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8th International Conference on Medical and Health Informatics, ICMHI 2024

作者： Huang, Hsien-Cheng Chang, Hsiao-Huang Ke, Xiang-Wei Chien, Ting-Ying Department of Computer Science and Engineering Yuan Ze University Taoyuan City Taiwan Cardiovascular Surgery Department Taipei Veterans General Hospital Taipei City Taiwan Taipei Medical University Taipei City Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan City Taiwan Innovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan City Taiwan

ISBN: (纸本)9798400716874

Coronary artery disease is the most prevalent type of heart disease and has a considerable mortality rate. In diagnosing and assessing coronary artery disease, physicians must integrate a variety of clinical information and test results to evaluate the degree of coronary artery obstruction and continually learn and improve their skills in clinical practice to ensure the most accurate assessment of patients. Currently, the use of information technology to assist medical staff allows more effective and accurate analysis of symptoms. This approach not only serves as an auxiliary standard for physician diagnosis, thereby reducing the possibility of misdiagnosis, but also significantly improves patient treatment outcomes and medical quality while reducing the time burden on physicians for diagnosing patients. This study utilized image processing techniques and thinning algorithms to automate the detection of coronary artery width and, following the recommended methods of physicians in related fields, used specific vessel widths to calculate the degree of coronary artery narrowing. This provides a more accurate and efficient assessment method for evaluating the degree of vascular constriction in patients with coronary artery disease. © 2024 Copyright ACM.

关键词： Patient treatment

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Efficient and High-quality Recommendations via Momentum-incorporated Parallel Stochastic Gradient Descent-Based Learning

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IEEE/CAA Journal of Automatica Sinica 2021年第2期8卷 402-411页

作者： Xin Luo Wen Qin Ani Dong Khaled Sedraoui MengChu Zhou the School of Computer Science and Technology Dongguan University of TechnologyDongguan 523808 Hengrui(Chongqing)Artificial Intelligence Research Center Department of Big Data Analyses TechniquesCloudwalkChongqing 401331China the School of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqing 400065 Chongqing Engineering Research Center of Big Data Application for Smart Cities Chongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714China the Department of Computer and Information Science City College of Dongguan University of TechnologyDongguan 523419China the Department of Electrical and Computer Engineering Faculty of Engineeringand Center of Research Excellence in Renewable Energy and Power SystemsKing Abdulaziz UniversityJeddah 21481Saudi Arabia the Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA the Center of Research Excellence in Renewable Energy and Power Systems King Abdulaziz UniversityJeddah 21481Saudi Arabia

A recommender system(RS)relying on latent factor analysis usually adopts stochastic gradient descent(SGD)as its learning ***,owing to its serial mechanism,an SGD algorithm suffers from low efficiency and scalability when handling large-scale industrial *** at addressing this issue,this study proposes a momentum-incorporated parallel stochastic gradient descent(MPSGD)algorithm,whose main idea is two-fold:a)implementing parallelization via a novel datasplitting strategy,and b)accelerating convergence rate by integrating momentum effects into its training *** it,an MPSGD-based latent factor(MLF)model is achieved,which is capable of performing efficient and high-quality *** results on four high-dimensional and sparse matrices generated by industrial RS indicate that owing to an MPSGD algorithm,an MLF model outperforms the existing state-of-the-art ones in both computational efficiency and scalability.

关键词： big data industrial application industrial data latent factor analysis machine learning parallel algorithm recommender system(RS) stochastic gradient descent(SGD)

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Reveal training performance mystery between Tensor Flow and PyTorch in the single GPU environment

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Science China(Information Sciences) 2022年第1期65卷 147-163页

作者： Hulin DAI Xuan PENG Xuanhua SHI Ligang HE Qian XIONG Hai JIN National Engineering Research Center for Big Data Technology and System Service Computing Technology and System LabSchool of Computer Science and Technology Huazhong University of Science and Technology Department of Computer Science University of Warwick

Deep learning has gained tremendous success in various fields while training deep neural networks(DNNs) is very compute-intensive, which results in numerous deep learning frameworks that aim to offer better usability and higher performance to deep learning practitioners. Tensor Flow and Py Torch are the two most popular frameworks. Tensor Flow is more promising within the industry context, while Py Torch is more appealing in academia. However, these two frameworks differ much owing to the opposite design philosophy:static vs dynamic computation graph. Tensor Flow is regarded as being more performance-friendly as it has more opportunities to perform optimizations with the full view of the computation graph. However, there are also claims that Py Torch is faster than Tensor Flow sometimes, which confuses the end-users on the choice between them. In this paper, we carry out the analytical and experimental analysis to unravel the mystery of comparison in training speed on single-GPU between Tensor Flow and Py Torch. To ensure that our investigation is as comprehensive as possible, we carefully select seven popular neural networks, which cover computer vision, speech recognition, and natural language processing(NLP). The contributions of this work are two-fold. First, we conduct the detailed benchmarking experiments on Tensor Flow and Py Torch and analyze the reasons for their performance difference. This work provides the guidance for the end-users to choose between these two frameworks. Second, we identify some key factors that affect the performance,which can direct the end-users to write their models more efficiently.

