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3rd Symposium on Algorithmic Foundations of Dynamic Networks, SAND 2024

作者： Zheng, Haozhi Eguchi, Ryota Ooshita, Fukuhito Inoue, Michiko Graduate School of Science and Technology Nara Institute of Science and Technology Japan Faculty of Engineering Fukui University of Technology Japan

ISBN: (纸本)9783959773157

The gathering problem requires multiple mobile agents in a network to meet at a single location. This paper investigates the gathering problem in carrier graphs, a subclass of recurrence of edge class of time-varying graphs. By focusing on three subclasses of single carrier graphs – circular, simple, and arbitrary – we clarify the conditions under which the problem can be solved, considering prior knowledge endowed to agents and obtainable online information, such as the count and identifiers of agents or sites. We propose algorithms for solvable cases and analyze the complexities and we give proofs for the impossibility for unsolvable cases. We also consider general carrier graphs with multiple carriers and propose an algorithm for arbitrary carrier graphs. To the best of our knowledge, this is the first work that investigates the gathering problem in carrier graphs. © Haozhi Zheng, Ryota Eguchi, Fukuhito Ooshita, and Michiko Inoue.

关键词： Mobile agents

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Knowledge Transfer Learning via Dual Density Sampling for Resource-Limited Domain Adaptation

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IEEE/CAA Journal of Automatica Sinica 2023年第12期10卷 2269-2291页

作者： Zefeng Zheng Luyao Teng Wei Zhang Naiqi Wu Shaohua Teng the School of Computer Science and Technology Guangdong University of TechnologyGuangzhou 510006China the School of Information Engineering Guangzhou Panyu PolytechicGuangzhou 511483China the Faculty of Information Technology Monash University20 Exhibition Walk ClaytonVIC 3800Australia IEEE the Institute of Systems Engineering and Collaborative Laboratory for Intelligent Science and Systems Macao University of Science and TechnologyMacao 999078China

Most existing domain adaptation(DA) methods aim to explore favorable performance under complicated environments by ***,there are three unsolved problems that limit their efficiencies:ⅰ) they adopt global sampling but neglect to exploit global and local sampling simultaneously;ⅱ)they either transfer knowledge from a global perspective or a local perspective,while overlooking transmission of confident knowledge from both perspectives;and ⅲ) they apply repeated sampling during iteration,which takes a lot of *** address these problems,knowledge transfer learning via dual density sampling(KTL-DDS) is proposed in this study,which consists of three parts:ⅰ) Dual density sampling(DDS) that jointly leverages two sampling methods associated with different views,i.e.,global density sampling that extracts representative samples with the most common features and local density sampling that selects representative samples with critical boundary information;ⅱ)Consistent maximum mean discrepancy(CMMD) that reduces intra-and cross-domain risks and guarantees high consistency of knowledge by shortening the distances of every two subsets among the four subsets collected by DDS;and ⅲ) Knowledge dissemination(KD) that transmits confident and consistent knowledge from the representative target samples with global and local properties to the whole target domain by preserving the neighboring relationships of the target *** analyses show that DDS avoids repeated sampling during the *** the above three actions,confident knowledge with both global and local properties is transferred,and the memory and running time are greatly *** addition,a general framework named dual density sampling approximation(DDSA) is extended,which can be easily applied to other DA *** experiments on five datasets in clean,label corruption(LC),feature missing(FM),and LC&FM environments demonstrate the encouraging performance of KTL-DDS.

关键词： Cross-domain risk dual density sampling intra-domain risk maximum mean discrepancy knowledge transfer learning resource-limited domain adaptation

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Adaptive Graph Embedding With Consistency and Specificity for Domain Adaptation

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IEEE/CAA Journal of Automatica Sinica 2023年第11期10卷 2094-2107页

作者： Shaohua Teng Zefeng Zheng Naiqi Wu Luyao Teng Wei Zhang School of Computer Science and Technology Guangdong University of TechnologyGuangzhou 510006China Institute of Systems Engineering and Collaborative Laboratory for Intelligent Science and Systems Macao University of Science and TechnologyMacao 999078China School of Information Engineering Guangzhou Panyu PolytechicGuangzhou 511483China Faculty of Information Technology Monash University20 Exhibition Walk ClaytonVIC 3800Australia IEEE

Domain adaptation(DA) aims to find a subspace,where the discrepancies between the source and target domains are reduced. Based on this subspace, the classifier trained by the labeled source samples can classify unlabeled target samples *** approaches leverage Graph Embedding Learning to explore such a subspace. Unfortunately, due to 1) the interaction of the consistency and specificity between samples, and 2) the joint impact of the degenerated features and incorrect labels in the samples, the existing approaches might assign unsuitable similarity, which restricts their performance. In this paper, we propose an approach called adaptive graph embedding with consistency and specificity(AGE-CS) to cope with these issues. AGE-CS consists of two methods, i.e., graph embedding with consistency and specificity(GECS), and adaptive graph embedding(AGE).GECS jointly learns the similarity of samples under the geometric distance and semantic similarity metrics, while AGE adaptively adjusts the relative importance between the geometric distance and semantic similarity during the iterations. By AGE-CS,the neighborhood samples with the same label are rewarded,while the neighborhood samples with different labels are punished. As a result, compact structures are preserved, and advanced performance is achieved. Extensive experiments on five benchmark datasets demonstrate that the proposed method performs better than other Graph Embedding methods.

