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DRAFT DESCRIPTION OF PASCAL

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SOFTWARE-PRACTICE & EXPERIENCE 1979年第5期9卷 381-424页

作者： ADDYMAN, AM BREWER, R BURNETTHALL, DG DEMORGAN, RM FINDLAY, W JACKSON, MI JOSLIN, DA REES, MJ WATT, DA WELSH, J WICHMANN, BA Department of Computer Science The University Oxford Road Manchester M13 9PL Software Department GEC Computers Ltd. Elstree Way Borehamwood Hertfordshire Department of Computer Science University of York Heslington York Y01 5DD European Software Engineering Digital Equipment Company Ltd. Fountain House The Butts Centre Reading RG1 7QN Computing Science Department University of Glasgow Glasgow G12 8QQ International Data Engineering Centre ITT Business Systems and Communications Group Latimer House Potters Bar Herts EN6 5DN Hull College of Higher Education Inglemire Avenue Hull HU6 7LJ Computer Studies Group The University Southampton S09 5NH Department of Computer Science Queen's University Belfast BT7 1NN Department of Industry National Physical Laboratory Teddington Middlesex TW1 0LW

This paper provides a description of the programming language Pascal. It has been published to enable those without easy access to the official BSI ‘draft for comment’ to comment on the description.

关键词： Pascal Standards

Enhanced prairie dog optimization with Levy flight and dynamic opposition-based learning for global optimization and engineering design problems

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Neural computing and Applications 2024年第19期36卷 11137-11170页

作者： Biswas, Saptadeep Shaikh, Azharuddin Ezugwu, Absalom El-Shamir Greeff, Japie Mirjalili, Seyedali Bera, Uttam Kumar Abualigah, Laith Department of Mathematics National Institute of Technology Agartala Tripura Agartala India Unit for Data Science and Computing North-West University 11 Hoffman Street Potchefstroom2520 South Africa Faculty of Natural and Agricultural Sciences School of Computer Science and Information Systems North-West University Vanderbijlpark South Africa Centre for Artificial Intelligence Research and Optimization Torrens University Australia Fortitude Valley BrisbaneQLD4006 Australia Research Center University of Tabuk Tabuk71491 Saudi Arabia Computer Science Department Al al-Bayt University Mafraq25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Applied Science Research Center Applied Science Private University Amman11931 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia College of Engineering Yuan Ze University Taoyuan32003 Taiwan MEU Research Unit Middle East University Amman11831 Jordan

This study proposes a new prairie dog optimization algorithm version called EPDO. This new version aims to address the issues of premature convergence and slow convergence that were observed in the original PDO algorithm. To improve performance, several modifications are introduced in EPDO. First, a dynamic opposite learning strategy is employed to increase the diversity of the population and prevent premature convergence. This strategy helps the algorithm avoid falling into local optima and promotes global optimization. Additionally, the Lévy dynamic random walk technique is utilized in EPDO. This modified Lévy flight with random walk reduces the algorithm’s running time for the test function’s ideal value, accelerating its convergence. The proposed approach is evaluated using 33 benchmark problems from CEC 2017 and compared against seven other comparative techniques: GWO, MFO, ALO, WOA, DA, SCA, and RSA. Numerical results demonstrate that EPDO produces good outcomes and performs well in solving benchmark problems. To further validate the results and assess reliability, the authors employ average rank tests, the measurement of alternatives, and ranking according to the compromise solution (MARCOS) method, as well as a convergence report of EPDO and other algorithms. Furthermore, the effectiveness of the EPDO algorithm is demonstrated by applying it to five design problems. The results indicate that EPDO achieves impressive outcomes and proves its capability to address practical issues. The algorithm performs well in solving benchmark and practical design problems, as supported by the numerical results and validation methods used in the study. © The Author(s) 2024.

关键词： Global optimization

CDRAS: An approach to dealing with Man-in-the-Middle attacks in the context of voice over IP

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CDRAS: An approach to dealing with Man-in-the-Middle attacks...

