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IEEE World Forum on Internet of Things (WF-IoT)

作者： Bobo Ju Kun Yang Yang Liu Jing Liu Peng Sun Wei Ni Liang Song Academy for Engineering & Technology Fudan University Shanghai China Innovation Platform for Academicians of Hainan Province Haikou China Department of Computer Science University of Toronto Toronto Ontario Canada School of Information Science and Technology Fudan University Shanghai China Division of Natural and Applied Sciences Duke Kunshan University Suzhou China Shanghai East Bund Research Institute on NSAI Shanghai China

ISBN: (数字)9798350373011

ISBN: (纸本)9798350373028

Under the architecture of Networking Systems of AI (NSAI), we propose a three-tier edge computing platform called Open Service for NSAI (OpenSAI), which can jointly configure the application execution and compute-storage-network resources, for online application-network adaption, to ensure the efficiency and reliability of the services. In this research, we investigate a specific task-device adaptation optimization problem in OpenSAI, which recommend application tasks to distributed computing devices. We evaluate the performance of various optimization and search algorithms in recommending optimal task-device combinations, which show significant differences among the algorithms in terms of recommendation runtime costs and task adaptability. Overall, the hybrid recommendation of SCORE Algorithm, Particle Swarm Optimization, and Item-Item Collaborative Filtering demonstrates the best performance in both time efficiency and fitness scores across multiple scenarios.

关键词： Performance evaluation Runtime Scheduling algorithms Heuristic algorithms Collaboration computer architecture Reliability engineering Partitioning algorithms Artificial intelligence Optimization

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NTIRE 2023 Image Shadow Removal Challenge Report

NTIRE 2023 Image Shadow Removal Challenge Report

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2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Vasluianu, Florin-Alexandru Seizinger, Tim Timofte, Radu Cui, Shuhao Huang, Junshi Tian, Shuman Fan, Mingyuan Zhang, Jiaqi Zhu, Li Wei, Xiaoming Wei, Xiaolin Luo, Ziwei Gustafsson, Fredrik K. Zhao, Zheng Sjölund, Jens Schön, Thomas B. Dong, Xiaoyi Zhang, Xi Sheryl Li, Chenghua Leng, Cong Yeo, Woon-Ha Oh, Wang-Taek Lee, Yeo-Reum Ryu, Han-Cheol Luo, Jinting Jiang, Chengzhi Han, Mingyan Wu, Qi Lin, Wenjie Yu, Lei Li, Xinpeng Jiang, Ting Fan, Haoqiang Liu, Shuaicheng Xu, Shuning Song, Binbin Chen, Xiangyu Zhang, Shile Zhou, Jiantao Zhang, Zhao Zhao, Suiyi Zheng, Huan Gao, Yangcheng Wei, Yanyan Wang, Bo Ren, Jiahuan Luo, Yan Kondo, Yuki Miyata, Riku Yasue, Fuma Naruki, Taito Ukita, Norimichi Chang, Hua-En Yang, Hao-Hsiang Chen, Yi-Chung Chiang, Yuan-Chun Huang, Zhi-Kai Chen, Wei-Ting Chen, I-Hsiang Hsieh, Chia-Hsuan Kuo, Sy-Yen Xianwei, Li Fu, Huiyuan Liu, Chunlin Ma, Huadong Fu, Binglan He, Huiming Wang, Mengjia She, Wenxuan Liu, Yu Nathan, Sabari Kansal, Priya Zhang, Zhongjian Yang, Huabin Wang, Yan Zhang, Yanru Phutke, Shruti S. Kulkarni, Ashutosh Khan, Md Raqib Murala, Subrahmanyam Vipparthi, Santosh Kumar Ye, Heng Liu, Zixi Yang, Xingyi Liu, Songhua Wu, Yinwei Jing, Yongcheng Yu, Qianhao Zheng, Naishan Huang, Jie Long, Yuhang Yao, Mingde Zhao, Feng Zhao, Bowen Ye, Nan Shen, Ning Cao, Yanpeng Xiong, Tong Xia, Weiran Li, Dingwen Xia, Shuchen Computer Vision Lab Ifi Caidas University of Würzburg Germany Computer Vision Lab Eth Zürich Switzerland Meituan Group China Department of Information Technology Uppsala University Sweden Institute of Automation Chinese Academy of Sciences Beijing China Nanjing China Maicro Nanjing China Department of Artificial Intelligence Convergence Sahmyook University Seoul Korea Republic of Megvii Technology China University of Electronic Science and Technology of China China University of Macau China China Toyota Technological Institute Japan Graduate Institute of Electronics Engineering National Taiwan University Taiwan Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan ServiceNow United States Beijing University of Post and Teleconmunication Beijing China Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education China Couger Inc. Computer Vision and Pattern Recognition Lab Indian Institute of Technology Ropar Punjab Rupnagar India Research Institute Singapore National University of Singapore Singapore Research Institute Singapore University of Sydney Australia Brain-Inspired Vision Laboratory Information Science and Technology Institution University of Science and Technology of China China State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou310027 China Key Laboratory of Advanced Manufacturing Technology of Zhejiang Province School of Mechanical Engineering Zhejiang University Hangzhou310027 China South China University of Technology China

