Pressure-sensitive smart textiles are widely applied in the fields of healthcare, sports monitoring, and intelligent homes. The integration of devices embedded with pressure sensing arrays is expected to enable comprehensive scene coverage and multi-device integration. However, the implementation of identity recognition, a fundamental function in this context, relies on extensive device-specific datasets due to variations in pressure distribution across different devices. To address this challenge, we propose a novel user identification method based on contrastive learning. We design two parallel branches to facilitate user identification on both new and existing devices respectively, employing supervised contrastive learning in the feature space to promote domain unification. When encountering new devices, extensive data collection efforts are not required; instead, user identification can be achieved using limited data consisting of only a few simple postures. Through experimentation with two 8-subject pressure datasets (BedPressure and ChrPressure), our proposed method demonstrates the capability to achieve user identification across 12 sitting scenarios using only a dataset containing 2 postures. Our average recognition accuracy reaches 79.05%, representing an improvement of 2.62% over the best baseline model.
Robust Smart Contract Vulnerability Detection via Contrastive Learning-Enhanced Granular-ball Training
Chongqing Key Laboratory of Computational Intelligence（重庆计算智能重点实验室）；Key Laboratory of Cyberspace Big Data Intelligent Security, Ministry of Education（教育部网络空间大数据智能安全重点实验室）；Key Laboratory of Big Data Intelligent Computing（大数据智能计算重点实验室）；Chongqing University of Posts and Telecommunications（重庆邮电大学）；School of Cyber Science and Engineering, Huazhong University of Science and Technology（华中科技大学网络空间安全学院）；School of Computer Science and Artificial Intelligence, Zhengzhou University（郑州大学计算机与人工智能学院）；College of Computer and Information Science, Southwest University（西南大学计算机与信息科学学院）
SMART: Semantic Matching Contrastive Learning for Partially View-Aligned Clustering
College of Computer Science and Technology, Huaqiao University（华侨大学计算机科学与技术学院）；Department of Computer Science, Shantou University（汕头大学计算机科学系）；Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University（西南大学电子信息工程学院非线性电路与智能信息处理重点实验室）；School of Computer Science and Engineering, South China University of Technology（华南理工大学计算机科学与工程学院）；Department of Computer Science, City University of Hong Kong（香港城市大学计算机科学系）
Contrastive Self-Supervised Learning Based Approach for Patient Similarity: A Case Study on Atrial Fibrillation Detection from PPG Signal
Bangladesh University of Engineering and Technology（孟加拉国工程技术大学）；United International University（联合国际大学）；University of Maryland（马里兰大学）；United Arab Emirates University（阿联酋大学）
Energy-Efficient Prediction in Textile Manufacturing: Enhancing Accuracy and Data Efficiency With Ensemble Deep Transfer Learning
Department of Electrical Engineering, National Tsing Hua University（国立清华大学电子工程系）；School of Information Technology, Faculty of Science, Engineering and Built Environment, Deakin University（德克萨斯大学信息科技学院）；Industrial Technology Research Institute（工业技术研究所）
Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data
Large Wireless Localization Model (LWLM): A Foundation Model for Positioning in 6G Networks
Department of Electrical Engineering, Chalmers University of Technology（楚德大学电气工程系）；School of Communication and Information Engineering, Shanghai University（上海大学通信与信息工程学院）；Chalmers AI Research Center Consortium (CHAIR)（楚德人工智能研究中心联盟）；National Academic Infrastructure for Supercomputing in Sweden (NAISS)（瑞典国家学术超级计算基础设施）；SNS JU project 6G-DISAC under the EU’s Horizon Europe research and innovation Program（欧盟地平线欧洲研究与创新计划下的SNS JU项目6G-DISAC）；Swedish Foundation for Strategic Research (SSF)（瑞典战略研究基金会）；Chalmers Areas of Advance in ICT and Transport（楚德信息与交通领域先进领域）
Fitbeat: COVID-19 Estimation based on Wristband Heart Rate
University of Augsburg（奥格斯堡大学）；University of Cambridge（剑桥大学）；University of Zaragoza（萨拉戈萨大学）；University of Bergamo（贝加莫大学）；Medical University of Graz（格拉茨医科大学）
