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arXiv 2021年

作者： Mohammed, Hardi M. Abdul, Zrar Kh Rashid, Tarik A. Alsadoon, Abeer Bacanin, Nebojsa Applied Computer Department College of Medicals and Applied Sciences Charmo University Sulaimani Chamchamal KRG Iraq Technical College of Informatics Sulaimani Polytechnic University Sulaimani KRG Iraq Computer Science and Engineering University of Kurdistan Hewler Erbil KRG Iraq Charles Sturt University Sydney Australia Sydney Australia Singidunum University Belgrade Serbia

Purpose: This paper aims at studying meta-heuristic algorithms. One of the common meta-heuristic optimization algorithms is called grey wolf optimization (GWO). The key aim is to enhance the limitations of the wolves’ searching process of attacking gray wolves. Design/methodology/approach: The development of meta-heuristic algorithms has increased by researchers to use them extensively in the field of business, science, and engineering. In this paper, the K-means clustering algorithm is used to enhance the performance of the original GWO;the new algorithm is called K-means clustering gray wolf optimization (KMGWO). Findings: Results illustrate the efficiency of KMGWO against the GWO. To evaluate the performance of the KMGWO, KMGWO was applied to solve CEC2019 benchmark test functions. Originality/value: Results prove that KMGWO is superior to GWO. KMGWO is also compared to cat swarm optimization (CSO), whale optimization algorithm-bat algorithm (WOA-BAT), WOA, and GWO so KMGWO achieved the first rank in terms of performance. Also, the KMGWO is used to solve a classical engineering problem and it is superior. © 2021, CC BY-NC-ND.

关键词： K-means clustering

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Anytime diagnosis for reconfiguration

arXiv

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arXiv 2021年

作者： Felfernig, Alexander Walter, Rouven Galindo, José A. Benavides, David Erdeniz, Seda Polat Atas, Müslüm Reiterer, Stefan Applied Software Engineering Group Institute for Software Technology TU Graz Austria Symbolic Computation Group WSI Informatics Universität Tübingen Germany Computer Languages and Systems Department University of Sevilla Spain SelectionArts Austria

Many domains require scalable algorithms that help to determine diagnoses efficiently and often within predefined time limits. Anytime diagnosis is able to determine solutions in such a way and thus is especially useful in real-time scenarios such as production scheduling, robot control, and communication networks management where diagnosis and corresponding reconfiguration capabilities play a major role. Anytime diagnosis in many cases comes along with a trade-off between diagnosis quality and the efficiency of diagnostic reasoning. In this paper we introduce and analyze FLEXDIAG which is an anytime direct diagnosis approach. We evaluate the algorithm with regard to performance and diagnosis quality using a configuration benchmark from the domain of feature models and an industrial configuration knowledge base from the automotive domain. Results show that FLEXDIAG helps to significantly increase the performance of direct diagnosis search with corresponding quality tradeoffs in terms of minimality and accuracy. Copyright © 2021, The Authors. All rights reserved.

关键词： Scheduling

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Preface

Smart Innovation, Systems and Technologies

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Smart Innovation, Systems and Technologies 2023年 301卷 v-vi页

作者： Das, Biplab Patgiri, Ripon Balas, Valentina Emilia Department of Mechanical Engineering National Institute of Technology Silchar Assam Silchar India Department of Automation and Applied Informatics Aurel Vlaicu University of Arad Arad Romania Department of Computer Science and Engineering National Institute of Technology Silchar Assam Silchar India

