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Collaborative Speculations on Future Themes for Participatory Design in Germany

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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A novel facial expression recognition method based on cross direction attention network

引用

Engineering Applications of Artificial Intelligence 2025年 157卷

作者： Cheng Peng Guodong Li Likang Lin Bowen Zhang Kun Zou Sio Long Lo Ah Chung Tsoi School of Computer Science University of Electronic Science and Technology of China Zhongshan Institute No. 1 Xueyuan Road Zhongshan 528402 Guangdong China School of Computer Science and Engineering University of Electronic Science and Technology of China Xiyuan Avenue High-tech Zone Chengdu 610000 Sichuan China College of Mathematics and Informatics South China Agricultural University 483 Wushan Road GuangZhou 510642 Guangdong China College of Big Data and Internet Shenzhen Technology University 3002 Lantian Road Shenzhen 510642 Guangdong China Faculty of Innovation Engineering Macau University of Science and Technology Avenida Wai Long Taipa Macau 999078 Macao Special Administrative Region of China School of Computing and Information Technology University of Wollongong Northfields Ave Wollongong NSW 2522 New South Wales Australia

Facial expression recognition (FER) is an area of growing interest in computer vision research. This paper extends the framework provided by the ‘Distract your Attention Network’ (DAN) which consists of multiple parallel branches, each branch composes of a spatial attention (SA) module followed by a channel attention (CA) module, and then these multiple branches are fused together before being passed into a classifier module. The spatial attention module of DAN has an internal channel dimension of 1, while our proposed Cross Directional Attention Network (CDAN)-I and CDAN-II contain respectively an internal channel dimension of 512 (same as the channel dimension of the input), and internal channel dimension of 1024 (double that of the channel dimension of the input). These increases in internal channel dimension allow extraction of more features, before they are being made to conform with the input channel dimension. Despite these seemingly simple modifications from that of DAN, both CDAN-I and CDAN-II are found to outperform those of DAN, a state-of-the-art FER method, on four popular FER benchmark datasets: RAF-DB (Real world Affective Face-database), AffectNet-7 (AffectNet with Seven Categories) AffectNet-8 ( AffectNet with Eight Categories), and CK+ (Cohn–Kanada Extended). Moreover, we make use of three statistical indexes for clustering analysis, and verified that the CDAN-I and CDAN-II modules have been able to increase the inter-cluster distances, and decrease the intra-cluster distances, when compared with those obtained by the backbone ResNet-18 network (Residual Network with 18 Layers) , thus providing a quantitative analysis technique in this area.

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Dual encoding for video retrieval by text

arXiv

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arXiv 2020年

作者： Dong, Jianfeng Li, Xirong Xu, Chaoxi Yang, Xun Yang, Gang Wang, Xun Wang, Meng College of Computer and Information Engineering Zhejiang Gongshang University Hangzhou310035 China Key Lab of Data Engineering and Knowledge Engineering Renmin University of China The AI and Media Computing Lab School of Information Renmin University of China Beijing100872 China School of Computing National University of Singapore Singapore37580 Singapore School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China

This paper attacks the challenging problem of video retrieval by text. In such a retrieval paradigm, an end user searches for unlabeled videos by ad-hoc queries described exclusively in the form of a natural-language sentence, with no visual example provided. Given videos as sequences of frames and queries as sequences of words, an effective sequence-to-sequence cross-modal matching is crucial. To that end, the two modalities need to be first encoded into real-valued vectors and then projected into a common space. In this paper we achieve this by proposing a dual deep encoding network that encodes videos and queries into powerful dense representations of their own. Our novelty is two-fold. First, different from prior art that resorts to a specific single-level encoder, the proposed network performs multi-level encoding that represents the rich content of both modalities in a coarse-to-fine fashion. Second, different from a conventional common space learning algorithm which is either concept based or latent space based, we introduce hybrid space learning which combines the high performance of the latent space and the good interpretability of the concept space. Dual encoding is conceptually simple, practically effective and end-to-end trained with hybrid space learning. Extensive experiments on four challenging video datasets show the viability of the new method. Code is available at https://***/danieljf24/hybrid_space. Copyright © 2020, The Authors. All rights reserved.

