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7th International Conference on Informatics and Computing, ICIC 2022

作者： Syahidi, Aulia Akhrian Supianto, Ahmad Afif Hirashima, Tsukasa Watanobe, Yutaka Politeknik Negeri Banjarmasin Smart City Information System Applied Undergraduate Program Banjarmasin City Indonesia Research Center for Data and Information Sciences Bandung City Indonesia Graduate School of Advanced Science and Engineering Hiroshima University Kagamiyama Learning Engineering Laboratory Higashihiroshima Japan University of Aizu Division of Information Systems Department of Computer Science and Engineering Fukushima AizuWakamatsu Japan

ISBN: (纸本)9798350345711

Learning activities are an indicator of the learner's desire to learn during the learning process. The pattern of learner action is related to learning activities. In this case, in extracting the learning process, it is necessary to collect a lot of data through analysis of the learning process. The purpose of this study is to recommend and report the performance of an activity tracking system equipped with visual artifacts as an educational data mining approach to analyze action patterns when learners complete the arrangement of program code lines in each programming problem. In this study, data were taken from activity recordings when learners used interactive learning media for a basic programming subject called TOLSYASUPI-EduMed which had problem-solving learning models embedded in their interactions. Learning Analytics is used as a method of this research and data relationships from learner actions during the learning process using the 'if-then' rule. Log data is used for recording and detecting activities carried out by learners. We confirm that the results of the study are based on the findings of the data relationship that if the lines of program code are too long (more than five lines) then almost all learners experience a bottleneck condition. The completion time of each question and the number of iterations in completing the program code lines for each question have also been recorded and have a pattern of connection between the data. The information that has been obtained is then forwarded to teachers or stakeholders as key information to make appropriate feedback to learners based on the results as a means of analyzing and evaluating the programming learning process. © 2022 IEEE.

关键词： Data mining

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Attachable cybernic unit for transfemoral amputees

Transactions of Japanese Society for Medical and Biological ...

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Transactions of Japanese Society for Medical and Biological engineering 2020年第1期58卷 14-20页

作者： Inuzuka, Kento Kawamoto, Hiroaki Sankai, Yoshiyuki Graduate School of Systems and Information Engineering University of Tsukuba Japan Faculty of Engineering Information and Systems Center for Cybernics Research University of Tsukuba Japan

Commercially available above-knee prosthetic legs prescribed to above-knee amputees are composed of springs and dampers, which restrict the daily movements of transfemoral amputees, such as stair ascent. We propose a novel device that can be attached to multi-or single-axis prosthetic legs and maneuvered actively according to the userʼs movements. The objective of this study was to develop an attachable cybernic unit and to confirm the basic capability of the system by evaluation with a stair ascent test. Using a four-linkage mechanism, the device can be operated without precise adjustment of the rotation axes of the prosthetic knee and the device. In addition, the dead point of the system can be avoided by designing a spring-operated dead point prevention mechanism and a thigh-fixing part with freedom of movement. In the stair ascent experiment, we confirmed that using the system, the subject ascended the steps as an able-bodied person. In conclusion, we developed an attachable cybernic unit that can be attached to multi-or single-axis prosthetic legs and maneuvered actively according to the userʼs movements, and confirmed that the attachable cybernic unit facilitates difficult movements. © 2020, Japan Soc. of Med. Electronics and Biol. engineering. All rights reserved.

关键词： Stairs

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Operation to Achieve Robust Networks

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NTT Technical Review 2023年第12期21卷 17-23页

作者： Okamoto, Jun Shibata, Tomoko Tahara, Mitsuho Fujiwara, Masakatsu Masuda, Masataka Executive Research Engineer Supervisor Communication Traffic Quality and Operation Research Project NTT Network Service Systems Laboratories Japan Executive Research Engineer Supervisor Access Network Operation Project NTT Access Network Service Systems Laboratories Japan Executive Manager Network Operation Project NTT Network Innovation Center Japan Planning Department NTT Information Network Laboratory Group Japan Senior Research Engineer Supervisor Traffic Engineering Research Group Communication Traffic Quality and Operation Research Project NTT Network Service Systems Laboratories. Japan

Large-scale communication-infrastructure failures have a tremendous impact on daily life and economic activities, requiring more resilient networks. We at NTT laboratories aim to achieve robust networks that minimize service impact by improving network adaptability to system failures and shorten recovery time by improving network resilience. This article describes our research and development efforts in operation-related technologies to achieve robust networks. © 2023 Nippon Telegraph and Telephone Corp.. All rights reserved.

关键词： Economics

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Traffic Pattern Sharing for Federated Traffic Flow Prediction with Personalization 24

Traffic Pattern Sharing for Federated Traffic Flow Predictio...

