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2021 International Conference on Information and Digital Technologies, IDT 2021

作者： Venkatachalapathy, Rajesh Zwick, Martin Slowik, Adam Brooks, Kai Mayers, Mikhail Minko, Roman Hull, Tyler Brass, Bliss Perkowski, Marek Portland State University System Science PortlandOR United States Koszalin University of Technology Computer Science Koszalin Poland Portland State University Department of Electrical and Computer Engineering United States

ISBN: (纸本)9781665436922

Paper presents a concept that is new to robotics education and social robotics. It is based on theatrical games, in which students create 'biological', 'characteristic' and natural motions for social robots and animatronic robots. Presented here motion model is based on Drift Differential Model from biology and Fokker-Planck equations. This model is used in various areas of science to describe many types of motion. The model was successfully verified on various simulated mobile robots and a motion game of three robots called 'Mouse and Cheese'. © 2021 IEEE.

关键词： Mammals

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XoT (Xducation of Things): Harnessing AI and edge computing to educate all things

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IEEE Access 2024年

作者： Nurtantyana, Rio Hwang, Wu-Yuin Hariyanti, Uun Research Center for Data and Information Sciences Bandung Indonesia Telkom University School of Computing Indonesia Telkom University Center of Excellence Artificial Intelligence for Learning and Optimization Bandung Indonesia National Dong Hwa University College of Science and Engineering Department of Computer Science and Information Engineering Taiwan National Central University Graduate Institute of Network Learning Technology Taiwan Universitas Brawijaya Faculty of Computer Science Department of Information System Indonesia

Most English foreign language (EFL) studies focus solely on human beings. This research explores how edge computing can facilitate learning for all things. The XoT (Xducation of Things) framework was proposed to educate both human and all things. All things encompass two terms: AI-Agent and smartthings (covering physical and digital smart objects). At the core of this framework is Smart Question Answer Forwarding Mechanism (SQA-Forwarding), specifically designed to assist all things in building knowledge. To demonstrate this, the smartXoT environment was developed based on XoT framework, and its impacts on EFL learners was assessed. A quasi-experimental study involving 26 EFL learners, divided into an experimental group (EG) and a control group (CG), examined the differences in learning achievement of smartthings and EFL learners when using the smartXoT environment with/without SQA-Forwarding. Findings, on one hand, indicated that smartthings in the EG developed knowledge bases greater than those in the CG. On the other hand, the interaction between EFL learners and smartthings with SQA-Forwarding significantly improved learners' writing skills, with revisions playing a crucial role in enhancing writing quality. Thus, the XoT framework offers a novel and promising approach to educating both humans and all things. © 2013 IEEE.

关键词： Contrastive Learning

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Unit-Level AC Induced Magnetic Behavior in the Low-Frequency Regime: Measurement and Characterization

Unit-Level AC Induced Magnetic Behavior in the Low-Frequency...

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2022 ESA Workshop on Aerospace EMC, Aerospace EMC 2022

作者： Spantideas, Sotirios T. Giannopoulos, Anastasios E. Baklezos, Anargyros T. Nikolopoulos, Christos D. Nicoletto, Marco Marziali, Ilario Boschetti, Demis Capsalis, Christos N. National Technical University of Athens School of Electrical and Computer Engineering 15780 Greece Hellenic Mediterranean University Department of Electronic Engineering Crete73133 Greece Thales Alenia Space Italia Domain Exploration and Science: Electrical System and EMC Turin10146 Italy

ISBN: (纸本)9789082684742

In this paper, the measurement and characterization of the unit-level, low-frequency induced magnetic response for purposes of magnetic cleanliness is presented. Firstly, we concretely describe the test measurement procedure, including the configuration of the measurement setup and the specifications required to accurately obtain the weak induced magnetic signature of a unit. Furthermore, the post-processing pipeline of the measured data for denoising purposes is illustrated through measurements of representative spacecraft equipment (RF Switch unit). Finally, the frequency domain characterization of the induced unit magnetization is performed based on dipole fitting modeling and the induced magnetization tensor is calculated as a linear combination for different orientations of the applied magnetic field. © 2022 ESA ESTEC.

