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2022 Conference on Lasers and Electro-Optics, CLEO 2022

作者： Chang, Lin Shu, Haowen Lao, Chenghao Shen, Bitao Xie, Weiqiang Zhang, Xuguang Jin, Ming Tao, Yuanshun Chen, Ruixuan Tao, Zihan Yu, Shaohua Yang, Qi-Fan Wang, Xingjun Bowers, John E. Department of Electrical and Computer Engineering University of California Santa BarbaraCA93106 United States State Key Laboratory of Advanced Optical Communications System and Networks Peking University Beijing100871 China State Key Laboratory for Artificial Microstructure and Mesoscopic Physics School and Physics Peking University Beijing100871 China Peng Cheng Laboratory Shenzhen518055 China Frontiers Science Center for Nano-Optoelectronics Peking University Beijing100871 China

We demonstrate coherent microcomb generation based on an AlGaAs-on-insulator microresonator operating at sub-milliwatt pump power regime, that can be directly driven by on-chip laser. © Optica Publishing Group 20... 详细信息

ISBN: (纸本)9781957171050

关键词： Aluminum gallium arsenide

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Dfier: A Directed Vulnerability Verifier for Ethereum Smart Contracts

SSRN

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SSRN 2023年

作者： Wang, Zeli Dai, Weiqi Li, Ming Choo, Kim-Kwang Raymond Zou, Deqing Chongqing Key Laboratory of Computational Intelligence Key Laboratory of Big Data Intelligent Computing Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing40065 China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Department of Information Systems and Cyber Security University of Texas at San Antonio San Antonio United States

Existing smart contract vulnerability identification approaches mainly focus on complete program detection. Consequently, lots of known potentially vulnerable locations need manual verification, which is energy-exhaustive and error-prone. Fuzzing is an effective way to detect vulnerabilities. However, current fuzzers cannot effectively probe these suspicious targets because they overlook two key factors: exploring all paths toward the targets and providing high-quality directed seed inputs. This paper presents a Directed vulnerability veriFier (DFier), elaborating effective transaction sequences with directed inputs for the fuzzer, to focus on analyzing target paths and automatically detect whether the given locations are vulnerable. Specifically, DFier fuses the effectiveness of static analysis and the efficiency of fuzzing. The static analysis helps locate target paths and generate directed inputs for the fuzzer under three heuristic strategies. The first prefers inputs with the larger possibility of triggering potential vulnerabilities based on target features. The second guides the value mutation to satisfy the path constraints. The third adequately leverages execution information to help find exploits faster. Extensive experiments demonstrate that DFier is effective in verifying contract security, compared with three existing contract fuzzers (i.e., contractFuzzer, sFuzz, and conFuzzius). And the performance losses are in an acceptable range. © 2023, The Authors. All rights reserved.

关键词： Smart contract

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Continual Test-Time Adaptation for Single Image Defocus Deblurring via Causal Siamese Networks

arXiv

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arXiv 2025年

作者： Cui, Shuang Li, Yi Li, Jiangmeng Tang, Xiongxin Su, Bing Xu, Fanjiang Xiong, Hui National Key Laboratory of Space Integrated Information System Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences China Beijing Key Laboratory of Big Data Management and Analysis Methods Gaoling School of Artificial Intelligence Renmin University of China China Department of Computer Science & Engineering the Hong Kong University of Science and Technology Hong Kong

Single image defocus deblurring (SIDD) aims to restore an all-in-focus image from a defocused one. Distribution shifts in defocused images generally lead to performance degradation of existing methods during out-of-distribution inferences. In this work, we gauge the intrinsic reason behind the performance degradation, which is identified as the heterogeneity of lens-specific point spread functions. Empirical evidence supports this finding, motivating us to employ a continual test-time adaptation (CTTA) paradigm for SIDD. However, traditional CTTA methods, which primarily rely on entropy minimization, cannot sufficiently explore task-dependent information for pixel-level regression tasks like SIDD. To address this issue, we propose a novel Siamese networks-based continual test-time adaptation framework, which adapts source models to continuously changing target domains only requiring unlabeled target data in an online manner. To further mitigate semantically erroneous textures introduced by source SIDD models under severe degradation, we revisit the learning paradigm through a structural causal model and propose Causal Siamese networks (CauSiam). Our method leverages large-scale pre-trained vision-language models to derive discriminative universal semantic priors and integrates these priors into Siamese networks, ensuring causal identifiability between blurry inputs and restored images. Extensive experiments demonstrate that CauSiam effectively improves the generalization performance of existing SIDD methods in continuously changing domains. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantics

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Blockchain-assisted Puncturable Signcryption for Cloud and Fog-Hosted Industrial Cyber-Physical systems

