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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhang, Wentao Zhao, Defeng Zheng, Wei-Shi Wang, Ruixuan Sun Yat-sen University School of Computer Science and Engineering Guangzhou China Peng Cheng Laboratory Shenzhen China Moe Key Laboratory of Machine Intelligence and Advanced Computing Guangzhou China

ISBN: (纸本)9798350386226

Current intelligent diagnostic systems often catastrophically forget old knowledge when learning new diseases only from the training dataset of the new diseases. Inspired by human learning of visual classes with the effective help of language, we propose a continual learning framework based on a pre-trained visual-language model (VLM) without storing any image of previously learned diseases. In this framework, textual prior knowledge of each new disease can be obtained by utilizing the frozen VLM's text encoder, and then used to guide the visual learning of the new disease. This framework innovatively utilizes the textual prior knowledge of all previously learned diseases as out-of-distribution (OOD) information to help differentiate currently being-learned diseases from others. Extensive empirical evaluations on both medical and natural image datasets confirm the superiority of the proposed method over existing state-of-the-art methods in continual learning of new visual classes. The source code is available at https://***/OpenMedIA/TexCIL. © 2024 IEEE.

关键词： Contrastive Learning

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Plant Disease Detection in Precision Agriculture: Deep Learning Approaches 2

Plant Disease Detection in Precision Agriculture: Deep Learn...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Dey, Puja Mahmud, Tanjim Nahar, Sultana Rokeya Hossain, Mohammad Shahadat Andersson, Karl University of Chittagong Department of Computer Science and Engineering Chittagong4331 Bangladesh Rangamati Science and Technology University Department of Computer Science and Engineering Rangamati4500 Bangladesh Department of Computer Science and Engineering Dhaka1212 Bangladesh Luleå University of Technology Pervasive and Mobile Computing Laboratory Luleå97187 Sweden

ISBN: (纸本)9798350327533

Plants affect economy, agriculture industry and climate of any country. Therefore it is very important to take care of plants. Like human being, plants are also infected by various disease that are resulted from virus, bacteria, and fungi. It is necessary to identify these diseases in time and cure them immediately to counteract the destruction of whole plant. In recent times, deep learning networks have attained tremendous improvement in classifying image and detecting object. Therefore, in our research, some pre-Trained Convolution Neural Networks (AlexNet, VGG16, VGG19) have been utilised along with transfer learning for detecting plant diseases. First of all, pre-processing technique has been applied to improve image quality and increase the accuracy. After the training process, testing has been done for validating the results. To train these models, PlantVillage dataset has been used which contains both diseased and healthy leaves. 80% data has been utilised in training step and other 20% has been used in testing. In this research, precision, recall and f1-score are also computed besides the accuracy. Using AlexNet, VGG16, and VGG19 96.63%, 95.05%, and 95.22% testing accuracy have been achieved respectively. As we can see that among all models the modified AlexNet performed best and got 96.63% as accuracy, 92 % as precision, 91 % as recall, and 91 % as f1-score. © 2024 IEEE.

关键词： Deep Learning Fully Connected Histogram equalization Pooling RELU

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Stability of connected and automated vehicles platoon considering communications failures

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Chinese Physics B 2023年第7期32卷 598-609页

作者：刘润坤于海洋任毅龙崔志勇 School of Transportation Science and Engineering Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety ControlBeihang UniversityBeijing 100191China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang UniversityBeijing 100191China

As a form of a future traffic system,a connected and automated vehicle(CAV)platoon is a typical nonlinear physical *** can communicate with each other and exchange ***,communication failures can change the platoon system *** characterize this change,a dynamic topology-based car-following model and its generalized form are proposed in this ***,a stability analysis method is ***,taking the dynamic cooperative intelligent driver model(DC-IDM)for example,a series of numerical simulations is conducted to analyze the platoon stability in different communication topology *** results show that the communication failures reduce the stability,but information from vehicles that are farther ahead and the use of a larger desired time headway can improve ***,the critical ratio of communication failures required to ensure stability for different driving parameters is studied in this work.

