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6th World Symposium on Communication Engineering, WSCE 2023

作者： Lioupa, Anna Memos, Vasileios A. Stergiou, Christos L. Ishibashi, Yutaka Psannis, Konstantinos E. School of Information Sciences University of Macedonia Dept. of Applied Informatics Thessaloniki Greece Graduate School of Engineering Nagoya Institute of Technology Dept. of Computer Science Nagoya Japan

ISBN: (纸本)9798350339505

The technological advances impact the future classrooms and the whole education system. The evolution of 6G promises faster speeds based on terahertz, wireless communication, the leverage of artificial intelligence (AL), virtually reality (VR) and generally a fully connected world. The use of applications of IoT and AIoT improves the education environment, but the intelligent network services undoubtedly gather, store and analyze big data which are vulnerable to attackers. As a solution we may consider the technology of blockchain. The trust and security of next generation intelligent networks are essential part of their design. In this article we suggest a data security system based on blockchain for AL applications in 6G networks. First, we present two Al-enabled applications, indoor positioning and mobile payment within the framework of 6G in the university area. We present the efficiency of blockchain in data security for Al-enabled systems using two case studies of indoor positioning system and mobile payment. The 6G system which is fully supported by Al and the integration of this in technology of blockchain, is ensured the security in future wireless communication. © 2023 IEEE.

关键词： Blockchain

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Analysis of Import Substitution Processes Taking into Account Industry Specifics in the Regional Economy 14th

Analysis of Import Substitution Processes Taking into Acco...

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14th International Conference on Optimization and Applications, OPTIMA 2023

作者： Rassokha, Anastasiia Obrosova, Natalia Moscow Institute of Physics and Technology Dolgoprudny Russia Federal Research Center "Computer Science and Control" of RAS Moscow Russia Center of Fundamental and Applied Mathematics Lomonosov MSU Moscow Russia

ISBN: (纸本)9783031478581

Analysis of import substitution processes is one of the urgent problems for many countries in the context of deglobalization of the world economy. Mathematical methods of convex analysis make it possible to construct a model of input-output balance with non-linear production functions. It can be identified from the official statistics of input-output tables. This model allows us to describe the substitution of production factors. The elasticity of substitution can be estimated using the solution of the Yang dual problem. With the help of the developed model for industries with a significant share of imports, the possibility of replacing imported production factors with domestic ones is studied on the example of the Russian economy. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Regional planning

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Urban Area Detection Based on Cross-Domain Lightweight Parallel Fusion Network for Orbita 'Zhuhai-1' Hyperspectral Satellite 10

Urban Area Detection Based on Cross-Domain Lightweight Paral...

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10th IEEE Smart World Congress, SWC 2024

作者： Wang, Jianing Zhang, Wan Liu, Yichen Hua, Zheng Yao, Yuqiong Hao, Shengjia Zhao, Zhicao Li, Lin Xidian University School of Computer Science and Technology Xian China Xidian University School of Artificial Intelligence Xian China Xian Institute of Applied Optics Xian China Northwestern Polytechnical University School of Automation Xian China

ISBN: (纸本)9798331520861

In order to ensure the stabilization issues of efficient deployment in orbit and cross-domain real-time detection. In this paper, a lightweight domain adaptation parallel fusion network (LAPNet) for urban detection of Orbita 'Zhuhai-1' (OHS) remote sensing data has been proposed. Point convolution and dynamic convolution are employed to fully exploit spatial-spectral fusion features from local-global and pixel-semantic perspective. At the same time, cross-scene domain alignment from the public dataset to the target dataset is achieved by the adversarial domain adaptation strategy. Based on the hyperspectral remote sensing data from the 'Zhuhai-1' satellite, we created three urban detection datasets for the experiment, where the public dataset and the dataset generated by hyperspectral reconstruction algorithm as the pre-training datasets for urban area detection. To verify the effectiveness of LAPNet for domain adaptation, we evaluate the LAPNet for the urban area detection task on the target domain 'Zhuhai- 1 ' hyperspectral dataset. Compared to related comparative algorithms, the LAPNet improves the urban detection performance and efficiency for hyperspectral remote sensing satellite on each dataset with effectively reducing the number of model parameters. © 2024 IEEE.

