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- research-articleJuly 2024
CryptGraph: An Efficient Privacy-Enhancing Solution for Accurate Shortest Path Retrieval in Cloud Environments
ASIA CCS '24: Proceedings of the 19th ACM Asia Conference on Computer and Communications SecurityPages 1660–1674https://doi.org/10.1145/3634737.3656293With the widespread adoption of cloud computing, it is a popular trend to migrate shortest path and distance (SPD) retrieval on large-scale graphs to cloud environments, harnessing their immense computational capabilities. To protect sensitive ...
- research-articleJuly 2024
Lightweight Privacy-Preserving Cross-Cluster Federated Learning With Heterogeneous Data
IEEE Transactions on Information Forensics and Security (TIFS), Volume 19Pages 7404–7419https://doi.org/10.1109/TIFS.2024.3435476Federated Learning (FL) eliminates data silos that hinder digital transformation while training a shared global model collaboratively. However, training a global model in the context of FL has been highly susceptible to heterogeneity and privacy concerns ...
- research-articleFebruary 2024
Reimagining Public Utilities through AI-Driven User-Centric Multimodal Interaction: A Case Study on the Lighthouse System
CHCHI '23: Proceedings of the Eleventh International Symposium of Chinese CHIPages 81–96https://doi.org/10.1145/3629606.3629616As public utilities become deeply intertwined with the digital sphere, we present the Lighthouse system, an innovative, user-centric, large-scale multimodal audio-visual interactive public utility. Leveraging Artificial Intelligence technologies, the ...
- research-articleOctober 2023
Privacy-Enhancing and Robust Backdoor Defense for Federated Learning on Heterogeneous Data
IEEE Transactions on Information Forensics and Security (TIFS), Volume 19Pages 693–707https://doi.org/10.1109/TIFS.2023.3326983Federated learning (FL) allows multiple clients to train deep learning models collaboratively while protecting sensitive local datasets. However, FL has been highly susceptible to security for federated backdoor attacks (FBA) through injecting triggers ...
- ArticleAugust 2023
- research-articleJune 2023
FedHAR: Semi-Supervised Online Learning for Personalized Federated Human Activity Recognition
IEEE Transactions on Mobile Computing (ITMV), Volume 22, Issue 6Pages 3318–3332https://doi.org/10.1109/TMC.2021.3136853The advancement of smartphone sensors and wearable devices has enabled a new paradigm for smart human activity recognition (HAR), which has a broad range of applications in healthcare and smart cities. However, there are four challenges, <italic>privacy ...
- research-articleApril 2023
Physical-priors-guided DehazeFormer
AbstractSingle-image dehazing is a challenging task in several machine-vision applications. Methods based on physical models and prior knowledge fail under certain conditions, resulting in defects such as color distortion. Transformer-based ...
Highlights- The proposed network is guided by physical priors for feature learning.
- The ...
- ArticleJanuary 2023
- ArticleNovember 2022
EdgeViT: Efficient Visual Modeling for Edge Computing
Wireless Algorithms, Systems, and ApplicationsPages 393–405https://doi.org/10.1007/978-3-031-19211-1_33AbstractWith the rapid growth of edge intelligence, a higher level of deep neural network computing efficiency is required. Visual intelligence, as the core component of artificial intelligence, is particularly worth more exploration. As the cornerstone ...
- research-articleJanuary 2022
The Efficacy and Safety of Mizoribine versus Mycophenolate Mofetil for the Treatment of Renal Transplantation: A Systematic Review and Meta-Analysis
Background. Mizoribine (MZR) is widely used in Asia due to its high safety and low cost, and comparative studies of its safety and efficacy with the first-line drug mycophenolate mofetil (MMF) have been carried out. This paper aimed to compare the ...
- research-articleOctober 2021
DCAP: Deep Cross Attentional Product Network for User Response Prediction
CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge ManagementPages 221–230https://doi.org/10.1145/3459637.3482246User response prediction, which aims to predict the probability that a user will provide a predefined positive response in a given context such as clicking on an ad or purchasing an item, is crucial to many industrial applications such as online ...
- doctoral_thesisJanuary 2021
Learning Knowledge Sharing Schemes for Time Series Modeling
AbstractIn IoT mobile sensing world, with the rapidly growing volume of Internet-connected sensory devices, the IoT generates massive data characterized by its velocity in terms of spatial and temporal dependency. Analyzing these mobile time series data ...