (3) a neural network is used on the sentence level to determine the meaning of the sentence. The system is also able to learn new object words during performance.
Classification of Ligase Function Based on Multi-parametric Feature Extracted from Protein Sequence
Authors: Bum Ju Lee, Heon Gyu Lee, Moon Sun Shin, Keun Ho Ryu
Venue: Proceedings of the international conference on Computational Science and Its Applications, Part II (2008)

One of the important goals of bioinformatics is to classify and predict the functions of proteins that have no sequence homolog of known functions. The purpose of this paper is to classify protein function by using multi-parametric feature, without sequence similarity. Firstly, we propose a method for generating novel features that present various local information of protein sequence based on positively and negatively charged residues. Then, we introduce a process of making optimal feature subset through combination of traditional and novel features extracted from protein sequence. Finally, we classify ligase enzymes by support vector machine (SVM). In experiment, only 375 out of 483 features were selected by feature selection, and the classification accuracy for 4th sub-classes in Enzyme Commission (EC) number is 98.35%. Our results demonstrate that most of novel features are valuable for specific enzyme function classification.
Multiobjectivization by Decomposition of Scalar Cost Functions
Authors: Julia Handl, Simon C. Lovell, Joshua Knowles
Venue: Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X (2008)

The term `multiobjectivization' refers to the casting of a single-objec-tive optimization problem as a multiobjective one, a transformation that can be achieved by the addition of supplementary objectives or by the decomposition of the original objective function. In this paper, we analyze how multiobjectivization by decomposition changes the fitness landscape of a given problem and affects search. We find that decomposition has only one possible effect: to introduce plateaus of incomparable solutions. Consequently, multiobjective hillclimbers using no archive `see' a smaller (or at most equal) number of local optima on a transformed problem compared to hillclimbers on the original problem. When archived multiobjective hillclimbers are considered this effect may partly be reversed. Running time analyses conducted on four example functions demonstrate the (positive and negative) influence that both the multiobjectivization itself, and the use vs. non-use of an archive, can have on the performance of simple hillclimbers. In each case an exponential/polynomial divide is revealed.
Simulation of Employee Behavior Based on Cellular Automata Model
Authors: Yue Jiao, Shaorong Sun, Xiaodong Sun
Venue: Proceedings of the 7th international conference on Computational Science, Part IV: ICCS 2007 (2007)

The aim of this current paper is to research the interactive influence of employee behavior in a given organization. First, we define three kinds of employee behavior called Positive Behavior, Zero Behavior and Negative Behavior. Then, we give a new cellular description of behavior states and define the evolution rules for this cellular automata (CA) model. In order to find what may influence the employee behavior and how, we consider two cellular attributes: behavior's influence force, recoded as Influence and behavior's insistence force, recorded as Insistence. Finally, we use this improved CA model to simulate how employee behavior evolves, and how encouragement rules and punishment rules influence employee behavior.
Improvement of Prediction Ability of Multicomponent Regression Model
Authors: Ling Gao, Shouxin Ren
Venue: Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 5 - Volume 05 (2008)

A novel method named wavelet packet transform based Elman recurrent neural network (WPTERNN) was proposed for simultaneous determination of Co (II), Zn (II) and Cu (II) by combining wavelet packet denoising with Elman recurrent neural network. Wavelet packet representations of signals provided a local time–frequency description, thus in the wavelet packet domain, the quality of the noise removal can be improved. Elman recurrent network was applied for non-linear multivariate calibration. In this case, by trials, wavelet function, decomposition level and numbers of hidden nodes for WPTERNN method were selected as Daubechies 2, 3 and 9 respectively. A program PWPTERNN was designed to perform simultaneous determination of Co (II), Zn (II) and Cu (II). The relative standard errors of prediction (RSEP) for all components with WPTERNN, Elman recurrent neural network (ERNN) and partial least squares (PLS), principal component regression (PCR) and Fourier transform based PCR (FTPCR) were 6.7, 14.7, 9.2, 25.6 and 25.2 % respectively. Experimental results demonstrated that the WPTERRN method had the best performance among the five methods.
A new hybrid electro-numerical model of the left ventricle
Authors: Maciej Kozarski, Gianfranco Ferrari, Krzysztof Zieliński, Krystyna Górczyńska, Krzysztof J. Pałko, Arkadiusz Tokarz, Marek Darowski
Venue: Computers in Biology and Medicine (2008)

