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2nd International Symposium on Brain, Vision, and Artificial Intelligence, BVAI 2007

作者： Lima, Priscila M. V. Morveli-Espinoza, M. Mariela M. França, Felipe M. G. LAM - Computer Architecture and Microelectronics Laboratory Universidade Federal Do Rio de Janeiro Brazil Departament d'Informàtica Universitat Autònoma de Barcelona Spain COPPE - Systems Engineering and Computer Science Program Universidade Federal Do Rio de Janeiro Brazil

ISBN: (纸本)3540755543

This paper presents the implementation of ARQ-PROP II, a limited-depth propositional reasoner, via the compilation of its specification into an exact formulation using the satyrus platform. satyrus' compiler takes as input the definition of a problem as a set of pseudo-Boolean constraints and produces, as output, the Energy function of a higher-order artificial neural network. This way, satisfiability of a formula can be associated to global optima. In the case of ARQ-PROP II, global optima is associated to Resolution-based refutation, in such a way that allows for simplified abduction and prediction to be unified with deduction. Besides experimental results on deduction with ARQ-PROP II, this work also corrects the mapping of satisfiability into Energy minima originally proposed by Gadi Pinkas. © 2007 Springer-Verlag Berlin Heidelberg.

关键词： Neural networks

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EvolTrack: Improving design evolution awareness in software development

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Journal of the Brazilian computer Society 2010年第2期16卷 117-131页

作者： de Cepêda, Rafael da Silva Viterbo Magdaleno, Andréa Magalhães Murta, Leonardo Gresta Paulino Werner, Cláudia Maria Lima COPPE/UFRJ-Systems Engineering and Computer Science Department Zip 21945-970 P.O. Box 68511 Rio de Janeiro RJ Brazil IC/UFF-Instituto de Computação Universidade Federal Fluminense Niterói RJ Brazil

Software differs from most manufactured products because it is intangible. This characteristic makes it difficult to detect, control, and understand how it evolves. This paper presents an approach based on software visualization that can detect and externalize design evolution made in a software project during its initial development or at any further phase. By using this approach, a developer can be aware of the current state of the software as a whole and can additionally verify if the current design, also called emerging design, is evolving according to the team expectations and leader guidance, preventing problems caused by misunderstandings of the expected software solution. The approach was evaluated with free/open source software (FOSS) projects. The results indicate that the approach behaves as expected when applied to real software development projects, with minor performance bottlenecks. © 2010 The Brazilian computer Society.

关键词： Software design

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DeepDTAGen: a multitask deep learning framework for drug-target affinity prediction and target-aware drugs generation

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Nature communications 2025年第1期16卷 5021页

作者： Pir Masoom Shah Huimin Zhu Zhangli Lu Kaili Wang Jing Tang Min Li School of Computer Science and Engineering Central South University Changsha China. School of Computer Science and Technology Donghua University Shanghai China. Research Program in Systems Oncology Faculty of Medicine University of Helsinki Helsinki Finland. Department of Biochemistry and Developmental Biology Faculty of Medicine University of Helsinki Helsinki Finland. School of Computer Science and Engineering Central South University Changsha China. limin@***. Xiangjiang Laboratory Changsha China. limin@***. Furong Laboratory Central South University Changsha China. limin@***.

Identifying novel drugs that can interact with target proteins is a highly challenging, time-consuming, and costly task in drug discovery and development. Numerous machine learning-based models have recently been utilized to accelerate the drug discovery process. However, these existing methods are primarily uni-tasking, either designed to predict drug-target interaction (DTI) or generate new drugs. Through the lens of pharmacological research, these tasks are intrinsically interconnected and play a critical role in effective drug development. Therefore, the learning models must be utilized in such a manner to learn the structural properties of drug molecules, the conformational dynamics of proteins, and the bioactivity between drugs and targets. To this end, this paper develops a novel multitask learning framework that can predict drug-target binding affinities and simultaneously generate new target-aware drug variants, using common features for both tasks. In addition, we developed the FetterGrad algorithm to address the optimization challenges associated with multitask learning particularly those caused by gradient conflicts between distinct tasks. Comprehensive experiments on three real-world datasets demonstrate that the proposed model provides an effective mechanism for predicting drug-target binding affinities and generating novel drugs, thus greatly facilitating the drug discovery process.

