Transactions on Mass-Data Analysis of Images and Signals P-ISSN1868-6451, E-ISSN 2509-9353, ISBN 978-3-942952-80-4 Volume 11 - Number 1 - September 2020 - Page 3-26
Data transmission between two or more digital devices in industry and government demands secure and agile technology. Digital information distribution often requires deployment of Internet of Things (IoT) devices and Data Fusion techniques which have also gained popularity in both, civilian and military environments, such as, emergence of Smart Cities and Internet of Battlefield Things (IoBT). This usually requires capturing and consolidating data from multiple sources. Because datasets do not necessarily originate from identical sensors, fused data typically results in a complex Big Data problem. Due to potentially sensitive nature of IoT datasets, Blockchain technology is used to facilitate secure sharing of IoT datasets, which allows digital information to be distributed, but not copied. However, blockchain has several limitations related to complexity, scalability, and excessive energy consumption. We propose an approach to hide information (sensor signal) by transforming it to an image or an audio signal. In one of the latest attempts to the military modernization, we investigate sensor fusion approach by investigating the challenges of enabling an intelligent identification and detection operation and demonstrates the feasibility of the proposed Deep Learning and Anomaly Detection models that can support future application for specific hand gesture alert system from wearable devices.
Robust Physics-Informed Neural Network Approach for Estimating Heterogeneous Elastic Properties from Noisy Displacement Data
Department of Industrial and Systems Engineering, Texas A&M University（工业与系统工程系，德克萨斯大学阿马尔科分校）；Department of Mechanical Engineering, Texas A&M University（机械工程系，德克萨斯大学阿马尔科分校）
Learning Cautiously in Federated Learning with Noisy and Heterogeneous Clients
The Future Network of Intelligence Institute（未来网络智能研究院）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Zhejiang University（浙江大学）；Peng Cheng Laboratory（鹏城实验室）
Learning Curves for Noisy Heterogeneous Feature-Subsampled Ridge Ensembles
Biophysics Graduate Program（生物物理学研究生项目）；Center for Brain Science（脑科学中心）；John A. Paulson School of Engineering and Applied Sciences（约翰·A·保尔森工程与应用科学学院）；Kempner Institute for the Study of Natural and Artificial Intelligence（肯普纳自然与人工智能研究所）；Harvard University（哈佛大学）
FedCova: Robust Federated Covariance Learning Against Noisy Labels
Department of Information Engineering（信息工程系）；The Chinese University of Hong Kong（香港中文大学）；National Key Laboratory on Wireless Communications（无线通信国家重点实验室）；University of Electronic Science and Technology of China（电子科学与技术大学）
FedRG: Unleashing the Representation Geometry for Federated Learning with Noisy Clients
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；TMLR Group, Hong Kong Baptist University（香港浸会大学TMLR组）；Beihang University（北京航空航天大学）；The Hong Kong University of Science and Technology（香港科技大学）
HGAurban: Heterogeneous Graph Autoencoding for Urban Spatial-Temporal Learning
The University of Hong Kong（香港大学）；The University of Queensland（昆士兰大学）；University of California, Los Angles（加州大学洛杉矶分校）；National University of Singapore（新加坡国立大学）
THeGAU: Type-Aware Heterogeneous Graph Autoencoder and Augmentation
Data Science Degree Program National Taiwan University；Graduate Institute of Communication Engineering National Taiwan University Taipei Taiwan；University of Illinois Urbana-Champaign Electrical \& Computer Engineering Illinois United States；Research Center for Information Technology Innovation Academia Sinica Taipei Taiwan；Department of Electrical Engineering National Taiwan University Taipei Taiwan；National Taiwan University；University of Illinois Urbana-Champaign
Modeling Multi-Hop Semantic Paths for Recommendation in Heterogeneous Information Networks
The Chinese University of Hong Kong（香港中文大学）；University of Pennsylvania（宾夕法尼亚大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；Brown University（布朗大学）；Shanghai Jiao Tong University（上海交通大学）；University of Southern California（南加州大学）
Disentangling Imperfect: A Wavelet-Infused Multilevel Heterogeneous Network for Human Activity Recognition in Flawed Wearable Sensor Data
School of Computer Science and Engineering, Tianjin University of Technology（天津理工大学计算机科学与工程学院）；Technical University of Denmark（丹麦技术大学）；Aalborg University（奥尔堡大学）
FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise
Academy for Engineering and Technology, Fudan University（复旦大学工程学院）；School of Computer Science, China University of Geosciences（中国地质大学（武汉）计算机学院）；College of Science, Beijing Forestry University（北京林业大学理学院）
Learning with Dual-level Noisy Correspondence for Multi-modal Entity Alignment
College of Computer Science, Sichuan University（四川大学计算机学院）；National Key Laboratory of Fundamental Algorithms and Models for Engineering Numerical Simulation, Sichuan University（四川省工程数值模拟基础算法与模型国家重点实验室）
