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Model Ownership Protection for Healthcare Consumer Electronics in Federated Edge Learning

IEEE Transactions on Consumer Electronics 2024年

作者： Wang, Rong Liang, Junchuan Feng, Chaosheng Chakraborty, Chinmay Alqahtani, Fayez Yu, Keping Sichuan Normal University College of Computer Science Chengdu610101 China Sichuan Normal University Visual Computing and Virtual Reality Key Laboratory of Sichuan Chengdu610068 China Birla Institute of Technology Mesra India King Saud University College of Computer and Information Sciences Software Engineering Department Riyadh12372 Saudi Arabia Hosei University Graduate School of Science and Engineering Tokyo184-8584 Japan

With the rising demand for intelligent services and privacy protection in consumer artificial intelligence (AI), federated edge learning has emerged as a beacon for privacy-preserving distributed machine learning. This non-centralized approach offers distinct data privacy advantages, making it especially suitable for applications with strict privacy policies, such as consumer-centric healthcare systems. However, consumer electronics/devices in federated edge learning might unlawfully violate model ownership by duplicating or distributing models. Another concern is that a central server, if compromised, can become a conduit for model leakage, whether for profit motives or due to external intrusions, thus exacerbating the model ownership violation. To our knowledge, few works have focused on model ownership protection for federated edge learning in the consumer-centric healthcare domain. In this paper, we introduce a federated model ownership protection (FedMOP) approach. FedMOP is a model ownership safeguard and a formidable fortress built upon two core innovations. First, FedMOP can swiftly embed and replace watermarks during local training, allowing for the accurate identification and subsequent flagging of deceitful consumer devices. Second, FedMOP ensures that the server only accesses encrypted versions of the federated model, keeping the model a secret. Extensive evaluations demonstrate the robustness and effectiveness of FedMOP. We hope that FedMOP can light the way for surmounting similar ethical challenges in the broader realm of consumer-edge AI. © 1975-2011 IEEE.

关键词： ethical AI Federated edge learning healthcare consumer electronicss model ownership protection

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S2WAT: Image Style Transfer via Hierarchical Vision Transformer Using Strips Window Attention

arXiv

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arXiv 2022年

作者： Zhang, Chiyu Xu, Xiaogang Wang, Lei Dai, Zaiyan Yang, Jun Sichuan Normal University China Nanjing University of Aeronautics and Astronautics China Zhejiang Lab China Zhejiang University China Visual Computing and Virtual Reality Key Laboratory of Sichuan Provience China

Transformer's recent integration into style transfer leverages its proficiency in establishing long-range dependencies, albeit at the expense of attenuated local modeling. This paper introduces Strips Window Attention Transformer (S2WAT), a novel hierarchical vision transformer designed for style transfer. S2WAT employs attention computation in diverse window shapes to capture both short- and long-range dependencies. The merged dependencies utilize the "Attn Merge" strategy, which adaptively determines spatial weights based on their relevance to the target. Extensive experiments on representative datasets show the proposed method's effectiveness compared to state-of-the-art (SOTA) transformer-based and other approaches. The code and pre-trained models are available at https://***/AlienZhang1996/S2WAT. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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Slimmable transformer with hybrid axial-attention for medical image segmentation

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Computers in Biology and Medicine 2024年 173卷 108370-108370页

作者： Hu, Yiyue Mu, Nan Liu, Lei Zhang, Lei Jiang, Jingfeng Li, Xiaoning College of Computer Science Sichuan Normal University Chengdu610101 China School of Science and Engineering The Chinese University of Hong Kong Shenzhen Shenzhen518172 China Department of Biomedical Engineering Michigan Technological University HoughtonMI49931 United States Visual Computing and Virtual Reality Key Laboratory of Sichuan Sichuan Normal University Chengdu610068 China Education Big Data Collaborative Innovation Center of Sichuan 2011 Chengdu610101 China