ISBN: (纸本)9798350302493

This work reviews the results of the NTIRE 2023 Challenge on Image Shadow Removal. The described set of solutions were proposed for a novel dataset, which captures a wide range of object-light interactions. It consists of 1200 roughly pixel aligned pairs of real shadow free and shadow affected images, captured in a controlled environment. The data was captured in a white-box setup, using professional equipment for lights and data acquisition sensors. The challenge had a number of 144 participants registered, out of which 19 teams were compared in the final ranking. The proposed solutions extend the work on shadow removal, improving over the performance level describing state-of-the-art methods. © 2023 IEEE.

关键词： Data acquisition

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Random resistive memory-based deep extreme point learning machine for unified visual processing

arXiv

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arXiv 2023年

作者： Wang, Shaocong Gao, Yizhao Li, Yi Zhang, Woyu Yu, Yifei Wang, Bo Lin, Ning Chen, Hegan Zhang, Yue Jiang, Yang Wang, Dingchen Chen, Jia Dai, Peng Jiang, Hao Lin, Peng Zhang, Xumeng Qi, Xiaojuan Xu, Xiaoxin So, Hayden Wang, Zhongrui Shang, Dashan Liu, Qi Cheng, Kwang-Ting Liu, Ming Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong Key Laboratory of Microelectronic Devices & Integrated Technology Institute of Microelectronics Chinese Academy of Sciences Beijing100029 China ACCESS - AI Chip Center for Emerging Smart Systems InnoHK Centers Hong Kong Science Park Hong Kong University of Chinese Academy of Sciences Beijing100049 China Frontier Institute of Chip and System Fudan University Shanghai200433 China College of Computer Science and Technology Zhejiang University Zhejiang310027 China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

Visual sensors, including 3D LiDAR, neuromorphic DVS sensors, and conventional frame cameras, are increasingly integrated into edge-side intelligent machines. Realizing intensive multi-sensory data analysis directly on edge intelligent machines is crucial for numerous emerging edge applications, such as augmented and virtual reality and unmanned aerial vehicles, which necessitates unified data representation, unprecedented hardware energy efficiency and rapid model training. However, multi-sensory data are intrinsically heterogeneous, causing significant complexity in the system development for edge-side intelligent machines. In addition, the performance of conventional digital hardware is limited by the physically separated processing and memory units, known as the von Neumann bottleneck, and the physical limit of transistor scaling, which contributes to the slowdown of Moore's law. These limitations are further intensified by the tedious training of models with ever-increasing sizes. In this study, we propose a novel hardware-software co-design, random resistive memory-based deep extreme point learning machine (DEPLM), that offers efficient unified point set analysis. Data-wise, the multi-sensory data are unified as point sets and can be processed universally. Software-wise, most weights of deep extreme point learning machines are exempted from training, which significantly reduce training complexity. Hardware-wise, nanoscale resistive memory not only enables collocation of memory and processing, mitigating the von Neumann bottleneck and the slowdown of Moore's law, but also leverages the inherent programming stochasticity for generating the random and sparse weights of the DEPLM, which also lessens the impact of read noise. We demonstrate the system's versatility across various data modalities and two different learning tasks. Compared to a conventional digital hardware-based system, our co-design system achieves 5.90×, 21.04×, and 15.79× energy efficiency improv

关键词： Cost reduction

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Robust Safeguarding of Frequency Regulation and Market Operations Via SVDD and Feature engineering

Robust Safeguarding of Frequency Regulation and Market Opera...

