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2023 IEEE International Conference on Metaverse computing, Networking and Applications, MetaCom 2023

作者： Rao, Wenbin Yuan, Jiantao Zhou, Meng Wu, Celimuge Ji, Yusheng Lu, Weidang Yin, Rui Zhejiang University of Technology College of Information Engineering China Zhejiang Engineering Research Center of Building's Digital Carbon Neutral Technology School of Information & Electronic Engineering Hangzhou City College Hangzhou310013 China The University of Electro-Communications Graduate School of Informatics and Engineering Japan National Institute of Informatics Information Systems Architecture Research Division Japan

ISBN: (纸本)9798350333336

As the licensed spectrum has been scarce, the unlicensed spectrum is deemed as a strong supplement to the licensed band, particularly for diversified sensors connected to the metaverse. Moreover, as one of the key components in future communication networks, the massive connection is crucial to the massive sensors connected to the metaverse with object of maximizing the number of connected devices while minimizing their power consumption. Therefore, both licensed and unlicensed bands are used for metaverse users and sensors in this paper. First, a feasible implementation framework is proposed to minimize power consumption and maximize the number of connected sensors while guaranteeing their data rate requirements. Accordingly, a joint power, spectrum allocation and user association scheme is proposed. Finally, the simulation results are provided to verify the effectiveness of the proposed algorithm. © 2023 IEEE.

关键词： Electric power utilization

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Optimising Random Forest Machine Learning Algorithms for User VR Experience Prediction Based on Iterative Local Search-Sparrow Search Algorithm 6

Optimising Random Forest Machine Learning Algorithms for Use...

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6th International Conference on Communications, Information System and Computer Engineering, CISCE 2024

作者： Tang, Xirui Li, Feiyang Cao, Zinan Yu, Qixuan Gong, Yulu College of Computer Sciences Northeastern University BostonMA02115 United States University of Illinois Urbana-Champaign Department of Computer Science ChampaignIL61820 United States The University of Tokyo Department of General Systems Studies Tokyo113-8654 Japan College of Computing Georgia Institute of Technology AtlantaGA30332-0280 United States School of Informatics Computing and Cyber Systems Northern Arizona University FlagstaffAZ86011-4092 United States

ISBN: (纸本)9798350353174

In this paper, an improved method for VR user experience prediction is investigated by introducing a sparrow search algorithm and a random forest algorithm improved by an iterative local search-optimised sparrow search algorithm. The study firstly conducted a statistical analysis of the data, and then trained and tested using the traditional random forest model, the random forest model improved by the sparrow search algorithm, and the random forest algorithm improved based on the iterative local search-sparrow search algorithm, respectively. The results show that the traditional random forest model has a prediction accuracy of 93% on the training set but only 73.3% on the test set, which is poor in generalisation;whereas the model improved by the sparrow search algorithm has a prediction accuracy of 94% on the test set, which is improved compared with the traditional model. What is more noteworthy is that the improved model based on the iterative local search-sparrow search algorithm achieves 100% accuracy on both the training and test sets, which is significantly better than the other two methods. These research results provide new ideas and methods for VR user experience prediction, especially the improved model based on the iterative local search-sparrow search algorithm performs well and is able to more accurately predict and classify the user's VR experience. In the future, the application of this method in other fields can be further explored, and its effectiveness can be verified through real cases to promote the development of AI technology in the field of user experience. © 2024 IEEE.

关键词： Prediction models

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Build-It-Here: Utilizing 2D Inpainting Models for On-Site 3D Object Generation

Build-It-Here: Utilizing 2D Inpainting Models for On-Site 3D...

