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2023 International Conference on computer science, Information Technology and Engineering, ICCoSITE 2023

作者： Wijaya, Santo Gaol, Ford Lumban Wibowo, Antoni Zarlis, Muhammad Bina Nusantara University BINUS Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program - Master of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program - Master of Information System Management Information System Management Department Jakarta11480 Indonesia

ISBN: (纸本)9798350320954

Real-world practical systems inherently exhibit non-linearities in their dynamics. Also, it is known that a time-varying delay exists in the system state or input-output. Combined, it affects the stability of the closed-loop system. It also increases the complexity of the controller design. In-depth controller design research on the class of Nonlinear Systems with Time-Varying Delay (NSTVD) has been the focus of the control community for many years. However, there is a lack of Systematic Literature Review (SLR) and classifications of the papers on this topic. This paper aims to review controller design utilizing a neural network model for the class of NSTVD systems. The study employs Kitchenham's SLR method to gather, analyze and synthesize published papers from reliable databases between 2017 and 2021. The bibliometric analysis for the selected 38 papers reveals the prolific authors, countries, affiliations, publishers, co-authorship network, co-occurrences of keywords, and ten most-cited papers. Finally, this paper developed a conceptual map outlining six multi-layered findings: the addressed problem, control design method, nonlinear system properties, time-varying delay properties, system constraint properties, and actuator limit properties. A brief qualitative analysis of the ten most-cited papers is performed based on the map. The findings highlighted that the proposed methods have shown encouraging results in the simulation domain and can be used as a source of inspiration for future studies and implementation of the neural controller design of the NSTVD system. © 2023 IEEE.

关键词： Nonlinear systems

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Space/Air Covert Communications:Potentials,Scenarios,and Key Technologies

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China Communications 2024年第3期21卷 1-18页

作者： Mao Haobin Liu Yanming Zhu Lipeng Mao Tianqi Xiao Zhenyu Zhang Rui Han Zhu Xia Xianggen School of Electronic and Information Engineering Beihang UniversityBeijing 100191China Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore School of Science and Engineering Shenzhen Research Institute of Big Datathe Chinese University of Hong KongShenzhenGuangdong 518172China Department of Electrical and Computer Engineering University of HoustonHoustonTX 77004USA Department of Computer Science and Engineering Kyung Hee UniversitySeoulSouth Korea446-701 Department of Electrical and Computer Engineering University of DelawareNewarkDE 19716USA

Space/air communications have been envisioned as an essential part of the next-generation mobile communication networks for providing highquality global connectivity. However, the inherent broadcasting nature of wireless propagation environment and the broad coverage pose severe threats to the protection of private data. Emerging covert communications provides a promising solution to achieve robust communication security. Aiming at facilitating the practical implementation of covert communications in space/air networks, we present a tutorial overview of its potentials, scenarios, and key technologies. Specifically, first, the commonly used covertness constraint model, covert performance metrics, and potential application scenarios are briefly introduced. Then, several efficient methods that introduce uncertainty into the covert system are thoroughly summarized, followed by several critical enabling technologies, including joint resource allocation and deployment/trajectory design, multi-antenna and beamforming techniques, reconfigurable intelligent surface(RIS), and artificial intelligence algorithms. Finally, we highlight some open issues for future investigation.

关键词： artificial intelligence(AI) sixth generation(6G) space-air-ground integrated networks(SAGINs) space/air covert communications

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Understanding the Definitions of Microcredentials in Higher Education: Systematic Literature Review

Understanding the Definitions of Microcredentials in Higher ...