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Roadmap on Neuromorphic Photonics

arXiv

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arXiv 2025年

作者： Brunner, Daniel Shastri, Bhavin J. Al-Qadasi, Mohammed A. Ballani, H. Barbay, Sylvain Biasi, Stefano Bienstman, Peter Bilodeau, Simon Bogaerts, Wim Böhm, Fabian Brennan, G. Buckley, Sonia Cai, Xinlun Strinati, Marcello Calvanese Canakci, B. Charbonnier, Benoit Chemnitz, Mario Chen, Yitong Cheung, Stanley Chiles, Jeff Choi, Suyeon Christodoulides, Demetrios N. Chrostowski, Lukas Chu, J. Clegg, J.H. Cletheroe, D. Conti, Claudio Dai, Qionghai Di Lauro, Luigi Diamantopoulos, Nikolaos-Panteleimon Dinc, Niyazi Ulas Ewaniuk, Jacob Fan, Shanhui Fang, Lu Franchi, Riccardo Freire, Pedro Gentilini, Silvia Gigan, Sylvain Giorgi, Gian Luca Gkantsidis, C. Gladrow, J. Goi, Elena Goldmann, M. Grabulosa, A. Gu, Min Guo, Xianxin Hejda, Matěj Horst, F. Hsieh, Jih-Liang Hu, Jianqi Hu, Juejun Huang, Chaoran Hurtado, Antonio Jaurigue, Lina Kalinin, K.P. Kamalian-Kopae, Morteza Kelly, D.J. Khajavikhan, Mercedeh Kremer, H. Laydevant, Jeremie Lederman, Joshua C. Lee, Jongheon Lenstra, Daan Li, Gordon H.Y. Li, Mo Li, Yuhang Lin, Xing Lin, Zhongjin Lis, Mieszko Lüdge, Kathy Lugnan, Alessio Lupo, Alessandro Lvovsky, A.I. Manuylovich, Egor Marandi, Alireza Marchesin, Federico Massar, Serge McCaughan, Adam N. McMahon, Peter L. Moralis-Pegios, Miltiadis Morandotti, Roberto Moser, Christophe Moss, David J. Mukherjee, Avilash Nikdast, Mahdi Offrein, B.J. Oguz, Ilker Oripov, Bakhrom O'Shea, G. Ozcan, Aydogan Parmigiani, F. Pasricha, Sudeep Pavanello, Fabio Pavesi, Lorenzo Peserico, Nicola Pickup, L. Pierangeli, Davide Pleros, Nikos Porte, Xavier Primavera, Bryce A. Prucnal, Paul Psaltis, Demetri Puts, Lukas Qiao, Fei Rahmani, B. Raineri, Fabrice Ríos Ocampo, Carlos A. Robertson, Joshua Romeira, Bruno Roques-Carmes, Charles Rotenberg, Nir Rowstron, A. Schoenhardt, Steffen Schwartz, Russell L.T. Shainline, Jeffrey M. Shekhar, Sudip Skalli, A. Sohoni, Mandar M. Sorger, Volker J. Soriano, Miguel C. Spall, James Stabile, Ripalta Stiller, Birgit Sunada, Satoshi Tefas, Anastasios Tossoun, Bassem Tsakyridis, Apostolos Turitsyn, Sergei K. Van der Sande, G Université Marie et Louis Pasteur CNRS UMR 6174 Institut FEMTO-ST Besançon25000 France Centre for Nanophotonics Department of Physics Engineering Physics & Astronomy Queen's University Canada Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada Microsoft Research Cambridge United Kingdom Université Paris-Saclay CNRS Centre de Nanosciences et de Nanotechnologies France Nanoscience Laboratory Department of Physics University of Trento Italy Photonics Research Group Department of Information Technology Ghent University imec Belgium Princeton University NJ United States Hewlett Packard Labs Hewlett Packard Enterprise Böblingen Germany National Institute of Standards and Technology BoulderCO United States State Key Laboratory of Optoelectronic Materials and Technologies School of Electronics and Information Technology Sun Yat-sen University China Enrico Fermi Research Center Rome Italy Université Grenoble-Alpes CEA Leti Grenoble France Leibniz-Institute of Photonic Technology Jena Germany Institute of Applied Optics and Biophysics Jena Germany Department of Automation Tsinghua University Beijing China Department of Electrical and Computer Engineering North Carolina State University NC United States Department of Electrical Engineering Stanford University CA United States University of Southern California Los AngelesCA United States Department of Physics Sapienza University Rome Italy Institute for Complex Systems National Research Council Rome Italy Varennes Canada NTT Device Technology Labs NTT Corporation Kanagawa Atsugi Japan Institute of Electrical and Microengineering School of Engineering École Polytechnique Fédérale de Lausanne Switzerland Edward L. Ginzton Laboratory Stanford University StanfordCA United States Department of Electronic Engineering Tsinghua University China Beijing National Research Center for Information Science and Technology Tsinghua University China Aston Univ