关键词： Signal encoding

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Corrigendum to “Molecular subtypes classification of breast cancer in DCE-MRI using deep features” [Expert Syst. Appl. 236 (2024) 121371]

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Expert Systems with Applications 2025年 270卷

作者： Ali M. Hasan Noor K.N. Al-Waely Hadeel K. Aljobouri Hamid A. Jalab Rabha W. Ibrahim Farid Meziane College of Medicine Al-Nahrain University Baghdad Iraq Biomedical Engineering Department College of Engineering Al-Nahrain University Iraq Faculty of Computer Science and Information Technology University of Malaya 50603 Kuala Lumpur Malaysia Information and Communication Technology Research Group Scientific Research Center Al-Ayen University Nile Street 64001 Thi-Qar Iraq Department of Computer Science and Mathematics Lebanese American University Beirut 1102 2801 Lebanon Near East University Mathematics Research Center Department of Mathematics Near East Boulevard PC: 99138 Nicosia/Mersin 10 Turkey Data Science Research Centre School of Computing and Engineering University of Derby United Kingdom

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Assertion-based approaches to auditing complex elections, with application to party-list proportional elections

arXiv

引用

arXiv 2021年

作者： Blom, Michelle Budurushi, Jurlind Rivest, Ronald L. Stark, Philip B. Stuckey, Peter J. Teague, Vanessa Vukcevic, Damjan School of Computing and Information Systems University of Melbourne Parkville Australia Cloudical Deutschland GmbH Berlin Germany Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology CambridgeMA United States Department of Statistics University of California BerkeleyCA United States Department of Data Science and AI Monash University Clayton Australia Thinking Cybersecurity Pty. Ltd. Melbourne Australia School of Mathematics and Statistics University of Melbourne Parkville Australia Melbourne Integrative Genomics University of Melbourne Parkville Australia

Risk-limiting audits (RLAs), an ingredient in evidence-based elections, are increasingly common. They are a rigorous statistical means of ensuring that electoral results are correct, usually without having to perform an expensive full recount - at the cost of some controlled probability of error. A recently developed approach for conducting RLAs, SHANGRLA, provides a flexible framework that can encompass a wide variety of social choice functions and audit strategies. Its flexibility comes from reducing sufficient conditions for outcomes to be correct to canonical 'assertions' that have a simple mathematical form. Assertions have been developed for auditing various social choice functions including plurality, multi-winner plurality, super-majority, Hamiltonian methods, and instant runoff voting. However, there is no systematic approach to building assertions. Here, we show that assertions with linear dependence on transformations of the votes can easily be transformed to canonical form for SHANGRLA. We illustrate the approach by constructing assertions for party-list elections such as Hamiltonian free list elections and elections using the D'Hondt method, expanding the set of social choice functions to which SHANGRLA applies directly. Copyright © 2021, The Authors. All rights reserved.

关键词： Hamiltonians

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Fusing Bluetooth with Pedestrian Dead Reckoning: A Floor Plan-Assisted Positioning Approach

arXiv

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arXiv 2025年

作者： Pan, Wenxuan Yang, Yang Chen, Mingzhe Wei, Dong Guo, Caili Mao, Shiwen Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China Department of Electrical and Computer Engineering the Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China Wireless Engineering Research and Education Center Auburn University AuburnAL36849 United States