关键词： Magnetization

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Large Language Model for Multi-objective Evolutionary Optimization

arXiv

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arXiv 2023年

作者： Liu, Fei Lin, Xi Wang, Zhenkun Yao, Shunyu Tong, Xialiang Yuan, Mingxuan Zhang, Qingfu Department of Computer Science City University of Hong Kong Hong Kong School of System Design and Intelligent Manufacturing the Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen518055 China Huawei Noah’s Ark Lab Canada

Multiobjective evolutionary algorithms (MOEAs) are major methods for solving multiobjective optimization problems (MOPs). Many MOEAs have been proposed in the past decades, of which the search operators need a carefully handcrafted design with domain knowledge. Recently, some attempts have been made to replace the manually designed operators in MOEAs with learning-based operators (e.g., neural network models). However, much effort is still required for designing and training such models, and the learned operators might not generalize well on new problems. To tackle the above challenges, this work investigates a novel approach that leverages the powerful large language model (LLM) to design MOEA operators. With proper prompt engineering, we successfully let a general LLM serve as a black-box search operator for decomposition-based MOEA (MOEA/D) in a zero-shot manner. In addition, by learning from the LLM behavior, we further design an explicit white-box operator with randomness and propose a new version of decomposition-based MOEA, termed MOEA/D-LO. Experimental studies on different test benchmarks show that our proposed method can achieve competitive performance with widely used MOEAs. It is also promising to see the operator only learned from a few instances can have robust generalization performance on unseen problems with quite different patterns and settings. The results reveal the potential benefits of using pre-trained LLMs in the design of MOEAs. To foster reproducibility and accessibility, the source code is https://***/FeiLiu36/LLM4MOEA. Copyright © 2023, The Authors. All rights reserved.

关键词： Multiobjective optimization

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Integrated Parallel Chaos driven LiDAR system with High Interference Tolerance

Integrated Parallel Chaos driven LiDAR System with High Inte...

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Chen, Ruixuan Shen, Bitao Shu, Haowen Chang, Lin Xie, Weiqiang Liao, Wenchao Tao, Zihan Bowers, John Wang, Xingjun State Key Laboratory of Advanced Optical Communications System and Networks School of Electronics Peking University Beijing100871 China Department of Electrical and Computer Engineering University of California Santa BarbaraCA93106 United States Frontiers Science Center for Nano-Optoelectronics Peking University Beijing100871 China

We introduce a chaotic microcomb based high interference-tolerance parallel LiDAR. Parallel chaos with record-high noise spectra beyond 7 GHz leads to sub-cm-level ranging accuracy. The intrinsic orthogonality permits... 详细信息

ISBN: (纸本)9781957171258

关键词： Optical radar

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Deep Learning Techniques for Advanced Large-Scale Data Processing

Deep Learning Techniques for Advanced Large-Scale Data Proce...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Mp Sunil Jyoti Shekhawat T. Vanaja Vazeer Ali Mohammed Shailesh J. Thaware Vaishali Singh Department of Electronics and Communication Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Information Technology Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Computer & Information System Lewis University Illinois USA Department of Computer Engineering Vishwakarma Institute of Technology Pune INDIA Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Deep getting to know techniques have become increasingly more popular due to their potential to improve huge-scale records processing performance. Leveraging specialized hardware which includes GPUs, deep getting to know algorithms can attain mind-blowing pace will increase compared to traditional algorithms. further, deep gaining knowledge of fashions can discover complicated non-linear relationships in big datasets, permitting greater accurate overall performance and less fake predictions. Latest advances in deep mastering have enabled it to be implemented to remedy a plethora of information processing duties, from photo recognition to natural language processing. CNNs, RNNs, and GANs are a number of the maximum famous deep gaining knowledge of fashions used in tackling complex data processing obligations. agencies consisting of Google, fb, and Amazon use these sophisticated frameworks to build predictive models that help improve numerous programs. Deep getting to know strategies offer exquisite ability to take massive-scale information processing to the following stage. corporations are increasingly more becoming privy to the emerging opportunities, and actively investing in deep mastering to liberate the untapped strength of their information.

关键词： Deep learning Accuracy Scalability Information processing Predictive models Data processing Prediction algorithms Hardware Natural language processing Optimization

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Computational Intelligence and AI-FML Experience Model for Pre-university Student Learning and Practice 18th

Computational Intelligence and AI-FML Experience Model for P...

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18th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2022

作者： Lee, Chang-Shing Wang, Mei-Hui Chang, Rin-Pin Liu, Hsiao-Chi Chiu, Szu-Chi Chang, Yu-Cheng Lin, Lu-An Chen, Shen-Chien Department of Computer Science and Information Engineering National University of Tainan Tainan Taiwan KWS Center National University of Tainan Tainan Taiwan St. Dominic Catholic High School Kaohsiung Taiwan Taipei First Girls High School Taipei Taiwan Z-System Technology Kaohsiung Taiwan