The paper presents a new project of a hybrid numerical-physical model of the left ventricle. A physical part of the model can be based on electrical or hydraulic structures. Four variants of the model with numerical and physical heart valves have been designed to investigate an effect of a heart assistance connected in series and in parallel to the natural heart. The LabVIEW^T^M real time environment has been used in the model to increase its accuracy and reliability. A prototype of the hybrid electro-numerical model of the left ventricle has been tested in an open loop and closed loop configuration.
Enhance Computational Efficiency of Neural Network Predictive Control Using PSO with Controllable Random Exploration Velocity
Authors: Xin Chen, Yangmin Li
Venue: Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks (2007)

NNPC has been used widely to control nonlinear systems. However traditional gradient decent algorithm (GDA) needs a large computational cost, so that NNPC is not acceptable for systems with rapid dynamics. To apply NNPC in fast control of mobile robots, the paper proposes an improved optimization technique, particle swarm optimization with controllable random exploration velocity (PSO-CREV), to replace of GDA in NNPC. Therefore for one cycle of control, PSO-CREV needs less iterations than GDA, and less population size than conventional PSO. Hence the computational cost of NNPC is reduced by using PSO-CREV, so that NNPC using PSO-CREV is more feasible for the control of rapid processes. As an example, a test of trajectory tracking using mobile robots is chosen to compare performance of PSO-CREV with other algorithms to show its advantages, especially on the aspect of computational time.
Effective image retrieval using dominant color descriptor and fuzzy support vector machine
Authors: Rui Min, H. D. Cheng
Venue: Pattern Recognition (2009)

Image retrieval is an active research area in image processing, pattern recognition, and computer vision. Relevance feedback has been widely accepted in the field of content-based image retrieval (CBIR) as a method to boost the retrieval performance. Recently, many researchers have employed support vector machines (SVMs) for relevance feedback. This paper presents a fuzzy support vector machine (FSVM) that is more robust to the four major problems encountered by the conventional SVMs: small size of samples, biased hyperplane, over-fitting, and real-time. To improve the performance, a dominant color descriptor (DCD) is also proposed. Experimental results based on a set of Corel images demonstrate that the proposed system performs much better than the previous methods. It achieves high accuracy and reduces the processing time greatly.
Approximate Compilation of Constraints into Multivalued Decision Diagrams
Authors: Tarik Hadzic, John N. Hooker, Barry O'Sullivan, Peter Tiedemann
Venue: Proceedings of the 14th international conference on Principles and Practice of Constraint Programming (2008)

We present an incremental refinement algorithm for approximate compilation of constraint satisfaction models into multivalued decision diagrams (MDDs). The algorithm uses a vertex splitting operation that relies on the detection of equivalent paths in the MDD. Although the algorithm is quite general, it can be adapted to exploit constraint structure by specializing the equivalence tests for partial assignments to particular constraints. We show how to modify the algorithm in a principled way to obtain an approximate MDD when the exact MDD is too large for practical purposes. This is done by replacing the equivalence test with a constraint-specific measure of distance. We demonstrate the value of the approach for approximate and exact MDD compilation and evaluate its benefits in one of the main MDD application domains, interactive configuration.
Early Computers at IBM
Authors: Cuthbert C. Hurd
Venue: IEEE Annals of the History of Computing (1981)

This paper describes the principal computers designed by IBM beginning with its first computer, the 701 delivered in 1952, through its first transistorized computer, STRETCH, and its first commercial process-control computer, the 1710 delivered in 1961. The ideas behind some of IBM's methods and decisions are described.
Detecting Patterns of Poor Design Solutions Using Constraint Propagation
Authors: Ghizlane El-Boussaidi, Hafedh Mili
Venue: Proceedings of the 11th international conference on Model Driven Engineering Languages and Systems (2008)