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International Conference on Computational Intelligence and Applications (ICCIA)

作者： Zhennan Wu Hiroyuki Sato Graduate School of Engineering The University of Tokyo Tokyo Japan Information Systems Architecture Science Research Division National Institute of Informatics Japan Tokyo Japan

ISBN: (数字)9798350352214

ISBN: (纸本)9798350352221

Significant progress has been made in both 2D and 3D content creation recently. In order to integrate the advantages of both, we present Build-it-here, a novel 3D generation method for open vocabulary furniture creation and placing inside empty rooms. By modulating textual input, we achieve few-shot prompting large language models for layout planning. The output room layout is represented by bounding boxes in the empty house. Upon sampling camera view around bounding-box for rendering, we perform text-guided inpainting on masked 2D images. The inpainted object is then reconstructed in 3D and back projected to the room. Qualitative experiments have demonstrated that our method not only achieved open vocabulary diversity but also ensuring the rationality of placements. The scenes generated through this method is photo-realistic and ready for applications like game industry asset usage.

关键词： Industries Vocabulary Solid modeling Three-dimensional displays Large language models Layout Games Rendering (computer graphics) Planning Image reconstruction

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A Cross-Organizational Identity Proofing System for Seamless Online ID Re-Binding Leveraging Individual Number Cards

A Cross-Organizational Identity Proofing System for Seamless...

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IEEE Annual International Computer Software and Applications Conference (COMPSAC)

作者： Sayako Shimizu Hiroyuki Sato Motonori Nakamura Hukofumi Suzuki Yasuo Okabe Information Systems Architecture Science Research National Institute of Informatics Tokyo Japan Institute for Information Management and Communication Kyoto University Kyoto Japan Cyber Science Infrastructure Development Department Academic Authentication Systems Office National Institute of Informatics Tokyo Japan Academic Center for Computing and Media Studies Kyoto University Kyoto Japan

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

This study focuses on the issue of ID management when researchers change their organizational affiliations, aiming at the effective utilization of research data in educational and research organizations. Although research data management systems are being developed at each organization, the continuous use of research data remains an issue for researchers. For example, when a researcher moves from one organization to another, the research data that was previously available to him or her becomes unavailable. In response, there is a need for a system that allows researchers to continue using their research data smoothly even if they move from one organization to another. To solve this problem, the authors propose a cross-organizational identity proofing system that ensures the continuity of IAL2/ AAL2 and completes the ID transfer online. The idea is to use researcher ID numbers, ORCID, and public personal authentication to verify the user's identity when linking the IDs before and after the transfer. To facilitate this, the study performs identity verification using an Individual Number Card (a Japanese governmental system) to enable easy ID linkage between IdPs with high identity confirmation and authentication strength. The implementation has been carried out in the development environment of Orthros, an ID linkage service provided by the National institute of informatics (NIl), utilizing the API service of xID Co., which is a solution for linking with the Individual Number Card. This approach is expected to overcome ID management issues associated with researcher transfers and improve the efficiency of research data use.

关键词： Couplings Digital transformation Employment Education Authentication Organizations Software

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An Efficient Collaborative Task Offloading Approach Based on Multi-Objective Algorithm in MEC-Assisted Vehicular Networks

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IEEE Transactions on Vehicular Technology 2025年

作者： Chen, Shuaijie Li, Wenfeng Sun, Jingtao Pace, Pasquale He, Lijun Fortino, Giancarlo Wuhan University of Technology State Key Laboratory of Maritime Technology and Safety School of Transportation and Logistics Engineering Hubei Wuhan430063 China National Institute of Informatics Information Systems Architecture Research Division Tokyo Japan University of Calabria Department of Informatics Modeling Electronics and Systems Rende87036 Italy