Neuromorphic photonics are processors inspired by the human brain and enabled by light (photons) instead of traditional electronics. Neuromorphic photonics and its associated concepts are experiencing a significant resurgence, building on foundational research from the 1980s and 1990s. This renewed momentum is driven by breakthroughs in photonic integration, nonlinear optics, and advanced materials, alongside the growing necessity of neuro-inspired computing in numerous applications of economic and societal relevance. The increasing demand for energy-efficient artificial intelligence (AI) solutions underscores the need for innovation and a cohesive vision to address key challenges, including scalability, energy efficiency, precision, and standardized performance benchmarks. Together, these efforts present an opportunity to establish a unique photonic advantage with practical, real-world applications. This roadmap consolidates recent advances while exploring emerging applications, reflecting the remarkable diversity of hardware platforms, neuromorphic concepts, and implementation philosophies reported in the field. It emphasizes the critical role of cross-disciplinary collaboration in this rapidly evolving field. The roadmap introduces various approaches to embedding the high-complexity transformations central to neuromorphic computing, focusing on frequency, delay, and spectral embeddings. This is followed by a discussion of architectures of photonic neural networks (PNNs) and an in-depth analysis of methods for implementing these architectures in photonic hardware. Dedicated sections delve into integrated photonic hardware, the realization of photonic weights and memories, and the optimization of training processes for photonic neuromorphic architectures. The roadmap concludes by exploring numerous potential applications, highlighting the challenges and advances necessary to transition neuromorphic photonic computing from a primarily academic pursuit to a technolog

关键词： Philosophical aspects

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Evaluating Security of MQTT Protocol in Internet of Things

Evaluating Security of MQTT Protocol in Internet of Things

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Canadian Conference on Electrical and computer engineering (CCECE)

作者： Ayman Al-Ani Wong Kang Shen Ahmed K. Al-Ani Shams A. Laghari Omar E. Elejla Cybersecurity Research Group Faculty of Computing and Informatics Universiti Malaysia Sabah Jalan UMS Kota Kinabalu Sabah Malaysia School of Computing and Data Science Xiamen University Malaysia Sepang Selangor Malaysia School of Engineering Technology and Applied Science Centennial College Toronto Ontario Canada National Advanced IPv6 Centre (NAv6) Universiti Sains Malaysia (USM) Gelugor Penang Malaysia Department of Computer Science Al-Aqsa University Gaza Palestine

The Internet of Things (IoT) has revolutionized the way people interact, communicate, and perform daily activities in various domains ranging from households to industries and cities. MQTT is one of the commonly adopted protocols for implementing IoT. However, IoT systems that are connected through MQTT are susceptible to security breaches as MQTT was not originally designed with security as a priority. The credentials and messages transmitted in plaintext by default, thereby compromising data confidentiality and integrity. This study presents a comprehensive analysis of the MQTT protocol, including experimentation on an MQTT system using various cryptographic implementations, such as AES-CBC, RSA, and ECC AES Hybrid Scheme, to assess the processing time and message size. The findings indicate that payload encryption increases processing time and message bytes. Among the cryptographic implementations, RSA incurs the highest processing time, followed by ECC AES Hybrid Scheme and AES- CBC. Furthermore, the study demonstrates the effectiveness of attack prevention between standard MQTT and secured MQTT implementations by simulating various IoT attacks, such as black-box penetration attack, identity spoofing, DoS attack, and MITM attack. The results and subsequent discussion provide insights that answer the research question, revealing the cryptographic algorithms that result in the most overhead on the standard MQTT implementation and their capacity to resist common attacks.