关键词： connected and automated vehicle car-following model communication failures string stability

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Never Lost Keys:A Novel Key Generation Scheme Based on Motor Imagery EEG in End-Edge-Cloud System

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China Communications 2022年第7期19卷 172-184页

作者： Yichuan Wang Dan Wu Xiaoxue Liu Xinhong Hei School of computer science and Engineering Xi’an University of TechnologyXi’an 710048China Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an 710048China

Biometric key is generated from the user’s unique biometric features,and can effectively solve the security problems in ***,the current prevailing biometric key generation techniques such as fingerprint recognition and facial recognition are poor in randomness and can be forged *** to the characteristics of Electroencephalographic(EEG)signals such as the randomness,nonlinear and non-stationary etc.,it can significantly avoid these *** paper proposes a novel method to generate keys based on EEG signals with end-edgecloud collaboration *** sensors to measure motor imagery EEG data,the key is generated via pre-processing,feature extraction and *** show the total time consumption of the key generation process is about *** scheme is practical and feasible,which provides a research route to generate biometric keys using EEG data.

关键词： EEG biometric key generation end-edgecloud system information security

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Bioinspired Polarized Skylight Orientation Determination Artificial Neural Network

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Journal of Bionic engineering 2023年第3期20卷 1141-1152页

作者： Huaju Liang Hongyang Bai Ke Hu Xinbo Lv School of Energy and Power Engineering Nanjing University of Science and TechnologyNanjing210094China Neuromorphic Computing Laboratory China Nanhu Academy of Electronics and Information TechnologyJiaxing314001China Guidance Radar General Department Xi’an Electronic Engineering Research InstituteXi’an710100China School of Automation Nanjing University of Science and TechnologyNanjing210094China

This paper proposes an artificial neural network to determine orientation using polarized skylight. This neural network has specific dilated convolution, which can extract light intensity information of different polarization directions. Then, the degree of polarization (DOP) and angle of polarization (AOP) are directly extracted in the network. In addition, the exponential function encoding of orientation is designed as the network output, which can better reflect the insect’s encoding of polarization information and improve the accuracy of orientation determination. Finally, training and testing were conducted on a public polarized skylight navigation dataset, and the experimental results proved the stability and effectiveness of the network.

关键词： Polarized light navigation Orientation determination Artificial neural network

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Competition on robust deep learning

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National science Review 2023年第6期10卷 13-15页

作者： Yinpeng Dong Chang Liu Wenzhao Xiang Hang Su Jun Zhu Department of Computer Science and Technology Institute for AI Tsinghua-Bosch Joint ML Center THBI Lab BNRist Center Tsinghua University Institute of Image Communication and Networks Engineering in the Department of Electronic Engineering (EE) Shanghai Jiao Tong University Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Peng Cheng Laboratory Pazhou Laboratory (Huangpu)

PROBLEM In recent years,the rapid development of artificial intelligence (AI) technology,especially machine learning and deep learning, is profoundly changing human production and *** various fields,such as robotics,face recognition,autonomous driving and healthcare,AI is playing an important ***,although AI is promoting the technological revolution and industrial progress,its security risks are often *** studies have found that the wellperforming deep learning models are extremely vulnerable to adversarial examples [1-3].The adversarial examples are crafted by applying small,humanimperceptible perturbations to natural examples,but can mislead deep learning models to make wrong *** vulnerability of deep learning models to adversarial examples can raise security and safety threats to various realworld applications.

关键词： robustness adversarial example adversarial training deep learning

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DRL-Based Time-Varying Workload Scheduling With Priority and Resource Awareness

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IEEE Transactions on Network and Service Management 2025年第3期22卷 2838-2852页

作者： Liu, Qifeng Fan, Qilin Zhang, Xu Li, Xiuhua Wang, Kai Xiong, Qingyu Chongqing University School of Big Data and Software Engineering Chongqing400044 China Chongqing University Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education Chongqing400044 China Nanjing University School of Electronic Science and Engineering Nanjing210023 China Haihe Laboratory of Information Technology Application Innovation Tianjin300072 China Harbin Institute of Technology School of Computer Science and Technology Weihai264209 China Shandong Key Laboratory of Industrial Network Security Weihai264209 China

With the proliferation of cloud services and the continuous growth in enterprises' demand for dynamic multi-dimensional resources, the implementation of effective strategy for time-varying workload scheduling has become increasingly significant. In this paper, we propose a deep reinforcement learning (DRL)-based method for time-varying workload scheduling, aiming to allocate resources efficiently across servers in the cluster. Specifically, we integrate a classifier and queue scorer to construct a priority queue that exploits temporal resource utilization patterns across different workload classes. Then, we design parallel graph attention layers to capture the dimensional features and temporal dynamics of cloud server cluster. Moreover, we propose a DRL algorithm to generate scheduling strategies that can adapt to dynamic environments. Validation on real-world traces from Google cluster demonstrates that our method outperforms existing approaches in key metrics of cloud server cluster management. © 2004-2012 IEEE.

