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arXiv 2023年

作者： Xu, Rui Liu, Zhi Luo, Yong Hu, Han Shen, Li Du, Bo Kuang, Kaiming Yang, Jiancheng The School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Hubei Luojia Laboratory Wuhan China The Department of Computer and Network Engineering The University of Electro-Communications 1-5-1 Chofugaoka Chofushi Tokyo182-8585 Japan The School of Information and Electronics Beijing Insititute of Technology Beijing100081 China The JD Explore Academy Beijing China The Dianei Technology Shanghai200120 China The Shanghai Jiao Tong University Shanghai China Dianei Technology Shanghai China EPFL Lausanne Switzerland

Lung cancer is the leading cause of cancer death worldwide. The best solution for lung cancer is to diagnose the pulmonary nodules in the early stage, which is usually accomplished with the aid of thoracic computed tomography (CT). As deep learning thrives, convolutional neural networks (CNNs) have been introduced into pulmonary nodule detection to help doctors in this labor-intensive task and demonstrated to be very effective. However, the current pulmonary nodule detection methods are usually domain-specific, and cannot satisfy the requirement of working in diverse real-world scenarios. To address this issue, we propose a slice grouped domain attention (SGDA) module to enhance the generalization capability of the pulmonary nodule detection networks. This attention module works in the axial, coronal, and sagittal directions. In each direction, we divide the input feature into groups, and for each group, we utilize a universal adapter bank to capture the feature subspaces of the domains spanned by all pulmonary nodule datasets. Then the bank outputs are combined from the perspective of domain to modulate the input group. Extensive experiments demonstrate that SGDA enables substantially better multi-domain pulmonary nodule detection performance compared with the state-of-the-art multi-domain learning methods. © 2023, CC BY.

关键词： Convolutional neural networks

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Multi-View Self-Supervised Learning Enhances Automatic Sleep Staging from EEG Signals

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IEEE Transactions on Biomedical Engineering 2025年 PP卷 PP页

作者： Yu, Tianyou Hu, Xinxin He, Yanbin Wu, Wei Gu, Zhenghui Yu, Zhuliang Li, Yuanqing Wang, Fei Xiao, Jun South China University of Technology School of Automation Science and Engineering Guangzhou510640 China Pazhou Laboratory Research Center for Brain-Computer Interface Guangzhou510330 China Guangdong Work Injury Rehabilitation Hospital Department of Traumatic Brain Injury Rehabilitation and Severe Rehabilitation Guangzhou510440 China Shanghai Jiao Tong University School of Medicine Songjiang Hospital Songjiang Research Institute Shanghai Key Laboratory of Emotions and Affective Disorders Shanghai201600 China South China Normal University School of Software Guangzhou510631 China South China University of Technology School of Electric Power Engineering Guangzhou510640 China

Deep learning-based methods for automatic sleep staging offer an efficient and objective alternative to costly manual scoring. However, their reliance on extensive labeled datasets and the challenge of generalization to new subjects and datasets limit their widespread adoption. Self-supervised learning (SSL) has emerged as a promising solution to address these issues by learning transferable representations from unlabeled data. This study highlights the effectiveness of SSL in automated sleep staging, utilizing a customized SSL approach to train a multi-view sleep staging model. This model includes a temporal view feature encoder for raw EEG signals and a spectral view feature encoder for time-frequency features. During pretraining, we incorporate a cross-view contrastive loss in addition to a contrastive loss for each view to learn complementary features and ensure consistency between views, enhancing the transferability and robustness of learned features. A dynamic weighting algorithm balances the learning speed of different loss components. Subsequently, these feature encoders, combined with a sequence encoder and a linear classifier, enable sleep staging after finetuning with labeled data. Evaluation on three publicly available datasets demonstrates that finetuning the entire SSL-pretrained model achieves competitive accuracy with state-of-the-art methods—86.4%, 83.8%, and 85.5% on SleepEDF-20, SleepEDF-78, and MASS datasets, respectively. Notably, our framework achieves near-equivalent performance with only 5% of the labeled data compared to full-label supervised training, showcasing SSL's potential to enhance automated sleep staging efficiency. © 1964-2012 IEEE.

关键词： Self-supervised learning

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A differential evolution-based approach for effort-Aware just-in-Time software defect prediction 1

A differential evolution-based approach for effort-Aware jus...