关键词： deep learning performance comparison TensorFlow PyTorch

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Evaluation of Deep Learning Algorithms for Instance Segmentation on Cassava Root

Evaluation of Deep Learning Algorithms for Instance Segmenta...

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IEEE International Conference on Knowledge Innovation and Invention (ICKII)

作者： Chanowat Tanasaksakul Khajonpong Akkarajitsakul Tobias Wojciechowski Department of Computer Engineering King Mongkut’s University of Technology Thonburi Bangkok Thailand Big Data Experience Center and Department of Computer Engineering King Mongkut’s University of Technology Thonburi Bangkok Thailand Institute of Bio- and Geosciences – Plant Sciences (IBG-2) Forschungszentrum Jülich GmbH Jülich Germany

Different algorithms were developed and evaluated for segmenting images of cassava roots. Cassava CSRs (CSR) have different shapes and sizes. Existing methods rely on image processing algorithms to segment CSR, but these often have problems with overlapping fibrous roots. In this study, deep learning architectures for instance segmentation HTC, SOLOv2, and Mask R-CNN were applied to CSR data to investigate whether these models can overcome the specific challenges of this domain. We created a custom dataset with 1,227 training, 100 validation, and 100 test images of CSRs with different root systems and high-quality segmentation masks. Then, we compared the result of the SOLOv2 model to the Mask R-CNN and HTC models using the same network architecture. The standard metric for comparing instance segmentation models in computer vision such as mean average precision, was used for the evaluation. The results showed that all tested models applied to the considered area showed good prediction accuracy. SOLOv2 showed the best results on the test set with the computation of the model less than that of HTC. Increasing the model complexity and size did not impact the prediction accuracy in the instance segmentation of CSR. HTC achieved almost the same result as that of the SOLOv2 model but required almost doubling the number of parameters. The visualization of the predictions showed quality differences in terms of the accuracy of the segmentation masks. The SOLOv2 model showed the best masks overall. However, each model had problems in assigning the segmentation masks due to the overlap of a CSR. Many root systems still need to be measured for gene discovery and subsequent breeding. The results demonstrated the potential of deep learning instance segmentation models for CSRs needs to be increased with further research.

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RAUIE: A Relation-Augmented Document-Level Event Extraction Model Based on UIE

RAUIE: A Relation-Augmented Document-Level Event Extraction ...

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Artificial Intelligence, Networking and Information Technology (AINIT), International Seminar on

作者： Yiyuan Liang Hong Jia Licai Wang Yangchen Huang Zhe Yang Big data research and development center North China Institute of Computing Technology Beijing China Department of Computer Science and Technology Tsinghua University Beijing China Department of Computer Science and Engineering The Chinese University of Hong Kong China

ISBN: (数字)9798350385557

ISBN: (纸本)9798350385564

In document-level event extraction tasks, multiple event types often coexist within a document (the multi-event problem), and arguments may play different roles across different events (the argument intertwining problem). Existing methods often suffer from significantly reduced event extraction accuracy, mainly when focusing on scenarios with scarce annotated data and imbalanced event-type distributions. This paper emphasizes enhancing the correlation between event triggers and arguments to address these challenges. Therefore, we propose the Relation-Augmented UIE Model (RADIE), which introduces entity relationships into prompts using prompt-based learning and designs a relation-augmentation algorithm to improve the accuracy and completeness of event extraction. Experimental results demonstrate that this approach significantly outperforms baseline methods, mainly showcasing excellent performance on datasets with long-tail distributions.

关键词： Seminars Accuracy Correlation Focusing Feature extraction data mining Task analysis

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Scenario, Role, and Persona: A Scoping Review of Design Strategies for Socially Intelligent AI Agents

Scenario, Role, and Persona: A Scoping Review of Design Stra...

