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Towards Adversarial Robustness and Reducing Uncertainty Bias through Expert Regularized Pseudo-Bidirectional Alignment in Transductive Zero Shot Learning 27th

Towards Adversarial Robustness and Reducing Uncertainty Bias...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Kumar Sinha, Abhishek Mishra, Deepak Moorthi, S. Manthira Signal and Image Processing Area Space Applications Center Ahmedabad India Indian Institute of Space Science and Technology Thiruvananthapuram India

ISBN: (纸本)9783031781827

Transductive zero-shot learning (TZSL) aims to minimize the domain shift between the learned and true distribution of the unseen classes by allowing access to the unpaired samples from unseen classes. While many distribution alignment based methods attempt to align both visual and semantic spaces to train the classifier, their performance is still limited by confirmation bias. Additionally, bidirectional alignment approaches are based on the strong assumption that the intrinsic dimensions of visual and semantic spaces are the same, which is rarely true. In this work, we first highlight the limitations of bidirectional alignment in terms of intrinsic dimensionality. We then present a pseudo-bidirectional approach that, without any underlying assumptions on these spaces, utilizes the learned visual-to-attribute mapping to minimize the distribution shift between learned and true unseen visual feature distributions. We further utilize an entangled loss between semantic and visual space to minimize the confirmation or uncertainty bias and improve the adversarial robustness. We, theoretically and empirically, show the performance gain in addition to the adversarial robustness under the proposed setting. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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On Cognitive Radar Jamming for countering Cognitive Electromagnetic Protection systems

On Cognitive Radar Jamming for countering Cognitive Electrom...

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2024 IEEE International Workshop on Technologies for Defense and Security, TechDefense 2024

作者： Banerjee, Hemanga Ilioudis, Christos Clemente, Carmine Williams, Chris University of Strathclyde Centre for Signal and Image Processing Glasgow United Kingdom Defence Science and Technology Laboratory Porton Down Wiltshire United Kingdom

ISBN: (纸本)9798331505585

The paper introduces a novel model of a fully adaptive cognitive radar jamming approach, designed for the purpose of countering modern cognitive radars. Cognitive radar systems have been widely used in modern electromagnetic warfare and also serve as an effective counter to traditional jamming techniques. Cognitive jamming makes use of machine learning and adaptive signal processing strategies to dynamically adapt jamming strategies in response to real-time changes in target radar transmissions. The proposed model may be used to incorporate advanced reinforcement learning algorithms, signal feature extraction techniques, and waveform optimization techniques to create a cohesive system that can optimize jamming strategies with minimal prior knowledge of the electromagnetic environment. The paper also discusses the state-of-the-art in cognitive radar jamming technologies, and their efficiency in the face of intelligent radar equipped with advanced anti-jamming measures. Furthermore, it addresses current challenges and future directions in the field with considerations for defense and security applications. © 2024 IEEE.

关键词： Jamming

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Bagging and Boosting Fine-Tuning for Ensemble Learning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第4期5卷 1728-1742页

作者： Zhao, Changming Peng, Ruimin Wu, Dongrui Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074 China and alsowith the ShenzhenHuazhong University of Science and Technology Research Institute Shenzhen 518029 China

Ensemble learning aggregates outputs from multiple base learners for better performance. Bootstrap aggregating (bagging) and boosting are two popular such approaches. They are suitable for integrating unstable base learners with large variance and weak base learners with large bias, respectively, but not base learners with small variance and/or bias, e.g., support vector machine, regularized logistic regression, and ridge regression. This article proposes two novel ensemble-learning-based fine-tuning approaches, boosting fine-tuning (BF) and bagging and boosting fine-tuning (BBF), to fine-tune learners with small variance and/or bias for better performance. BF embeds boosting in a single hidden layer neural network. In each iteration, BF first uses the Newton's method to generate a temporary training set, and then trains a boosting learner on it. BBF combines BF and bagging. It first uses bootstrap to obtain multiple replicas of the training set, and then trains a BF learner on each replica. Extensive experiments on 46 real-world datasets demonstrated that BBF is flexible, robust, and effective, and can fine-tune many popular classifiers to achieve better generalization performance. © 2020 IEEE.

关键词： Support vector machines

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Development of an Intelligent System for Detection of Chronic Stress from Biological signal processing 9th

Development of an Intelligent System for Detection of Chroni...