The transformer architecture has achieved remarkable success in medical image analysis owing to its powerful capability for capturing long-range dependencies. However, due to the lack of intrinsic inductive bias in modeling visual structural information, the transformer generally requires a large-scale pre-training schedule, limiting the clinical applications over expensive small-scale medical data. To this end, we propose a slimmable transformer to explore intrinsic inductive bias via position information for medical image segmentation. Specifically, we empirically investigate how different position encoding strategies affect the prediction quality of the region of interest (ROI) and observe that ROIs are sensitive to different position encoding strategies. Motivated by this, we present a novel Hybrid Axial-Attention (HAA) that can be equipped with pixel-level spatial structure and relative position information as inductive bias. Moreover, we introduce a gating mechanism to achieve efficient feature selection and further improve the representation quality over small-scale datasets. Experiments on LGG and COVID-19 datasets prove the superiority of our method over the baseline and previous works. Internal workflow visualization with interpretability is conducted to validate our success better;the proposed slimmable transformer has the potential to be further developed into a visual software tool for improving computer-aided lesion diagnosis and treatment planning. © 2024 Elsevier Ltd

关键词： COVID-19

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Dynamic Power Distribution Controlling for Directional Chargers

Dynamic Power Distribution Controlling for Directional Charg...

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IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Yuzhuo Ma Dié Wu Jing Gao Wen Sun Jilin Yang Tang Liu College of Computer Science Sichuan Normal University Chengdu China Visual Computing and Virtual Reality Key Lab Sichuan Normal University Chengdu China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China School of Cyberspace Security Northwestern Polytechnical University Xi’an China

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

Recently, deploying static chargers to construct timely and robust Wireless Rechargeable Sensor Networks (WRSNs) has become an important research issue for solving the limited energy problem of wireless sensor networks. However, the established fixed power distribution lacks flexibility in response to dynamic charging requests from sensors and may render some sensors to be continuously impacted by destructive wave interference. This results in a gap between energy supply and practical demand, making the charging process less efficient. In this paper, we focus on the real-time sensor charging requests and formulate a dynamic power disTributIon controlling for Directional chargErs (TIDE) problem to maximize the overall charging utility. To solve the problem, we first build a charging model for directional chargers while considering wave interference and extract the candidate charging orientations from the continuous search space. Then we propose the neighbor set division method to narrow the scope of calculation. Finally, we design a dynamic power distribution controlling algorithm to update the neighbor sets timely and select optimal orientations for chargers. Our experimental results demonstrate the effectiveness and efficiency of the proposed scheme, it outperforms the comparison algorithms by 142.62% on average.

关键词： Wireless communication Wireless sensor networks Heuristic algorithms Computational modeling Power distribution Interference Search problems

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Fast Collection of Target Tags Information for Large-Scale RFID Systems

Fast Collection of Target Tags Information for Large-Scale R...

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International Conference on Wavelet Active Media Technology and Information Processing (ICWAMTIP)

作者： Chu Chu Ning Fu Rui Xu College of Computer Science Sichuan Normal University Chengdu China The Visual Computing and Virtual Reality Key Laboratory of Sichuan Province Chengdu China School of Network and Communication Engineering Chengdu Technological University Chengdu China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China

Radio Frequency Identification (RFID) technology is greatly supporting a variety of life and industry. Enhanced by sensors, RFID tags can monitor real-time information about attached objects and the environment, making efficient collection of tag information an important task. However, most works focus on collecting all tags but lack effective mechanisms to avoid interfering from irrelevant tags when the application only cares about a part of the tags in some cases. Besides, in more common scenarios with multiple RFID readers, existing works fail to effectively determine the surrounding tags for each reader, increasing the execution time. To conquer these defects, we propose a new solution called the efficient Vector-based target tag Information Collection (VIC) protocol to accelerate the collection rate. First, we introduce a composite vector to silence irrelevant tags and determine the local tags. During this process, the expected empty slots are skipped and the useless information transmitted by tags is reduced. Second, we use an allocation vector to collect tag information by resolving tag collision, thus improving the time efficiency. Finally, we provide theoretical analyses to optimize the parameter settings. Extensive experiments prove that VIC outperforms existing solutions by up to 32% increase in time efficiency.

