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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Jiang, Zongli Wang, Zhenbo Zhang, Jinli Wub, Man Li, Chen Yamanishi, Yoshihiro Beijing University of Technology Faculty of Information Technology China Keio University Department of Information and Computer Science Japan Nagoya University Graduate School of Informatics Japan

ISBN: (纸本)9798350337488

De novo drug design is a challenging task that involves understanding the principles of chemistry, chemical properties, and the rules that govern molecular interactions. Deep learning-based generative models, such as MolGAN, offer a promising approach for generating new molecules with the desired chemical properties from molecular graphs. Such models often combine a discrete generative adversarial network (GAN) and reinforcement learning (RL) to produce highly valid and novel molecules. However, the severe mode collapse problem leads to low performance. This study aims to alleviate and investigate the effect of multiple factors on mode collapse. We conducted experiments on different sampling methods, training epochs, and datasets of various volumes and evaluated the experimental results using performance metrics such as validity, uniqueness, novelty, and diversity. The experimental results demonstrate that noise sampling distributions, training epochs, and training data volumes affect performance. The experimental results provide a direction for mitigating the mode collapse problem for RL-based discrete GANs. © 2023 IEEE.

关键词： Generative adversarial networks (GANs) Mode collapse Molecular generation Reinforcement learning (RL)

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Mapping the Landscape of Social Learning: Integrating Key Success Factors, Challenges, and Digital Platforms 9

Mapping the Landscape of Social Learning: Integrating Key Su...

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9th International Conference on Information Management and technology, ICIMTech 2024

作者： Marcel Meyliana Warnars, Harco Leslie Hendric Spits Mursitama, Tirta Nugraha BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia BINUS Business School Doctor of Research in Management Bina Nusantara University Management Department Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

This research develops a conceptual model to understand the influence of digitalization on social learning, especially in the context of community empowerment. This model maps the relationship between factors that influence success, challenges faced, and variations in the use of digital platforms in social learning. By using a literature study approach, the research results show that effective communication, collaboration and empowerment are the main keys to the success of social learning in society. Key challenges identified include limited access to information, resource constraints, and digital literacy barriers, all of which require specific strategies to overcome. By examining the relationship between success factors, challenges, and use of digital platforms, this research provides new insights that support the development of more effective strategies for using digital technology to support social learning that leads to community empowerment. This research has great potential to increase people's access to learning opportunities and information, allowing them to be more active in the social learning process. © 2024 IEEE.

关键词： Federated learning

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Farthest-Point Voronoi Diagrams in the Presence of Rectangular Obstacles 38

Farthest-Point Voronoi Diagrams in the Presence of Rectangul...

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38th International Symposium on Computational Geometry, SoCG 2022

作者： Kim, Mincheol Seo, Chanyang Ahn, Taehoon Ahn, Hee-Kap Department of Computer Science and Engineering Pohang University of Science and Technology Korea Republic of Graduate School of Artificial Intelligence Pohang University of Science and Technology Korea Republic of

ISBN: (纸本)9783959772273

We present an algorithm to compute the geodesic L1 farthest-point Voronoi diagram of m point sites in the presence of n rectangular obstacles in the plane. It takes O(nm + n log n + m log m) construction time using O(nm) space. This is the first optimal algorithm for constructing the farthest-point Voronoi diagram in the presence of obstacles. We can construct a data structure in the same construction time and space that answers a farthest-neighbor query in O(log(n + m)) time. © Mincheol Kim, Chanyang Seo, Taehoon Ahn, and Hee-Kap Ahn;licensed under Creative Commons License CC-BY 4.0

关键词： Computational geometry

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How do programmers Use the Internet? Discovering Domain Knowledge from Browsing and Coding Behaviors 15

How do Programmers Use the Internet? Discovering Domain Know...

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2022 IEEE Congress on Cybermatics: 15th IEEE International Conferences on Internet of Things, iThings 2022, 18th IEEE International Conferences on Green Computing and Communications, GreenCom 2022, 2022 IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2022 and 8th IEEE International Conference on Smart Data, SmartData 2022

作者： Watanabe, Ko Matsuda, Yuki Nakamura, Yugo Arakawa, Yutaka Ishimaru, Shoya Department of Computer Science University of Kaiserslautern and Dfki GmbH Kaiserslautern Germany Nara Institute of Science and Technology Graduate School of Science and Technology Nara Japan Graduate School and Faculty of Information Science and Electrical Engineering Kyushu University Fukuoka Japan

ISBN: (数字)9781665454179

ISBN: (纸本)9781665454179

The Internet is an effective tool for learners to gain new knowledge. Often, people use search engines (e.g., Google) rather than accessing websites directly. People have their search techniques to find specific information. In particular, people with domain knowledge tend to search more efficiently than novices. By understanding the gap between people with domain knowledge and novices, the novice can understand the path to becoming an expert. Therefore, in this study, we wanted to know what differences exist in search and programming behavior with and without domain knowledge. In this experiment, we asked a group with and without domain knowledge to solve ten programming problems and collected search logs (input knowledge) and compilation logs (output knowledge). Specifically, the first dataset consisted of 13 participants who had taken a university programming class. The second dataset consisted of 20 participants who had not taken a programming class and had no domain knowledge. We examined differences in search and compilation behavior based on participants' domain knowledge from this data. Since we observed a difference between each group when referring to the correlation coefficient, we performed a binary classification of novice and experienced participants using Random Forest, and achieved an average precision of 0.95, indicating that there were different trends in behavior with and without domain knowledge. © 2022 IEEE.