2011 6th International Conference on Availability, Reliability and Security, ARES 2011

作者： Hofbauer, Stefan Quirchmayr, Gerald Wills, Christopher C. Danube Data Center GmbH Network Department Floridsdorfer Hauptstrasse 1 1210 Vienna Austria University of Vienna Multimedia Information Systems Research Group Liebiggasse 4/3-4 1010 Vienna Austria University of South Australia Division of Information Technology School of Computer and Information Science Mawson Lakes SA 5095 Australia University of South Australia Division of Information Technology School of Computer and Information Science GPO Box 2471 Adelaide SA 5001 Australia Kingston University Faculty of Computing Information Systems and Mathematics Penrhyn Road Surrey KT1 2EE London United Kingdom

ISBN: (纸本)9780769544854

This paper describes the CDRAS (Call Detail Records Analysis System) system, the motivation behind it, its approach and its background. The system aims at dealing with the notorious Man-in-the-Middle attack in the context of VoIP. As the use of such a system constitutes leads to an interference with the privacy of users, the most relevant legal aspects related to this work are also briefly discussed. © 2011 IEEE.

关键词： Voice/data communication systems

Idiosyncratic properties of Australian STV election counting

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arXiv 2024年

作者： Conway, Andrew Blom, Michelle Ek, Alexander Stuckey, Peter J. Teague, Vanessa J. Vukcevic, Damjan Melbourne Australia School of Computing and Information Systems University of Melboure Parkville Australia Department of Econometrics and Business Statistics Monash University Clayton Australia Department of Data Science and AI Monash University Clayton Australia Australian National University Thinking Cybersecurity Pty Ltd Australia

Single Transferable Vote (STV) counting, used in several jurisdictions in Australia, is a system for choosing multiple election winners given voters’ preferences over candidates. There are a variety of different versions of STV legislated and/or applied across Australia. This paper shows some of the unintuitive properties of some of these systems. Copyright © 2024, The Authors. All rights reserved.

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An improved finite-time analysis of temporal difference learning with deep neural networks 24

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An improved finite-time analysis of temporal difference lear...

Proceedings of the 41st International Conference on Machine Learning

作者： Zhifa Ke Zaiwen Wen Junyu Zhang Center for Data Science Peking University China Beijing International Center for Mathematical Research Center for Machine Learning Research and Changsha Institute for Computing and Digital Economy Beijing China Department of Industrial Systems Engineering and Management National University of Singapore Singapore

Temporal difference (TD) learning algorithms with neural network function parameterization have well-established empirical success in many practical large-scale reinforcement learning tasks. However, theoretical understanding of these algorithms remains challenging due to the nonlinearity of the action-value approximation. In this paper, we develop an improved nonasymptotic analysis of the neural TD method with a general L-layer neural network. New proof techniques are developed and an improved new Õ(ε-1) sample complexity is derived. To our best knowledge, this is the first finite-time analysis of neural TD that achieves an Õ(ε-1) complexity under the Markovian sampling, as opposed to the best known Õ(ε-2) complexity in the existing literature.

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An Improved Finite-time Analysis of Temporal Difference Learning with Deep Neural Networks

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arXiv 2024年

作者： Ke, Zhifa Wen, Zaiwen Zhang, Junyu Center for Data Science Peking University China Beijing International Center for Mathematical Research Center for Machine Learning Research Changsha Institute for Computing and Digital Economy Beijing China Department of Industrial Systems Engineering and Management National University of Singapore Singapore

Temporal difference (TD) learning algorithms with neural network function parameterization have well-established empirical success in many practical large-scale reinforcement learning tasks. However, theoretical understanding of these algorithms remains challenging due to the nonlinearity of the action-value approximation. In this paper, we develop an improved non-asymptotic analysis of the neural TD method with a general L-layer neural network. New proof techniques are developed and an improved new Õ(ϵ−1) sample complexity is derived. To our best knowledge, this is the first finite-time analysis of neural TD that achieves an Õ(ϵ−1) complexity under the Markovian sampling, as opposed to the best known Õ(ϵ−2) complexity in the existing literature. Copyright © 2024, The Authors. All rights reserved.

关键词： Asymptotic analysis

Mean-field games among teams

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arXiv 2023年

作者： Subramanian, Jayakumar Kumar, Akshat Mahajan, Aditya Media and Data Science Research Lab Digital Experience Cloud Adobe Inc. Uttar Pradesh Noida India School of Computing and Information Systems The Singapore Management University Singapore Department of Electrical and Computer Engineering McGill University Montreal Canada

In this paper, we present a model of a game among teams. Each team consists of a homogeneous population of agents. Agents within a team are cooperative while the teams compete with other teams. The dynamics and the costs are coupled through the empirical distribution (or the mean field) of the state of agents in each team. This mean-field is assumed to be observed by all agents. Agents have asymmetric information (also called a non-classical information structure). We propose a mean-field based refinement of the Team-Nash equilibrium of the game, which we call mean-field Markov perfect equilibrium (MF-MPE). We identify a dynamic programming decomposition to characterize MF-MPE. We then consider the case where each team has a large number of players and present a mean-field approximation which approximates the game among large-population teams as a game among infinite-population teams. We show that MF-MPE of the game among teams of infinite population is easier to compute and is an Ε-approximate MF-MPE of the game among teams of finite population. Copyright © 2023, The Authors. All rights reserved.