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International Conference on ICT in Business Industry & Government (ICTBIG)

作者： Jagmeet Sohal Dimple Bahri Y.V. Dongre D. Anandhasilambarasan Sidhant Das Latha B Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India Department of Civil Engineering JAIN School of Engineering and Technology Deemed to be University Bangalore Karnataka India Vishwakarma Institute of Technology Pune India Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of IT Prince Shri Venkateshwara Padmavathy Engineering College Chennai India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

The power grid's cyber-security is becoming more important as our reliance on real-time interaction and communication technology continues to expand. Any malfunction in these technologies might cause the automated generation control (AGC) system to fail entirely since they are essential to the system's functioning. Cyberattacks, if successful, may halt power grid operations and cause major financial and personal data breaches. If an attacker wants to cause power outages, they may, for instance, use false data injection (FDI) attacks to tamper with the communication channel's network settings and send out incorrect dispatches. This introduces a novel market-operation assault template that makes use of exponential functions to enable stealthy attackers to obtain bigger cash settlements. To effectively identify FDIs aimed against the AGC system, it presents a new intrusion detection approach based on SVDD. This method employs a number of learning strategies, such as K-means clustering, SMOTE, and a number of Support Vector Data Description models serving as foundational learners. The detection technique is enhanced to capture varied patterns during standard and endangered grid states by the introduction of a new feature variable. Additionally, a novel market operation assault based on foreign direct investment (FDI) is shown in the chapter. This attack maintains frequency grid checks while unjustly increasing the output of particular GENCOs for financial gain.

关键词： Training Power system dynamics Training data Prototypes Automatic generation control Feature extraction Real-time systems Vectors Standards Investment

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A Fast Light Baking System for Mobile VR Game Based on Edge Computing Framework 21

A Fast Light Baking System for Mobile VR Game Based on Edge ...

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Proceedings of the 2021 ACM International Conference on Intelligent Computing and its Emerging Applications

作者： Ma Cheng Wang Lu School of Information Engineering Shandong Youth University of Political Science China Qilu University of Technology (Shandong Academy of Sciences) Shandong Computer Science Center (National Supercomputer Center in Jinan) Shandong Provincial Key Laboratory of Computer Networks

ISBN: (纸本)9781450391603

Metaverse development has often focused on bettering virtual reality technologies due to benefits in establishing immersion in virtual environment1. Its dependency on VR technology brings limitations including the lack of high-quality graphics and a lack of mobility. The mobile platform is the most prevalent interface for users to gain VR experience. There is a big challenge that mobile VR must face: it has limited computing power, but VR requires high rendering speed and image quality. The traditional solution is to complete lighting and shading computation offline and bake the result to light map. Thus, only texture lookups are needed at runtime which allows very high rendering speed. The problem of the traditional baking is that they usually take seconds to minutes to finish. In this paper, we introduce a novel edge computing framework to reduce the baking time to less than a second. Our method uses reservoir-based spatiotemporal importance resampling (ReSTIR) to bake low resolution light maps at edge server. At mobile side, the light maps are enlarged using super resolution technique. We also use denoiser to make sure the baked image is noise-free. The experimental results show that our method can have near real-time performance for scene with average complexity. The bottleneck is the bandwidth of mobile device when performing super resolution. Once it gets improved, we can expect our entire edge computing framework becomes real-time.

关键词： ray tracing virtual reality Edge computing

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Automation Through Machine Learning: Reinvigorating Cyber Security

Automation Through Machine Learning: Reinvigorating Cyber Se...