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2025 CHI Conference on Human Factors in Computing Systems, CHI EA 2025

作者： Niu, Ruowen Hu, Jiaxiong Peng, Siyu Cao, Caleb Chen Liu, Chengzhong Han, Sirui Guo, Yike Hong Kong Generative AI Research and Development Center Hong Kong Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong The Hong Kong University of Science and Technology Hong Kong Big Data Institute Hong Kong University of Science and Technology Hong Kong

ISBN: (纸本)9798400713958

As artificial intelligence (AI) agents have often been perceived as social actors, there is a growing expectation for them to demonstrate social intelligence. Social intelligence encompasses the ability of AI agents to align their actions with human intentions and behave in socially and culturally appropriate ways. This study conducts a scoping review to analyze the design strategies employed in current AI agent designs that support the exhibition of social intelligence. Our findings reveal three design strategy themes that interdependently and collectively shape the capabilities and behaviors of AI agents: scenario, role, and persona. These findings provide a structured perspective and actionable insights for designing AI agents that effectively integrate social intelligence into human-AI interactions. © 2025 Copyright held by the owner/author(s).

关键词： AI Agents Design Strategy Scoping Review Social Intelligence

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Optimization of 3D Holographic Projection for a Medical Image System using Taguchi Method and Fuzzy Inference System

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Journal of Imaging Science and Technology 2023年第4期67卷 1-7页

作者： Chou, Ming-Chung Huang, Tian-Hsiang Tsai, Jinn-Tsong Chen, Yen-Ming J. Hwang, Kao-Shing Huang, Wei-Tai Ho, Wen-Hsien Department of Medical Imaging and Radiological Sciences Kaohsiung Medical University Kaohsiung Taiwan Center for Big Data Research Kaohsiung Medical University Kaohsiung Taiwan Department of Medical Research Kaohsiung Medical University Hospital Kaohsiung Taiwan Department of Computer Science and Information Engineering National Penghu University of Science and Technology Penghu Taiwan Department of Computer Science and Artificial Intelligence National Pingtung University Pingtung Taiwan Department of Healthcare Administration and Medical Informatics Kaohsiung Medical University Kaohsiung Taiwan Department of Information Management National Kaohsiung University of Science and Technology Kaohsiung Taiwan Department of Electrical Engineering National Sun Yat-Sen University Kaohsiung Taiwan Department of Mechanical Engineering National Pingtung University of Science and Technology Pingtung Taiwan College of Professional Studies National Pingtung University of Science and Technology Pingtung Taiwan

Two dimensional projections of three dimensional (3D) objects are used to teach anatomy. These projections are relatively unrealistic, and medical students cannot easily and accurately grasp the position and shape of each anatomical structure. As a remedy, 3D holographic projections allow for 3D anatomical structures to be projected in space without the use of worn contraptions. This study designed a method for optimizing 3D holographic projections with respect to the quality characteristics of 3D imaging definition and stereoscopic quality. The Taguchi method was used to determine the factors that most greatly influenced these quality characteristics, and the Delphi method and fuzzy theory were used to quantify image quality. Among three main factors of material of the projection panel, thickness of the projection panel, and angle made by the projection panel with the horizontal plane, thickness was the most influential. The optimal combination of factors was acrylic material, a thickness of 1 mm, and an angle of 45°. This paper's method aids medical pedagogy and can be used by the holographic projection manufacturing industry to optimize 3D imaging definition and stereoscopic quality. © 2023 Society for Imaging Science and Technology.

关键词： Taguchi methods

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Independent Pitch Controller based on Fuzzy Adaptive Tuning PI for Load Mitigation of Large Land Wind Turbine 2

Independent Pitch Controller based on Fuzzy Adaptive Tuning ...

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2023 2nd International Conference on Energy Internet and Power Systems, ICEIPS 2023

作者： Li, Shengshan Du, Ming Li, Yanggui He, Jun Ma, Fuliang Chen, Qingyun Department of Computer Science Qinghai University Qinghai Xining China Geospatial and Natural Resources Big Data Center Qinghai Xining China Key Laboratory of Geospatial Information Technology and Application Qinghai Xining China College of Chemical Engineering Qinghai University Qinghai Xining China Qinghai Telecom Qinghai Xining China

With the development of wind power generation units towards the mega-scale, the blade root and tower have large load and vibration problems during the operation of wind turbines. In order to extend the operational life of wind turbines, a PI independent pitch control (IPC) strategy with fuzzy adaptive tuning (FAT) is introduced based on the existing wind turbine model and control strategy to achieve optimal control of wind turbine loads. Simulations are carried out by the FAST software of the US Renewable Energy Laboratory to compare the independent pitch and the cooperative pitch. The results show that IPC reduces the load on the blade roots and improves the quality of the electrical energy. © 2023 Published under licence by IOP Publishing Ltd.

