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IEEE International Conference on Communications (ICC)

作者： Nan An Fang Yang Ling Cheng Jian Song Zhu Han Department of Electronic Engineering Beijing National Research Center for Information Science and Technology Tsinghua University Beijing P. R. China School of Electrical and Information Engineering University of the Witwatersrand Johannesburg South Africa Shenzhen International Graduate School Tsinghua University Shenzhen China Department of Electrical and Computer Engineering University of Houston Houston TX USA

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Motivated by the ability of optical intelligent reflective surface (OIRS) to alter optical channels, the effectiveness of OIRS on aggregated visible light communication (VLC)-radio frequency (RF) systems is explored, and the system model is established in detail. With the aim of improving the energy efficiency (EE) of the aggregated system with OIRS, the EE maximization problem is formulated with diverse constraints, followed by the proposed block coordinate descent (BCD)-based resource allocation algorithm. Particularly, the EE maximization problem is partitioned into two subproblems, which are OIRS configuration and joint power allocation, and then are solved in the iteration process. Additionally, the substantial enhancement in EE brought by OIRS for the aggregated VLC-RF system, as well as the convergence and efficacy of the proposed BCD-based algorithm are demonstrated by comprehensive simulation results.

关键词： Integrated optics Radio frequency Simulation Energy efficiency Partitioning algorithms Resource management Optical reflection

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Leveraging BERT and Social Media Data to Analyze Pre- and Post-COVID-19 Accommodation Reviews in Japan

Leveraging BERT and Social Media Data to Analyze Pre- and Po...

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International Conference on Applied System Innovation (ICASI)

作者： Liang-Tseng Liu Yang-Chou Juan Tomoya Kawakami Yi-Chung Chen Wei-Ting Chen Jia-Ching Ying Department of Industrial Engineering and Management National Yunlin University of Science and Technology Yunlin Taiwan Department of Computer Science and Engineering National Chung Hsing University Taichung Taiwan Graduate School of Engineering University of Fukui Fukui Japan Department of Management Information Systems National Chung Hsing University Taichung Taiwan

ISBN: (数字)9798350394924

ISBN: (纸本)9798350394931

This study applied the BERT model to the analysis of social media data to enhance the process of reviewing Japanese accommodation options. Text processing and sentiment analysis revealed shifts in the preferences and perceptions of Taiwanese tourists before and after the COVID-19 pandemic. These findings demonstrating the impact of the pandemic on traveler behavior as well as the utility of AI in elucidating these changes.

关键词： COVID-19 Analytical models Technological innovation Sentiment analysis Social networking (online) Pandemics Reviews

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International Conference of computer and Informatics engineering (IC2IE)

作者： Wandy Wandy Kusworo Adi Media Anugerah Ayu Doctoral Program of Information Systems School of Postgraduate Studies Diponegoro University Semarang Indonesia Department of Physics Faculty of Science and Mathematics Diponegoro University Semarang Indonesia Department of Computer Science Faculty of Engineering and Technology Sampoerna University South Jakarta Indonesia

ISBN: (数字)9798331505530

ISBN: (纸本)9798331505547

Full-marathon and Half-marathon distances are categorized as road running. Full-marathon running is becoming increasingly popular, and Half-marathon is increasing worldwide in both sexes and all age groups. Some aspects might relate to Full-marathon and Half-marathon running performance during training and races. Technology also plays an essential role in supporting runners and running races. Technology like artificial intelligence (AI) now supports the running athlete, not only predicting performance and results. It can also be used later to help the coach generate training programs for the athlete. This research aimed to find many aspects of marathons and performance and analyze them to see if artificial intelligence could later support them. It used secondary data and a systematic literature review proposed by Kitchenham. Out of the 58 articles, 21 of them (36.21%) received a score of 1 from Q1. Additionally, 19 articles (32.76%) received a score of 1 from both Q2 and Q3. Among the 58 articles, 9 (15.52%) received a total score of 3, with all three Q1, Q2, and Q3 scores being 1. This indicates that artificial intelligence will likely support the content of these nine articles. Several factors were also discovered to be connected to marathons and athletic performance. These findings suggested that additional investigation into marathons and performance, later backed by artificial intelligence, remained pertinent and essential.

关键词： Training Roads Artificial intelligence Informatics

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Multiple-replica integrity auditing schemes for cloud data storage

Multiple-replica integrity auditing schemes for cloud data s...