关键词： Convolutional neural networks

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Deep Learning Approaches for Recognizing Daily Human Activities Using Smart Home Sensors 8

Deep Learning Approaches for Recognizing Daily Human Activit...

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Joint 8th International Conference on Digital Arts, Media and technology and 6th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications Engineering, ECTI DAMT and NCON 2023

作者： Sanpote, Worrakit Jantawong, Ponnipa Hnoohom, Narit Jitpattanakul, Anuchit Mekruksavanich, Sakorn School of Information and Communication Technology Department of Computer Science University of Phayao Phayao Thailand School of Information and Communication Technology Department of Computer Engineering University of Phayao Phayao Thailand Mahidol University Image Information and Intelligence Laboratory Faculty of Engineering Department of Computer Engineering Nakorn Pathom Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (纸本)9798350396546

Nowadays, one of the most important objectives in health-related research is the improvement of the living condition and well-being of people. Smart home systems can provide health protection for residents based on the results of daily activity recognition. Recent advances and developments in sensor technology have increased the need for sensor-compatible goods and services in smart homes. Consequently, the ever-increasing volume of data requires the field of deep learning (DL) for auto-matic human motion recognition. Recent research has modeled spatiotemporal sequences gathered by smart home sensors using long short-term memory networks. In this work, ResNeXt-based models that learn to classify human activities in smart homes were proposed to improve recognition performance. Experiments conducted on Center for Advanced Studies in Adaptive Systems (CASAS) data, a publicly available benchmark dataset, shows that the proposed ResNeXt-based techniques are significantly superior to the existing DL methods and provide better results compared to the existing literature. The ResNeXt model achieved the averaged accuracy over the benchmark method to 84.81%, 93.57%, and 90.38% for the CASAS-Cairo, CASAS-Milan and CASAS-Kyoto3 datasets, respectively. © 2023 IEEE.

关键词： Deep learning

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A deep neural network based surrogate model for damage identification in full-scale structures with incomplete noisy measurements

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Frontiers of Structural and Civil Engineering 2024年第3期18卷 393-410页

作者： Tram BUI-NGOC Duy-Khuong LY Tam T TRUONG Chanachai THONGCHOM T.NGUYEN-THOI Laboratory for Computational Mechanics Institute for Computational Science and Artificial IntelligenceVan Lang UniversityHo Chi Minh City 70000Vietnam Faculty of Mechanical-Electrical and Computer Engineering School of TechnologyVan Lang UniversityHo Chi Minh City 70000Vietnam Faculty of Civil Engineering School of TechnologyVan Lang UniversityHo Chi Minh City 70000Vietnam Department of Computer Science Aarhus UniversityAarhus 8000Denmark Thammasat University research unit in structural and foundation engineering Department of Civil EngineeringThammasat UniversityPathumthani 12120Thailand Laboratory for Applied and Industrial Mathematics Institute for Computational Science and Artificial IntelligenceVan Lang UniversityHo Chi Minh City 70000Vietnam Thammasat School of Engineering Thammasat UniversityPathumthani 12120Thailand

The paper introduces a novel approach for detecting structural damage in full-scale structures using surrogate models generated from incomplete modal data and deep neural networks(DNNs).A significant challenge in this field is the limited availability of measurement data for full-scale structures,which is addressed in this paper by generating data sets using a reduced finite element(FE)model constructed by SAP2000 software and the MATLAB programming *** surrogate models are trained using response data obtained from the monitored structure through a limited number of measurement *** proposed approach involves training a single surrogate model that can quickly predict the location and severity of damage for all potential *** achieve the most generalized surrogate model,the study explores different types of layers and hyperparameters of the training algorithm and employs state-of-the-art techniques to avoid overfitting and to accelerate the training *** approach’s effectiveness,efficiency,and applicability are demonstrated by two numerical *** study also verifies the robustness of the proposed approach on data sets with sparse and noisy measured ***,the proposed approach is a promising alternative to traditional approaches that rely on FE model updating and optimization algorithms,which can be computationally *** approach also shows potential for broader applications in structural damage detection.