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Analysis of a multi-component system with double threshold control policy

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Journal of Control and Decision 2021年第3期8卷 343-352页

作者： Wenqing Wu Gang He Wenxin Yu Mengxin Wang Kang Xu School of Science Southwest University of Science and TechnologyMianyangPeople’s Republic of China Visual Computing and Virtual Reality Key Laboratory of Sichuan Province Sichuan Normal UniversityChengduPeople’s Republic of China School of Computer Science and Technology Southwest University of Science and TechnologyMianyangPeople’s Republic of China Zhongtai Securities Institute of Financial Studies Shandong UniversityShandongPeople’s Republic of China

This paper investigates a multi-component repairable system with double threshold control *** system is composed of n identical and independent components which operate simultaneously at the beginning,and it is down when the number of operating components decreases to k−1(k≤n).When the number of failed components is less than the value L,the repairman repairs them with a low repair *** high repair rate is activated as soon as L failed components present,and continues until the number of failed components drops to the value N−*** the matrix analytical method,the Laplace transform technique and the properties of the phase type distribution,various performance measures including the availability,the rate of occurrence of failures,and the reliability are derived in transient and stationary ***,numerical examples are reported to show the behaviour of the system.

关键词： Multi-component system double threshold control policy matrix analytical method reliability measures

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Cross-Angle Facial Individual Verification of Giant Panda

Cross-Angle Facial Individual Verification of Giant Panda

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IEEE International Conference on Systems, Man and Cybernetics

作者： Jiawei Wu Han Su Peng Min Mengnan He Pengcheng Wu Gai Luo Rong Hou Peng Chen College Computer Science of Sichuan Normal University Chengdu China College of Computer Science Sichuan Normal University Chengdu China and also with Visual Computing and visual reality Key Laboratory of Sichuan Province Chengdu China College of Sichuan Normal University Chengdu China Chengdu Research Base of Giant Panda Chengdu Research Base of Giant Panda Breeding Sichuan Normal University Chengdu China Chengdu Research Base of Giant Panda Breeding

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Enhancing the protection of giant pandas necessitates a precise means of verifying their individual information. With the development of deep learning technology, some individual recognition methods for giant pandas have emerged. However, these methods often neglect cross-angle facial recognition, a common occurrence in natural settings, and predominantly focus on identification rather than verification, rendering them unsuitable for wild populations of unknown individuals. Cross-angle facial verification poses novel challenges, notably feature misalignment and geometric deformation induced by varying angles, particularly in the verification process reliant on comparing feature discrepancies. To address this issue, we developed the CrossPandaFace model. This model employs a Pixel Drift Unit (PDU) to adjust feature pixels and utilizes template features generated by the Template Generation Module (TGM) as a reference to align giant pandas from different angles onto a unified template. Furthermore, Multi-scale Feature Supplement (MSFS) compensates for the potential risk of losing local features of aligned features. Experimental results show state-of-the-art performance, thus affirming the efficacy of our model for cross-angle giant panda verification.

关键词： Deep learning Deformation Face recognition Rendering (computer graphics) Protection Cybernetics

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Unraveling the neural dynamics of mathematical interference in english reading: A novel approach with deep learning and fNIRS data

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Brain research bulletin 2025年 227卷 111398页

作者： Zhijie Liang Ling Wang Jianyu Su Bo Sun Daifa Wang Juan Yang School of Computer Science Sichuan Normal UniversityChengdu 610066 China Visual Computing and Virtual Reality Key Laboratory of Sichuan Province Chengdu 610066 China. College of Foreign Languages Sichuan Normal UniversityChengdu 610066 China. School of Biological Science and Medical Engineering Beihang University Beijing 100083 China. Visual Computing and Virtual Reality Key Laboratory of Sichuan Province Chengdu 610066 China. Electronic address: Jkxy_yjuan@***.

English has emerged as the predominant global language, driving efforts to optimize its acquisition through interdisciplinary cognitive research. While behavioral studies suggest a link between English learning and mathematical cognition, the neural mechanisms underlying this relationship remain poorly understood. To bridge this gap, the present study employs functional near-infrared spectroscopy (fNIRS) to construct a novel dataset on mathematical interference in English acquisition. Utilizing this dataset, a novel deep learning model named AC-LSTM is proposed, amalgamating Transformer and LSTM architectures to identify residual mathematical cognition during the English learning process. The AC-LSTM model achieves an exceptional accuracy rate of 99.8 %, surpassing other machine learning and deep learning models. Moreover, a multi-class classification experiment is conducted to discern algebra, geometry, and quantitative reasoning interference, with the AC-LSTM model achieving the highest accuracy of 75.9 % in this classification task. Furthermore, crucial brain channels for interference detection are pinpointed through grid search, and alterations in vital brain regions (R-Broca and L-Broca) are unveiled via association rule analysis. By integrating fNIRS, deep learning, and data mining techniques, this study delves into cognitive interference in English learning, providing valuable insights for educational neuroscience and data mining research.