Floor plans can provide valuable prior information that helps enhance the accuracy of indoor positioning systems. However, existing research typically faces challenges in efficiently leveraging floor plan information and applying it to complex indoor layouts. To fully exploit information from floor plans for positioning, we propose a floor plan-assisted fusion positioning algorithm (FP-BP) using Bluetooth low energy (BLE) and pedestrian dead reckoning (PDR). In the considered system, a user holding a smartphone walks through a positioning area with BLE beacons installed on the ceiling, and can locate himself in real time. In particular, FP-BP consists of two phases. In the offline phase, FP-BP programmatically extracts map features from a stylized floor plan based on their binary masks, and constructs a mapping function to identify the corresponding map feature of any given position on the map. In the online phase, FP-BP continuously computes BLE positions and PDR results from BLE signals and smartphone sensors, where a novel grid-based maximum likelihood estimation (GML) algorithm is introduced to enhance BLE positioning. Then, a particle filter is used to fuse them and obtain an initial estimate. Finally, FP-BP performs post-position correction to obtain the final position based on its specific map feature. Experimental results show that FP-BP can achieve a real-time mean positioning accuracy of 1.19 m, representing an improvement of over 28% compared to existing floor plan-fused baseline algorithms. Copyright © 2025, The Authors. All rights reserved.

关键词： Photomapping

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基于大语言模型的糖尿病管理:潜力与展望

引用

science Bulletin 2024年第5期69卷 583-588页

作者：盛斌管洲榆 Lee-Ling Lim 江泽铧 Nestoras Mathioudakis 李佳佳刘茹涵包玉倩 Yong Mong Bee 王亚星郑颖丰 Gavin Siew Wei Tan 纪宏伟 Josip Car 王海波 David C.Klonoff 李华婷覃宇宗黄天荫贾伟平 Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases Department of Computer Science and EngineeringSchool of ElectronicInformationand Electrical EngineeringShanghai Jiao Tong UniversityDepartment of Endocrinology and MetabolismShanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghai Diabetes InstituteShanghai Clinical Center for DiabetesShanghai 200240China MOE Key Laboratory of Artificial Intelligence Artificial Intelligence InstituteShanghai Jiao Tong UniversityShanghai 200240China Department of Medicine Faculty of MedicineUniversity of MalayaKuala Lumpur 50603Malaysia Department of Medicine and Therapeutics The Chinese University of Hong KongHong KongChina Asia Diabetes Foundation Hong KongChina Tsinghua Medicine of Tsinghua University Beijing 100084China School of Clinical Medicine Beijing Tsinghua Changgung HospitalBeijing 102218China Division of Endocrinology Diabetes&MetabolismJohns Hopkins University School of MedicineBaltimoreMD 21211USA Department of Endocrinology Singapore General HospitalSingapore 169608Singapore Beijing Institute of Ophthalmology Beijing Tongren HospitalCapital University of Medical ScienceBeijing Ophthalmology and Visual Sciences Key LaboratoryBeijing 100730China State Key Laboratory of Ophthalmology Zhongshan Ophthalmic CenterSun Yat-sen UniversityGuangdong Provincial Key Laboratory of Ophthalmology and Visual ScienceGuangzhou 510060China Singapore Eye Research Institute Singapore National Eye CentreSingapore 168751Singapore Department of Cardiology the Affiliated Hospital of Qingdao UniversityQingdao 266011China Centre for Population Health Sciences Lee Kong Chian School of MedicineNanyang Technological University SingaporeSingapore 639798Singapore Department of Primary Care and Public Health School of Public HealthImperial College LondonLondon SW72BUUK Clinical Trial Unit Research Centre of Big Data and Artificial Intelligence in Medicine

The increasing prevalence of diabetes has become a global public health concern in the 21st *** 2021,it was estimated that 537 million people had diabetes,and this number is projected to reach 643 million by 2030,and 783 million by 2045[1].Such a huge burden of diabetes brings great challenges in its prevention and management,including early diagnosis,timely interventions,and regular monitoring of risk factor control and complications *** self-care support and patient empowerment can enhance clinical and psychobehavioural outcomes[2],although these require additional resources including manpower,infrastructure(hard and technology),and *** emergence of digital health technologies(DHTs),especially artificial intelligence(AI),may help address these obstacles and alleviate the burden of diabetes[3].Large language models(LLMs),a generative AI that can accept image and text inputs and produce text outputs,have shown promise in various aspects of medical care.

