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FedLGMatch: Federated semi-supervised learning via joint local and global pseudo labeling

Knowledge-Based Systems 2025年 320卷

作者： Zhao, Qing Chu, Jielei Li, Zhaoyu Huang, Wei Luo, Zhipeng Li, Tianrui School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Southwest Jiaotong University Chengdu611756 China China Railway Engineering Group Limited Beijing100039 China China Railway Eryuan Engineering Group Co. Ltd. Sichuan Chengdu610031 China College of Computer and Data Science Fuzhou University China

The bulk of existing Federated Learning (FL) algorithms pay attention to supervised setting and assume that clients have fully labeled data. However, it may be impractical for all clients to obtain plenty of labels due to high annotation costs. Hence, the Federated Semi-Supervised Learning (FSSL) as a promising paradigm has better prospect in many realistic applications (e.g. medical scenario). Under Labels-at-Server scenario, most pseudo labeling based FSSL approaches use only the global model to generate pseudo-labels for unlabeled data, while the local models are ignored. When the local data distribution is much more different from the central server (e.g., Non-IID setting), the generated pseudo-labels may contain much noise, thus, resulting in more serious confirmation bias. To tackle the crucial issue, a novel Federated Semi-Supervised Learning via Joint Local and Global Pseudo Labeling (FedLGMatch) framework is proposed in this paper. The prominent advantage of the proposed FedLGMatch is that it allows local models trained in the last communication round to assist global model in generating pseudo-labels, which successfully emphasizes more clean pseudo-label learning at each client. Experimental results also show that FedLGMatch achieves significant performance improvements than other state-of-the-art models on the standard benchmark datasets. © 2025 Elsevier B.V.

关键词： Semi-supervised learning

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Realistic Adversarial data Augmentation for MR Image Segmentation

arXiv

引用

arXiv 2020年

作者： Chen, Chen Qin, Chen Qiu, Huaqi Ouyang, Cheng Wang, Shuo Chen, Liang Tarroni, Giacomo Bai, Wenjia Rueckert, Daniel BioMedIA Group Department of Computing Imperial College London United Kingdom CitAI Research Centre Department of Computer Science City University of London United Kingdom Data Science Institute Imperial College London United Kingdom Department of Brain Sciences Imperial College London United Kingdom

Neural network-based approaches can achieve high accuracy in various medical image segmentation tasks. However, they generally require large labelled datasets for supervised learning. Acquiring and manually labelling a large medical dataset is expensive and sometimes impractical due to data sharing and privacy issues. In this work, we propose an adversarial data augmentation method for training neural networks for medical image segmentation. Instead of generating pixel-wise adversarial attacks, our model generates plausible and realistic signal corruptions, which models the intensity inhomogeneities caused by a common type of artefacts in MR imaging: bias field. The proposed method does not rely on generative networks, and can be used as a plug-in module for general segmentation networks in both supervised and semi-supervised learning. Using cardiac MR imaging we show that such an approach can improve the generalization ability and robustness of models as well as provide significant improvements in low-data scenarios. Copyright © 2020, The Authors. All rights reserved.

关键词： Image segmentation

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Match2: A Matching over Matching Model for Similar Question Identification

arXiv

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arXiv 2020年

作者： Wang, Zizhen Fan, Yixing Guo, Jiafeng Yang, Liu Zhang, Ruqing Lan, Yanyan Cheng, Xueqi Jiang, Hui Wang, Xiaozhao University of Chinese Academy of Sciences Beijing China CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Massachusetts Amherst MA United States Alibaba Group Beijing China

Community Question Answering (CQA) has become a primary means for people to acquire knowledge, where people are free to ask questions or submit answers. To enhance the efficiency of the service, similar question identification becomes a core task in CQA which aims to find a similar question from the archived repository whenever a new question is asked. However, it has long been a challenge to properly measure the similarity between two questions due to the inherent variation of natural language, i.e., there could be different ways to ask a same question or different questions sharing similar expressions. To alleviate this problem, it is natural to involve the existing answers for the enrichment of the archived questions. Traditional methods typically take a one-side usage, which leverages the answer as some expanded representation of the corresponding question. Unfortunately, this may introduce unexpected noises into the similarity computation since answers are often long and diverse, leading to inferior performance. In this work, we propose a two-side usage, which leverages the answer as a bridge of the two questions. The key idea is based on our observation that similar questions could be addressed by similar parts of the answer while different questions may not. In other words, we can compare the matching patterns of the two questions over the same answer to measure their similarity. In this way, we propose a novel matching over matching model, namely Match2, which compares the matching patterns between two question-answer pairs for similar question identification. Empirical experiments on two benchmark datasets demonstrate that our model can significantly outperform previous state-of-the-art methods on the similar question identification *** CONCEPTS •Information systems → Question answering;Copyright © 2020, The Authors. All rights reserved.