关键词： Data Mining Deep Learning English Language Learners FNIRS Mathematical Interference

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A Study on the Mechanism and Kinetic of Nitrate Reduction by the nZVI-g-C3N4/TiO2 composite under the simulated sunlight

Research Square

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Research Square 2021年

作者： Wei, Linyu Tian, Jing Wang, Qing Liu, Yuanyuan Yu, Yi Yang, Chun College of Chemistry and Materials Science Sichuan Normal University Chengdu610066 China Sichuan Engineering Laboratory of Livestock Manure Treatment and Recycling Chengdu140028001 China Visual Computing and Virtual Reality Key Laboratory of Sichuan Province Sichuan Normal University Chengdu610068 China

g-C3N4/TiO2 composite has excellent photoelectric properties and is considered as a good carrier of nanoparticles. A novel composite of nZVI-g-C3N4/TiO2 was successfully synthesized through in-situ growth nZVI on the surface of g-C3N4/TiO2 with liquid phase reduction method. The composite was characterized by TEM, XRD, EDS and evaluated its nitrate removal efficiency. The effects of composite dosage, solution initial pH and HCOOH concentration on nitrate reduction were investigated. The results showed that nitrate was rapidly reduced by nZVI-g-C3N4/TiO2 composite. The dosage of 4 g/L nZVI-g-C3N4/TiO2 composite and 3.0 mM of HCOOH concentration was more suitable for nitrate reduction. Solution initial pH had little impact on the nitrate reduction efficiency, but affected the proportion of the nitrate reduction products. The mechanism of nitrate reduction in the nZVI-gC3N4/TiO2/HCOOH-Xe-lamp system was proposed. The nZVI-gC3N4/TiO2 composite could be considered as a viable and promising technology for water pollution remediation. © 2021, CC BY.

关键词： Nitrates

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Three-level and three-way uncertainty measurements and attribute reductions on neighborhood multi-granulation in incomplete neighborhood decision systems

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Information Sciences 2025年 718卷

作者： Xianyong Zhang Qian Wang School of Mathematical Sciences Sichuan Normal University Chengdu 610066 China Research Center of Sichuan Normal University National-Local Joint Engineering Laboratory of System Credibility Automatic Verification Chengdu 610066 China Visual Computing and Virtual Reality Key Laboratory of Sichuan Province Sichuan Normal University Chengdu 610066 China

Uncertainty measurements underlie attribute reductions for classification learning. The neighborhood multi-granulation dependency degree and joint entropy are fused in incomplete neighborhood decision systems, and the obtained measure (called PDJE) generates an effective attribute reduction algorithm (called PDJE-AR). PDJE and PDJE-AR utilize only the pessimistic neighborhood multi-granulation rough set (NMRS), while introducing the optimistic NMRS can motivate systematic measurements and improved reductions. We combine pessimistic and optimistic NMRSs to propose neighborhood multi-granulation dependency joint entropies (DJEs), so we establish three-level and three-way uncertainty measurements and attribute reductions. At the combination-basis level, the optimistic DJE is proposed by simulating the pessimistic DJE; at the attitude-extension level, pessimism-trend and optimism-trend DJEs are proposed via preferential tolerances; at the equilibrium-synthesis level, the compromise-system DJE is proposed via the compromise criterion. Three-level DJEs (containing five DJE-relevant measures) imply three-way measures on pessimism, optimism, compromise, and they acquire dependency factors, calculation algorithms, size relationships, coefficient extensions, multi-granulation non-monotonicity. Furthermore, three-level and three-way DJEs induce three-level and three-way heuristic reduction algorithms (with five DJE-relevant algorithms). Finally, relevant uncertainty measurements and attribute reductions are validated by table examples and data experiments, and four new reduction algorithms outperform current PDJE-AR and recent contrast algorithms for classification performances.

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