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IEEE Network 2025年

作者： Ma, Qingru Shen, Jian Pandi, Vijayakumar Gupta, Brij B. Arya, Varsha Mahalinghum, Maheswari Zhejiang Sci-Tech University School of Computer Science and Technology School of Artificial Intelligence Hangzhou310018 China Zhejiang Sci-Tech University School of Information Science and Engineering School of Cyber Science and Technology Zhejiang Key Laboratory of Digital Fashion and Data Governance Zhejiang Provincial International Cooperation Base for Science and Technology on Cloud Computing Security and Data Aggregation Hangzhou310018 China Department of Computer Science and Engineering Tamil Nadu Tiruchirappalli620009 India Asia University Department of Computer Science and Information Engineering Taichung413 Taiwan China Medical University Hospital China Medical University Department of Medical Research Taichung Taiwan Pune412115 India School of Cybersecurity Korea University Seoul Korea Republic of Kyung Hee University Seoul02447 Korea Republic of Lebanese American University Beirut1102 Lebanon Center for Interdisciplinary Research Dehradun India Sathyabama Institute of Science and Technology School of Computing Department of Computer Science and Engineering Tamil Nadu Chennai India

The widespread adoption of emerging technologies, such as interconnected sensors and advanced automation systems, has led to rapid advancements in smart industrial environments. While industrial cyber-physical systems (ICPSs) bring efficiency and innovation to industrial processes, the security risks are also of great concern. The existing work provides many methods for data security and authentication for ICPSs. However, since the authentication status of ICPSs is changeable, the up-to-date methods can hardly meet the requirements for rapid authentication and secure data transmission. What’s more, largescale message revocation incurs significant communication and computational costs. This paper proposes a blockchain-assisted identity-based puncturable signcryption (BC-IBPSC) scheme for cloud and fog-hosted ICPSs. First, based on the concepts of puncturable encryption and signature, a novel identity-based puncturable signcryption method is presented to achieve efficient verifiable data encryption. Second, the blockchain is utilized to record all valid and punctured messages, to achieve evidence storage and traceability. The results of the analysis indicate that the scheme ensures security and operates efficiently. In addition, experiments show that the size of the signcrypted files does not affect the value of Gas consumption on the blockchain when uploading proofs. © 2025 IEEE. All rights reserved.

关键词： Authentication

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Sub-milliwatt coherent microcomb generation

Sub-milliwatt coherent microcomb generation

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CLEO: Science and Innovations, S and I 2022

作者： Chang, Lin Shu, Haowen Lao, Chenghao Shen, Bitao Xie, Weiqiang Zhang, Xuguang Jin, Ming Tao, Yuanshun Chen, Ruixuan Tao, Zihan Yu, Shaohua Yang, Qi-Fan Wang, Xingjun Bowers, John E. Department of Electrical and Computer Engineering University of California Santa BarbaraCA93106 United States State Key Laboratory of Advanced Optical Communications System and Networks Peking University Beijing100871 China State Key Laboratory for Artificial Microstructure and Mesoscopic Physics School and Physics Peking University Beijing100871 China Peng Cheng Laboratory Shenzhen518055 China Frontiers Science Center for Nano-optoelectronics Peking University Beijing100871 China

ISBN: (纸本)9781557528209

关键词： Aluminum gallium arsenide

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The influence of proton radiation on the performance of silicon Mach-Zehnder modulator

The influence of proton radiation on the performance of sili...

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2021 Asia Communications and Photonics Conference, ACP 2021

作者： Han, Changhao Hu, Zhaoyi Tao, Yuansheng Fu, Engang Wang, Xingjun State Key Laboratory of Advanced Optical Communications System and Networks Department of Electronics School of Electronics Engineering and Computer Science Peking University Beijing100871 China State Key Laboratory of Nuclear Physics and Technology School of Physics Peking University Beijing100871 China

The influence of proton radiation on silicon Mach-Zehnder modulator performance was studied for space environment. The modulator was sensitive to proton radiation while still demonstrated a certain working ability aft... 详细信息

ISBN: (纸本)9781957171005

关键词： Silicon

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Prototype-Wise Self-Knowledge Distillation for Few-Shot Segmentation

SSRN

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SSRN 2023年

作者： Chen, Yadang Xu, Xinyu Wei, Chenchen Yang, Zhi-Xin School of Computer and Software Nanjing University of Information Science and Technology Nanjing210044 China Engineering Research Center of Digital Forensics Ministry of Education Nanjing University of Information Science and Technology Nanjing210044 China The State Key Laboratory of Internet of Things for Smart City Department of Electromechanical Engineering University of Macau 999078 China

Few-shot segmentation was proposed to obtain segmentation results for a image with an unseen class by referring to a few labeled samples. However, due to the limited number of samples, many few-shot segmentation models suffer from poor generalization. Prototypical network-based few-shot segmentation still has issues with spatial inconsistency and prototype bias. Since the target class has different appearance in each image, some specific features in the prototypes generated from the support image and its mask do not accurately reflect the generalized features of the target class. To address the support prototype consistency issue, we put forward two modules: Data Augmentation Self-knowledge Distillation (DASKD) and Prototype-wise Regularization (PWR). The DASKD module focuses on enhancing spatial consistency by using data augmentation and self-knowledge distillation. Self-knowledge distillation helps the model acquire generalized features of the target class and learn hidden knowledge from the support images. The PWR module focuses on obtaining a more representative support prototype by conducting prototype-level loss to obtain support prototypes closer to the category center. Broad evaluation experiments on PASCAL-5i and COCO-20i demonstrate that our model outperforms the prior works on few-shot segmentation. Our approach surpasses the state of the art by 7.5% in PASCAL-5i and 4.2% in COCO-20i. © 2023, The Authors. All rights reserved.