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24th IEEE International Conference on Data Mining, ICDM 2024

作者： Zhou, Hang Yu, Wentao Wan, Sheng Tong, Yongxin Gu, Tianlong Gong, Chen School of Computer Science and Engineering Nanjing University of Science and Technology China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education China Jiangsu Key Laboratory of Image and Video Understanding for Social Security China SKLSDE Beihang University China Jinan University Engineering Research Center of Trustworthy AI Ministry of Education China

ISBN: (纸本)9798331506681

Accurate Traffic Flow Prediction (TFP) is crucial for enhancing the efficiency and safety of transportation systems, so it has attracted intensive researches by exploiting spatial-temporal dependencies within road networks. However, existing works only consider the case of centralized data collection with all traffic data observed, which may raise privacy concerns as each region of a city may have its own traffic administration department and the traffic data is not allowed to distribute. Therefore, this paper proposes to use Federated Learning (FL) to address this issue by allowing all clients (i.e., traffic administration departments in all regions in our problem) to collaboratively train TFP models without exchanging raw data, thereby offering a solution in maintaining data privacy. Nevertheless, most existing FL methods aim to learn a global model that performs well universally, so they cannot well handle the non-Independent and Identically Distributed (non-IID) traffic data naturally over different regions. To cope with this problem, this paper develops a new FL framework termed 'personalized Federated learning with Traffic Pattern Sharing' (FedTPS) to solve federated TFP problem. Our FedTPS critically exploits the underlying common traffic patterns (e.g., morning and evening rush hours) shared across different city regions and meanwhile maintaining the region-specific data characteristics in a personalized FL manner. Specifically, to extract the common traffic patterns, we decompose the traffic data in each client via using discrete wavelet transform, where the low-frequency components uncover the stable traffic dynamics of different regions and thus can be considered as the common traffic patterns. These common patterns are then shared among different clients through traffic pattern repositories on the server side to aid the global collaborative traffic flow modeling. Moreover, the model components capturing spatial-temporal dependencies in traffic data are r

关键词： Highway administration

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TERMS: Task management policies to achieve high performance for mixed workloads using surplus resources

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Journal of Parallel and Distributed Computing 2022年第0期170卷 74-85页

作者： Yu, Jinyu Tong, Wei Lv, Pengze Feng, Dan Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology School of Computer Science and Technology Huazhong University of Science and Technology Ministry of Education of China Hubei Wuhan China

Resource contentions and performance interferences can lead to workload performance degradation in mixed-workload deployment clusters. Previous work guarantees the resource requirements of latency-sensitive tasks and reduces performance losses to batch jobs by reclaiming surplus resources from over-provisioned tasks. While the fragmentation of resources leads to a mismatch between provisioned resources and task requirements, resulting in high operation overheads and losses of task fairness. This paper proposes TERMS, the task management policies based on task relevance, resource distribution, and task fairness to achieve efficient and low-cost task management. TERMS mainly includes three types of management policies. The task scheduling policy can schedule new tasks according to task relevance. Task selection strategies select tasks for resource provisioning and task resumption based on resource requirements and task fairness. If necessary, the node selection strategy can be used to choose befitting target nodes based on task relevance and node resource information for task migration when eliminating straggler tasks. Evaluation results show that TERMS can further improve the performance of latency-sensitive services and batch jobs, reduce management overheads, and avoid operation failures. © 2022 Elsevier Inc.

关键词： Scheduling

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Multi-StudentNet: a Semi-supervised Deep-Learning Framework for Endometrial Segmentation in Transvaginal Ultrasound Images

Multi-StudentNet: a Semi-supervised Deep-Learning Framework ...

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2024 IEEE International Conference on E-Health Networking, Application and Services, HealthCom 2024

作者： Peng, Boyuan Liu, Yiyang Zhu, Xin Huang, Jie Wang, Wenwen Fang, Li Zhou, Qin Graduate Department of Computer Science and Engineering The University of Aizu Fukushima Aizu-Wakamatsu Japan Department of AI Technology Development M&D Data Science Center Institute of Science Tokyo Chiyodaku Tokyo Japan Graduate Department of Computer Science and Engineering Division of Information Systems The University of Aizu Fukushima Aizu-Wakamatsu Japan Department of Obstetrics and Gynecology Tongji Hospital Huazhong University of Science and Technology Hubei Wuhan China

ISBN: (纸本)9798350350548

Semi-supervised learning (SSL) is a promising solution for the problem of insufficient medical labeled data. However, it is still a challenging task to segment transvaginal ultrasound (TVUS) images because of their poor contrast and speckle noise. In this study, we propose Multi-StudentNet, a semi-supervised deep-learning framework for endometrial segmentation in TVUS images. This method employs multiple student models to effectively integrate labeled and unlabeled data, improving the reliability and performance of segmentation while significantly reducing the need for extensive manual labeling. By leveraging the weights of multiple models, our framework facilitates feature sharing, which enhances model robustness. Specifically, Multi-StudentNet achieves a Dice coefficient (DSC) of 0.81 and a specificity of 0.99. Our multi-student models consistently outperform single-student models across various conditions, including normal, polyp, and cancer cases. Extensive experiments demonstrate that Multi-StudentNet achieves state-of-the-art performance in both accuracy and robustness. © 2024 IEEE.

关键词： Self-supervised learning

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L-band tunable mode-locked fiber laser achieving up to 19 kHz wavelength sweeping rate

L-band tunable mode-locked fiber laser achieving up to 19 kH...