关键词： Adaptive adjustment consistency and specificity domain adaptation graph embedding geometrical and semantic metrics

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A Non-Work Conserving Algorithm for Dynamic Scheduling of Moldable Gang Tasks on Multicore systems

A Non-Work Conserving Algorithm for Dynamic Scheduling of Mo...

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2024 International Conference on Electronics, Information, and Communication, ICEIC 2024

作者： Shimizu, Tomoki Nishikawa, Hiroki Kong, Xiangbo Tomiyama, Hiroyuki Graduate School of Science and Engineering Ritsumeikan University Japan Graduate School of Information Science and Technology Osaka University Japan Toyama Prefectural University Faculty of Engineering Japan

ISBN: (纸本)9798350371888

In this paper, we consider a dynamic scheduling algorithm for moldable gang tasks on multicore systems. While previous algorithms have been proposed, they predominantly adhere to work-conserving scheduling algorithms. In this paper, we introduce a non-work conserving dynamic scheduling algorithm aiming to reduce the makespan. Our proposed algorithm employs beam-search to perform non-work conserving scheduling. Through experiments, we demonstrate that under certain conditions, our method improves the makespan by 10% compared to existing methods. Furthermore, we discuss the variance in results based on the characteristics of the input tasks. © 2024 IEEE.

关键词： Scheduling algorithms

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CASE STUDY OF SPACE UTILIZATION AND WASTE REDUCTION IN BUILDINGS USING ULTRA-THIN PRECAST PRESTRESSED CONCRETE MEMBERS

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AIJ Journal of technology and Design 2025年第77期31卷 444-448页

作者： Hosoya, Jin Imamoto, Kei-Ichi Ridengaoqier, E. Kiyohara, Chizuru Dept. of Architecture Graduate School of Engineering Tokyo Univ. of Science Japan Dept. of Architecture Faculty of Engineering Tokyo Univ. of Science Japan Research Institute for Science & Technology Tokyo Univ. of Science Japan

This paper presents case study on implementing ultra-thin precast prestressed concrete members, with tension introduced by CFRP strand as prefabricated partition materials in architectural spaces. These members enhanced the efficiency of architectural spaces and significantly reduced waste materials at construction sites. The paper also discussed the mechanical properties of the thin-walled members through bending tests, demonstrating their high bending strength and safety as partition materials. Furthermore, implementation examples in a pediatric clinic have illustrated the practical benefits, including increased effective area, reduced construction time, and decreased waste. © 2025 Architectural Institute of Japan. All rights reserved.

关键词： Thin walled structures

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Embedded Topic Models Enhanced by Wikification 1

Embedded Topic Models Enhanced by Wikification

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1st Workshop on Advancing Natural Language Processing for Wikipedia, WikiNLP 2024

作者： Shibuya, Takashi Utsuro, Takehito Degree Programs in Systems and Information Engineering Graduate School of Science and Technology University of Tsukuba Japan

ISBN: (纸本)9798891761889

Topic modeling analyzes a collection of documents to learn meaningful patterns of words. However, previous topic models consider only the spelling of words and do not take into consideration the homography of words. In this study, we incorporate the Wikipedia knowledge into a neural topic model to make it aware of named entities. We evaluate our method on two datasets, 1) news articles of New York Times and 2) the AIDA-CoNLL dataset. Our experiments show that our method improves the performance of neural topic models in generalizability. Moreover, we analyze frequent terms in each topic and the temporal dependencies between topics to demonstrate that our entity-aware topic models can capture the time-series development of topics well. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Millimeter-Wave Fast Beam Tracking Enabled by RAN/V2X Cooperation 6

Millimeter-Wave Fast Beam Tracking Enabled by RAN/V2X Cooper...