Mobile edge computing assisting vehicular networks to achieve collaborative computing is a significant research area, as it can effectively expand the on-board computational resources, thereby improving the responsiveness of vehicular applications and reducing energy consumption. However, the existing task offloading schemes still have various gaps and face challenges that should be addressed because task offloading problems are usually multi-objective optimization problems (MOOP) and NP-hard problems. In this respect, we formulate a new multi-objective model for vehicle-centered task offloading tacking into account the queuing and computing processes of tasks. The minimization of the average delay time, average energy consumption, and average payment cost for each vehicle is defined as a MOOP. We propose an efficient collaborative offloading approach based on task triage strategy and multi-objective optimization to address the model. Specifically, this approach can be decomposed into two stages. First, a task triage strategy is developed based on a comprehensive analysis of task urgency and task criticality to make offloading decisions for triaged tasks with extreme characteristics. Then, we propose a novel multi-objective optimization framework for the MOOP. Finally, we develop a multi-objective hybrid genetic algorithm that integrates the associative learning immediate memory strategy and genetic operations to solve the MOOP. Extensive experiments and performance evaluations based on real-world traces of taxis demonstrate the effectiveness of the proposed approach. The results indicate that the approach outperforms six other well-known multi-objective algorithms in solving the MOOP. © 1967-2012 IEEE.

关键词： Mobile edge computing

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Securing the Future after Pager Bombs:Lifecycle Protection of Smart Devices via Blockchain Intelligence

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IEEE/CAA Journal of Automatica Sinica 2024年第12期11卷 2355-2358页

作者： Fei Lin Qinghua Ni Jing Yang Juanjuan Li Nan Zheng Levente Kovács Radu Prodan Mariagrazia Dotoli Qing-Long Han Fei-Yue Wang the Department of Engineering Science Faculty of Innovation EngineeringMacao University of Science and TechnologyMacao 999078China the State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China the School of Artificial Intelligence University of Chinese Academy of SciencesBei-jing 100049China IEEE Obuda University John von Neumann Faculty of InformaticsBecsi Str.96/bH-1034 BudapestHungary the Institute of Information Technology University of Klagenfurt9020 Klagenfurt am WortherseeAustria the Department of Electrical and Information Engineering Polytechnic of Bari70126 BariItaly the School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourne VIC 3122Australia the State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190

THE Lebanese wireless device explosion incident has drawn widespread attention,involving devices such as pagers,walkie-talkies,and other common devices[1].This event has revealed and highlighted the security vulnerabilities in global supply chains from raw material manufacturing and distribution to the usage of devices and equipment,signaling the onset of a new wave of"supply chain warfare"[2].

关键词： Smart cycle chains

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Causal Deep Operator Networks for Data-Driven Modeling of Dynamical systems

Causal Deep Operator Networks for Data-Driven Modeling of Dy...

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2023 IEEE International Conference on systems, Man, and Cybernetics, SMC 2023

作者： Nghiem, Truong X. Nguyen, Thang Nguyen, Binh T. Nguyen, Linh School of Informatics Computing and Cyber Systems Northern Arizona University FlagstaffAZ United States Texas A&m University-Corpus Christi Department of Engineering Corpus ChristiTX United States Institute of Innovation Science and Sustainability Federation University Australia ChurchillVIC Australia

ISBN: (纸本)9798350337020

The deep operator network (DeepONet) architecture is a promising approach for learning functional operators, that can represent dynamical systems described by ordinary or partial differential equations. However, it has two major limitations, namely its failures to account for initial conditions and to guarantee the temporal causality - a fundamental property of dynamical systems. This paper proposes a novel causal deep operator network (Causal-DeepONet) architecture for incorporating both the initial condition and the temporal causality into data-driven learning of dynamical systems, overcoming the limitations of the original DeepONet approach. This is achieved by adding an independent root network for the initial condition and independent branch networks conditioned, or switched on/off, by time-shifted step functions or sigmoid functions for expressing the temporal causality. The proposed architecture was evaluated and compared with two baseline deep neural network methods and the original DeepONet method on learning the thermal dynamics of a room in a building using real data. It was shown to not only achieve the best overall prediction accuracy but also enhance substantially the accuracy consistency in multistep predictions, which is crucial for predictive control. © 2023 IEEE.