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1st ACM SIGSOFT International Workshop on Representation Learning for software Engineering and Program Languages, RL+SE and PL 2020, co-located with ESEC/FSE 2020

作者： Yang, Xingguang Yu, Huiqun Fan, Guisheng Yang, Kang East China University of Science and Technology Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China East China University of Science and Technology Shanghai Engineering Research Center of Smart Energy Shanghai China East China University of Science and Technology Shanghai China

ISBN: (纸本)9781450381253

software defect prediction technology is an effective method to improve software quality. Effort-Aware just-in-Time software defect prediction (JIT-SDP) aims to identify more defective changes in limited effort. Although many methods have been proposed for JIT-SDP, the prediction performance of existing prediction models still needs to be improved. To improve the effort-Aware prediction performance, we propose a new method called DEJIT based on differential evolution algorithm. First, we propose a metric called density-percentile-Average (DPA), which is used as the optimization objective of models on the training set. Then, we use logistic regression to build models and use the differential evolution algorithm to determine coefficients of logistic regression. We conduct empirical research on six open source projects. Empirical results demonstrate that the proposed method significantly outperforms the state-of-The-Art 4 supervised models and 4 unsupervised models. © 2020 ACM.

关键词： Defects

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AuNP-Modulated qPCR: An Optimized System for Detecting MIR Biophotons Released in DNA Replication

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CHEMISTRY-A EUROPEAN JOURNAL 2023年第8期29卷 e202203513-e202203513页

作者： Yang, Yu Peng, Daoling Gu, Zhenglong Jiang, Lei Song, Bo State Key Laboratory of Genetic Engineering Collaborative Innovation Center of Genetics and Development School of Life Sciences Fudan University Shanghai P. R. China Key Laboratory of Theoretical Chemistry of Environment Ministry of Education School of Environment South China Normal University Guangzhou P. R. China Key Laboratory of Optical Technology and Instrument for Medicine Ministry of Education Shanghai Key Lab of Modern Optical System School of Optical-Electrical Computer Engineering University of Shanghai for Science and Technology Shanghai P. R. China Key Laboratory of Bio-inspired Materials and Interfacial Science Technical Institute of Physics and Chemistry Chinese Academy of Sciences Beijing P. R. China School of Future Technology University of Chinese Academy of Sciences Beijing P. R. China Key Laboratory of Optical Technology and Instrument for Medicine Ministry of Education Shanghai Key Lab of Modern Optical System School of Optical-Electrical Computer Engineering University of Shanghai for Science and Technology Shanghai P. R. China

Living systems can utilize energy with a high efficiency. Biophotons are proposed to modulate biological functions with such efficiency;however, the underlying mechanism remains unexplored, especially due to the challenge of ultraweak mid-infrared (MIR) light detection and the theoretical perturbation from spontaneous MIR emission. Here, we proposed an optimized system to detect MIR biophotons generated in the hydrolysis of deoxynucleotide triphosphates (dNTPs, energy-storing molecules similar to ATP). The system used a quantitative polymerase chain reaction (qPCR) that was modulated by gold nanoparticle (AuNP) concentration and thus by the inter-AuNP distance, which depends on the concentration above. The measurements indicate that 33- and 84-THz photons are released by dNTP hydrolysis, which can drive DNA replication. Our findings provide a novel chain-reaction-based method for detecting MIR photons in solution, and pave a way for photon-based insights to understand the highly efficient energy utilization of biology.

关键词： DNA replication energy utilization efficiency gold nanoparticles MIR biophoton detection qPCR

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Vehicle Check-In Data-driven POI Recommendation Based on Improved SVD and Graph Convolutional Network

Vehicle Check-In Data-driven POI Recommendation Based on Imp...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Yuwen Liu Jie Zhang Ruihan Dou Xiaokang Zhou Xiaolong Xu Shoujin Wang Lianyong Qi College of Computer Science and Technology China University of Petroleum (East China) Qingdao China Department of Computer Science and Technology State Key Laboratory for Novel Software Technology Nanjing University China Shanghai Meteorological Information and Technology Support Center China Faculty of Mathematics University of Waterloo Waterloo Canada Faculty of Data Science Shiga University Japan RIKEN Center for Advanced Intelligence Project RIKEN Japan School of Computer and Software Nanjing University of Information Science and Technology China School of Computing Macquarie University Australia School of Computer Science Qufu Normal University China

The application of automobile tool software is becoming increasingly widespread, and its functions are becoming increasingly diverse. Using vehicle traffic data to give users with recommendations for Points-of-interest (POI) is becoming an intriguing research challenge. However, the current POI recommendation is either a sequence-based method, ignoring the interaction information between users and POIs. Or it needs a lot of model training time to use graph correlation method to mine the interaction between users or vehicles and POIs. In order to address these issues, we propose a method for vehicle check-in data-driven POI recommendation based on improved Singular Value Decomposition (SVD) and Graph Convolutional Network (GCN). First, we apply SVD to replace the neighborhood aggregation update method in traditional GCN, update the node embedding with fewer parameters and faster speed. Second, we propose an information enhancement strategy to make up for the information loss caused by the SVD. Experiments on large vehicle check-in datasets demonstrate that our method is both faster and more effective when making POI recommendations.