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9th Latin American Congress on Biomedical Engineering, CLAIB 2022 and 28th Brazilian Congress on Biomedical Engineering, CBEB 2022

作者： Junqueira, Luis Pina, Marta Biomedical Engineering Laboratory of Signal Processing and Medical Image SP São Paulo Brazil Federal Institute of Education Science and Technology IFSP SP São Paulo Brazil

ISBN: (纸本)9783031494031

Our objective in this work was to develop an intelligent system able to perform an automated detection of chronic stress, based on biological signals processing and features extraction, confronted with Hans Selye clinical model of stress phase’s assessment. We recorded biological signals of blood pressure, skin surface temperature, galvanic skin resistance and heart rate of 120 health adult volunteers. Also the heart rate variability parameters were extracted for the time-domain and frequency-domain analysis. A Multi-Layer Perceptron Artificial Neural Network with a supervised learning approach was applied to generate the mathematical model. The system classification produced a precision index of 89% for identifying the stressed class, when applying the skin temperature and the heart rate variability parameters as input of the neural network, providing a satisfactory initial performance in the discrimination of stressed individuals. A worth training set with more examples mighty potentially increases the precision for identifying the stressed individuals. Despite study limitations, we consider that the use of intelligent systems to classify biological signals and identify the long term stress presence in human organism could contribute to a more objective analysis of such physiologic characteristics associated to the chronic stress and its implications in human health and performance. Future works should include different settle of physiological parameters and different machine learning techniques for analysis and classification of signals. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Intelligent systems

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A Privacy Preservation Method for Attributed Social Network Based on Negative Representation of Information

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Computer Modeling in Engineering & sciences 2024年第7期140卷 1045-1075页

作者： Hao Jiang Yuerong Liao Dongdong Zhao Wenjian Luo Xingyi Zhang Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and TechnologyAnhui UniversityHefei230601China Chongqing Research Institute School of Computer Science and Artificial IntelligenceWuhan University of TechnologyWuhan430070China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies School of Computer Science and TechnologyHarbin Institute of TechnologyShenzhen518055China

Due to the presence of a large amount of personal sensitive information in social networks,privacy preservation issues in social networks have attracted the attention of many *** by the self-nonself discrimination paradigmin the biological immune system,the negative representation of information indicates features such as simplicity and efficiency,which is very suitable for preserving social network ***,we suggest a method to preserve the topology privacy and node attribute privacy of attribute social networks,called ***,a negative survey-based method is developed to disturb the relationship between nodes in the social network so that the topology structure can be kept ***,a negative database-based method is proposed to hide node attributes,so that the privacy of node attributes can be preserved while supporting the similarity estimation between different node attributes,which is crucial to the analysis of social *** evaluate the performance of the AttNetNRI,empirical studies have been conducted on various attribute social networks and compared with several state-of-the-art methods tailored to preserve the privacy of social *** experimental results show the superiority of the developed method in preserving the privacy of attribute social networks and demonstrate the effectiveness of the topology disturbing and attribute hiding *** experimental results show the superiority of the developed methods in preserving the privacy of attribute social networks and demonstrate the effectiveness of the topological interference and attribute-hiding components.

关键词： Attributed social network topology privacy node attribute privacy negative representation of information negative survey negative database

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Recursive Least Squares Identification With Variable-Direction Forgetting via Oblique Projection Decomposition

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IEEE/CAA Journal of Automatica Sinica 2022年第3期9卷 547-555页

作者： Kun Zhu Chengpu Yu Yiming Wan Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and Technology Beijing Institute of Technology Chongqing Innovation Center

In this paper,a new recursive least squares(RLS)identification algorithm with variable-direction forgetting(VDF)is proposed for multi-output *** objective is to enhance parameter estimation performance under non-persistent *** proposed algorithm performs oblique projection decomposition of the information matrix,such that forgetting is applied only to directions where new information is *** proofs show that even without persistent excitation,the information matrix remains lower and upper bounded,and the estimation error variance converges to be within a finite ***,detailed analysis is made to compare with a recently reported VDF algorithm that exploits eigenvalue decomposition(VDF-ED).It is revealed that under non-persistent excitation,part of the forgotten subspace in the VDF-ED algorithm could discount old information without receiving new data,which could produce a more ill-conditioned information matrix than our proposed *** simulation results demonstrate the efficacy and advantage of our proposed algorithm over this recent VDF-ED algorithm.

关键词： Non-persistent excitation oblique projection recursive least squares(RLS) variable-direction forgetting(VDF)

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Semisupervised Transfer Boosting (SS-TrBoosting)

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3431-3444页

作者： Deng, Lingfei Zhao, Changming Du, Zhenbang Xia, Kun Wu, Dongrui Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan 430074 China and also with Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen 518063 China

Semisupervised domain adaptation (SSDA) aims at training a high-performance model for a target domain using few labeled target data, many unlabeled target data, and plenty of auxiliary data from a source domain. Previous works in SSDA mainly focused on learning transferable representations across domains. However, it is difficult to find a feature space where the source and target domains share the same conditional probability distribution. Additionally, there is no flexible and effective strategy extending existing unsupervised domain adaptation (UDA) approaches to SSDA settings. In order to solve the above two challenges, we propose a novel fine-tuning framework, semisupervised transfer boosting (SS-TrBoosting). Given a well-trained deep learning-based UDA or SSDA model, we use it as the initial model, generate additional base learners by boosting, and then use all of them as an ensemble. More specifically, half of the base learners are generated by supervised domain adaptation, and half by semisupervised learning. Furthermore, for more efficient data transmission and better data privacy protection, we propose a source data generation approach to extend SS-TrBoosting to semisupervised source-free domain adaptation (SS-SFDA). Extensive experiments showed that SS-TrBoosting can be applied to a variety of existing UDA, SSDA, and SFDA approaches to further improve their performance. © 2020 IEEE.