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2024 International Conference on TVET Excellence and Development, ICTeD 2024

作者： Wihendro Warnars, Harco Leslie Hendric Spits Prabowo, Harjanto Sfenrianto Bina Nusantara University BINUS Graduate Program- Computer Science Information Systems Department Jakarta Indonesia Bina Nusantara University BINUS Graduate Program- Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University BINUS Business School Undergraduate Program Management Department Jakarta Indonesia Bina Nusantara University BINUS Graduate Program-Master of Information Systems Management Information Systems Management Department Jakarta Indonesia

ISBN: (纸本)9798331516413

The growing focus on microcredentials emphasizes the urgent need for precise and widely accepted definitions, as existing uncertainties hinder their effective implementation. This research aims to investigate the comprehension of microcredentials definitions in the context of higher education by conducting a systematic literature review. The goal is to identify current definitions of microcredentials to facilitate standardization initiatives. Following The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) methodology to systematically search in Scopus database within 2019-2024 resulting 940 articles, by removing the duplicates, 280 articles were initially reviewed, with 29 selected for detailed thematic analysis based on specific inclusion and exclusion criteria. By adapting the six phases of thematic analysis, selected articles were identified analyzed, organized, described, and themes found the data set were reported. Key findings reveal that, while a universally accepted definition is lacking, common themes emerge: microcredentials are competency-based, modular, portable, stackable, and often include quality assurance. These attributes highlight microcredentials as valuable for upskilling and reskilling in a flexible manner. The study concludes that establishing standard definitions can increase the recognition and utility of microcredentials across educational and professional sectors and recommends further research to strengthen core elements for consistency. © 2024 IEEE.

关键词： Competency-based Higher Education Microcredentials Modular Reskilling Upskilling

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Boosting Team Spirit: The Impact of Ambidextrous LMS Usage on Indonesian Academics 4

Boosting Team Spirit: The Impact of Ambidextrous LMS Usage o...

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4th International Conference on Innovative Research in Applied science, Engineering and Technology, IRASET 2024

作者： Herlina, Maria Grace Iskandar, Karto Binus Business School Undergraduate Program Bina Nusantara University Management Department Jakarta11480 Indonesia School Of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350309508

The study explores the role of Learning Management Systems (LMS) in enhancing academic teamwork and ambidextrous learning in Indonesian higher education. It examines how different uses of LMS, both explorative and exploitative, affect academics' morale and teamwork. The study employs Structural Equation Modeling to analyse questionnaire data from academics in Greater Jakarta, Indonesia. Key findings reveal significant correlations between the exploration and exploitation of LMS, ambidextrous learning, and the spirit of teamwork. The research contributes to understanding how LMS fosters collaborative learning and innovation in academic settings, highlighting its importance in modern education. © 2024 IEEE.

关键词： Human resource management

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Outlier-Efficient Hopfield Layers for Large Transformer-Based Models 41

Outlier-Efficient Hopfield Layers for Large Transformer-Base...

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41st International Conference on Machine Learning, ICML 2024

作者： Hu, Jerry Yao-Chieh Chang, Pei-Hsuan Luo, Haozheng Chen, Hong-Yu Li, Weijian Wang, Wei-Po Liu, Han Department of Computer Science Northwestern University Evanston United States Department of Physics National Taiwan University Taipei Taiwan Department of Statistics and Data Science Northwestern University Evanston United States

We introduce an Outlier-Efficient Modern Hopfield Model (termed OutEffHop) and use it to address the outlier inefficiency problem of training gigantic transformer-based models. Our main contribution is a novel associative memory model facilitating outlier-efficient associative memory retrievals. Interestingly, this memory model manifests a model-based interpretation of an outlier-efficient attention mechanism (Softmax1): it is an approximation of the memory retrieval process of OutEffHop. Methodologically, this allows us to introduce novel outlier-efficient Hopfield layers as powerful alternatives to traditional attention mechanisms, with superior post-quantization performance. Theoretically, the Outlier-Efficient Modern Hopfield Model retains and improves the desirable properties of standard modern Hopfield models, including fixed point convergence and exponential storage capacity. Empirically, we demonstrate the efficacy of the proposed model across large-scale transformer-based and Hopfield-based models (including BERT, OPT, ViT, and STanHop-Net), benchmarking against state-of-the-art methods like ClippedSoftmax and GatedAttention. Notably, OutEffHop achieves an average reduction of 22+% in average kurtosis and 26+% in the maximum infinity norm of model outputs across four models. Code is available at GitHub;future updates are on arXiv. Copyright 2024 by the author(s)

关键词： Associative storage

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A Comprehensive Exploration of Real-Time 3-D View Reconstruction Methods