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A survey on deep learning in medical image reconstruction

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Intelligent Medicine 2021年第3期1卷 118-127页

作者： Emmanuel Ahishakiye Martin Bastiaan Van Gijzen Julius Tumwiine Ruth Wario Johnes Obungoloch Department of Computer Science Kyambogo UniversityP.O Box 1KyambogoKampalaUganda Delft University of Technology Delft Institute of Applied MathematicsMekelweg 42628 CDthe Netherlands Department of Mathematics Mbarara University of Science and TechnologyMbarara 1410Uganda Department of Informatics University of Free StateSouth Africa Department of Biomedical Sciences and Engineering Mbarara University of Science and TechnologyMbarara 1410Uganda

Medical image reconstruction aims to acquire high-quality medical images for clinical usage at minimal cost and risk to the *** learning and its applications in medical imaging,especially in image reconstruction have received considerable attention in the literature in recent *** study reviews records obtained elec-tronically through the leading scientific databases(Magnetic Resonance Imaging journal,Google Scholar,Scopus,Science Direct,Elsevier,and from other journal publications)searched using three sets of keywords:(1)Deep learning,image reconstruction,medical imaging;(2)Medical imaging,Deep learning,Image reconstruction;(3)Open science,Open imaging data,Open *** articles reviewed revealed that deep learning-based re-construction methods improve the quality of reconstructed images qualitatively and ***,deep learning techniques are generally computationally expensive,require large amounts of training datasets,lack decent theory to explain why the algorithms work,and have issues of generalization and *** challenge of lack of enough training datasets is currently being addressed by using transfer learning techniques.

关键词： Deep learning Open science Image reconstruction Medical imaging Machine Learning

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Secure $k$k-NN Query on Encrypted Cloud Data with Multiple Keys

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IEEE Transactions on Big Data 2021年第4期7卷 689-702页

作者： Ke Cheng Liangmin Wang Yulong Shen Hua Wang Yongzhi Wang Xiaohong Jiang Hong Zhong School of Computer Science and Technology Xidian University Xi’an Shaanxi China School of Computer Science and Technology Anhui University Hefei Anhui China Centre of Applied Informatics College of Engineering and Science Victoria University VIC Australia Department of Computer Science Taiyuan Normal University Jinzhong China School of Systems Information Science Future University Hakodate Hakodate Hokkaido Japan School of Computer Science and Technology Xidian University Xi‘an Shaanxi China

The k-nearest neighbors (k-NN) query is a fundamental primitive in spatial and multimedia databases. It has extensive applications in location-based services, classification & clustering and so on. With the promise of confidentiality and privacy, massive data are increasingly outsourced to cloud in the encrypted form for enjoying the advantages of cloud computing (e.g., reduce storage and query processing costs). Recently, many schemes have been proposed to support k-NN query on encrypted cloud data. However, prior works have all assumed that the query users (QUs) are fully-trusted and know the key of the data owner (DO), which is used to encrypt and decrypt outsourced data. The assumptions are unrealistic in many situations, since many users are neither trusted nor knowing the key. In this paper, we propose a novel scheme for secure k-NN query on encrypted cloud data with multiple keys, in which the DO and each QU all hold their own different keys, and do not share them with each other; meanwhile, the DO encrypts and decrypts outsourced data using the key of his own. Our scheme is constructed by a distributed two trapdoors public-key cryptosystem (DT-PKC) and a set of protocols of secure two-party computation, which not only preserves the data confidentiality and query privacy but also supports the offline data owner. Our extensive theoretical and experimental evaluations demonstrate the effectiveness of our scheme in terms of security and performance.