关键词： vibration-based damage detection deep neural network full-scale structures finite element model updating noisy incomplete modal data

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Intelligent System for Improving Scalability by Sharding for Agriculture Supply Chain 11

Intelligent System for Improving Scalability by Sharding for...

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11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Saranya, P. Vijaya, P. Maheswari, R. Palanisamy, Praba Mani, Joseph Vellore Institute of Technology Scope Tamilnadu Chennai India Modern College of Business and Science Bowshar Muscat Oman Symbiosis Skills and Professional University Csit Pune India University of Technology and Applied Sciences Information Technology Dept Muscat Al khuwair Muscat Oman Modern Colllege of Business and Science Department of Mathematics and Computer Science Bowshar Muscat Oman

ISBN: (纸本)9798350350357

Experts attribute the success of Bitcoin to its underlying public and permissionless blockchain technology. Permissionless blockchains offer solutions to various worldly challenges, from eCommerce to governance and arbitration. However, the agriculture supply chain is a crucial sector that deserves attention. Many blockchain-based agriculture traceability systems have been developed by recent researchers using blockchain and smart contracts. While existing systems achieve security and decentralization on a large scale, they suffer from severe performance and scalability limitations. This work introduces a blockchain traceability system for the Agriculture Supply Chain (ASC) using a Proof of Stake (PoS) sharding approach to enhance scalability and operational efficiency. The proposed system comprises a coord layer, sharding layer, cross shard, and communication layer implemented using Python and the MPICH message passing library. The system distributes the computational workload among a specified number of nodes across the network, leveraging the sharding mechanism to achieve higher transaction throughput limits. The simulation results reveal that with a consistent throughput, an increase in transactions leads to longer processing times when the shard count remains the same. However, when the shard count increases, there is a significant improvement in throughput, albeit with a higher number of nodes and longer processing times. Therefore, the proposed system demonstrates enhanced throughput in sharded systems compared to existing systems. © 2024 IEEE.

关键词： Blockchain

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Digital Twin Empowered Task Offloading for Vehicular Edge Computing 29

Digital Twin Empowered Task Offloading for Vehicular Edge Co...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Tang, Chaogang Wu, Huaming Zhu, Chunsheng Xiao, Shuo China University of Mining and Technology School of Computer Science and Technology Xuzhou221116 China Tianjin University Center for Applied Mathematics Tianjin300072 China Shenzhen Technology University College of Big Data and Internet Shenzhen518118 China

ISBN: (纸本)9798350330717

Vehicular edge computing (VEC) as a promising computing paradigm has accelerated the reformation of existing dominating computing infrastructures, enabling resource provisioning in close proximity to resource requestors. However, several challenges still exist, including efficient resource scheduling and management, dynamic wireless channel state, and limited bandwidth usage. To address these issues, we introduce the digital twin (DT) technology into VEC, enabling DTs of physical entities in VEC to achieve real-time offloading decision-making in the DT simulation cycle. In particular, we propose a DT-empowered VEC (DT-VEC) architecture, aiming to achieve efficient task offloading while considering extra latency incurred by task migration. We further put forward an efficient algorithm to minimize the response latency for all the tasks in the optimization period. The simulation results have proven that our approach outperforms the other two greedy approaches. © 2023 IEEE.

关键词： digital twin resource scheduling response latency task offloading Vehicular edge computing

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Selectively Sharing Experiences Improves Multi-Agent Reinforcement Learning 22

Selectively Sharing Experiences Improves Multi-Agent Reinfor...