关键词： Supervised learning

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Misspecified CRB for TDOA based Passive Localization 16

Misspecified CRB for TDOA based Passive Localization

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16th International Conference on signal processing Systems, ICSPS 2024

作者： Cao, Jingmin Wan, Qun Chen, Ying Du, Fen Liu, Yang Li, Wei University of Electronic Science and Technology of China Chengdu China Southwest China Institute of Electronic Technology Chengdu China National Key Laboratory on Blind Signal Processing Chengdu China

ISBN: (纸本)9781510689251

Misspecified CRB (MCRB) is a lower bound on the variance of estimators when the parametric mode is not fully characterizing the behavior of the measurements. This paper derives the TDOA localization model and the performance bound under misspecification of the mean value of the measurement noise. The MCRB can not only play the same role as the CRB but also be used to assess performance under misspecified model. It’s a generalization of the classical CRB to misspecified model framework. For comparison, the specified CRB for TDOA localization with non-zero-mean Gaussian model is aslo investigaed. MCRB is equate to the specified CRB when the mean value of the measurement noise is equal to zero. The simmulation results are satisfy the theoretical analysis. © 2025 SPIE.

关键词： Velocity measurement

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A Lightweight UAV Visual Obstacle Avoidance Algorithm Based on Improved YOLOv8

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Computers, Materials & Continua 2024年第11期81卷 2607-2627页

作者： Zongdong Du Xuefeng Feng Feng Li Qinglong Xian Zhenhong Jia College of Computer Science and Technology Xinjiang UniversityUrumqi830046China Signal Detection and Processing Autonomous Region Key Laboratory Xinjiang UniversityUrumqi830046China Xinjiang Uygur Autonomous Region Research Institute of Measurement and Testing Urumqi830000China

The importance of unmanned aerial vehicle(UAV)obstacle avoidance algorithms lies in their ability to ensure flight safety and collision avoidance,thereby protecting people and *** propose UAD-YOLOv8,a lightweight YOLOv8-based obstacle detection algorithm optimized for UAV obstacle *** algorithm enhances the detection capability for small and irregular obstacles by removing the P5 feature layer and introducing deformable convolution v2(DCNv2)to optimize the cross stage partial bottleneck with 2 convolutions and fusion(C2f)***,it reduces the model’s parameter count and computational load by constructing the unite ghost and depth-wise separable convolution(UGDConv)series of lightweight convolutions and a lightweight detection *** on this,we designed a visual obstacle avoidance algorithm that can improve the obstacle avoidance performance of UAVs in different *** particular,we propose an adaptive distance detection algorithm based on obstacle attributes to solve the ranging problem for multiple types and irregular obstacles to further enhance the UAV’s obstacle avoidance *** verify the effectiveness of the algorithm,the UAV obstacle detection(UAD)dataset was *** experimental results show that UAD-YOLOv8 improves mAP50 by 3.4%and reduces GFLOPs by 34.5%compared to YOLOv8n while reducing the number of parameters by 77.4%and the model size by 73%.These improvements significantly enhance the UAV’s obstacle avoidance performance in complex environments,demonstrating its wide range of applications.

关键词： Unmanned aerial vehicle obstacle detection obstacle avoidance algorithm

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Electrochemical protein biosensors for disease marker detection:progress and opportunities

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Microsystems & Nanoengineering 2024年第3期10卷 1-20页

作者： Lanpeng Guo Yunong Zhao Qing Huang Jing Huang Yanbing Tao Jianjun Chen Hua-Yao Li Huan Liu School of Integrated Circuits Wuhan National Laboratory for OptoelectronicsOptics Valley LaboratoryHuazhong University of Science and TechnologyWuhan 430074China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Integrated CircuitsAnhui UniversityHefei 230601China School of Optoelectronic Materials and Technology Jianghan UniversityWuhan 430056China Department of Otorhinolaryngology Union HospitalTongji Medical CollegeHuazhong University of Science and Technology1277 Jiefang AvenueWuhan 430022China Wenzhou Institute of Advanced Manufacturing Technology Huazhong University of Science and TechnologyWenzhou 325000China

The development of artificial intelligence-enabled medical health care has created both opportunities and challenges for next-generation biosensor *** are extensively used as biological macromolecular markers in disease diagnosis and the analysis of therapeutic *** protein biosensors have achieved desirable specificity by using the specific antibody–antigen binding principle in ***,the active centers of protein biomarkers are surrounded by a peptide matrix,which hinders charge transfer and results in insufficient sensor ***,electrode-modified materials and transducer devices have been designed to increase the sensitivity and improve the practical application prospects of electrochemical protein *** this review,we summarize recent reports of electrochemical biosensors for protein biomarker *** highlight the latest research on electrochemical protein biosensors for the detection of cancer,viral infectious diseases,inflammation,and other *** corresponding sensitive materials,transducer structures,and detection principles associated with such biosensors are also addressed ***,we present an outlook on the use of electrochemical protein biosensors for disease marker detection for the next few years.

关键词： diagnosis specificity Electrochemical

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