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IEEJ Transactions on Electronics, Information and systems 2025年第4期145卷 467-473页

作者： Tanabe, Akifumi Harachi, Kento Yamamoto, Yusuke Muramatsu, Ayumi Nagahara, Hajime Takemura, Noriko Shimojo, Shinji Furihata, Daisuke Mizuno-Matsumoto, Yuko Graduate School of Information Science University of Hyogo 7-1-28 Minatojima Minami Chuo-ku Hyogo Kobe650-0047 Japan Department of Medical Engineering Aino University 4-5-4 Higashi-Ohda Ibaraki Osaka567-0012 Japan D3 Center The University of Osaka 2-8 Yamadaoka Osaka Suita565-0871 Japan D3 Center The University of Osaka 5-1 Mihogaoka Ibaraki Osaka567-0047 Japan Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology 680-4 Kawazu Fukuoka Iizuka820-8502 Japan Faculty of Software and Information Technology Aomori University 2-3-1 Shachihata Aomori-shi Aomori030-0943 Japan

This research aimed to reveal the information process in the brain of the patients who suffered from mild cognitive impairment (MCI) using the power spectra and timelag analysis in electroencephalogram (EEG). The patients with MCI have memory or thinking problems, and are at a great risk of developing Alzheimer's disease or a related dementia. Ten patients with MCI and 13 healthy adults (non-MCI) were examined. The EEGs (122.88 seconds/subject) during a state of relaxation with the eyes closed, were analyzed using the Fourier analysis (power spectrum) and the timelag analysis. The results showed that the mean value of the power spectra in MCI was significantly higher than the mean value in non-MCI in the theta band. The mean value of the absolute timelag values (ATL) in MCI was significantly lower than the mean value in non-MCI in the frontal and left temporal areas. Our findings suggest that the features from EEG of MCI were abstracted in the theta band, and the propagation of information related to memory in brain would becoming fast in MCI. © 2025 The Institute of Electrical Engineers of Japan.

关键词： Electroencephalography

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Neural Airport Ground Handling

arXiv

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arXiv 2023年

作者： Wu, Yaoxin Zhou, Jianan Xia, Yunwen Zhang, Xianli Cao, Zhiguang Zhang, Jie The Department of Information Systems Faculty of Industrial Engineering and Innovation Sciences Eindhoven University of Technology Netherlands The School of Computer Science and Engineering Nanyang Technological University Singapore The School of Computing and Information Systems Singapore Management University Singapore

Airport ground handling (AGH) offers necessary operations to flights during their turnarounds and is of great importance to the efficiency of airport management and the economics of aviation. Such a problem involves the interplay among the operations that leads to NP-hard problems with complex constraints. Hence, existing methods for AGH are usually designed with massive domain knowledge but still fail to yield high-quality solutions efficiently. In this paper, we aim to enhance the solution quality and computation efficiency for solving AGH. Particularly, we first model AGH as a multiple-fleet vehicle routing problem (VRP) with miscellaneous constraints including precedence, time windows, and capacity. Then we propose a construction framework that decomposes AGH into sub-problems (i.e., VRPs) in fleets and present a neural method to construct the routing solutions to these sub-problems. In specific, we resort to deep learning and parameterize the construction heuristic policy with an attention-based neural network trained with reinforcement learning, which is shared across all sub-problems. Extensive experiments demonstrate that our method significantly outperforms classic meta-heuristics, construction heuristics and the specialized methods for AGH. Besides, we empirically verify that our neural method generalizes well to instances with large numbers of flights or varying parameters, and can be readily adapted to solve real-time AGH with stochastic flight arrivals. Our code is publicly available at: https://***/RoyalSkye/AGH. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Analyzing and Battling the Emerging Variants of Covid-19 Using Artificial Neural Network and Blockchain 18

Analyzing and Battling the Emerging Variants of Covid-19 Usi...