关键词： Cloud computing Protocols computer science Encryption Servers Distributed databases

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Mathematical modeling and simulation of magnetized bioconvective nanoliquid flow capturing Brownian motion, multiple slip, thermophoresis and gyrotactic microorganisms configured by rotating disk

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Partial Differential Equations in applied Mathematics 2024年 12卷

作者： Garalleh, Hakim AL Khan, Sami Ullah Waqas, M. Zamri, Nurnadiah Abdullaeva, Barno Gupta, Manish Department of Mathematical Science College of Engineering University of Business and Technology Jeddah 21361 Saudi Arabia Department of Mathematics Namal University Mianwali 42250 Pakistan NUTECH School of Applied Sciences and Humanities National University of Technology Islamabad 44000 Pakistan Department of Computer Science and Mathematics Lebanese American University Beirut Lebanon Faculty of Informatics and Computing University Sultan Zainal Abidin Besut Campus Terengganu Besut 22200 Malaysia Department of Mathematics and Information Technologies Vice-Rector for Scientific Affairs Tashkent State Pedagogical University Tashkent Uzbekistan Division of Research and Development Lovely Professional University Phagwara India

This analysis describes the bioconvective flow of nanofluid due to rotation of disk. A uniform suspension of nanofluid between microorganisms is considered to analyze the applications of bioconvection. The nanofluid assumed to be electrically conducting with amplification of magnetic force. The problem is entertained in presence of different slip features including velocity, temperature, concentration and microorganisms. The formulation of problem in simplified form is attained via dimensionless variables. Shooting numerical scheme is used to compute the simulations. Physical interpretation and visualization of results is observed in view of parameters. The observations concluded that interaction of slip effects reduces the velocity profile but enhances nanofluid temperature and concentration profiles. The temperature profile increases with thermophoresis parameter. Current results comprise applications in cooling of electronics devices, thin film coating, gas turbines engine, energy systems etc. © 2024

关键词： Bioconvection Multiple slip effects Nanoparticles Numerical computations Rotating disks

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Retraction Note: Multiclass skin lesion classification using deep learning networks optimal information fusion

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Discover applied Sciences 2025年第1期7卷 1-1页

作者： Khan, Muhammad Attique Hamza, Ameer Shabaz, Mohammad Kadry, Seifeine Rubab, Saddaf Bilal, Muhammad Abdullah Akbar, Muhammad Naeem Kesavan, Suresh Manic Department of Computer Science HITEC University Taxila Pakistan Model Institute of Engineering and Technology Jammu India Department of Applied Data Science Noroff University College Oslo Norway Department of Computer Engineering College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Department of CS SEECS NUST Islamabad Pakistan National University of Sciences and Technology (NUST) Islamabad Pakistan Department of Electrical and Communication Engineering National University of Science and Technology Muscat Sultanate of Oman

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PI Controller Tuning via Data-Driven Algorithms for Shape Memory Alloy Systems

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IFAC-PapersOnLine 2022年第40期55卷 181-186页

作者： Raul-Cristian Roman Radu-Emil Precup Stefan Preitl Alexandra-Iulia Szedlak-Stinean Claudia-Adina Bojan-Dragos Elena-Lorena-Hedrea Emil M. Petriu Department of Automation and Applied Informatics Politehnica University of Timisoara Bd. V. Parvan 2 300223 Timisoara Romania Center for Fundamental and Advanced Technical Research Romanian Academy – Timisoara Branch Bd. Mihai Viteazu 24 300223 Timisoara Romania School of Electrical Engineering and Computer Science University of Ottawa 800 King Eduard Ottawa Ontario K1N 6N5 Canada

The current paper proposes to compare the performances of a proportional-integral (PI) controller whose parameters are determined in a model-based way using a metaheuristic search algorithm, with the performances of a PI determined in a model-free way by using virtual reference feedback tuning (VRFT) algorithm and using iterative feedback tuning (IFT) algorithm. The PI controller is included in a control system structure to control the position of a shape memory alloy (SMA) laboratory equipment.

关键词： data-driven iterative feedback tuning shape memory alloy virtual reference feedback tuning

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