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Sparse KPCA for feature extraction in speech recognition

Sparse KPCA for feature extraction in speech recognition

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2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05

作者： Lima, A. Zen, H. Nankaku, Y. Tokuda, K. Kitamura, T. Resende, F.G. Nagoya Institute of Technology Department of Computer Science and Engineering Nagoya 466-8555 Japan Federal University of Rio de Janeiro Dept. of Electronics and Computer Engineering/EPoli Program of Electrical Engineering/COPPE Rio de Janeiro 21945-970 Brazil

ISBN: (纸本)0780388747

This paper presents an analysis of the applicability of Sparse Kernel Principal Component Analysis (SKPCA) for feature extraction in speech recognition, as well as, a proposed approach to make the SKPCA technique realizable for a large amount of training data, which is an usual context in speech recognition systems. Although the KPCA (Kernel Principal Component Analysis) has proved to be an efficient technique for being applied to speech recognition, it has the disadvantage of requiring training data reduction, when its amount is excessively large. The standard approach to perform this data reduction is to randomly choose frames from the original data set, which does not necessarily provide a good statistical representation of the original data set. In order to solve this problem a likelihood related re-estimation procedure was applied to the KPCA framework, thus creating the SKPCA. The experimental results show the efficiency of SKPCA technique with the proposed approach over the KPCA with the standard sparse solution using randomly chosen frames and the standard feature extraction techniques. © 2005 IEEE.

关键词： Speech recognition

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Optimal Deep Canonically Correlated Autoencoder-Enabled Prediction Model for Customer Churn Prediction

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computers, Materials & Continua 2022年第11期73卷 3757-3769页

作者： Olfat M.Mirza GJose Moses R.Rajender E.Laxmi Lydia Seifedine Kadry Cheadchai Me-Ead Orawit Thinnukool Department of Computer Science College of Computers and Information SystemsUmm Al-Qura UniversityMakkahSaudi Arabia Department of Computer Science Engineering Malla Reddy Engineering College500100India Department of Computer Science Engineering Lendi Institute of Engineering&TechnologyDenkada535005India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Applied Data Science Noroff University CollegeNorway Program in Statistics and Information Management Faculty of ScienceMaejo UniversityChiang Mai50290Thailand Department of Modern Management and Information Technology College of Arts Media and TechnologyChiang Mai UniversityChiang Mai50200Thailand

Presently,customer retention is essential for reducing customer churn in telecommunication *** churn prediction(CCP)is important to predict the possibility of customer retention in the quality of *** risks of customer churn also get essential,the rise of machine learning(ML)models can be employed to investigate the characteristics of customer ***,deep learning(DL)models help in prediction of the customer behavior based characteristic *** the DL models necessitate hyperparameter modelling and effort,the process is difficult for research communities and business *** this view,this study designs an optimal deep canonically correlated autoencoder based prediction(ODCCAEP)model for competitive customer dependent application *** addition,the O-DCCAEP method purposes for determining the churning nature of the *** O-DCCAEP technique encompasses preprocessing,classification,and hyperparameter ***,the DCCAE model is employed to classify the churners or ***,the hyperparameter optimization of the DCCAE technique occurs utilizing the deer hunting optimization algorithm(DHOA).The experimental evaluation of the O-DCCAEP technique is carried out against an own dataset and the outcomes highlighted the betterment of the presented O-DCCAEP approach on existing approaches.

关键词： Churn prediction customer retention deep learning machine learning archimedes optimization algorithm

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Efficient categorization of memory sharing patterns in software DSM systems 15

Efficient categorization of memory sharing patterns in softw...

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15th International Parallel and Distributed Processing Symposium, IPDPS 2001

作者： De Castro, M.C.S. De Amorim, C.L. Computer Science Department Federal University of Juiz de Fora Minas Gerais Brazil COPPE - Systems Engineering Federal University of Rio de Janeiro Rio de Janeiro Brazil

ISBN: (纸本)0769509908

This work introduces a new technique that enables SDSMs to categorize dynamically and accurately memory sharing patterns in both classes of regular and irregular applications. The categorization is carried out automatically at run-time on a per-page basis, requiring no user or compiler assistance. We evaluate the potential benefits of our technique using execution-driven simulations of 8 applications running on TrendMarks on a network of 8 workstations. Surprisingly, we found that producer-consumer(s) and migratory are the dominant patterns even in irregular applications. Preliminary results suggest that the categorization technique we propose is a promising option to further improve the performance of current adaptive SDSM systems. © 2001 IEEE.

关键词： adaptive software DSM systems latency-hiding techniques memory sharing patterns

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Context-based process line

Context-based process line

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作者： Nunes, Vanessa Werner, Claudia Santoro, Flavia Maria COPPE/UFRJ Systems Engineering and Computer Science Program P.O. Box 68511 Rio de Janeiro RJ Brazil NP2Tec and Postgraduate Information Systems Program UNIRIO Av. Pasteur 458 Urca - Rio de Janeiro RJ Brazil

ISBN: (纸本)9789898425065

Complexity and dynamism of day-to-day activities in organizations are inextricably linked, one impacting the other, increasing the challenges for constant adaptation of the way to organize work to address emerging demands. In this scenario, there are a variety of information, insight and reasoning being processed between people and systems, during process execution. We argue that process variations could be decided in real time, using context information collected. This paper presents a proposal for a business process line cycle, with a set of activities encapsulated in the form of components as central artefact. We explain how composition and adaptation of work may occur in real time and discuss a scenario for this proposal.