引用

18th International computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2021

作者： Khan, Riaz Ullah Haq, Amin Ul Hussain, Sardar Muhammad Ullah, Shakir Almakdi, Sultan Kumar, Rajesh Shah, Hasrat Hussain Li, Jianping 313001 China University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu China Department of Mathematical Sciences Quetta87300 Pakistan Chengdu University of Technology Chengdu China Najran University College of Computer Science and Information Systems Department of Computer Science Najran55461 Saudi Arabia

ISBN: (纸本)9781665413640

The fast expansion of the COVID-19 epidemic has revealed the shortcomings of current healthcare institutions in dealing with public emergency situations. One of the big reasons of Covid-19 spread is the lack of standard track and trace mechanisms in healthcare infrastructures. Furthermore, throughout the epidemic, the transmission of disinformation has accelerated, and existing platforms lacking capability of verifying the veracity of information, resulting to social unrest and illogical conduct. Therefore, building a track and trace system is critical to ensuring that data collected by the government and the public entities is accurate and dependable. It is obvious that implementing state-of-the-art predictive models like Artificial Neural Network and Blockchain-based traceable mechanisms can help to prevent the spreads of the new variants. In this paper, we proposed a Blockchain based traceable model to track and trace the infected cases so to help an effective planning to prevent the spread. © 2021 IEEE.

关键词： Blockchain

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Analyzing the Impact of Personalization on Fairness in Federated Learning for Healthcare 11

Analyzing the Impact of Personalization on Fairness in Feder...

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11th IEEE International Conference on Healthcare Informatics, ICHI 2023

作者： Wang, Tongnian Zhang, Kai Cai, Jiannan Gong, Yanmin Choo, Kim-Kwang Raymond Guo, Yuanxiong The University of Texas at San Antonio Department of Information Systems and Cyber Security San Antonio United States The University of Texas Health Science Center at Houston School of Biomedical Informatics Houston United States The University of Texas at San Antonio School of Civil and Environmental Engineering and Construction Management San Antonio United States The University of Texas at San Antonio Department of Electrical and Computer Engineering San Antonio United States

ISBN: (纸本)9798350302639

Machine learning (ML) models have the potential to generate biased outcomes, which may exacerbate existing health disparities. With privacy regulations leading to data silos in health data, federated learning (FL) has emerged as a promising solution for this issue by enabling collaborative ML without patient data sharing. Personalization within FL aims to handle performance degradation that arises due to heterogeneous data distributions across organizations. However, the relationship between personalized FL and fairness remains unclear. This paper aims to investigate and analyze the potential impact of personalized FL on fairness for healthcare. Our analysis is expected to have significant implications for fairness in federated learning and healthcare. © 2023 IEEE.

关键词： Hospital data processing

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Low-Complexity Decoding of a Class of Reed-Muller Subcodes for Low-Capacity Channels

arXiv

引用

arXiv 2022年

作者： Jamali, Mohammad Vahid Fereydounian, Mohammad Mahdavifar, Hessam Hassani, Hamed Department of Electrical Engineering and Computer Science University of Michigan United States Department of Electrical and Systems Engineering University of Pennsylvania United States

We present a low-complexity and low-latency decoding algorithm for a class of Reed-Muller (RM) subcodes that are defined based on the product of smaller RM codes. More specifically, the input sequence is shaped as a multi-dimensional array, and the encoding over each dimension is done separately via a smaller RM encoder. Similarly, the decoding is performed over each dimension via a low-complexity decoder for smaller RM codes. The proposed construction is of particular interest to low-capacity channels that are relevant to emerging low-rate communication scenarios. We present an efficient soft-input soft-output (SISO) iterative decoding algorithm for the product of RM codes and demonstrate its superiority compared to hard decoding over RM code components. The proposed coding scheme has decoding (as well as encoding) complexity of O(n log n) and latency of O(log n) for blocklength n. This research renders a general framework toward efficient decoding of RM codes. Copyright © 2022, The Authors. All rights reserved.