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Corrigendum to “Random projection ensemble conformal prediction for high-dimensional classification” [Chemometr. Intell. Lab. Syst. 253 (2024) 1–10, 105225]

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Chemometrics and Intelligent Laboratory Systems 2024年 254卷

作者： Xiaoyu Qian Jinru Wu Ligong Wei Youwu Lin School of Mathematics and Computing Science Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation Guilin University of Electronic Technology Guilin 541002 PR China Guangxi Academy of Sciences High-tech Group Co. LTD Nanning 530007 PR China Center for Applied Mathematics of Guangxi (GUET) Guilin 541002 PR China Guanghua School of Management Peking University Beijing PR China

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Retraction notice to “Fuzzy logic based on-line fault detection and classification method of substation equipment based on convolutional probabilistic neural network with discrete wavelet transform and fuzzy interference” [Optik 270 (2022) 169956]

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Optik 2023年 288卷

作者： Romany F. Mansour Eatedal Alabdulkreem Heba F. Eid K. Sathishkumar Mohd Abdul Rahim Khan Anil Kumar Department of Mathematics Faculty of Science New Valley University El-Kharja 72511 Egypt Department of Computer Science College of Computer and Information Science Princess Nourah Bint Abdulrahman University P.O.Box 84428 Riyadh 11671 Saudi Arabia Faculty of Science AL-Azhar University Cairo Egypt Department of Computer Science Gobi Arts & Science College(Autonomous) Gobichettipalayam Tamilnadu India Department of Computer Science College of Computer and Information Sciences Majmaah University Al-Majmaah 11952 Saudi Arabia Data Science Research Group School of Computing DIT University Dehradun India

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HSD Shared Task in VLSP Campaign 2019: Hate Speech Detection for Social Good

arXiv

引用

arXiv 2020年

作者： Vu, Xuan-Son Vu, Thanh Tran, Mai-Vu Le-Cong, Thanh Nguyen, Huyen T.M. Deep Data Mining Group Department of Computing Science Umeå University Sweden Australian E-Health Research Centre CSIRO Australia Vietnam National University Hanoi Viet Nam InfoRE Technology Company Vietnam National University Hanoi Viet Nam

The paper describes the organisation of the "Hate Speech Detection" (HSD) task at the VLSP workshop 2019 on detecting the fine-grained presence of hate speech in Vietnamese textual items (i.e., messages) extracted from Facebook, which is the most popular social network site (SNS) in Vietnam. The task is organised as a multi-class classification task and based on a large-scale dataset containing 25,431 Vietnamese textual items from Facebook. The task participants were challenged to build a classification model that is capable of classifying an item to one of 3 classes, i.e., "HATE’, "OFFENSIVE" and "CLEAN". HSD attracted a large number of participants and was a popular task at VLSP 2019. In particular, there were 71 teams signed up for the task, 14 of them submitted results with 380 valid submissions from 20th September 2019 to 4th October 2019. Copyright © 2020, The Authors. All rights reserved.

关键词： Social networking (online)

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MINI-Net: Multiple instance ranking network for video highlight detection

arXiv

引用

arXiv 2020年

作者： Hong, Fa-Ting Huang, Xuanteng Li, Wei-Hong Zheng, Wei-Shi School of Data and Computer Science Sun Yat-sen University China Peng Cheng Laboratory Shenzhen518005 China VICO Group University of Edinburgh United Kingdom Pazhou Lab Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

We address the weakly supervised video highlight detection problem for learning to detect segments that are more attractive in training videos given their video event label but without expensive supervision of manually annotating highlight segments. While manually averting localizing highlight segments, weakly supervised modeling is challenging, as a video in our daily life could contain highlight segments with multiple event types, e.g., skiing and surfing. In this work, we propose casting weakly supervised video highlight detection modeling for a given specific event as a multiple instance ranking network (MINI-Net) learning. We consider each video as a bag of segments, and therefore, the proposed MINI-Net learns to enforce a higher highlight score for a positive bag that contains highlight segments of a specific event than those for negative bags that are irrelevant. In particular, we form a max-max ranking loss to acquire a reliable relative comparison between the most likely positive segment instance and the hardest negative segment instance. With this max-max ranking loss, our MINI-Net effectively leverages all segment information to acquire a more distinct video feature representation for localizing the highlight segments of a specific event in a video. The extensive experimental results on three challenging public benchmarks clearly validate the efficacy of our multiple instance ranking approach for solving the problem. Copyright © 2020, The Authors. All rights reserved.