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 5915-5927页

作者： Agrawal, Arya Sharma, Teena Verma, Nishchal K. Vellore Institute of Technology School of Computer Science and Engineering Vellore632014 India Indian Institute of Technology Mehta Family School of Data Science and Artificial Intelligence Guwahati781039 India Indian Institute of Technology Kanpur Department of Electrical Engineering 208016 India

Real-time 3-D view reconstruction in an unfamiliar environment poses complexity for various applications due to varying conditions such as occlusion, latency, precision, etc. This article thoroughly examines and tests contemporary methodologies addressing challenges in 3-D view reconstruction. The methods being explored in this article are categorized into volumetric and mesh, generative adversarial network based, and open source library based methods. The exploration of these methods undergoes detailed discussions, encompassing methods, advantages, limitations, and empirical results. The real-time testing of each method is done on benchmarked datasets, including ShapeNet, Pascal 3D+, Pix3D, etc. The narrative highlights the crucial role of 3-D view reconstruction in domains such as robotics, virtual and augmented reality, medical imaging, cultural heritage preservation, etc. The article also anticipates future scopes by exploring generative models, unsupervised learning, and advanced sensor fusion to increase the robustness of the algorithms. © 2020 IEEE.

关键词： Generative adversarial networks

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Design and Implementation for Innovative Tourism Expert System based on Artificial Intelligence 8

Design and Implementation for Innovative Tourism Expert Syst...

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8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Prasanth, R. Anitha, R. Sarathamani, T. Manoj, M. Dr. Sns Rajalakshmi College of Arts and Science Department of Management Tamilnadu Coimbatore India Kolkata India Kpr College of Arts Science and Research Department of Computer Science with Data Analytics Tamilnadu Coimbatore India

ISBN: (纸本)9798350367904

The tourism sector has long been looked at as a vital engine of economic expansion. The most recent developments in the tourism sector have benefitted governments, international organizations, common people and academia. For developing the tourism sector, it is important to analyze and understand tourist visitor patterns. Tourist forecasting helps maximize the tourism-based economy. The international tourist movement has grown at an unprecedented rate in recent decades, and many countries are concentrating on the tourism economy. While tourists visit a tourist place, spend a significant amount of money on their enjoyment, company, and hotel accommodations. ML algorithms are making significant contributions to forecasting system, and several researchers are working to improve the model's accuracy. This research has proposed a machine learning approach for forecasting worldwide tourist arrivals that analyses prior quantitative information about visitors so that the governing body can arrange the necessary facilities for visitors accordingly. The proposed model is applied to a dataset compiled over a decade from all countries around the world. The detailed proposed approach and the various results of experiments are explained in this paper. © 2024 IEEE.

关键词： Artificial Intelligence Classification data Analysis data Mining Tourist Expert System

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MOCBOA:Multi-Objective Chef-Based Optimization Algorithm Using Hybrid Dominance Relations for Solving Engineering Design Problems

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computer Modeling in Engineering & sciences 2025年第4期143卷 967-1008页

作者： Nour Elhouda Chalabi Abdelouahab Attia Abdulaziz S.Almazyad Ali Wagdy Mohamed Frank Werner Pradeep Jangir Mohammad Shokouhifar Computer Science Department University of M’silaIchebiliaM’sila28000Algeria Computer ScienceDepartment Mohamed El Bachir El IbrahimiUniversity of Bordj BouArreridjBordj BouArreridj34000Algeria Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11931Jordan Faculty of Mathematics Otto-von-Guericke UniversityMagdeburg39016Germany University Centre for Research and Development Chandigarh UniversityMohali140413India Department of Electrical and Computer Engineering Shahid Beheshti UniversityTehran14399Iran DTU AI and Data Science Hub(DAIDASH) Duy Tan UniversityDa Nang550000Vietnam

Multi-objective optimization is critical for problem-solving in engineering,economics,and *** study introduces the Multi-Objective Chef-Based Optimization Algorithm(MOCBOA),an upgraded version of the Chef-Based Optimization Algorithm(CBOA)that addresses distinct *** approach is unique in systematically examining four dominance relations—Pareto,Epsilon,Cone-epsilon,and Strengthened dominance—to evaluate their influence on sustaining solution variety and driving convergence toward the Pareto *** comparison investigation,which was conducted on fifty test problems from the CEC 2021 benchmark and applied to areas such as chemical engineering,mechanical design,and power systems,reveals that the dominance approach used has a considerable impact on the key optimization measures such as the hypervolume *** paper provides a solid foundation for determining themost effective dominance approach and significant insights for both theoretical research and practical applications in multi-objective optimization.