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Noncontact Doppler Radar-based Heart Rate Detection on the SVD and ANC

Noncontact Doppler Radar-based Heart Rate Detection on the S...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Shi, Qiuxia Hu, Bin Tian, Fuze Zhao, Qinglin Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University China School of Computer and Communication Lanzhou University of Technology China Key Laboratory of Special Functional Materials and Structural Design Lanzhou University Ministry of Education Lanzhou China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences China Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University Institute of Semiconductors Chinese Academy of Sciences China Engineering Research Center of Open Source Software and Real-Time System Lanzhou University Ministry of Education China

ISBN: (纸本)9781665468190

In the Doppler biological radar-based applications of noncontact measurement of vital signs, effectively extracting heartbeat information from weak thoracic mechanical motion is an important problem to be solved. This study is aimed at extracting heartbeat signal via the technology combined with Short Time Fourier Transform (STFT), Singular Value Decomposition (SVD) and Adaptive Noise Canceller (ANC) from radar recording. The simulated data and the data collected by Doppler radar biosensor realized in laboratory are employed to validate the proposed method. The results show that the proposed method has the ability of detection for the heart rate and heart rate variability indexes in rest state, it has certain advantages in time-consuming and detection accuracy. Therefore, the current method provides another way to process vital sign signals recorded by Doppler radar. © 2022 IEEE.

关键词： Singular value decomposition

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A Privacy-Preserving Aggregation and Authentication Scheme Towards Mobile Users in Smart Grid

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Journal of shanghai Jiaotong university(Science) 2020年第1期25卷 37-43页

作者： GUO Fei CAO Zhenfu LIU Zhusen CAO Nanyuan Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China Software Engineering Institute East China Normal UniversityShanghai 200062China Cyberspace Security Research Center Peng Cheng LaboratoryShenzhen and Shanghai Institute of Intelligent Science and TechnologyTongji UniversityShanghai 200092China School of Information Management Shanghai Lixin University of Accounting and FinanceShanghai 201209China

With the enormous developmen t of imformation and communicat ion technology,more concerns are focused to achieve secure and reliable smart grids as the social infrastrueture,especially in the explosion era of mobile *** this paper,we propose an efficient scheme to satis勿the outdoor electrical demand of mobile *** scheme protects the privacy and integrity of users'electricity consumption ***,we encrypt users'electricity consumption data by a chosen-plai nt ext-at tack(CPA)secure public key encryption(PKE)scheme and aggregate ciphertexts by the aggregator(an untrusted third party).In the scheme,internal and external adversaries cannot obtain the electricity consumption ***,we require users to provide authentication and commitment of consumption data that can track who modifies the data,which protects the integrity of users1 electricity consumption data.

关键词： smart grid authentication commitment

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Effective Storm Surge Evacuation Planning Coupling GIS and Drl: A Case Study of Daya Bay Petrochemical Industrial Zone

SSRN

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SSRN 2023年

作者： Liu, Chuanfeng Li, Yan Qin, Hao Mu, Lin Wang, Si Liu, Darong Zhou, Kai College of Marine Science and Technology China University of Geosciences Wuhan430074 China College of Life Science and Oceanography Shenzhen University Shenzhen518061 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Shenzhen Ocean Development Research and Promotion Center Shenzhen China