关键词： Machine learning

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Ethical Aspects of Computational Modelling in science, Decision Support and Communication 15th

Ethical Aspects of Computational Modelling in Science, Decis...

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15th International Workshop on Artificial Life and Evolutionary Computation, WIVACE 2021

作者： Füchslin, Rudolf Marcel Ambühl, Jacques Faggian, Alessia Fellermann, Harold M. Flumini, Dandolo Geller, Armando Hanczyc, Martin M. Klinkert, Andreas Krütli, Pius Matuttis, Hans-Georg Ott, Thomas Scheidegger, Stephan Schmid, Gary Bruno Smieszek, Timo Schneider, Johannes J. Steiner, Albert Weyland, Mathias S. Institute of Applied Mathematics and Physics Zurich University of Applied Sciences Zurich Technikumstrasse 9 Winterthur8400 Switzerland European Centre for Living Technology Ca’ Bottacin Dorsoduro 3911 Calle Crosera Venice30123 Italy World Bank Group Geneva Switzerland University of Trento Trento38123 Italy School of Computing Newcastle University Science Square 1 Newcastle Upon TyneNE4 5TG United Kingdom GmbH Fraumünsterstrasse 11 Zurich8001 Switzerland Institute of Data Analysis and Process Design Zurich University of Applied Sciences Rosenstrasse 3 Winterthur8400 Switzerland Transdisciplinarity Lab ETH Zurich Zurich8092 Switzerland Department of Mechanical Engineering and Intelligent Systems The University of Electrocommunications Tokyo182-8585 Japan Institute of Computational Life Sciences Zurich University of Applied Sciences Schloss 1 Wädenswil8820 Switzerland Swiss NeuroCreations GmbH Hambergersteig 25 Zurich8008 Switzerland Muesmattweg 70 Allschwil4123 Switzerland

ISBN: (纸本)9783031239281

The development of data science, the increase of computational power, the availability of the internet infrastructure for data exchange and the urgency for an understanding of complex systems require a responsible and ethical use of computational models in science, communication and decision-making. Starting with a discussion of the width of different purposes of computational models, we first investigate the process of model construction as an interplay of theory and experimentation. We emphasise the different aspects of the tension between model variables and experimentally measurable observables. The resolution of this tension is a prerequisite for the responsible use of models and an instrumental part of using models in the scientific processes. We then discuss the impact of models and the responsibility that results from the fact that models support and may also guide experimentation. Further, we investigate the difference between computational modelling in an interdisciplinary science project and computational models as tools in transdisciplinary decision support. We regard the communication of model structures and modelling results as essential;however, this communication cannot happen in a technical manner, but model structures and modelling results must be translated into a "narrative." We discuss the role of concepts from disciplines such as literary theory, communication science, and cultural studies and the potential gains that a broader approach can obtain. Considering concepts from the liberal arts, we conclude that there is, besides the responsibility of the model author, also a responsibility of the user/reader of the modelling results. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Decision making

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Collaborative Speculations on Future Themes for Participatory Design in Germany

