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High-performance computing at a crossroads

science (New York, N.Y.) 2025年第6736期387卷 829-831页

作者： Deelman, Ewa Dongarra, Jack Hendrickson, Bruce Randles, Amanda Reed, Daniel Seidel, Edward Yelick, Katherine Information Sciences Institute University of Southern California Marina del Rey CA United States Department of Electrical Engineering and Computer Science University of Tennessee Knoxville TN United States Oak Ridge National Laboratory Oak Ridge TN United States Department of Mathematics University of Manchester Manchester United Kingdom Computing Directorate Lawrence Livermore National Laboratory Livermore CA United States Department of Biomedical Engineering Duke University Durham NC United States Scientific Computing and Imaging Institute University of Utah Salt Lake City UT United States Office of the President University of Wyoming Laramie WY United States Department of Electical Engineering and Computer Sciences University of California Berkeley Berkeley CA United States

Long-term plans and comprehensive vision are needed.

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The next generation software workshop - IPDPS'07

The next generation software workshop - IPDPS'07

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21st International Parallel and Distributed Processing Symposium, IPDPS 2007

作者： Darema, Frederica Computer and Information Science and Engineering Directorate National Science Foundation Arlington VA 22230

ISBN: (纸本)1424409101

This workshop provides a forum for an overview, project presentations, and discussion of the research fostered and funded initially by the NSF Next Generation Software (NGS) Program, and more recently through the Advanced Execution Systems (AES) and the Systems Modeling and Analysis (SMA) components of the follow-up computer Systems Research (CSR) Program1. The present workshop is part of the series of the Next Generation Software Workshops that have been conducted since 2001, on a yearly basis and in conjunction with IPDPS. The topics addressed in the workshop are on research in systems' software technology in the scope of NGS and of the AES and the SMA components, namely: methods for systems modeling and analysis, performance engineering, dependability, fault tolerance, and power-management, programming environments, enhanced compiler capabilities, tools for the development, dynamic runtime support and dynamic composition of complex applications executing on heterogeneous, parallel and distributed computing platform assemblies, such as computational grids, encompassing, advanced multiprocessing nodes, midrange and high-end platforms, clusters, embedded and sensor systems, and special purpose processing systems. © 2007 IEEE.

关键词： Technical presentations

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Broadening participation in computing: A critical perspective

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ACM Inroads 2016年第4期7卷 49-51页

作者： Bobb, Kamau Program Officer National Science Foundation Directorate of Computer and Information Science and Engineering 4201 Wilson Boulevard ArlingtonVA22230 United States

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Energy saving slot allocation-based multicast routing in cloud wireless mesh network

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International Journal of Cloud Computing 2023年第2-4期12卷 148-162页

作者： Jeyakarthic, M. Subalakshmi, N. Department of Computer and Information Science Annamalai University Tamil Nadu Annamalai Nagar India Computer Science and Engineering Wing Directorate of Distance Education Annamalai University Tamil Nadu Annamalai Nagar India

Many techniques of cloud computing are affected by surge load and time issues. This proposed method for virtual cloud planning utilise simple protocols in a single or multiple data centres. A new load balanced amortised multi-scheduling algorithm (LBAM) is proposed for assigning the task to cloud based on active load in the cloud environment. The proposed system calculates the multiple attribute weight for each workplace and introduces an application source that changes in application virtualisation environment. The system calculates the cloud data weight based upon the allocation of data and its consequence based on the processing efficiency of the cloud machine. This approach works effectively in most configurations, also virtual machines hold the ability to process load with high pace and efficient memory management. A detailed comparative analysis is made with the existing methods namely SLA, FPGGA and DJS algorithms. The obtained results indicated that the LBAM model is superior to compared methods under several aspects. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Cloud computing

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Understanding NSF funding opportunities 13

Understanding NSF funding opportunities

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44th ACM Technical Symposium on computer science Education, SIGCSE 2013

作者： Forbes, Jeffrey Piotrowski, Victor Prey, Jane Westbrook, D. Suzanne National Science Foundation Directorate of Computer and Information Science and Engineering United States National Science Foundation Directorate of Education and Human Resources United States

ISBN: (纸本)9781450320306

This session highlights programs in the National science Foundation's (NSF) Education and Human Resources (EHR) and computer and information science and engineering (CISE) directorates. The focus is on providing descriptions of several programs of interest to college faculty and discussing the requirements and guidelines for programs in these areas. The session includes a description of the proposal and review processes as well as strategies for writing competitive proposals. Participants are encouraged to discuss procedural issues with the presenters. Copyright © 2013 ACM.