Storm surge is one of the most destructive marine disasters, characterized by abnormal and temporary rises in water levels during intense storms, leading to extreme inland flooding in the coastal area. Emergency evacuation planning, based on storm surge risk assessments, plays a crucial role in saving lives and mitigating disasters. Conventional emergency evacuation plans primarily adopt the perspective of administrators, providing evacuees with complete environmental information. However, in practical situations, evacuees often lack access to complete environmental information and need to select appropriate paths based on their limited awareness of their surroundings. This study coupled a risk assessment of storm surges with a road network to optimize evacuation routes in the Daya Bay Petrochemical Industrial Zone, a low-lying coastal region of Huizhou City, China, which is frequently affected by storm surge-driven flooding. A combination of Deep Q-Network (DQN) model and raster environment was employed to develop real-time evacuation plans based on limited surrounding environments during storm surge events. To address the DQN model's convergence challenges, masked state space, and masked action space were proposed, significantly reducing the search space scale. The coupled ADCIRC-SWAN model and the Jelesnianski hurricane model were utilized to simulate storm surges for risk assessments under various typhoon scenarios. Additionally, potential safe shelters were identified to offer alternative evacuation options. Two distinct storm surge scenarios were employed as test environments, and the results indicate that the proposed method is highly effective in devising optimal evacuation routes with minimal deviation, offering valuable guidance for evacuees during storm surges. © 2023, The Authors. All rights reserved.

关键词： Risk assessment

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Super Resolution of Pathology Images with Hierarchical Feature Integration and Local Image Patterns

SSRN

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SSRN 2024年

作者： Gao, Chaofei Xu, Feng Li, Lei Wang, Shuyang Ling, Ren Zhang, Xie Deng, Xi Zhou, Mengzhe Ling, Jin Department of Gastroenterology The Affiliated Lihuili Hospital of Ningbo University Zhejiang Ningbo315040 China Pathological Diagnosis Department Meikang Medicalsystem Laboratory. Ningbo Zhejiang 315105 China Department of Pathology School of Basic Medical Sciences Fudan University Xuhui District Shanghai200032 China Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention Xuhui District Shanghai200032 China Department of Research and Development Shanghai Laizi Software Technology Co. LTD. Fengxian District Shanghai201499 China Ningbo Clinical Pathology Diagnosis Center Zhejiang Ningbo315020 China MOE Key Laboratory of Bioinformatics Bioinformatics Division BNRist Department of Automation Tsinghua University Beijing100084 China

The rapid development of artificial intelligence and digital pathology has demonstrated promise in precision diagnosis in clinical. However, pathology analysis at single cell and sub-cellular level is still limited by the image resolution as the high resolution image acquirement is expensive and time-consuming. Additionally, most super resolution models ignored the intrinsic characteristics of pathology images without reaching satisfactory performance. In this paper, we developed a hierarchical super resolution deep learning framework based on local image patterns (HLIP) to reconstruct super resolution images from the low resolution ones with arbitrary input size and magnification. HLIP integrates hierarchical feature maps and conducts reconstruction by considering the local image patterns of neighboring pixels. Benchmark analysis shows HLIP achieves the best performance on both the internal and external validation datasets and the generated super resolution images approximate high resolution ones, which have reached clinical quality. It is revealed that HLIP can be applied in multiple clinical scenes as a versatile tool to improve the performance of other models on the tasks including the gland segmentation, cell segmentation and Helicobacter pylori detection. Moreover, HLIP can be integrated directly into the existing computer-aided pathology diagnostic systems for real-time super resolution. © 2024, The Authors. All rights reserved.

关键词： Deep learning

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IEMT: Inequality-Based Metamorphic Testing for Autonomous Driving Models

IEMT: Inequality-Based Metamorphic Testing for Autonomous Dr...

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IEEE International Conference on software Quality, Reliability and Security Companion (QRS-C)

作者： Chao Xiong Zhiyi Zhang Yuqian Zhou Chen Liu Zhiqiu Huang Collage of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Ministry Key Laboratory for Safety-Critical Software Development and Verification Nanjing University of Aeronautics and Astronautics Nanjing China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing University Nanjing China School of Marxism Yangzhou University Yangzhou China

In the last ten years, the development of deep learning has promoted the progress of autonomous driving. Several major manufacturers, including Google, Tesla, Baidu, Audi, etc., are building and actively testing self-driving cars. However, the safety of autonomous driving still raises concerns. Recent research has used metamorphic testing to evaluate the robustness of autonomous driving models, but metamorphic relations defined during the test are basically based on equality, and there are very few inequality-based metamorphic relations. Our goal is to provide more inequality-based metamorphic relations to efficiently detect autonomous driving model violations. IEMT proposes additional inequality-based metamorphic relations and compares the robustness of autonomous driving models based on different neural network models. The experimental results show that the metamorphic relations we proposed can detect inconsistent behaviors of the driving model quite efficiently.

关键词： Neural networks Web and internet services software quality Robustness software reliability Safety Internet

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