关键词： Enterprise resource management

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Impact of Intention to Use to a Document Management System (Case Study Approach) 3

Impact of Intention to Use to a Document Management System (...

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3rd International Conference on Information Management and Technology, ICIMTech 2018

作者： Hartono, Sugiarto Kosala, Raymondus Raymond Supangkat, Suhono Harso Ranti, Benny Information Systems Department Computer Science Department BINUS Graduate Program-Doctor of Computer Science Jakarta11480 Indonesia Computer Science Department Bina Nusantara University BINUS Graduate Program-Doctor of Computer Science Jakarta11480 Indonesia Bandung Institute of Technology School of Electrical Engineering and Informatics Bandung Indonesia

ISBN: (纸本)9781538658215

Document management system makes user to access information anytime and anywhere. The purpose of this research is to analyze what variables have impact on the intention to use of document management system. To achieve the objectives conducted by identification problem on the research model developed from the model of Technology Acceptance Model and Unified Theory of Acceptance and Use of Technology to the use of document management system. Furthermore, the data obtained from the respondents tested outer model, inner model test, hypothesis test and to produce a comprehensive analysis will be conducted Importance Performance Analysis. This research will result in a comprehensive analysis and able to provide recommendations and solutions for improvement from the implementation DMS has been done earlier. © 2018 IEEE.

关键词： Information services

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Fuzzy Markov predictor in electric load forecasting

Fuzzy Markov predictor in electric load forecasting

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International Joint Conference on Neural Networks (IJCNN)

作者： M.A. Teixeira G. Zaverucha Systems Engineering and Computer Science Program Federal University of Rio de Janeiro Brazil Systems Engineering and Computer Science Program E28093 Graduate Programs in Engineering (COPPE) Federal University of Rio de Janeiro Brazil

We present the fuzzy Markov predictor (FMP), a hybrid system that is applied to the task of monthly electric load forecasting. The FMP is a modification we introduce in the hidden Markov model in order to enable it to predict numerical values. The FMP can be seen as an extension of the fuzzy Bayes predictor (FBP) that was modified from the naive Bayes classifier. For verifying the efficiency of the FMP's prediction, we compare it with the FBP, one fuzzy system and two traditional forecasting methods, Box-Jenkins and Winters exponential smoothing.

关键词： Load forecasting Fuzzy systems Hidden Markov models Power engineering and energy Smoothing methods Niobium compounds systems engineering and theory computer science Predictive models Electric variables measurement

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A Novel Human Interaction Framework Using Quadratic Discriminant Analysis with HMM

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computers, Materials & Continua 2023年第11期77卷 1557-1573页

作者： Tanvir Fatima Naik Bukht Naif Al Mudawi Saud S.Alotaibi Abdulwahab Alazeb Mohammed Alonazi Aisha Ahmed AlArfaj Ahmad Jalal Jaekwang Kim Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Information Systems Department Umm Al-Qura UniversityMakkahSaudi Arabia Department of Information Systems College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Convergence Program for Social Innovation Sungkyunkwan UniversitySuwon03063Korea

Human-human interaction recognition is crucial in computer vision fields like surveillance,human-computer interaction,and social *** enhances systems’ability to interpret and respond to human behavior *** research focuses on recognizing human interaction behaviors using a static image,which is challenging due to the complexity of diverse *** overall purpose of this study is to develop a robust and accurate system for human interaction *** research presents a novel image-based human interaction recognition method using a Hidden Markov Model(HMM).The technique employs hue,saturation,and intensity(HSI)color transformation to enhance colors in video frames,making them more vibrant and visually appealing,especially in low-contrast or washed-out *** filters reduce noise and smooth imperfections followed by silhouette extraction using a statistical *** extraction uses the features from Accelerated Segment Test(FAST),Oriented FAST,and Rotated BRIEF(ORB)*** application of Quadratic Discriminant Analysis(QDA)for feature fusion and discrimination enables high-dimensional data to be effectively analyzed,thus further enhancing the classification *** ensures that the final features loaded into the HMM classifier accurately represent the relevant human *** impressive accuracy rates of 93%and 94.6%achieved in the BIT-Interaction and UT-Interaction datasets respectively,highlight the success and reliability of the proposed *** proposed approach addresses challenges in various domains by focusing on frame improvement,silhouette and feature extraction,feature fusion,and HMM *** enhances data quality,accuracy,adaptability,reliability,and reduction of errors.

关键词： Human interaction recognition HMM classification quadratic discriminant analysis dimensionality reduction

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