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作者： Lin, Yangfei Li, Jie Jia, Xiaohua Ren, Kui Graduate School of System and Information Engineering University of Tsukuba Ibaraki Japan Department of Computer Science and Engineering Shanghai Jiaotong University Shanghai China Department of Computer Science City University of Hong Kong Hong Kong Department of Computer Science and Engineering State University of New York at Buffalo New YorkNY United States

Cloud computing has been an essential technology for providing on-demand computing resources as a service on the Internet. Not only enterprises but also individuals can outsource their data to the cloud without worrying about purchase and maintenance cost. The cloud storage system, however, is not fully trustable. Cloud data integrity auditing is crucial for defending against the security threats of data in the untrusted multicloud environment. Storing multiple replicas is a commonly used strategy for the availability and reliability of critical data. In this paper, we summarize and analyze the state-of-the-art multiple-replica integrity auditing schemes in cloud data storage. We present the system model and security threats of outsourcing data to the cloud with classification of ongoing developments. We also summarize the existing data integrity auditing schemes for multicloud data storage. The important open issues and potential research directions are addressed. © 2019 John Wiley & Sons, Ltd.

关键词： Cloud storage

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Analysis of temporal structure of laser chaos by Allan variance

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Physical Review E 2023年第1期107卷 014211-014211页

作者： Naoki Asuke Nicolas Chauvet André Röhm Kazutaka Kanno Atsushi Uchida Tomoaki Niiyama Satoshi Sunada Ryoichi Horisaki Makoto Naruse Department of Information Physics and Computing Graduate School of Information Science and Technology The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-8656 Japan Department of Information and Computer Sciences Saitama University 255 Shimo-Okubo Sakura-ku Saitama city Saitama 338-8570 Japan Faculty of Mechanical Engineering Institute of Science and Engineering Kanazawa University Kakuma Kanazawa city Ishikawa 920-1192 Japan

Allan variance has been widely utilized for evaluating the stability of the time series generated by atomic clocks and lasers, in time regimes ranging from short to extremely long. This multiscale examination capability of the Allan variance may also be beneficial in evaluating the chaotic oscillating dynamics of semiconductor lasers— not just for conventional phase stability analysis. In the present study, we demonstrated Allan variance analysis of the complex time series generated by a semiconductor laser with delayed feedback, including low-frequency fluctuations (LFFs), which exhibit both fast and slow dynamics. While the detection of LFFs is difficult with the conventional power spectrum analysis method in the low-frequency regime, the Allan variance approach clearly captured the appearance of multiple time-scale dynamics, such as LFFs. This study demonstrates that Allan variance can help in understanding and characterizing diverse laser dynamics, including LFFs, spanning a wide range of timescales.

关键词： Chaos

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Diffusion-based Reinforcement Learning for Edge-enabled AI-Generated Content Services

TechRxiv

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TechRxiv 2023年

作者： Du, Hongyang Li, Zonghang Niyato, Dusit Kang, Jiawen Xiong, Zehui Huang, Huawei Mao, Shiwen School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore School of Information and Communication Engineering University of Electronic Sciences and Technology of China Chengdu China School of Automation Guangdong University of Technology China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore School of Software Engineering Sun Yat-Sen University Zhuhai China Department of Electrical and Computer Engineering Auburn University Auburn United States

As Metaverse emerges as the next-generation Internet paradigm, the ability to efficiently generate content is paramount. AI-Generated Content (AIGC) emerges as a key solution, yet the resource-intensive nature of large Generative AI (GAI) models presents challenges. To address this issue, we introduce an AIGC-as-a-Service (AaaS) architecture, which deploys AIGC models in wireless edge networks to ensure broad AIGC services accessibility for Metaverse users. Nonetheless, an important aspect of providing personalized user experiences requires carefully selecting AIGC Service Providers (ASPs) capable of effectively executing user tasks, which is complicated by environmental uncertainty and variability. Addressing this gap in current research, we introduce the AI-Generated Optimal Decision (AGOD) algorithm, a diffusion model-based approach for generating the optimal ASP selection decisions. Integrating AGOD with Deep Reinforcement Learning (DRL), we develop the Deep Diffusion Soft Actor-Critic (D2SAC) algorithm, enhancing the efficiency and effectiveness of ASP selection. Our comprehensive experiments demonstrate that D2SAC outperforms seven leading DRL algorithms. Furthermore, the proposed AGOD algorithm has the potential for extension to various optimization problems in wireless networks, positioning it as a promising approach for future research on AIGC-driven services. The implementation of our proposed method is available at: https://***/Lizonghang/AGOD. © 2023, CC BY.