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International Conference on Computing and Networking technology (ICCNT)

作者： Jayashree V. Bagade Sukhvinder Singh Dari Rekha Dhivrani Santosh Chowhan G. Ravivarman V Janakiraman Department of Information Technology Vishwakarma Institute of Technology Pune india Symbiosis Law School Nagpur Symbiosis International Pune India Department of Computer Science & Engineering Vivekananda Global University Jaipur India Department of Data Analytics and Mathematical Sciences Jain (Deemed to be University) Bangalore Karnataka India Department of Electrical and Electronics Engineering Karpagam Academy of Higher Education Coimbatore Department of Mechanical Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Cyber security is predicated heavily on manual efforts to fight malicious assaults, but such processes are increasingly turning into overwhelmed by means of the quantity of threats - a hassle worsened in the course of the global COVID-19 pandemic. This has highlighted the importance of leveraging device studying (ML) to automate cyber protection methods. In this article, we speak how ML may be used to optimize the detection, prevention, and remediations of malicious assaults in an extensive kind of applications and offerings. We discover the various procedures being evolved that may assist make the identity of threats greater accurate and greater effortlessly to be had. We additionally recollect the capacity challenges associated with the software of ML strategies, along with the potential for unfair bias in opposition to positive instructions of customers, and become aware of methods for addressing those issues. Our end emphasizes the significance of leveraging ML in order to enable automation in cyber safety, as well as to make sure the detection of state-of-the-art new threats.

关键词： Automation Pandemics Prevention and mitigation Manuals Machine learning Turning Software Security Protection computer crime

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Octupole topological insulating phase in Brillouin three-dimensional real projective space

arXiv

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arXiv 2024年

作者： Qiu, Sichang Hu, Jinbing Yang, Yi Shang, Ce Liu, Shuo Jun Cui, Tie State Key Laboratory of Millimeter Waves Southeast University Nanjing210096 China College of Optical-Electrical Information and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China Department of Physics HK Institute of Quantum Science and Technology The University of Hong Kong Pokfulam Hong Kong Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China

Recent advancements in quantum polarization theory have propelled the exploration of topological insulators (TIs) into the realm of higher-order systems, leading to the study of the celebrated two-dimensional (2D) quadrupole and three-dimensional (3D) octupole TIs. Traditionally, these topological phases have been associated with the toroidal topology of the conventional Brillouin zone (BZ). This Letter reports on the discovery of a novel octupole topological insulating phase emerging within the framework of the Brillouin 3D real projective space (3). We theoretically propose the model and its corresponding topological invariant, experimentally construct this insulator within a topological circuit framework, and capture the octupole insulating phase as a localized impedance peak at the circuit's corner. Furthermore, our 3circuit stands out as a pioneering 3D model to simultaneously exhibit both intrinsic, termination-independent symmetry-protected topological phases (SPTPs) and extrinsic, termination-dependent boundary-obstructed topological phases (BOTPs), which broadly encompass 2D surface-obstructed topological phases (SOTPs) and 1D hinge-obstructed topological phases (HOTPs). Our results broaden the topological landscape and provide insights into the band theory within the manifold of the Brillouin 3 © 2024, CC BY.

关键词： Topological insulators

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Visible Light Positioning Method for Indoor Line-of-Sight Scene Based on Time Series Coding

Visible Light Positioning Method for Indoor Line-of-Sight Sc...

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IEEE International Conference on Data science in Cyberspace (DSC)

作者： Yonghao Yu Yongde Guo Dawei Zhao Kexue Fu Yongwei Tang Qinyuan Wang Faculty of Data Science City University of Macau China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center(National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China School of Mechanical Engineering Key Laboratory of High Efficiency and Clean Mechanical Manufacture Ministry of Education Shandong University Jinan China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

Existing indoor visible light positioning methods, such as those based on fingerprint database algorithms, are complex to construct and consume significant resources. In response to this issue, the text proposes an end-to-end indoor visible light positioning method that samples continuous signals at a small number of random coordinate positions in indoor environments. Through a deep learning method based on attention mechanism, a nonlinear mapping between the received signal and spatial position coordinates is constructed, and the time series properties in the received signal are encoded as a feature in the data. The method is further improved by combining residual networks to improve accuracy. Through simulation experiments, it has been verified that the deep learning based localization method described in this article can achieve sub meter level accuracy on small sample datasets, and it has been proven that time series features have a significant impact on localization accuracy in indoor visible light positioning algorithms based on deep learning.