ISBN: (纸本)9789819901043

This paper presents the computational intelligence (CI) and artificial intelligence (AI)–fuzzy markup language (CI&AI-FML) learning model, called the CI&AI-FML human and machine co-learning model, for pre-university students to experience and practice CI&AI-FML. Since 2019, we have engaged much in interacting and working with teachers to teach pre-university students the basic concepts of computational intelligence, such as fuzzy sets and evolutionary computing in real-world applications. To inspire and encourage more pre-university students to attend these CI&AI-FML learning and experience activities, we organized a CI&AI-FML Machine Learning Competition @ IEEE WCCI 2022 and a workshop on AI-FML Robotic Learning with AIoT (AI of Things) Applications @ 2022 IEEE CIS Summer School in Taiwan. In these events, we prepare the key but simple concepts of the CI for pre-university students learning and practice, for example, learners can practice AI-FML learning tools with Augmented Creativity (AR) and Google Teachable Machine to travel recommendation applications. From the experimental results, this kind of learning model is suitable for pre-university students to experience and practice CI for real-world applications. In future, we hope to encourage more young students and researchers to work with CI techniques and methods for real-world applications. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Students

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An Investigation of the Current State of Digital Platforms and Their Prospects for the Future

An Investigation of the Current State of Digital Platforms a...

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Interdisciplinary Humanitarian Conference for Sustainability (IIHC), International

作者： Mahainder Rao S Rakesh Kumar Department of Computer & System Sciences School of Computer & System Sciences Jaipur National University Jaipur India Department of Computer Science and Engineering Galgotias University Greater Noida Uttar Pradesh India

Researchers in the field of information systems are becoming more interested in this subject as a result of the increased use of digital platforms as a new way to organise social and commercial activities. As a result, over the past ten years, the literature on digital platforms has greatly increased. However, as platforms change and a number of problems remain unsolved, academics call for greater research on the subject. Therefore, a review of the existing literature is necessary to better understand what has been accomplished and the potential avenues for future study. A thorough review of the literature on digital platforms, which included 100 publications from IS and management journals, was done by our team. The study's key findings point to some ambiguity in how digital platforms are currently conceptualised. They also emphasise how multifaceted digital platforms are, which reflects the variety of ways they are used in real life.

关键词： Planning Econometrics Sustainable development Information systems

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Privacy-Preserving Digital Forensics: Balancing Investigation Needs and User Rights

Privacy-Preserving Digital Forensics: Balancing Investigatio...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Louay Karadsheh Hazem Al-Najjar Amine Nehari-Talet Information Security University of Petra Amman Jordan Computer Science Department University of Petra Amman Jordan Computer engineering Department AlBalqa Applied University Salt Jordan Management information system King Fahd University of Petroleum & Minerals Dhahran Kingdom of Saudi Arabia

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Modern criminal investigations are important and raise privacy issues in digital forensics. The goal of this study is to develop a privacy-preserving framework for digital forensics that makes it possible to balance effective investigation with individual privacy rights. In this work, we present an innovative framework for privacy-preserving digital forensics, which incorporates data minimization techniques with selective imaging and privacy-preserving data analysis methods. The approach is validated with a collection of case studies and a series of simulations, which show that the approach reduces privacy intrusions while maintaining investigative integrity. Results indicate up to a 40 % reduction in exposure of non-relevant personal data compared to traditional approaches with no loss of evidence collection quality. We recommend how to implement privacy-respecting digital forensic practices to policymakers. This paper adds practical solutions for investigators and legal professionals in ethical digital forensics.

关键词： Privacy Ethics Data privacy Data analysis Law Digital forensics Imaging Minimization Computational intelligence

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Development the Numerical Method to Solve the Inverse Initial Value Problem for the Thermal Conductivity Equation of Composite Materials

Development the Numerical Method to Solve the Inverse Initia...

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2020 Ibn Al-Haitham International Conference for Pure and Applied sciences, IHICPS 2020

作者： Al-Mahdawi, H.K. Department of System Programming School of Electronic Engineering and Computer Science South Ural State University Lenina 76 Chelyabinsk454080 Russia

In this paper, the heat conduction equation for composite materials posed and solved. This problem is known as an inverse initial value problem for the heat conduction equation. In order to solve and formulate this inverse problem, the function spaces must be defined and represented. By studying and solving the direct problem for the heat equation in composite materials, it is possible to determine the function spaces and solve the inverse initial value problem. Scientific methods used: the separation of variables method used to solve the direct problem for the heat equation. It found that method separation of variables does not completely lead to the solution of the inverse initial value problem, since this method leads to a divergent series of solutions and rather massive errors. The heat conduction problem can be formulated as Fredholm integral first kind equations. The discretization algorithm applied to reformulated the problem as linear operator problem as matrix and vectors form. Then, Tikhonov's regularization inversion method has been used to find an approximation solution. Finally, as shown in the numerical example the regularized approximate solutions obtained. © Published under licence by IOP Publishing Ltd.

关键词： Initial value problems

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