关键词： Signal encoding

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CHGNN: A Semi-Supervised Contrastive Hypergraph Learning Network

arXiv

引用

arXiv 2023年

作者： Song, Yumeng Gu, Yu Li, Tianyi Qi, Jianzhong Liu, Zhenghao Jensen, Christian S. Yu, Ge The School of Computer Science and Engineering Northeastern University Liaoning Shenyang110819 China The Department of Computer Science Aalborg University Aalborg9220 Denmark The School of Computing and Information Systems The University of Melbourne ParkvilleVIC3010 Australia

Hypergraphs can model higher-order relationships among data objects that are found in applications such as social networks and bioinformatics. However, recent studies on hypergraph learning that extend graph convolutional networks to hypergraphs cannot learn effectively from features of unlabeled data. To such learning, we propose a contrastive hypergraph neural network, CHGNN, that exploits self-supervised contrastive learning techniques to learn from labeled and unlabeled data. First, CHGNN includes an adaptive hypergraph view generator that adopts an auto-augmentation strategy and learns a perturbed probability distribution of minimal sufficient views. Second, CHGNN encompasses an improved hypergraph encoder that considers hyperedge homogeneity to fuse information effectively. Third, CHGNN is equipped with a joint loss function that combines a similarity loss for the view generator, a node classification loss, and a hyperedge homogeneity loss to inject supervision signals. It also includes basic and cross-validation contrastive losses, associated with an enhanced contrastive loss training process. Experimental results on nine real datasets offer insight into the effectiveness of CHGNN, showing that it outperforms 19 competitors in terms of classification accuracy consistently. Copyright © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

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IoT Network Intrusion Detection Using Machine Learning Techniques

IoT Network Intrusion Detection Using Machine Learning Techn...

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2022 International Conference on Smart Information systems and Technologies, SIST 2022

作者： Omarov, Bauyrzhan Auelbekov, Omirlan Koishiyeva, Tursynay Sadybekov, Ruslan Uxikbayev, Yerkebulan Bazarbayeva, Aizhan Al-Farabi Kazakh National University Department Of Inforamtion Systems Almaty Kazakhstan Institute Information And Computational Technologies Cs Mes Rk Almaty Kazakhstan Khoja Akhmet Yassawi International Kazakh-Turkish University Department Of Computer Engineering Turkestan Kazakhstan Khoja Akhmet Yassawi International Kazakh-Turkish University Department Of Computer Science Turkestan Kazakhstan

ISBN: (数字)9781665467902

ISBN: (纸本)9781665467902

The approaches that are directly suited for the implementation of the process of detecting anomalies in data are discussed in this article, and a comparison of the most widely used ones is carried out using the following criteria: the speed of implementation and the model's data needs. As a consequence, it can be stated that determining the quality of a model at the emission detection stage is challenging, and as a result, it must be examined at the final step (through subsequent analysis of the identified anomalies of various methods). Combinations of various strategies are the most often utilized and accurate procedures, as shown by experience. © 2022 IEEE.

关键词： Internet of things

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Defining Problem from Solutions: Inverse Reinforcement Learning (IRL) and Its Applications for Next-Generation Networking

arXiv

引用

arXiv 2024年

作者： Liu, Yinqiu Zhang, Ruichen Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui Kim, Dong In The School of Computer Science and Engineering Nanyang Technological University Singapore The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

Performance optimization is a critical concern in networking, on which Deep Reinforcement Learning (DRL) has achieved great success. Nonetheless, DRL training relies on precisely defined reward functions, which formulate the optimization objective and indicate the positive/negative progress towards the optimal. With the ever-increasing environmental complexity and human participation in Next-Generation Networking (NGN), defining appropriate reward functions become challenging. In this article, we explore the applications of Inverse Reinforcement Learning (IRL) in NGN. Particularly, if DRL aims to find optimal solutions to the problem, IRL finds a problem from the optimal solutions, where the optimal solutions are collected from experts, and the problem is defined by reward inference. Specifically, we first formally introduce the IRL technique, including its fundamentals, workflow, and difference from DRL. Afterward, we present the motivations of IRL applications in NGN and survey existing studies. Furthermore, to demonstrate the process of applying IRL in NGN, we perform a case study about human-centric prompt engineering in Generative AI-enabled networks. We demonstrate the effectiveness of using both DRL and IRL techniques and prove the superiority of IRL. © 2024, CC BY.

关键词： Reinforcement learning

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Single-Phase and Two-Phase Liquid Immersion Cooling of Data Center Power Supply Units for Heat Capture

Single-Phase and Two-Phase Liquid Immersion Cooling of Data ...