关键词： Location awareness Deep learning Meters Training Accuracy Attention mechanisms Time series analysis Training data Encoding Data models

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ImageALCAPA: A 3D Computed Tomography Image Dataset for Automatic Segmentation of Anomalous Left Coronary Artery from Pulmonary Artery

ImageALCAPA: A 3D Computed Tomography Image Dataset for Auto...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： An Zeng Chenxi Mi Dan Pan Qing Lu Xiaowei Xu School of Computer Science Guangdong University of Technology Guangzhou China Department of Computer Science Guangdong Polytechnic Normal University Guangzhou China Department of Computer Science and Engineering University of Notre Dame IN US Guangdong Provincial People’s Hospital Guangdong Academy of Medical Sciences Guangzhou China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Anomalous left coronary artery from pulmonary artery (ALCAPA) is a serious cardiac anomaly, and surgical repair is the main treatment for ALCAPA patients in clinical practice. Recently, 3D printing has been widely adopted in the surgical planning of ALCAPA, which can give surgeons an intuitive structure of the heart especially the coronary arteries. However, before 3D printing is conducted, experienced radiologists need to manually segment the coronary arteries on computed tomography angiography (CTA) images, which is time-consuming, tedious and biased. On the other hand, automatic coronary artery segmentation with normal structures has been extensively studied in the community, but cannot be effectively applied to ALCAPA due to the significant variation of coronary artery structure in ALCAPA. In this paper, we propose ImageALCAPA, the first 3D CTA image dataset of ALCAPA. The proposed dataset contains 30 ALCAPA CTA images, which is of decent size compared with existing medical imaging datasets. We further propose a baseline method that performs multi-task 2D- 3D ensemble for automatic segmentation of ALCAPA. It is shown by experiment that our baseline method outperforms popular existing works on coronary artery segmentation. However, as the highest average Dice Similarity Coefficient of coronary arteries is merely 65%, there is still much room for improvement. To facilitate further research on this challenging problem, our dataset and codes are released to the public [1].

关键词： Image segmentation Three-dimensional displays Codes Computed tomography Lung Surgery Three-dimensional printing

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Dynamic Spectrum Access in Cognitive Radio Networks: A Game-Theoretic Approach

Dynamic Spectrum Access in Cognitive Radio Networks: A Game-...

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International Conference on Computing and Networking technology (ICCNT)

作者： Dharmesh Dhabliya M. Vigenesh Deepak Minhas M Shalini Sidhant Das S RenukaJyothi Department of Information Technology Vishwakarma Institute of Information Technology Pune Maharashtra India Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore India Centre for Interdisciplinary Research for Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India Department of Biotechnology and Genetics School of Sciences JAIN (Deemed to be University) Bangalore Karnataka India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Cognitive radio networks (CRNs) are a promising technology which could offer better spectrum efficiency and utilization via permitting dynamic spectrum get right of entry to (DSA). Game idea has been an increasing number of hired as a tool for analyzing and addressing optimization problems in CRNs. This abstract discusses the utility of sport theory to the evaluation and optimization of DSA in CRNs. the main consciousness is to broaden a widespread recreation-theoretic approach for DSA that can model numerous styles of interactions among cognitive customers. Specifically, we speak 8 exceptional styles of games that may be used to model the DSA problem, including static, dynamic, and multi-attribute games. Moreover, we speak 3 critical DSA optimization issues: spectrum allocation, spectrum sharing, and energy manipulate. Sooner or later, we speak a few crucial challenges and possibilities for similarly research in this place. Using game principle for DSA in CRNs can assist to enhance the performance of CRNs in terms of throughput, fairness, energy efficiency, and security.

关键词： Games Interference Throughput Energy efficiency Cognitive radio Security Resource management Optimization Standards Sports

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