关键词： prevention diagnosis finance

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Strategic priorities for transformative progress in advancing biology with proteomics and artificial intelligence

arXiv

引用

arXiv 2025年

作者： Sun, Yingying Jun, A. Liu, Zhiwei Sun, Rui Qian, Liujia Payne, Samuel H. Bittremieux, Wout Ralser, Markus Li, Chen Chen, Yi Dong, Zhen Perez-Riverol, Yasset Khan, Asif Sander, Chris Aebersold, Ruedi Vizcaíno, Juan Antonio Krieger, Jonathan R. Yao, Jianhua Wen, Han Zhang, Linfeng Zhu, Yunping Xuan, Yue Sun, Benjamin Boyang Qiao, Liang Hermjakob, Henning Tang, Haixu Gao, Huanhuan Deng, Yamin Zhong, Qing Chang, Cheng Bandeira, Nuno Li, Ming Weinan, E. Sun, Siqi Yang, Yuedong Omenn, Gilbert S. Zhang, Yue Xu, Ping Fu, Yan Liu, Xiaowen Overall, Christopher M. Wang, Yu Deutsch, Eric W. Chen, Luonan Cox, Jürgen Demichev, Vadim He, Fuchu Huang, Jiaxing Jin, Huilin Liu, Chao Li, Nan Luan, Zhongzhi Song, Jiangning Yu, Kaicheng Wan, Wanggen Wang, Tai Zhang, Kang Zhang, Le Bell, Peter A. Mann, Matthias Zhang, Bing Guo, Tiannan Affiliated Hangzhou First People’s Hospital State Key Laboratory of Medical Proteomics School of Medicine Westlake University Zhejiang Province Hangzhou China Westlake Center for Intelligent Proteomics Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Province Hangzhou China Biology Department Brigham Young University ProvoUT84602 United States Department of Computer Science University of Antwerp Antwerp2020 Belgium Department of Biochemistry CharitéUniversitätsmedizin Berlin Berlin Germany Biomedicine Discovery Institute Department of Biochemistry and Molecular Biology Monash University MelbourneVICVIC 3800 Australia Wellcome Genome Campus Hinxton CambridgeCB10 1SD United Kingdom Harvard Medical School Ludwig Center at Harvard United States Harvard Medical School Broad Institute Ludwig Center at Harvard Dana-Farber Cancer Institute United States Department of Biology Institute of Molecular Systems Biology ETH Zürich Zürich Switzerland Bruker Ltd. MiltonONL9T 6P4 Canada AI for Life Sciences Lab Tencent Shenzhen518057 China State Key Laboratory of Medical Proteomics AI for Science Institute Beijing100080 China Beijing Institute of Lifeomics Beijing102206 China Thermo Fisher Scientific GmbH Hanna-Kunath Str. 11 Bremen28199 Germany Informatics and Predictive Sciences Research Bristol Myers Squibb United States Department of Chemistry Fudan University Songhu Road 2005 Shanghai200438 China Department of Computer Science Luddy School of Informatics Computing and Engineering Indiana University IN47408 United States ProCan® Children’s Medical Research Institute Faculty of Medicine and Health The University of Sydney WestmeadNSW Australia La Jolla CA United States Central China Institute of Artificial Intelligence University of Waterloo Canada AI for Science Institute Center for Machine Learning Research School of Mathematical Sciences Peking University China Research Institute of Intelligent Complex Systems Fudan U

Artificial intelligence (AI) is transforming scientific research, including proteomics. Advances in mass spectrometry (MS)-based proteomics data quality, diversity, and scale, combined with groundbreaking AI techniques, are unlocking new challenges and opportunities in biological discovery. Here, we highlight key areas where AI is driving innovation, from data analysis to new biological insights. These include developing an AI-friendly ecosystem for proteomics data generation, sharing, and analysis;improving peptide and protein identification and quantification;characterizing protein-protein interactions and protein complexes;advancing spatial and perturbation proteomics;integrating multi-omics data;and ultimately enabling AI-empowered virtual cells. © 2025, CC BY.