引用

Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic systems (ITHERM)

作者： Haoyun Qiu Pouya Kabirzadeh Vivek S. Garimella Arjit Bali Keerthivasan Gurusami Kang Joon Lee Andrew Stillwell Todd Salamon Nenad Miljkovic Department of Mechanical Science and Engineering University of Illinois Urbana-Champaign Urbana IL Department of Electrical and Computer Engineering University of Illinois Urbana-Champaign Urbana IL Optical Systems and Device Research Laboratory Nokia Bell Labs Murray Hill NJ

ISBN: (数字)9798350364330

ISBN: (纸本)9798350364347

Data center power supply units (PSU) have high power density due to the increasing power demand and their low profiles. Air cooling proves to be increasingly difficult in mitigating the hot spots, and it also consumes extra energy in fluid delivery, air conditioning, and rejection to the ambience. In this work, we redesign and modify the PSU cooling infrastructure and adopt quiescent immersion cooling. This design removes all active air-cooling elements and enables heat capture. The PSU used in the experiments is a 2 kW 80 Plus Platinum commercial unit (Bel PET2000-12-074RA). The cooling performance was characterized by tracking the steady-state case temperatures of the switching semiconductors. Flutec PP1 was used to study the cooling performance variations with respect to PSU load and capture temperature. Conventional fan-cooled PSU with heat sinks served as the benchmark. Experiments show that two-phase immersion cooling grants an overall enhancement of 4.4× over the original air-cooling approach at 40 °C heat capture. The results suggest that heat rejection from the dielectric fluid to the riding heatsink is the main bottleneck, and further reduction of the thermal resistance would allow heat capture at a temperature closer to the liquid boiling point. The PSU thermal management approach studied here can help lower data center power usage effectiveness (PUE) while increasing PSU power delivery density.

关键词： Power system measurements Cooling Density measurement Thermal resistance Thermomechanical processes Immersion cooling Switches Thermal management Resistance heating Data center power

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Unraveling the ‘Anomaly’ in Time Series Anomaly Detection: A Self-supervised Tri-domain Solution

arXiv

引用

arXiv 2023年

作者： Sun, Yuting Pang, Guansong Ye, Guanhua Chen, Tong Hu, Xia Yin, Hongzhi School of Electrical Engineering and Computer Science The University of Queensland Australia School of Computing and Information Systems Singapore Management University Singapore Research and Development Department Deep Neural Computing Company Limited Department of Computer Science Rice University United States

The ongoing challenges in time series anomaly detection (TSAD), notably the scarcity of anomaly annotations and the variability in anomaly lengths and shapes, have led to the need for a more all-encompassing and efficient solution. As limited anomaly labels pose a significant obstacle for traditional supervised models in anomaly classification, various state-of-the-art deep learning techniques, such as self-supervised learning, have been introduced to tackle this issue. However, they encounter difficulties handling variations in anomaly lengths and shapes, limiting their adaptability to diverse anomalies. Additionally, many benchmark datasets suffer from the problem of having explicit anomalies that even random functions can detect. This problem is exacerbated by ill-posed evaluation metrics, such as point adjustment (PA) prior to scoring, which can result in inflated model performance. In this context, we propose a novel self-supervised learning based Tri-domain Anomaly Detector (TriAD), which addresses these challenges by modeling features across three data domains – temporal, frequency, and residual domains – without relying on anomaly labels. Unlike traditional contrastive learning methods, TriAD employs both inter-domain and intra-domain contrastive loss to learn common attributes among normal data and differentiate them from anomalies. Additionally, our approach can detect anomalies of varying lengths by integrating with a discord discovery algorithm. It is worth noting that this study is the first to reevaluate the deep learning potential in TSAD, utilizing both rigorously designed datasets (i.e., UCR Time Series Anomaly Archive) and evaluation metrics (i.e., PA%K and affiliation). Through experimental results on the UCR dataset, TriAD demonstrates a significant improvement in prediction performance across diverse evaluation metrics, achieving an impressive three-fold increase in PA%K based F1 scores over state-of-the-art (SOTA) deep learning models. Moreover

关键词： Anomaly detection

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