关键词： Education computing

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Optimization of Electrocardiogram Classification Using Dipper Throated Algorithm and Differential Evolution

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computers, Materials & Continua 2023年第2期74卷 2379-2395页

作者： Doaa Sami Khafaga El-Sayed M.El-kenawy Faten Khalid Karim Sameer Alshetewi Abdelhameed Ibrahim Abdelaziz A.Abdelhamid D.L.Elsheweikh Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt General Information Technology Department Ministry of DefenseThe Executive AffairsExcellence Services DirectorateRiyadh11564Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Science Faculty of Specific EducationMansoura UniversityEgypt

Electrocardiogram(ECG)signal is a measure of the heart’s electrical ***,ECG detection and classification have benefited from the use of computer-aided systems by *** goal of this paper is to improve the accuracy of ECG classification by combining the Dipper Throated Optimization(DTO)and Differential Evolution Algorithm(DEA)into a unified algorithm to optimize the hyperparameters of neural network(NN)for boosting the ECG classification *** addition,we proposed a new feature selection method for selecting the significant feature that can improve the overall *** prove the superiority of the proposed approach,several experimentswere conducted to compare the results achieved by the proposed approach and other competing ***,statistical analysis is performed to study the significance and stability of the proposed approach using Wilcoxon and ANOVA *** results confirmed the superiority and effectiveness of the proposed *** classification accuracy achieved by the proposed approach is(99.98%).

关键词： Electrocardiogram differential evolution algorithm dipper throated optimization neural networks

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Optimized Weighted Ensemble Using Dipper Throated Optimization Algorithm in Metamaterial Antenna

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computers, Materials & Continua 2022年第12期73卷 5771-5788页

作者： Doaa Sami Khafaga El-Sayed M.El-kenawy Faten Khalid Karim Sameer Alshetewi Abdelhameed Ibrahim Abdelaziz AAbdelhamid Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt General Information Technology Department Ministry of DefenseThe Executive AffairsExcellence Services DirectorateRiyadh11564Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia

Metamaterial Antennas are a type of antenna that uses metamaterial to enhance *** bandwidth restriction associated with small antennas can be solved using metamaterial *** learning is gaining popularity as a way to improve solutions in a range of *** learning approaches are currently a big part of current research,and they’re likely to be huge in the *** model utilized determines the accuracy of the prediction in large *** goal of this paper is to develop an optimized ensemble model for forecasting the metamaterial antenna’s bandwidth and *** basic models employed in the developed ensemble are Support Vector Regression(SVR),K-NearestRegression(KNR),Multi-Layer Perceptron(MLP),Decision Trees(DT),and Random Forest(RF).The percentages of contribution of these models in the ensemble model are weighted and optimized using the dipper throated optimization(DTO)*** choose the best features from the dataset,the binary(bDTO)algorithm is *** proposed ensemble model is compared to the base models and results are recorded and analyzed *** addition,two other ensembles are incorporated in the conducted experiments for *** ensembles are average ensemble and K-nearest neighbors(KNN)-based *** comparison is performed in terms of eleven evaluation *** evaluation results confirmed the superiority of the proposed model when compared with the basic models and the other ensemble models.