关键词： Biotic

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CyberSentry: Enhancing SCADA security through advanced deep learning and optimization strategies

引用

International Journal of Critical Infrastructure Protection 2025年 50卷

作者： Alaa O. Khadidos Adil O. Khadidos Shitharth Selvarajan Taher Al-Shehari Nasser A Alsadhan Subhav Singh Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Center of Research Excellence in Artificial Intelligence and Data Science King Abdulaziz University Jeddah Saudi Arabia Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia School of Built Environment Engineering and Computing Leeds Beckett University LS1 3HE Leeds UK Department of Computer Science and Engineering Chennai Institute of Technology Chennai India Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India Computer Skills Department of Self-Development Skill Common First Year Deanship King Saud University 11362 Riyadh Saudi Arabia Computer Science Department College of Computer and Information Sciences King Saud University Riyadh 12372 Saudi Arabia Chitkara Centre for Research and Development Chitkara University Himachal Pradesh 174103 India Division of research and development Lovely Professional University Phagwara Punjab India Division of Research & innovation Uttaranchal University Dehradun India

SCADA systems form the core of infrastructural facilities, including power grids, water treatment facilities, and industrial processes. Changing cyber threats present increasingly sophisticated attacks against which traditional security models inadequately protect SCADA systems. These traditional models usually have drawbacks in the way of inadequate feature selection, inefficiency in detecting most attacks, and suboptimal parameter tuning, which cause vulnerabilities and reduce resilience in systems. This paper presents CyberSentry, a new security framework designed to overcome limitations so as to provide robust protection for SCADA systems. These three modules makeup CyberSentry: the RMIG feature selection model, tri-fusion net for attack detection, and Parrot-Levy Blend Optimization (PLBO) for parameter tuning. The Recursive Multi-Correlation-based information Gain (RMIG) feature selection model enhances accuracy in detection by optimizing the set of fatal features through recursive multi-correlation analysis by information Gain prioritization. The Tri-Fusion Net combines anomaly detection, signature-based detection, and machine learning classifiers to enhance the detection versatility and robustness. The PLBO module ensures efficient and dynamic tuning for the parameters through undocumented Parrot and Levy optimization techniques. The proposed CyberSentry framework integrates, within a unified architecture, anomaly detection, signature-based detection, and machine learning classifiers to enhance the security of SCADA systems against diverse cyber threats. Features extracted in this manner are analyzed using machine learning classifiers that exploit their predictive capabilities for robust threat classification. The proposed approaches are fused within the Tri-Fusion Net to complement each other in areas where the separate methods lack certain strengths. This, therefore, ensures broad threat detection, as is validated by extensive testing with various datasets

关键词： SCADA systems Security Cyber-attack Intrusion detection Deep learning Feature selection and Optimization

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computing-in-Memory Architecture Based on Field-Free SOT-MRAM with Self-Reference Method

Computing-in-Memory Architecture Based on Field-Free SOT-MRA...

引用

IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Chao Wang Zhaohao Wang Yansong Xu Jianlei Yang Youguang Zhang Weisheng Zhao Fert Beijing Research Institute Beihang University School of Electronics and Information Engineering Beihang University School of Microelectronics Beihang University Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University School of Computer Science and Engineering Beihang University Beijing 100191 China

ISBN: (数字)9781728133201

ISBN: (纸本)9781728133218

On the current computing platforms, the memory wall between processor and memory has become the toughest challenge for the traditional Von-Neumann computer architecture. computing-in-Memory (CIM) is taken as a promising approach to solving the above bottleneck in computing systems. In this paper, we propose a CIM platform with field-free spinorbit torque magnetic random access memory (SOT-MRAM). The self-reference (SelfRef) method is designed to enhance the read reliability and directly obtain logic results through memory-like read operations without adding logic cells. Memory read/write and logic operations, including NOT, AND/NAND and OR/NOR, can be implemented in the same SOT-MRAM chip. The speed and power penalties caused by SelfRef scheme are acceptable thanks to the ultrafast switching of the SOT. The read reliability and logic correctness of the proposed CIM are demonstrated by hybrid simulation on a 40 nm technology node.

关键词： Reliability Switches Resistance Magnetic tunneling Magnetization Torque Semiconductor device modeling

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