We are proposing an approach for applying design patterns that consists of recognizing occurrences of the modeling problem solved by the design pattern (problem pattern) in input models, which are then transformed according to the solution proposed by the design pattern (solution pattern). In this paper, we look at the issue of identifying instances of problem patterns in input models, and marking the appropriate entities so that the appropriate transformations can be applied. Model marking within the context of MDA is a notoriously difficult problem, in part because of the structural complexity of the patterns that we look for, and in part because of the required design knowledge--- and expertise. Our representation of design problem patterns makes it relatively easy to express the pattern matching problem as a constraint satisfaction problem. In this paper, we present our representation of design problem patterns, show how matching such patterns can be expressed as a constraint satisfaction problem, and present an implementation using ILOG JSolver, a commercial CSP solver.
Workload-Aware Histograms for Remote Applications
Authors: Tanu Malik, Randal Burns
Venue: Proceedings of the 10th international conference on Data Warehousing and Knowledge Discovery (2008)

Recently several database-based applications have emerged that are remote from data sources and need accurate histograms for query cardinality estimation. Traditional approaches for constructing histograms require complete access to data and are I/O and network intensive, and therefore no longer apply to these applications. Recent approaches use queries and their feedback to construct and maintain "workload aware" histograms. However, these approaches either employ heuristics, thereby providing no guarantees on the overall histogram accuracy, or rely on detailed query feedbacks, thus making them too expensive to use. In this paper, we propose a novel, incremental method for constructing histograms that uses minimum feedback and guarantees minimum overall residual error. Experiments on real, high dimensional data shows 30-40% higher estimation accuracy over currently known heuristic approaches, which translates to significant performance improvement of remote applications.
Ski rental with two general options
Authors: Zvi Lotker, Boaz Patt-Shamir, Dror Rawitz
Venue: Information Processing Letters (2008)

We define and solve a simple extension of the ski-rental problem [A.R. Karlin, M.S. Manasse, L. Rudolph, D.D. Sleator, Competitive snoopy caching, Algorithmica 3 (1) (1988) 77-119]. In the classical version, the algorithm needs to decide when to switch from renting to buying. In our version, no pure buy option is available: even after switching to the buy option, the algorithm needs to pay some reduced rent.
Searching Eye Centers Using a Context-Based Neural Network
Authors: Jun Miao, Laiyun Qing, Lijuan Duan, Wen Gao
Venue: Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks (2007)

Location of human features, such as human eye centers, is much important for face image analysis and understanding. This paper proposes a context-based method for human eye centers search. A neural network learns the contexts between human eye centers and their environment in images. For some initial positions, the distances between them and the labeled eye centers in horizontal and vertical directions are learned and remembered respectively. Given a new initial position, the system will predict the eye centers' positions according to the contexts that the neural network learned. Two experiments on human eye centers search showed promising results.
Research on SOA-Based Education Resource Service System
Authors: Feimin Li, Kebin Huang, Bin Yang, Huixia Wang, Zhaoxia Dai, Hui Ma
Venue: Proceedings of the 2008 Second International Conference on Genetic and Evolutionary Computing (2008)

SOA is a software architecture. It is a real solution to integrate education resources and enhance resource utilizes. This essay introduced SOA's principle, structure and reference model of application. Based on this, it designed a SOA-based education resource service system. It used Web Service, WSDL, UDDI, SOAP and CELTS to implement the system. Through the system, we can use and develop the education resource much more easier and quicker.
Experiment Databases: Towards an Improved Experimental Methodology in Machine Learning
Authors: Hendrik Blockeel, Joaquin Vanschoren
Venue: Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases (2007)

Machine learning research often has a large experimental component. While the experimental methodology employed in machine learning has improved much over the years, repeatability of experiments and generalizability of results remain a concern. In this paper we propose a methodology based on the use of experiment databases. Experiment databases facilitate large-scale experimentation, guarantee repeatability of experiments, improve reusability of experiments, help explicitating the conditions under which certain results are valid, and support quick hypothesis testing as well as hypothesis generation. We show that they have the potential to significantly increase the ease with which new results in machine learning can be obtained and correctly interpreted.
Inference of Gene Regulatory Network by Bayesian Network Using Metropolis-Hastings Algorithm
Authors: Khwunta Kirimasthong, Aompilai Manorat, Jeerayut Chaijaruwanich, Sukon Prasitwattanaseree, Chinae Thammarongtham
Venue: Proceedings of the 3rd international conference on Advanced Data Mining and Applications (2007)