关键词： Distillation

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Reinforcement Learning with Unbiased Policy Evaluation and Linear Function Approximation

arXiv

引用

arXiv 2022年

作者： Winnicki, Anna Srikant, R. Coordinated Science Laboratory The Department of Electrical and Computer Engineering University of Illinois Urbana-Champaign United States C3.ai Digital Transformation Institute United States

We provide performance guarantees for a variant of simulation-based policy iteration for controlling Markov decision processes that involves the use of stochastic approximation algorithms along with state-of-the-art techniques that are useful for very large MDPs, including lookahead, function approximation, and gradient descent. Specifically, we analyze two algorithms;the first algorithm involves a least squares approach where a new set of weights associated with feature vectors is obtained via least squares minimization at each iteration and the second algorithm involves a two-time-scale stochastic approximation algorithm taking several steps of gradient descent towards the least squares solution before obtaining the next iterate using a stochastic approximation algorithm. Copyright © 2022, The Authors. All rights reserved.

关键词： Markov processes

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Hardware design and tests of SMURF V1 platform for searching survivors in debris cones 18

Hardware design and tests of SMURF V1 platform for searching...

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18th International Conference on Information systems for Crisis Response and Management, ISCRAM 2021

作者： Watanabe, Masahiro Takahashi, Kenichi Kimura, Tetsuya Tadakuma, Kenjiro Mugaas, Terje Ozawa, Yu Takane, Eri Suzuki, Soichiro Marafioti, Giancarlo Koutsokeras, Miltiadis Tadokoro, Satoshi Tough Cyberphysical AI Research Center Tohoku University Japan Department of System Safety Nagaoka University of Technology Japan Department of Mathematics and Cybernetics SINTEF Digital Norway Graduate School of Information Sciences Tohoku University Japan Graduate School of Engineering Tohoku University Japan International Rescue System Institute United States Institute of Communication and Computer Systems Greece

ISBN: (纸本)9781949373615

When a large-scale disaster such as earthquake occurs, a huge number of victims will be trapped under debris in a wide area. Rescue activities in debris are technically not easy and endanger the first responders. There are several methods for improving safety and efficiency of rescue operation, but their availability is limited to a certain area or short operating time. Our project called CURSOR is developing tools to comprehensively search victims using a large number of ground-based robots entering debris transported by aerial drones. Here we show the development of the exploration robot collecting information with several sensors. The robot system was designed based on the requirements and performance was evaluated by ruggedization tests and mobility tests. No critical problem was found in the durability, and the mobility showed as the same as the ordinary wheel. To improve the mobility, we are planning to apply a proposed unique track mechanism. © 2021 Information systems for Crisis Response and Management, ISCRAM. All rights reserved.

关键词： Machine design

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Detection of Veterinary Drug Residues in Raw Milk: Machine Learning Modeling and Federated Learning Optimization

SSRN

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SSRN 2025年

作者： Cheng, Wenxu Song, Xiaodong Ding, Haohan Dong, Guanjun Cui, Xiaohui Yu, Wei Wilson, David I. School of Artificial Intelligence and Computer Science Jiangnan University Wuxi214122 China Key Laboratory of Dairy Quality Digital Intelligence Monitoring Technology State Administration for Market Regulation Hohhot011517 China Science Center for Future Foods Jiangnan University Wuxi214122 China School of Cyber Science and Engineering Wuhan University Wuhan430072 China Department of Chemical & Materials Engineering University of Auckland Auckland1010 New Zealand Electrical and Electronic Engineering Department Auckland University of Technology Auckland1010 New Zealand

Veterinary drug residues in raw milk can lead to serious safety problems, while traditional detection methods are inefficient and costly. Difference in regional agricultural practice and the need for data privacy lend towards a unified detection method. This study developed a detection method for veterinary drug residues in raw milk using machine learning and federated learning. Using raw milk quality monitoring data from 8 Chinese regions (2022-2024), we combined SMOTE with random under-sampling to solve the serious problem of data imbalance. We evaluated several machine learning classifiers with XGBoost showing the best performance, with psychrophilic bacteria count played a crucial role in distinguishing milk quality. The integration of federated learning and the XGBoost model enhanced stability across regions while preserving data privacy. These results demonstrate that the proposed and cost-effective method can help producers identify samples requiring further laboratory confirmation while reducing unnecessary testing. © 2025, The Authors. All rights reserved.

关键词： Federated learning

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