关键词： Dynamic programming

Distributed and Localized Model Predictive Control. Part II: Theoretical Guarantees

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arXiv 2022年

作者： Alonso, Carmen Amo Li, Jing Shuang Matni, Nikolai Anderson, James The Computing and Mathematical Sciences Department California Institute of Technology PasadenaCA91106 United States The Department of Electrical and Systems Engineering The University of Pennsylvania PhiladelphiaPA19104 United States The Department of Electrical Engineering The Data Science Institute Columbia University New YorkNY10027 United States

Engineered cyberphysical systems are growing increasingly large and complex. These systems require scalable controllers that robustly satisfy state and input constraints in the presence of additive noise - such controllers should also be accompanied by theoretical guarantees on feasibility and stability. In our companion paper, we introduced Distributed and Localized Model Predictive Control (DLMPC) for large-scale linear systems;DLMPC is a scalable closed-loop MPC scheme in which subsystems need only exchange local information in order to synthesize and implement local controllers. In this paper, we provide recursive feasibility and asymptotic stability guarantees for DLMPC. We leverage the System Level Synthesis framework to express the maximal positive robust invariant set for the closed-loop system and its corresponding Lyapunov function, both in terms of the closed-loop system responses. We use the invariant set as the terminal set for DLMPC, and show that this guarantees feasibility with minimal conservatism. We use the Lyapunov function as the terminal cost, and show that this guarantees stability. We provide fully distributed and localized algorithms to compute the terminal set offline, and also provide necessary additions to the online DLMPC algorithm to accommodate coupled terminal constraint and cost. In all algorithms, only local information exchanges are necessary, and computational complexity is independent of the global system size - we demonstrate this analytically and experimentally. This is the first distributed MPC approach that provides minimally conservative yet fully distributed guarantees for recursive feasibility and asymptotic stability, for both nominal and robust settings. Copyright © 2022, The Authors. All rights reserved.

关键词： Model predictive control

Digital Innovations Development: Causal factors and relations - A Case Study of Montenegro 28

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Digital Innovations Development: Causal factors and relation...

2024 28th International Conference on Information Technology, IT 2024

作者： Ognjanovic, Ivana Lakovic, Luka Sendelj, Ramo Bokor, Laszlo Kara, Peter A. Reich, Christoph Madan, Manav Roganovic, Milovan Zouilas, Emmanouil Mantas, John Erakovic, Jevto Radusinovic, Tanja Rakocevic, Nada University of Donja Gorica Faculty for Information Systems and Technologies Podgorica81000 Montenegro University of Donja Gorica Humanistic Studies Podgorica81000 Montenegro Budapest University of Technology and Economics Faculty of Electrical Engineering and Informatics Department of Networked Systems and Services Budapest1111 Hungary Furtwangen University Institute of Data Science Cloud Computing and It Security Furtwangen78120 Germany Clinic for Neurology Clinical Centre of Montenegro Podgorica81000 Montenegro National and Kapodistrian University of Athens Faculty of Nursing School of Health Sciences Goudi11527 Greece Chamber of Economy of Montenegro Podgorica81000 Montenegro

ISBN: (纸本)9798350369618

In order for economies to expand and develop globally, innovation is essential. A popular statistic for evaluating innovation between countries is the Global Innovation Index (GII), which provides a thorough analysis of the state of innovation worldwide. In this context, this paper aims to contribute to the literature by examining the multi-factoral effects of the innovation ecosystem on innovation development (internal causal effects), and the impact of global trends and crises on the innovation ecosystem (external causal effects) in Montenegro. The resources provided in this paper offer valuable insights into the innovation landscape in Montenegro and the Western Balkans, as well as the impact of globalization, global trends, and crises on innovation in the region. The paper also discusses the function of innovation hubs such as the DigNest project, in promoting cooperation between academic institutions, government agencies and corporations, and supporting the exchange of knowledge and fresh concepts. © 2024 IEEE.

关键词： Ecosystems