关键词： Domain Knowledge

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Restoration of Time-Varying Graph Signals using Deep Algorithm Unrolling 48

Restoration of Time-Varying Graph Signals using Deep Algorit...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Kojima, Hayate Noguchi, Hikari Yamada, Koki Tanaka, Yuichi Tokyo University of Agriculture and Technology Department of Electrical Engineering and Computer Science Japan Tokyo University of Science Department of Electrical Engineering Japan Osaka University Graduate School of Engineering Japan

ISBN: (纸本)9781728163277

In this paper, we propose a restoration method of time-varying graph signals, i.e., signals on a graph whose signal values change over time, using deep algorithm unrolling. Deep algorithm unrolling is a method that learns parameters in an iterative optimization algorithm with deep learning techniques. It is expected to improve convergence speed and accuracy while the iterative steps are still interpretable. In the proposed method, the minimization problem is formulated so that the time-varying graph signal is smooth both in time and spatial domains. The internal parameters, i.e., time domain FIR filters and regularization parameters, are learned from training data. Experimental results using synthetic data and real sea surface temperature data show that the proposed method improves signal reconstruction accuracy compared to several existing time-varying graph signal re- construction methods. © 2023 IEEE.

关键词： deep algorithm unrolling graph signal processing Time-varying graph signals

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Secure and Private Fault Detections of Wind Turbine through Blockchain-enabled Federated Learning 14

Secure and Private Fault Detections of Wind Turbine through ...

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14th IEEE Global Reliability and Prognostics and Health Management Conference, PHM-Hangzhou 2023

作者： Guo, Xinhao Xiang, Yangxia Long, Zhuo Ma, Xiaoguang Jia, Runda Jia, Jie School of Information Science and Engineering Northeast University Shenyang China School of Computer Science and Engineering Northeastern University Shenyang China School of Electrical and Information Engineering Changsha University of Science and Technology Changsha China Foshan Graduate School of Innovation Northeastern University Foshan China

ISBN: (纸本)9798350301359

Fault detection is a major issue in wind energy field, and data-driven methods required a large amount of IoT data, posing risks of data security and privacy. To address this issue, this paper proposed a blockchain-enhanced federated learning model, i.e., BEFL, for wind turbine fault detection, wherein traditional federated learning was improved by eliminating the reliance on a central server, ensuring better privacy protection and security, and verification mechanisms were included in blockchain to enhance defense against malicious attacks. Experimental results demonstrated the privacy-preserving advantages of the BEFL framework for fault detection data. Overall, the framework provided a secure, confidential, and reliable solution for the maintenance and operation of wind turbine systems. © 2023 IEEE.

关键词： ault detection blockchain federated learning wind turbine

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Relationship Between Mechanical Deformation and Contact Force Applied by Catheter Tip on Cardiac Muscle: Experimentation and computer Modeling 5

Relationship Between Mechanical Deformation and Contact Forc...

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5th IEEE Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability, ECBIOS 2023

作者： Ijima, Yukako Masnok, Kriengsak Perez, Juan J. Gonzalez-Suarez, Ana Berjano, Enrique Watanabe, Nobuo Master's Program Global Course of Engineering and Science Graduate School of Engineering and Science Shibaura Institute of Technology Saitama Japan Functional Control Systems Graduate School of Engineering and Science Shibaura Institute of Technology Biofluid Science and Engineering Laboratory Saitama Japan Universitat Politecnica de Valencia BioMIT Department of Electronic Engineering Valencia Spain School of Engineering University of Galway Translational Medical Device Lab Galway Ireland Functional Control Systems Graduate School of Engineering and Science College of Systems Engineering and Science Shibaura Institute of Technology Biofluid Science and Engineering Laboratory Department of Bio-Science and Engineering Saitama Japan

ISBN: (纸本)9798350320978

The cardiac muscle is elastic and deformable. Pushing a catheter in contact with the cardiac muscle surface to conduct focal energy-based ablative therapies, such as RF ablation, requires an adequate electrode-tissue contact surface to transfer the energy to the target site. In this regard, the relationship between the contact force (CF) and the resulting mechanical response is still unclear, in particular, the insertion depth (ID) and the diameter of the surface deformation. The objective of this study was to quantify these relationships using an ex vivo model and a computational model. A rigid bar with a 2.3 mm diameter blunt tip (mimicking a 7Fr standard ablation catheter) was placed at a perpendicular orientation on a fragment of the porcine heart. CF values ranged from 10 to 80g. We used ANSYS to build a Mooney-Rivlig model of 3 parameters based on hyperelastic material and to simulate the same conditions as in the experiments. The experimental results showed a strong linear correlation between CF and insertion depth ID ($\mathrm{R}^{2}=0.97, \mathrm{P} © 2023 IEEE.