关键词： Wind turbines

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Cloud-magnetic resonance imaging system:In the era of 6G and artificial intelligence

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Magnetic Resonance Letters 2025年第1期5卷 52-63页

作者： Yirong Zhou Yanhuang Wu Yuhan Su Jing Li Jianyu Cai Yongfu You Jianjun Zhou Di Guo Xiaobo Qu Department of Electronic Science Intelligent Medical Imaging R&D CenterFujian Provincial Key Laboratory of Plasma and Magnetic ResonanceNational Institute for Data Science in Health and MedicineXiamen UniversityXiamen361104China Department of Electronic Science Key Laboratory of Digital Fujian on IoT CommunicationXiamen UniversityXiamen361104China Shanghai Electric Group Co. LtdShanghai200002China China Telecom Group Quanzhou362018China China Mobile Group Xiamen361009China Department of Radiology Zhongshan Hospital(Xiamen)Fudan UniversityXiamen Municipal Clinical Research Center for Medical ImagingFujian Province Key Clinical Specialty Construction Project(Medical Imaging Department)Xiamen Key Laboratory of Clinical Transformation of Imaging Big Data and Artificial IntelligenceXiamen361006China School of Computer and Information Engineering Xiamen University of TechnologyXiamen361024China

Magnetic resonance imaging(MRI)plays an important role in medical diagnosis,generating petabytes of image data annually in large *** voluminous data stream requires a significant amount of network bandwidth and extensive storage ***,local data processing demands substantial manpower and hardware *** isolation across different healthcare institutions hinders crossinstitutional collaboration in clinics and *** this work,we anticipate an innovative MRI system and its four generations that integrate emerging distributed cloud computing,6G bandwidth,edge computing,federated learning,and blockchain *** system is called Cloud-MRI,aiming at solving the problems of MRI data storage security,transmission speed,artificial intelligence(AI)algorithm maintenance,hardware upgrading,and collaborative *** workflow commences with the transformation of k-space raw data into the standardized Imaging Society for Magnetic Resonance in Medicine Raw data(ISMRMRD)***,the data are uploaded to the cloud or edge nodes for fast image reconstruction,neural network training,and automatic ***,the outcomes are seamlessly transmitted to clinics or research institutes for diagnosis and other *** Cloud-MRI system will save the raw imaging data,reduce the risk of data loss,facilitate inter-institutional medical collaboration,and finally improve diagnostic accuracy and work efficiency.

关键词： Magnetic resonance imaging Cloud computing 6G bandwidth Artificial intelligence Edge computing Federated learning Blockchain

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Revenue and User Traffic Maximization in Mobile Short-Video Advertising 21

Revenue and User Traffic Maximization in Mobile Short-Video ...

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21st International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2022

作者： Ran, Dezhi Zheng, Weiqiang Li, Yunqi Bian, Kaigui Zhang, Jie Deng, Xiaotie School of Computer Science Peking University China Department of Computer Science Yale University United States School of Computer Science National Engineering Laboratory for Big Data Analysis and Applications Peking University China Electronics and Computer Science University of Southampton United Kingdom Center on Frontiers of Computing Studies Peking University China

ISBN: (纸本)9781713854333

A new mobile attention economy has emerged with the explosive growth of short-video apps such as TikTok. In this internet market, three types of agents interact with each other: the platform, influencers, and advertisers. A short-video platform encourages its influencers to attract users by creating appealing content through short-form videos and allows advertisers to display their ads in short-form videos. There are two options for the advertisers: one is to bid for platform advert slots in a similar way to search engine auctions;the other is to pay an influencer to make engaging short videos and promote them through the influencer's channel. The second option will generate a higher conversion ratio if advertisers choose the right influencers whose followers match their target market. Although displaying influencer ads will generate less revenue, it is more engaging than platform ads, which is better for maintaining user traffic. Therefore, it is crucial for a platform to balance these factors by establishing a sustainable business agreement with its influencers and advertisers. In this paper, we develop a two-stage solution for a platform to maximize short-term revenue and long-term user traffic maintenance. In the first stage, we estimate the impact of user traffic generated by displaying influencer ads and characterize the user traffic the platform should allocate to influencers for overall revenue maximization. In the second stage, we devise an optimal (1 - 1/e)-competitive algorithm for ad slot allocation. To complement this analysis, we examine the ratio of the revenue generated by our online algorithm to the optimal offline revenue. Our simulation results show that this ratio is 0.94 on average, which is much higher than (1 - 1/e) and outperforms four baseline algorithms. © 2022 International Foundation for Autonomous Agents and Multiagent Systems (***). All rights reserved

关键词： Multi agent systems

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