关键词： Metamaterial antenna dipper throated optimization feature selection parameters prediction

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A New Multi-Agent Feature Wrapper Machine Learning Approach for Heart Disease Diagnosis

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computers, Materials & Continua 2021年第4期67卷 51-71页

作者： Mohamed Elhoseny Mazin Abed Mohammed Salama A.Mostafa Karrar Hameed Abdulkareem Mashael S.Maashi Begonya Garcia-Zapirain Ammar Awad Mutlag Marwah Suliman Maashi Department of Computer Science College of Computer Information TechnologyAmerican University in the Emirates503000United Arab Emirates College of Computer Science and Information Technology University of AnbarRamadi31001Iraq Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn MalaysiaJohor86400Malaysia College of Agriculture Al-Muthanna UniversitySamawah66001Iraq Department of Software Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11451Saudi Arabia eVIDA Lab University of DeustoBilbao48007Spain Department of Pure Science Ministry of EducationGeneral Directorate of CurriculaBaghdadIraq Department of Medical Laboratory Science Faculty of Applied Medical ScienceKing Abdulaziz UniversityJeddah21589Saudi Arabia

Heart disease(HD)is a serious widespread life-threatening *** heart of patients with HD fails to pump sufcient amounts of blood to the entire *** the occurrence of HD early and efciently may prevent the manifestation of the debilitating effects of this disease and aid in its effective *** methods for diagnosing HD are sometimes unreliable and insufcient in analyzing the related *** an alternative,noninvasive medical procedures based on machine learning(ML)methods provide reliable HD diagnosis and efcient prediction of HD ***,the existing models of automated ML-based HD diagnostic methods cannot satisfy clinical evaluation criteria because of their inability to recognize anomalies in extracted symptoms represented as classication features from patients with *** this study,we propose an automated heart disease diagnosis(AHDD)system that integrates a binary convolutional neural network(CNN)with a new multi-agent feature wrapper(MAFW)*** MAFW model consists of four software agents that operate a genetic algorithm(GA),a support vector machine(SVM),and Naïve Bayes(NB).The agents instruct the GA to perform a global search on HD features and adjust the weights of SVM and BN during initial classication.A nal tuning to CNN is then performed to ensure that the best set of features are included in HD *** CNN consists of ve layers that categorize patients as healthy or with HD according to the analysis of optimized HD *** evaluate the classication performance of the proposed AHDD system via 12 common ML techniques and conventional CNN models by using across-validation technique and by assessing six evaluation *** AHDD system achieves the highest accuracy of 90.1%,whereas the other ML and conventional CNN models attain only 72.3%–83.8%accuracy on ***,the AHDD system proposed herein has the highest capability to identify patients with *** system can be used by medical

关键词： Heart disease machine learning multi-agent feature wrapper model heart disease diagnosis HD cleveland datasets convolutional neural network

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Code Generation for Collectible Card Games with Complex APIs 36th

Code Generation for Collectible Card Games with Complex APIs

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36th International Florida Artificial Intelligence Research Society Conference, FLAIRS-36 2023

作者： Licato, John Fields, Logan Hollis, Brayden Lab Department of Computer Science and Engineering University of South Florida United States Information Directorate Air Force Research Lab United States

Large pre-trained language models (LMs) such as GPT-3 Codex are able to generate code remarkably well given prompts of natural language text. But if we want to use such LMs to generate code compatible with a specific API or library (e.g., an API which provides the environments in which certain rules, laws, or orders are to be carried out), the amount of computational and data resources required to fine-tune such models can be cost prohibitive to most organizations. Given these practical limitations, is it possible to utilize these massive codegeneration LMs to write code compatible with a given API? We develop an algorithm that selects code examples using a smaller LM trained to predict which features of an API are likely to be used in the resulting code, which is a simpler problem than actually generating the code. The selected examples are then used to build a prompt for the larger LM, which in turn generates the final code. We demonstrate our results on a benchmark dataset derived from the collectible card game "Magic: the Gathering," and obtain state-of-the-art results. © 2023 by the authors. All rights reserved.

关键词： Machine learning

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information technology research: Transforming our future

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AIP Conference Proceedings 2001年第1期583卷 7-10页

作者： Dr. Ruzena Bajcsy Assistant Director Computer and Information Science and Engineering Directorate National Science Foundation

The information Age is transforming our economy and our lives. In its pathbreaking 1999 report to President Clinton, the Presidential information Technology Advisory Committee (PITAC) outlined the ten crucial ways that new technologies are transforming society in the U.S. It is clear that the Federal government will need to provide the critical R&D investments that will help retain and bolster the U.S. technological lead in the 21st century. These investments will also support efforts to make new technologies and their benefits available to all U.S. citizens.

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