关键词： Catheters

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Translating Motion to Notation: Hand Labanotation for Intuitive and Comprehensive Hand Movement Documentation 24

Translating Motion to Notation: Hand Labanotation for Intuit...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Li, Ling Yang, WenRui Yu, Xinchun Xing, Junliang Zhang, Xiao-Ping Shenzhen Key Laboratory of Ubiquitous Data Enabling Shenzhen International Graduate School Tsinghua University Shenzhen China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (纸本)9798400706868

Symbols play a pivotal role in the documentation and dissemination of art. For instance, we use musical scores and dance notation to document musical compositions and choreographic movements. Existing hand representations do not fit well with hand movement documentation since (1) data-oriented representations, e.g., coordinates of hand keypoints, are not intuitive and vulnerable to noise, and (2) the sign language, another widely adopted representation for hand movements, focuses solely on semantic interaction rather than action encoding. To balance intuitiveness and precision, we propose a novel notation system, named Hand Labanotation (HL), for hand movement documentation. We first introduce a new HL dataset comprising 4M annotated images. Thereon, we propose a novel multi-view transformer architecture for automatically translating hand movements to HL. Extensive experiments demonstrate the promising capacity of our method for representing hand movements. This makes our method a general tool for hand movement documentation, driving various downstream applications like using HL to control robotic hands. © 2024 ACM.

关键词： Semantics

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Generating comprehension checking problems using mutation analysis compensating for shakyo-style learning defects: A case study on binary operators with evaluating its effectiveness

Computer Software

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computer Software 2021年第1期38卷 111-139页

作者： Kamei, Ryota Yoshizuka, Takahiro Sasano, Isao Komiya, Seiichi Department of Electrical Engineering and Computer Science Graduate School of Engineering and Science Shibaura Institute of Technology Emeritus Shibaura Institute of Technology

When learning some programming language it is often the case that novice programmers input some sample code and check the execution results. This is called Shakyo-Style learning, which tends to be a monotonous task without thinking and thus results in insufficient understanding of the syntax and semantics of the language. In this study, we present a new learning method with making minute changes to source code. The changes are made by the techniques in mutation test. In this work we change some binary operators in the original source code and let novice programmers write the standard output of the generated code. Based on the method, we developed a tool and evaluated it by an experiment, which shows that the group with learning by the tool in addition to Shakyo-Style learning improved learning effectiveness, compared with the group with solving exercises similar to problems in the examinations in the experiments. Furthermore, by the results of questionnaires, we found that our method improves the motivation of novice programmers. We also report the implementation of the tool. © 2021 Japan Society for Software science and technology. All rights reserved.

关键词： Semantics

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Detecting Bipolar Disorder based on Crowdsourced Symptoms using Machine Learning with Shapley Additive Explanations 9th

Detecting Bipolar Disorder based on Crowdsourced Symptoms us...

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9th International Conference on computer science and Computational Intelligence, ICCSCI 2024

作者： Martawidjaja, Marvel Lefrandt, Matthew Paramita, Ni Luh Putu Satyaning Pradnya Zhan, Rongfang Komalasari, Renata Kurniadi, Felix Indra Sihotang, Erna Fransisca Angela Anggreainy, Maria Susan Cahyono, Dwy Bagus Jati, Putu Hadi Purnama Computer Science Department School of Computer Science Bina Nusantara University Jakarta11530 Indonesia Department of Rehabilitation and Health Services University of North Texas Denton76203 United States College of Nursing Pennsylvania State University State College16802 United States Statistics Department School of Computer Science Bina Nusantara University Jakarta11530 Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta11530 Indonesia Meta Lab Jakarta10310 Indonesia

Bipolar disorder is a mental health condition characterized by extreme mental states ranging from manic highs to depressive lows. Early intervention is crucial to prevent progression and complications of bipolar disorder, which can lead to significant loss. This study proposes a framework to detect bipolar disorder based on crowdsourced symptoms in the form of free texts. We extract the features by transforming these free-text symptoms into binary features using a set of natural language processing techniques. We build support vector machine models with different kernel functions and penalized logistic regression models with different penalty functions, where the best models have a precision of 0.7, recall of 0.78, F1 score of 0.74, and accuracy of 0.88. Moreover, the models are explainable since we incorporate a model-agnostic explanation method called Shapley additive explanations to understand the symptoms that mostly contributed to the prediction of bipolar disorder. The models presented in this study can be implemented in the initial screening process of bipolar disorder for further examination. © 2024 The Authors.

关键词： Adversarial machine learning

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