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A novel analytical model for estimating vehicle delay at isolated signalised intersections

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International Journal of Simulation and Process Modelling 2022年第1-2期19卷 71-85页

作者： Qiao, Feng Liu, Huixin Luo, Dan Sun, Haochen Chen, Yinong School of Information and Control Engineering Shenyang Institute of Technology Fushun113122 China Faculty of Information and Control Engineering Shenyang Jianzhu University Shenyang100168 China School of Information Engineering Henan University of Science and Technology Henan Luoyang471000 China School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ85287-8809 United States

This paper proposes a novel analytical model to estimate the average vehicle delay at signalised intersections under saturated or oversaturated conditions based on the investigation and analysis of the existing methods to deal with the problems arising in the process of acceleration, deceleration, and the transmissibility of the cycle-by-cycle average vehicle delay. The proposed model employs and combines the operating and queuing characteristics of vehicles to produce the analytic formula. To verify the effectiveness of the proposed model, simulation experiments are conducted, and the error rates and the correlation coefficients are investigated, which confirm that the proposed model possesses certain significant advantages over the existing models in saturated and oversaturated conditions. The results of research work show that the proposed model can provide transportation engineers or professionals with an effective tool for analysing, timing and managing the saturated or oversaturated signalised intersections. © 2022 Inderscience Enterprises Ltd.

关键词： Analytical models

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Empowering Consumer Healthcare Through Sensor-Rich Devices using Federated Learning for Secure Resource Recommendation

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IEEE Transactions on Consumer Electronics 2025年第1期71卷 1563-1570页

作者： Fathima, Afifa Salsabil Basha, Syed Muzamil Ahmed, Syed Thouheed Khan, Surbhi Bhatia Asiri, Fatima Basheer, Shakila Shukla, Madhu School of Computer Science and Engineering REVA University Karnataka Bengaluru India School of Computing and Information Technology REVA University Karnataka Bengaluru India Department of Data Science School of Science Engineering and Environment University of Salford Manchester United Kingdom Centre for Research Impact and Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India College of computer science Informatics and computer systems department King Khalid University Saudi Arabia Department of Information Systems College of computer and Information science Princess Nourah bint Abdulrahman University P.O. BOX 84428 Riyadh11671 Saudi Arabia the Department of CSE - AI ML & DS Marwadi University Gujarat Rajkot360003 India

When implementing zero-trust edge computing, offloading computational tasks and data access through traditional model training and usage approaches can lead to increased latency. Since the traditional methods often involve extensive communication with a central server, creating additional network hopping stations/nodes resulting in increased latency. The challenge is bound to allocate a befitting resource at a given consumer demand. In this proposed system, a federated learning model based data offloading and consumer medical resource recommendation of IoT is discussed and validated. The user/consumer group and local training models are aligned with edge servers for data preprocessing and customization with a series of resources demand creation and coordination. The consumer resource allocating priorities are fine-grained with the proposed blockchain based priority analyzer for recommendation and allocation. The computational parameter such as resource pool, average waiting time, energy consumption and transmission trust delays are observed and validated. The proposed framework fetches consumer resources logs and synchronizes the centralized training model for effective scheduling and allocation of resources with an accuracy of 94.92% under the 5G operating spectrum. The technique has demonstrated minimal latency in offloading the data request demand and resource allocation at the cloud servers. © 2025 institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Federated learning

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Assessing microbiome engraftment extent following fecal microbiota transplant with q2-fmt

arXiv

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arXiv 2024年

作者： Herman, Chloe Bolyen, Evan Simard, Anthony Gehret, Liz Caporaso, J. Gregory Pathogen and Microbiome Institute Northern Arizona University FlagstaffAZ United States School of Informatics Computing and Cyber Systems Northern Arizona University FlagstaffAZ United States

We present q2-fmt, a QIIME 2 plugin that provides diverse methods for assessing the extent of microbiome engraftment following fecal microbiota transplant. The methods implemented here were informed by a recent literature review on approaches for assessing FMT engraftment, and cover aspects of engraftment including Chimeric Asymmetric Community Coalescence, Donated Microbiome Indicator Features, and Temporal Stability. q2-fmt is free for all use, and detailed documentation illustrating worked examples on a real-world data set are provided in the project’s documentation. © 2024, CC BY.

关键词： Microorganisms

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