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i-com 2022年第2期21卷 283-298页

作者： Mucha, Henrik Correia De Barros, Ana Benjamin, Jesse Josua Benzmüller, Christoph Bischof, Andreas Buchmüller, Sandra De Carvalho, Alexandra Dhungel, Anna-Katharina Draude, Claude Fleck, Marc-Julian Jarke, Juliane Klein, Stefanie Kortekaas, Caroline Kurze, Albrecht Linke, Diane Maas, Franzisca Marsden, Nicola Melo, Ricardo Michel, Susanne Müller-Birn, Claudia Pröbster, Monika Rießenberger, Katja Antonia Schäfer, Mirko Tobias Sörries, Peter Stilke, Julia Volkmann, Torben Weibert, Anne Weinhold, Wilhelm Wolf, Sara Zorn, Isabel Heidt, Michael Berger, Arne Fraunhofer-Institut für Optronik Systemtechnik und Bildauswertung IOSB Karlsruhe Germany Fraunhofer Center for Assistive Information and Communication Solutions-AICOS Porto Portugal University of Twente Enschede Netherlands Freie Universität Berlin Fachbereich Mathematik und Informatik Berlin Germany Chemnitz University of Technology Computer Science Chemnitz Germany TU Braunschweig University Institute of Flight Guidance Braunschweig Germany Universität Witten Herdecke Fakultät für Gesundheit Witten Germany Universität zu Lübeck Institut für Multimediale und Interaktive Systeme Lubeck Germany Fachbereich Elektrotechnik/Informatik Kassel Germany Hochschule Heilbronn Fakultät Informatik Heilbronn Germany Bremen Germany Katholische Hochschule Nordrhein Westfalen Forschung und Innovation in der Sozialen Arbeit Koln Germany Fakultät Angewandte Sozialwissenschaften Koln Germany Freie Universität Berlin Institut für Informatik Human-Centered Computing Research Group Berlin Germany Julius-Maximilians-Universität Würzburg Lehrstuhl für Psychologische Ergonomie Würzburg Germany Hochschule Heilbronn Fakultät Informatik Forschungsprofessur Sozioinformatik Heilbronn Germany Charité Universitätsmedizin Berlin Institute of the History of Medicine and Ethics in Medicine Berlin Germany Freie Universität Berlin Human-Centered Computing Berlin Germany OST-Ostschweizer Fachhochschule Institut für Altersforschung St Gallen Switzerland Utrecht University Utrecht Data School Governing the Digital Society Utrecht Netherlands Universität Siegen Lehrstuhl für Wirtschaftsinformatik und Neue Medien Siegen Germany Universität Würzburg Philosophie und Öffentliches Recht Wurzburg Germany Julius-Maximilians-Universität Würzburg Institut Mensch-Computer-Medien Würzburg Germany TH Köln Institut für Medienforschung und Medienpädagogik Leiterin des Forschungsschwerpunkts Digitale Technologien und Soziale Dienste Koln Germany Hochschule Anhalt Computer Science and Languages

Participatory Design means recognizing that those who will be affected by a future technology should have an active say in its creation. Yet, despite continuous interest in involving people as future users and consumers into designing novel and innovative future technology, participatory approaches in technology design remain relatively underdeveloped in the German HCI community. This article brings together the diversity of voices, domains, perspectives, approaches, and methods that collectively shape Participatory Design in Germany. In the following, we (1) outline our understanding of participatory practice and how it is different from mere user involvement;(2) reflect current issues of participatory and fair technology design within the German Participatory Design community;and (3) discuss tensions relevant to the field, that we expect to arise in the future, and which we derived from our 2021 workshop through a speculative method. We contribute an introduction and an overview of current themes and a speculative outlook on future issues of Participatory Design in Germany. It is meant to inform, provoke, inspire and, ultimately, invite participation within the wider Computer science community. © 2022 Walter de Gruyter GmbH, Berlin/Boston.

关键词： Human computer interaction

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Urban green space and happiness in developed countries

arXiv

引用

arXiv 2021年

作者： Kwon, Oh-Hyun Hong, Inho Yang, Jeasurk Wohn, Donghee Yvette Jung, Woo-Sung Cha, Meeyoung Department of Physics Pohang University of Science and Technology Pohang37673 Korea Republic of Center for Humans and Machines Max Planck Institute for Human Development Berlin14195 Germany Department of Geography National University of Singapore Singapore119260 Singapore Department of Informatics New Jersey Institute of Technology NewarkNJ07103 United States Department of Industrial and Management Engineering Pohang University of Science and Technology Pohang37673 Korea Republic of Asia Pacific Center for Theoretical Physics Pohang37673 Korea Republic of Data Science Group Institute for Basic Science Daejeon34126 Korea Republic of School of Computing Korea Advanced Institute of Science and Technology Daejeon34141 Korea Republic of

Urban green space has been regarded as contributing to citizen happiness by promoting physical and mental health. However, how urban green space and happiness are related across many countries of different socioeconomic conditions has not been explained well. By measuring urban green space score (UGS) from high-resolution Sentinel-2 satellite imagery of 90 global cities that in total cover 179,168 km2 and include 230 million people in 60 developed countries, we reveal that the amount of urban green space and the GDP can explain the happiness level of the country. More precisely, urban green space and GDP are each individually associated with happiness;happiness in the 30 wealthiest countries is explained only by urban green space, whereas GDP alone explains happiness in the 30 other countries in this study. Lastly, we further show that the relationship between urban green space and happiness is mediated by social support and that GDP moderates the relationship between social support and happiness, which underlines the importance of maintaining urban green space as a place for social cohesion in promoting people’s happiness. © 2021, CC BY.

关键词： Satellite imagery

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