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CLEO: Science and Innovations in CLEO 2024, CLEO: S and I 2024 - Part of Conference on Lasers and Electro-Optics

作者： Ye, Guanyu Chow, Kin Kee Liu, Bowen Ma, Yifan Dai, Maolin Shirahata, Takuma Yamashita, Shinji Set, Sze Yun Research Center for Advanced Science and Technology The University of Tokyo Meguro-ku Tokyo153-8904 Japan Department of Electrical Engineering and Information Systems The University of Tokyo Bunkyo-ku Tokyo113-8656 Japan Department of Engineering Manchester Metropolitan University Chester Street ManchesterM1 5GD United Kingdom

For the first time, we introduce a rapid wavelength-swept L-band tunable mode-locked fiber laser, boasting an exceptional wavelength sweeping rate of up to 19 kHz, enabled by external modulation of the pump current. &... 详细信息

关键词： Mode-locked fiber lasers

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Two-Stage Robust Optimization Under Decision Dependent Uncertainty

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IEEE/CAA Journal of Automatica Sinica 2022年第7期9卷 1295-1306页

作者： Yunfan Zhang Feng Liu Yifan Su Yue Chen Zhaojian Wang João P.S.Catalão State Key Laboratory of Power System and Generation Equipment the Department of Electrical EngineeringTsinghua UniversityBeijing 100084China Department of Mechanical and Automation Engineering the Chinese University of Hong KongHong Kong SARChina Ministry of Education Key Laboratory of System Control and Information Processing the Department of AutomationShanghai Jiao Tong Universityand also with Shanghai Engineering Research Center of Intelligent Control and ManagementShanghai 200240China Faculty of Engineering of the University of Porto and Institute for Systems and Computer Engineering Technology and Science(INESC TEC)Porto 4200-465Portugal IEEE

In the conventional robust optimization(RO)context,the uncertainty is regarded as residing in a predetermined and fixed uncertainty *** many applications,however,uncertainties are affected by decisions,making the current RO framework *** paper investigates a class of two-stage RO problems that involve decision-dependent *** introduce a class of polyhedral uncertainty sets whose right-hand-side vector has a dependency on the here-and-now decisions and seek to derive the exact optimal wait-and-see decisions for the second-stage problem.A novel iterative algorithm based on the Benders dual decomposition is proposed where advanced optimality cuts and feasibility cuts are designed to incorporate the uncertainty-decision *** computational tractability,robust feasibility and optimality,and convergence performance of the proposed algorithm are guaranteed with theoretical *** motivating application examples that feature the decision-dependent uncertainties are ***,the proposed solution methodology is verified by conducting case studies on the pre-disaster highway investment problem.

关键词： Benders decomposition decision-dependent uncertainty endogenous uncertainty robust optimization(RO)

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A Hybrid Approach for Object Localization Combining Mask R-CNN and Halcon in an Assembly Scenario 8

A Hybrid Approach for Object Localization Combining Mask R-C...

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8th IEEE International Conference on Industrial engineering and Applications, ICIEA 2021

作者： Timmermann, David Heid, Dominik Friedel, Thomas Roennau, Arne Dillmann, Ruediger Intelligent Systems and Production Engineering Fzi Research Center for Information Technology Karlsruhe Germany

ISBN: (纸本)9781665428958

Robust and accurate object localization is a key component in the context of robotic grasping. Especially in collaborative assembly scenarios it is important that the robot is able to detect objects precisely to grasp them quickly and reliable. Therefore, classic image-based methods for object localization are not always applicable, as they may experience bad localization rates in large scenes or long runtimes at high camera resolutions. The focus of this paper is to provide a hybrid approach for the localization of objects in a scene by using a deep neural network for classification and segmentation of objects and classic shape-based matching for exact localization in the detected region of interest. We use Mask R-CNN as a deep neural network pixel based classifier and Halcon as an example for a well-known classic image processing program. Last but not least the solution is evaluated in comparison to only using Halcon by evaluating the performance regarding execution time and reliability of the object pose localization. © 2021 IEEE.

关键词： Deep neural networks

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Modeling and evaluation design of heterogeneous media networks considering quality of experience

Modeling and evaluation design of heterogeneous media networ...

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2024 China Automation Congress, CAC 2024

作者： Cong, Kun Huang, Luzhen He, Ziqi Ding, Lu Huang, Dan Song, Lei School of Aeronautics and Astronautics Shanghai Jiao Tong University Shanghai China Architecture & Technical Innovation Dept Huawei Cloud Computing Technologies Co. Ltd Shenzhen China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Center for aerospace systems engineering Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9798350368604

Aiming at social media platforms with different commercial scenarios, especially for "cloud gaming", this paper proposes a modeling and analysis framework for heterogeneous media networks considering quality of experience, which addresses the challenges of quantifying human impacts. Through standardized definitions of humans and equipment, man-machine information transmission is classified formally. Then, the evaluation of quality of experience for entities and media networks are defined. Finally, a case study about analysis and optimization for media network in the scenario of "cloud gaming" is derived. Different simulation results and the experiment’s comparison imply the applicability and effectiveness of the framework proposed in this paper. © 2024 IEEE.

关键词： Cloud platforms

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