关键词： Multi-objective optimization chef-based optimization algorithm(CBOA) pareto dominance epsilon dominance cone-epsilon dominance strengthened dominance

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PipeMoE: Accelerating Mixture-of-Experts through Adaptive Pipelining 42

PipeMoE: Accelerating Mixture-of-Experts through Adaptive Pi...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Shi, Shaohuai Pan, Xinglin Chu, Xiaowen Li, Bo Harbin Institute of Technology School of Computer Science and Technology Shenzhen China Hong Kong Baptist University Department of Computer Science Hong Kong Data Science and Analytics Thrust Hong Kong The Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong

ISBN: (纸本)9798350334142

Large models have attracted much attention in the AI area. The sparsely activated mixture-of-experts (MoE) technique pushes the model size to a trillion-level with a sub-linear increase of computations as an MoE layer can be equipped with many separate experts, but only one or two experts need to be trained for each input data. However, the feature of dynamically activating experts of MoE introduces extensive communications in distributed training. In this work, we propose PipeMoE to adaptively pipeline the communications and computations in MoE to maximally hide the communication time. Specifically, we first identify the root reason why a higher pipeline degree does not always achieve better performance in training MoE models. Then we formulate an optimization problem that aims to minimize the training iteration time. To solve this problem, we build performance models for computation and communication tasks in MoE and develop an optimal solution to determine the pipeline degree such that the iteration time is minimal. We conduct extensive experiments with 174 typical MoE layers and two real-world NLP models on a 64-GPU cluster. Experimental results show that our PipeMoE almost always chooses the best pipeline degree and outperforms state-of-the-art MoE training systems by 5%-77% in training time. © 2023 IEEE.

关键词： Pipelines

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S5ELBP: Supervised SAR Image Classification using SVM from SSELBP Texture Features

S5ELBP: Supervised SAR Image Classification using SVM from S...

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2023 International Conference on data science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Bandi, Sudheer Reddy Anbarasan, M. Merlin Linda, G. Panimalar Engineering College Department of Artificial Intelligence and Data Science Chennai India Swarna Bharathi Institute of Science and Technology Department of Computer Science and Engineering Khammam India

ISBN: (纸本)9798350348910

Synthetic Aperture Radar (SAR) and Optical sensors are very popular depending on their strengths and application needs. The ocean surface will be covered with clouds most of the time and optical sensors cannot capture the information as they cannot operate in dust and cloudy conditions. So, SAR sensors come into existence to capture the atmospheric phenomena in those regions. There is a wide range of texture extraction techniques such as traditional-based wavelet analysis, statistical analysis, local pixel-based approaches, and dimensional reduction approaches. However, these approaches have limitations in boundary extraction, high-dimensional feature extraction, and uniformity representation. In recent times, deep learning (DL) approaches to extract features which varies based on the applications, image interpretation, and so on. At the same time, DL approaches are limited in transparency, computation requirements, and hyperparameter tuning. In contrast, the proposed S5ELBP i.e., Supervised SAR image classification using the integration of Support Vector Machine (SVM) and Scale Selective Extended Local Binary Pattern (SSELBP) extraction techniques shall overcome various challenges of existing traditional and DL approaches such as adaptive thresholding, less computational resources, and noise robustness. Hence, the objective of the proposed methodology is to extract the image features from the Scale Selective Extended Local Binary Pattern (SSELBP). Also, the SSELBP features are integrated with SVM to show the strength of the proposed approach in SAR image classification. Additionally, the performance of the present work is enhanced by comparing the extracted results with various classifiers. Finally, the work is concluded by recommending the scope of future work. © 2023 IEEE.

关键词： Support vector machines

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