关键词： Reinforcement learning

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MSIr: Automatic Registration Service for Mass Spectrometry Imaging and Histology

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Analytical Chemistry 2023年第6期95卷 3317-3324页

作者： Lin, Bo-Jhang Kuo, Tien-Chueh Chung, Hsin-Hsiang Huang, Ying-Chen Wang, Ming-Yang Hsu, Cheng-Chih Yao, Po-Yang Tseng, Yufeng Jane Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei10617 Taiwan The Metabolomics Core Laboratory Centers of Genomic and Precision Medicine National Taiwan University Taipei10617 Taiwan Department of Chemistry National Taiwan University Taipei10617 Taiwan Department of Surgery National Taiwan University Hospital Taipei10002 Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei10617 Taiwan School of Pharmacy College of Medicine National Taiwan University Taipei10002 Taiwan

Mass spectrometry imaging (MSI) is a powerful tool that can be used to simultaneously investigate the spatial distribution of different molecules in samples. However, it is difficult to comprehensively analyze complex biological systems with only a single analytical technique due to different analytical properties and application limitations. Therefore, many analytical methods have been combined to extend data interpretation, evaluate data credibility, and facilitate data mining to explore important temporal and spatial relationships in biological systems. Image registration is an initial and critical step for multimodal imaging data fusion. However, the image registration of multimodal images is not a simple task. The property difference between each data modality may include spatial resolution, image characteristics, or both. The image registrations between MSI and different imaging techniques are often achieved indirectly through histology. Many methods exist for image registration between MSI data and histological images. However, most of them are manual or semiautomatic and have their prerequisites. Here, we built MSI Registrar (MSIr), a web service for automatic registration between MSI and histology. It can help to reduce subjectivity and processing time efficiently. MSIr provides an interface for manually selecting region of interests from histological images;the user selects regions of interest to extract the corresponding spectrum indices in MSI data. In the performance evaluation, MSIr can quickly map MSI data to histological images and help pinpoint molecular components at specific locations in tissues. Most registrations were adequate and were without excessive shifts. MSIr is freely available at https://*** and https://***/CMDM-Lab/MSIr. © 2023 The Authors. Published by American Chemical Society.

关键词： Histology

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An Improved Sample Selection Framework for Learning with Noisy Labels

SSRN

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SSRN 2023年

作者： Zhang, Qian Zhu, Yi Yang, Ming Jin, Ge Zhu, Yingwen Lu, Yanjun Zou, Yu Chen, Qiu School of Information Technology Jiangsu Open University Nanjing210036 China School of Computer and Electronic Information Nanjing Normal University Nanjing China School of Artificial Intelligence School of Future Technology Nanjing University of Information Science & Technology Nanjing China Department of Electrical Engineering and Electronics Graduate School of Engineering Kogakuin University Tokyo163-8677 Japan

Since the powerful memory capabilities of deep neural networks, they tend to overfit noisy labels, resulting in the degradation of discrimination. Sample selection methods that filter out the possibly clean labels have been the mainstream of learning with noisy labels. Since the large gap between the size of the filtered, possibly clean subset and the unlabeled subset, which is particularly obvious under high noise rates, the label-free samples in sample selection methods cannot be fully used, leaving space for performance improvement. This paper proposes an improved sample Selection framework with the OverSampling strategy, SOS, to overcome this deficiency. It mines the useful information carried in label-free instances to boost models’ performance by combining an oversampling strategy with the existing SOTA sample selection methods. We demonstrate the effectiveness of SOS through extensive experimental results on both synthetic noisy datasets and real-world datasets. The code will be available at https://***/LanXiaoPang613/SOS. © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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An Improved Sample Selection Framework for Learning with Noisy Labels

SSRN

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SSRN 2023年

关键词： Deep neural networks

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Demo Abstract: Real-Time Out-of-Distribution Detection on a Mobile Robot

arXiv

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arXiv 2022年

作者： Yuhas, Michael Easwaran, Arvind School of Computer Science and Engineering Singapore Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore

In a cyber-physical system such as an autonomous vehicle (AV), machine learning (ML) models can be used to navigate and identify objects that may interfere with the vehicle’s operation. However, ML models are unlikely to make accurate decisions when presented with data outside their training distribution. Out-of-distribution (OOD) detection can act as a safety monitor for ML models by identifying such samples at run time. However, in safety critical systems like AVs, OOD detection needs to satisfy real-time constraints in addition to functional requirements. In this demonstration, we use a mobile robot as a surrogate for an AV and use an OOD detector to identify potentially hazardous samples. The robot navigates a miniature town using image data and a YOLO object detection network. We show that our OOD detector is capable of identifying OOD images in real-time on an embedded platform concurrently performing object detection and lane following. We also show that it can be used to successfully stop the vehicle in the presence of unknown, novel samples. Copyright © 2022, The Authors. All rights reserved.

关键词： Mobile robots

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