A5107062167- Recommender Systems and Techniques
- Privacy-Preserving Technologies in Data
- Perovskite Materials and Applications
- Geotechnical and Geomechanical Engineering
- Graphene and Nanomaterials Applications
- Solid-state spectroscopy and crystallography
- Mining and Gasification Technologies
- Cryptography and Data Security
- Optical properties and cooling technologies in crystalline materials
- Drilling and Well Engineering
- Organic Light-Emitting Diodes Research
- High-pressure geophysics and materials
- earthquake and tectonic studies
- Advanced Drug Delivery Systems
- Free Radicals and Antioxidants
- Indoor and Outdoor Localization Technologies
- Oxidative Organic Chemistry Reactions
- Advanced Image and Video Retrieval Techniques
- Target Tracking and Data Fusion in Sensor Networks
- Earthquake Detection and Analysis
- Organic Chemistry Cycloaddition Reactions
- Advanced Nanomaterials in Catalysis
- Image Retrieval and Classification Techniques
- Anesthesia and Pain Management
- GNSS positioning and interference
Chengdu University of Technology
2023
State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation
2023
Wuhan National Laboratory for Optoelectronics
2023
Griffith University
2023
Fourth People’s Hospital of Jinan
2021-2023
Jilin Normal University
2019-2020
Qiqihar University
2019
Tianjin University
2018
Indoor positioning is a thriving research area which slowly gaining market momentum. Its applications are mostly customised, ad hoc installations; ubiquitous analogous to GNSS for outdoors not available because of the lack generic platforms, widely accepted standards and interoperability protocols. In this context, Positioning Navigation (IPIN) competition only long-term, technically sound initiative monitor state art real systems by measuring their performance in realistic environment. Most...
Federated recommender systems (FedRecs) have been widely explored recently due to their capability safeguard user data privacy. These enable a central server collaboratively learn recommendation models by sharing public parameters with clients, providing privacy-preserving solutions. However, this collaborative approach also creates vulnerability that allows adversaries manipulate FedRecs. Existing works on FedRec security already reveal items can easily be promoted malicious users via model...
The CsPb<sub>1−x</sub>Fe<italic>x</italic>Cl<sub>3</sub> NCs were synthesized and an appropriate amount of Fe<sup>2+</sup> doping can enhance PLQY average PL lifetimes. Meanwhile, obvious hysteresis behavior has been observed for the NCs.
Abstract Earthquakes often occur along faults in the presence of hot, pressurized water. Here we exploit a new experimental device to study friction gabbro with water vapor, liquid and supercritical states (water temperature pressure up 400 °C 30 MPa, respectively). The are sheared over slip velocities from 1 μm/s 100 mm/s distances 3 m (seismic deformation conditions). Here, show vapor state, fault decreases increasing distance velocity. However, when is or distance, regardless We propose...
All inorganic perovskite nanocrystals (NCs) have wide practical applications for their remarkable optoelectronic properties. To obtain blue-emitting perovskites with high photoluminescence quantum yield and room-temperature ferromagnetism, CsPb1-x Fe x (Br1-y Cl y )3 NCs were synthesized using a hot injection method. The effects of the cation-anion co-exchange on structural, luminescent magnetic properties CsPbBr3 studied by X-ray diffraction spectroscopy, transmission electron microscopy,...
Federated recommender systems (FedRecs) have emerged as a popular research direction for protecting users' privacy in on-device recommendations. In FedRecs, users keep their data locally and only contribute local collaborative information by uploading model parameters to central server. While this rigid framework protects raw during training, it severely compromises the recommendation model's performance due following reasons: (1) Due power law distribution nature of user behavior data,...
With the growing concerns regarding user data privacy, Federated Recommender System (FedRec) has garnered significant attention recently due to its privacy-preserving capabilities. Existing FedRecs generally adhere a learning protocol in which central server shares global recommendation model with clients, and participants achieve collaborative by frequently communicating model's public parameters. Nevertheless, this framework two drawbacks that limit practical usability: (1) It necessitates...