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6th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2024

作者： Maruta, Kazuki Nakazato, Jin Suzuki, Kengo Dou, Hu Iwaki, Ryo Ozawa, Sojin Sasaki, Yuki So, Hideya Tsukada, Manabu Graduate School of Engineering Tokyo University of Science Department of Electrical Engineering Tokyo Japan Graduate School of Information Science and Technology The University of Tokyo Tokyo Japan Shonan Institute of Technology Faculty of Engineering Kanagawa Japan

ISBN: (纸本)9798350344349

Automated driving has the same limitations as human drivers because it functions as a replacement for humans and operates based on local information using onboard sensors and computers. Cooperative automated vehicles (CAVs) are expected to achieve both safety and efficiency, which could not be achieved by imitating human driving, by sharing sensor information from roadside equipment and other vehicles. Since such sensor information is enormous, it is desirable to utilize mm W, which are capable of high-capacity transmission. However, wireless communication systems for CAV have the challenge of radio quality degradation due to vehicle movement. Our research project aims to realize stable millimeter-wave transmission by incorporating open radio access network (O-RAN) and vehicle-To-everything (V2X) functions. This paper presents the overall proposed concept and an example of validation;we show the results of evaluating our previously proposed fast beam following scheme in a handover environment with multiple roadside units. © 2024 IEEE.

关键词： Vehicle to Everything

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DFNet: Dense fusion convolution neural network for plant leaf disease classification

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Agronomy Journal 2024年第3期116卷 826-838页

作者： Faisal, Muhamad Leu, Jenq-Shiou Avian, Cries Prakosa, Setya Widyawan Köppen, Mario Department of Electronic and Computer Engineering (ECE) National Taiwan University of Science and Technology Taipei City Taiwan Department of Computer Science and Systems Engineering (CSSE) Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Fukuoka Iizuka-shi Japan

The early identification of plant diseases is crucial for preventing the loss of crop production. Recently, the advancement of deep learning has significantly improved the identification of plant leaf diseases. However, most approaches depend on a single convolutional neural network (CNN) to extract the leaf features, ignoring the opportunity to take full advantage of the feature richness available in the images. This paper explores a novel CNN model with multiple automated feature extractors, namely, dense fusion CNN (DFNet), for classifying plant leaf diseases. DFNet aims to increase the diversity of extracted features in order to improve discrimination. Instead of using a single-CNN model, DFNet relies on a double-pretrained CNN model, MobileNetV2 and NASNetMobile, as the feature extractor. The features extracted from each CNN are fused in the fusion layer using a fully connected network. The proposed method was evaluated using corn (Zea mays L.) and coffee (Coffea canephora) leaf disease datasets and compared to the existing models. The experiment showed that DFNet is superior and consistent to other CNN methods by achieving an accuracy of 97.53% for corn leaf diseases and 94.65% for coffee leaf diseases. © 2023 The Authors. Agronomy Journal © 2023 American Society of Agronomy.

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A STUDY ON TYPES OF EXPECTED USE AND SUPPLY OF FACILITY FOR THE ELDERLY IN THE NATIONAL COMPILATION OF THE 8TH LONG-TERM CARE SERVICE PLANS IN JAPAN

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AIJ Journal of technology and Design 2023年第72期29卷 975-980页

作者： Nishino, Tatsuya Nozaki, Katsuhiro Faculty of Geosciences and Civil Engineering Institute of Natural Science and Technology Kanazawa Univ. Japan Division of Geosciences and Civil Engineering Graduate School of Natural Science and Technology Kanazawa Univ Japan

This study identified regional types of the expected use of long-term care services in Japan. In addition, the expected use and supply of facilities were compiled, and the level of achievement was assessed by comparison with the 2025 Reform targets. The 2021-2023 long-term care service plans were nationally collected. As a result, in metropolitan areas, regions with the largest average rate of home-visit users accounted for about 80%, whilst those of day-care users accounted for more than 50% in local cities and depopulated areas. The 2025 estimates show that the facilities will be curtailed compared to the 2025 Reform targets. © 2023 Architectural Institute of Japan. All rights reserved.

关键词： Expected use Facility for the elderly Long-term care service Supply

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Control Displaying of Web Push Notifications Inducing Users to Phishing Websites 7

Control Displaying of Web Push Notifications Inducing Users ...

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7th IEEE Conference on Dependable and Secure Computing, DSC 2024

作者： Sato, Masaya Mandai, Ryuki Okayama Prefectural University Faculty of Computer Science and Systems Engineering Soja Japan Graduate School of Computer Science and Systems Engineering Okayama Prefectural University Soja Japan

ISBN: (纸本)9798331540289

The number of phishing websites is increasing, and the methods used to direct users to these sites are becoming more diverse. As web push notifications become more widespread, adversaries have begun using them as a method to direct users to phishing websites, known as phishing web push notifications. While protection against other phishing methods has been widely studied, countermeasures against phishing web push notifications remain an ongoing research topic. In this paper, we explore a method to control the display of web push notifications to protect users from phishing attempts. To minimize the cost of implementing and introducing the control mechanism, we utilized a browser extension to manage the display of web push notifications. We employed a keyword-based approach to detect and prevent the display of suspicious web push notifications. Our experimental results demonstrated that the proposed method effectively blocks phishing notifications. © 2024 IEEE.

关键词： Phishing

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