关键词： Cloud platforms

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Adaptive Evolutionary Reinforcement Learning Algorithm with Early Termination Strategy 23

Adaptive Evolutionary Reinforcement Learning Algorithm with ...

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23rd International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2024

作者： Wu, Xiaoqiang Zhu, Qingling Lin, Qiuzhen Chen, Weineng Li, Jianqiang College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China School of Computer Science and Engineering South China University of Technology Guangzhou China

Evolutionary reinforcement learning algorithms (ERLs), which combine evolutionary algorithms (EAs) with reinforcement learning (RL), have demonstrated significant success in enhancing RL performance. However, most ERLs rely heavily on Gaussian mutation operators to generate new individuals. When the standard deviation is too large or small, this approach will result in the production of poor or highly similar offspring. Such outcomes can be detrimental to the learning process of the RL agent, as too many poor or similar experiences are generated by these individuals. In order to alleviate these issues, this paper proposes an Adaptive Evolutionary Reinforcement Learning (AERL) method that adaptively adjusts both the standard deviation and the evaluation process. By tracking the performance of new individuals, AERL maintains the mutation strength within a suitable range without the need for additional gradient computations. Moreover, the proposed AERL approach early terminates unnecessary evaluations and discards experiences arising from poor individuals, thereby resulting in enhanced learning efficiency. Empirical assessments conducted on a variety of continuous control problems demonstrate the effectiveness of the AERL method. © 2024 International Foundation for Autonomous Agents and Multiagent Systems.

关键词： Reinforcement learning

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High-efficiency multi-channel focusing and imaging enabled by polarization-frequency multiplexing non-interleaved metasurfaces

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Photonics Research 2025年第4期13卷 976-986页

作者： XUNJUN HE MINGZHONG WU GUANGJUN LU YING ZHANG ZHAOXIN GENG School of Electrical and Electronic Engineering Harbin University of Science and Technology Guangxi Key Laboratory of Brain-Inspired Computing and Intelligent Chips School of Electronic and Information EngineeringGuangxi Normal University College of Science Harbin University of Science and Technology School of Information Engineering Minzu University of China

In microwave communication systems, focusing and imaging have attracted widespread attention due to their application prospects in the information processing and communication fields. Most existing multi-channel focusing and imaging are implemented by interleaved metasurfaces. However, the disadvantages of their large size and low efficiency limit their practical applications in large-capacity and low-loss integrated systems.

关键词： Fourier transforms Imaging systems Imaging techniques Information processing Optical elements Spatial light modulators

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Plausible Heterogeneous Graph k-Anonymization for Social Networks

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Tsinghua science and Technology 2022年第6期27卷 912-924页

作者： Kaiyang Li Ling Tian Xu Zheng Bei Hui School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731China School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu 610054China Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province Chengdu 610000China

The inefficient utilization of ubiquitous graph data with combinatorial structures necessitates graph embedding methods,aiming at learning a continuous vector space for the graph which is amenable to be adopted in traditional machine learning algorithms in favor of vector *** embedding methods build an important bridge between social network analysis and data analytics as social networks naturally generate an unprecedented volume of graph data *** social network data not only bring benefit for public health,disaster response,commercial promotion,and many other applications,but also give birth to threats that jeopardize each individual’s privacy and ***,most existing works in publishing social graph embedding data only focus on preserving social graph structure with less attention paid to the privacy issues inherited from social *** be specific,attackers can infer the presence of a sensitive relationship between two individuals by training a predictive model with the exposed social network *** this paper,we propose a novel link-privacy preserved graph embedding framework using adversarial learning,which can reduce adversary’s prediction accuracy on sensitive links while persevering sufficient non-sensitive information such as graph topology and node attributes in graph *** experiments are conducted to evaluate the proposed framework using ground truth social network datasets.

关键词： social network graph embedding privacy preservation adversarial learning

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