Bayesian networks are widely used to infer genes regulatory network from their transcriptional expression data. Bayesian network of the best score is usually chosen as genes regulatory model. However, without the hint from biological ground truth, and given a small number of transcriptional expression observations, the resulting Bayesian networks might not correspond to the real one. To deal with these two constrains, this paper proposes a stochastic approach to fit an existing hypothetical gene regulatory network, derived from biological evidence, with few available amount of transcriptional expression levels of the genes. The hypothetical gene regulatory network is set as an initial model of Bayesian network and fitted with transcriptional expression data by using Metropolis-Hastings algorithm. In this work, the transcriptional regulation of gene CYC1 by co-regulators HAP2 HAP3 HAP4 of yeast (Saccharomyces Cerevisiae) is considered as example. Due to the simulation results, ten probable gene regulatory networks which are similar to the given hypothetical model are obtained. This shows that Metropolis-Hastings algorithm can be used as a simulation model for gene regulatory network.
The use of electronic data processing systems in the life insurance business
Authors: M. E. Davis
Venue: Papers and discussions presented at the Dec. 8-10, 1953, eastern joint AIEE-IRE computer conference: information processing systems---reliability and requirements (1953)

It was under the impetus of the last war that several rapid computing devices were developed. They did very difficult and important mathematical work for the government and, in doing so, demonstrated a capacity to handle work similar to that found in many offices. It is not surprising, then, that at the end of the war some of us began to feel that machinery of this kind might be very useful in the life insurance business. The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc. Terms of Usage Privacy Policy Code of Ethics Contact Us Useful downloads: Adobe Acrobat QuickTime Windows Media Player Real Player
Scalable Multiparty Computation with Nearly Optimal Work and Resilience
Authors: Ivan Damgård, Yuval Ishai, Mikkel Krøigaard, Jesper Buus Nielsen, Adam Smith
Venue: Proceedings of the 28th Annual conference on Cryptology: Advances in Cryptology (2008)

We present the first general protocol for secure multiparty computation in which the total amount of work required by n players to compute a function f grows only polylogarithmically with n (ignoring an additive term that depends on n but not on the complexity of f). Moreover, the protocol is also nearly optimal in terms of resilience, providing computational security against an active, adaptive adversary corrupting a (1/2 ¿ ¿) fraction of the players, for an arbitrary ¿> 0.
Proportional integral sliding mode control for the half-car active suspension system with hydraulic actuator
Authors: Y. M. Sam, N. M. Suaib, J. H. S. Osman
Venue: Proceedings of the 8th WSEAS International Conference on Robotics, Control and Manufacturing Technology (2008)

The purposes of this paper are to present a new mathematical model and control technique for modeling and control of an active suspension system with hydraulic dynamics for a half car model. In this paper the new mathematical model is presented which will give a much more complete mathematical representation of hydraulically actuated suspension system for the half car model. The models presented taking into account all the pressure difference parameters inherent in the hydraulic cylinder. However, from the derived mathematical expression for the systems, it is found that the system suffer from mismatched condition problem due to the nature of the road disturbance which is not in phase with the control input. Thus, a new control technique is proposed that will be able to overcome the mismatched condition problem inherent in the system. In order to achieve the desired ride comfort and road handling and to solve the mismatched condition, a proportional-integral sliding mode control (PISMC) technique is presented to deal with the system and uncertainties. Extensive simulations are performed for different road profiles and the results showed that the proposed controller performed well in improving the ride comfort and road handling for the half car model using the hydraulically actuated suspension system. The results also showed that the system is completely insensitive to the external disturbance due to the road surface irregularities. Thus, it is also proved that the proposed controller is capable to overcome the mismatched condition problem that present in the active suspension system.
Characterisation of the Surfactant Shell Stabilising Calcium Carbonate Dispersions in Overbased Detergent Additives: Molecular Modelling and Spin-Probe-ESR Studies
Authors: Francesco Frigerio, Luciano Montanari
Venue: Proceedings of the 7th international conference on Computational Science, Part II (2007)

The surfactant shell stabilising the calcium cabonate core in overbased detergent additives of lubricant base oils was characterised by computational and experimental methods, comprising classical force-field based molecular simulations and spin-probe Electron Spin Resonance spectroscopy. An atomistic model is proposed for the detergents micelle structure. The dynamical behaviour observed during diffusion simulations of three nitroxide spin-probe molecules into micelle models could be correlated to their mobility as determined from ESR spectra analysis. The molecular mobility was found to be dependent on the chemical nature of the surfactants in the micelle external shell.