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22nd International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2023

作者： Gerstgrasser, Matthias Danino, Tom Keren, Sarah John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA United States The Taub Faculty of Computer Science Technion - Israel Institute of Technology Haifa Israel

We present a novel multi-agent RL approach, Selective Multi-Agent Prioritized Experience Relay, in which agents share with other agents a limited number of transitions they observe during training. The intuition behind this is that even a small number of relevant experiences from other agents could help each agent learn. Unlike many other multi-agent RL algorithms, this approach allows for largely decentralized training, requiring only a limited communication channel between agents. We show that our approach outperforms baseline no-sharing decentralized training and state-of-the art multi-agent RL algorithms. Further, sharing only a small number of highly relevant experiences outperforms sharing all experiences between agents, and the performance uplift from selective experience sharing is robust across a range of hyperparameters and DQN variants. A reference implementation is available under https://***/mgerstgrasser/super. © 2023 International Foundation for Autonomous Agents and Multiagent Systems (***). All rights reserved.

关键词： Reinforcement learning

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The Impact of Collaborative Learning Virtual Environments on Student's Performance

The Impact of Collaborative Learning Virtual Environments on...

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Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE), IEEE International Conference on

作者： Ourania Koutzampasopoulou Xanthidou Nadine Aburumman Hanêne Ben-Abdallah Computer Science Brunel University London London U.K. Computer Information Science & Applied Media Higher Colleges of Technology Dubai U.A.E.

ISBN: (数字)9798350378009

ISBN: (纸本)9798350378016

This research explored the potential of collaborative virtual environments (CVEs) in delivering and assessing course content in higher education. The study examined the role of “asymmetric” (i.e., one participant interacting within the virtual reality (VR) setting and the other “guiding” outside of it) and “symmetric” (i.e., both participants taking a quiz within the same VR setting) interactions in the overall VR experience. It also investigated participants' preferences for a collaborative VR setting over a traditional physical lab quiz. A convenience sample of 184 participants/trainees participated in a VR experiment in single-user, asymmetric collaboration, and symmetric collaboration modalities, with 102 from the computer science department and 82 from the Aviation Engineering department of a higher education institution in the U.A.E. The VR experiment (quan-titative) was followed by a survey (qualitative) to gather their reflections on the experiment. The results indicate a significant improvement in success rates for completing the VR experiment in collaboration modalities compared to the same in single-user VR mode. They also suggest that, while participants enjoyed the VR experience, they prefer a blended approach to delivering and assessing course content in higher education. These findings have substantial implications for decision-making processes in higher education institutions.

关键词： Surveys Federated learning Extended reality Education Decision making Collaboration Virtual environments Neural engineering Metrology Reflection

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LARSA22 at Qur’an QA 2022: Text-to-Text Transformer for Finding Answers to Questions from Qur’an 5

LARSA22 at Qur’an QA 2022: Text-to-Text Transformer for Fin...

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5th Workshop Open-Source Arabic Corpora and Processing Tools with Shared Tasks on Qur'an QA and Fine-Grained Hate Speech Detection, OSACT 2022

作者： Mellah, Youssef Touahri, Ibtissam Kaddari, Zakaria Haja, Zakaria Berrich, Jamal Bouchentouf, Toumi LARSA Laboratory National School of Applied Sciences Oujda Morocco Department of Computer Science Superior School of Technology Meknes Morocco

ISBN: (纸本)9791095546757

Question Answering (QA) is one of the main foсuses of Natural Language Proсessing (NLP) researсh. However, Arabiс Question Answering is still not within reaсh. The сhallenges of the Arabiс language and the laсk of resourсes have made it diffiсult to provide powerful Arabiс QA systems with high aссuraсy. While low aссuraсy may be aссepted for general purpose systems, it is сritiсal in some fields suсh as religious affairs. Therefore, there is a need for speсialized aссurate systems that target these сritiсal fields. In this paper, we propose a Transformer-based QA system using the mT5 Language Model (LM). We finetuned the model on the Qur'aniс Reading Сomprehension Dataset (QRСD) whiсh was provided in the сontext of the Qur'an QA 2022 shared task. The QRСD dataset сonsists of question-passage pairs as input, and the сorresponding adequate answers provided by expert annotators as output. Evaluation results on the same DataSet show that our best model сan aсhieve 0.98 (F1 Sсore) on the Dev Set and 0.40 on the Test Set. We disсuss those results and сhallenges, then propose potential solutions for possible improvements. The sourсe сode is available on our repository1 © European Language Resources Association (ELRA).

关键词： Statistical tests

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