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4th International Conference on Computer Vision, Application, and Algorithm, CVAA 2024

作者： Xu, Xiaoxiang Li, Fanzhang Zhang, Li School of Computer Science and Technology Joint International Research Laboratory of Machine Learning and Neuromorphic Computing Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou China

ISBN: (纸本)9781510687615

For a long time, people have believed that representation problems are one of the bottlenecks in the field of machine learning. Therefore, it is a long-term and exploratory work to study machine learning representation methods. Due to this, we use category theory to study the fusion representation of data dimensionality reduction. We propose the basic concept of category representation for data dimensionality reduction and provide a data dimensionality reduction fusion representation framework. We have conducted research and analysis on algorithms such as PCA, KPCA, and LDA, identified the essential connection between PCA, KPCA, and LDA and proposed a data dimensionality reduction fusion representation algorithm based on a data dimensionality reduction fusion representation framework. Finally, we demonstrated the feasibility of the proposed method through experiments. © 2025 SPIE.

关键词： Dimensionality reduction

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Security within Security: Attack Detection Model with Defenses Against Attacks Capability for Zero-trust Networks

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IEEE Journal on Selected Areas in Communications 2025年第6期43卷 2312-2327页

作者： Wang, Tingting Fang, Kai Cai, Jijing Wang, Lina Tian, Jinyu Feng, Hailin Li, Jianqing Guizani, Mohsen Wang, Wei Macau University of Science and Technology Faculty of Innovation Engineering School of Computer Science and Engineering Macau999078 China Zhejiang A&F University College of Mathematics and Computer Science Hangzhou311300 China Machine Learning Department United Arab Emirates Shenzhen MSU-BIT University Guangdong-Hong Kong-Macao Joint Laboratory for Emotion Intelligence and Pervasive Computing Artificial Intelligence Research Institute Shenzhen518172 China Beijing Institute of Technology School of Medical Technology Beijing100081 China

Traditional traffic anomaly-based attack detection methods in Zero-trust Networks (ZTN) suffer from inherent security vulnerabilities, as they neglect considerations regarding their security defenses. Compromising the attack detection model itself can result in the breakdown of normal attack detection capabilities. Ensuring the security of the attack detection model during runtime presents a novel challenge. To address these shortcomings, we propose a novel attack detection model, termed Security within Security: Attack Detection Model with Defenses Against Attacks Capability for Zero-Trust Networks (SWS), aimed at enhancing the security of ZTN. SWS focuses on achieving attack detection in non-secure detection environments, to maintain its detection capability even when under attack. By employing a soft thresholding method, SWS adapts to the dynamic changes in network traffic, thus reducing the interference of attack signals. The incorporation of an attention mechanism enables SWS to concentrate on analyzing the most indicative traffic features of attack behavior. Additionally, we integrate Residual Networks (ResNet) and Bidirectional Long Short-Term Memory (BiLSTM) to enhance the robustness of identifying complex network attack behaviors. The effectiveness of the SWS is validated through ablation studies, model comparisons, experiments conducted over different training epochs, and experiments conducted on various components of the dataset. Experimental results demonstrate that compared to existing attack detection models, SWS achieves improvements in detection accuracy and recall rate by 13.4% and 10.6%, respectively, while reducing the False Positive Rate (FPR) by 16.9%. © 1983-2012 IEEE.

关键词： Economic and social effects

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Wildlife Small Object Detection based on Enhanced Network in Ecological Surveillance 33

Wildlife Small Object Detection based on Enhanced Network in...

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第33届中国控制与决策会议

作者： Wan Dai Hongpeng Wang Yulin Song Yunwei Xin College of Computer Science Nankai University Tianjin Key Laboratory of Intelligent Robotics College of Artificial Intelligence Nankai University Research Laboratory of Machine Learning and Pervasive Computing Nankai University

ISBN: (纸本)9781665431293

Visual tele-observation is an effective way for intelligent monitoring and protection of ecology and the natural *** from pedestrian or rigid body detection,wildlife detection in natural scenes face more complex problems,such as the existence of wild background clutter,local or global vegetation occlusion of animals,small object,rotation,deformation,and other interfering *** this paper,We mainly propose a novel method for the small object *** our small object wild species data set,we use an SSD detector for object ***,the K-means algorithm is used to adjust the anchor box of the SSD ***,for the situation that SSD is not good for small object detection,in conv4 layers of SSD,the feature enhancement module is ***,aiming at the size of a small object,the network level of SSD is *** have proved the feasibility of each of the three methods through experiments and combined with the three methods to verify,the recognition rate of the small target has increased by 2.67%.

关键词： anchor feature enhancement hierarchical design wildlife detection

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Laplacain Pair-Weight Vector Projection with Adaptive Neighbor Graph for Semi-supervised learning 1

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3rd International Conference on Neural computing for Advanced Applications, NCAA 2022

作者： Xue, Yangtao Zhang, Li The School of Computer Science and Technology and Joint International Research Laboratory of Machine Learning and Neuromorphic Computing Soochow University Suzhou215006 China

ISBN: (数字)9789811961427

ISBN: (纸本)9789811961410

Recently, Laplacian pair-weight vector projection (LapPVP) algorithm was proposed for semi-supervised classification. Although LapPVP achieves a good classification performance for semi-supervised learning, it may be sensitive to noise and outliers for using the neighbor graph with a fixed similarity matrix. To remedy it, this paper proposes a novel method named Laplacain pair-weight vector projection with adaptive neighbor graph (ANG-LapPVP), in which the graph induced by the Laplacian manifold regularization is adaptively constructed by solving an optimization problem. For binary classification problems, ANG-LapPVP learns a pair of projection vectors by solving the pair-wise optimal formulations in which we maximize the between-class scatter and minimize both the within-class scatter and the adaptive neighbor graph (ANG) regularization. The ANG regularization is to learn the ANG whose similarity matrix varies with iterations, which may solve the issue of LapPVP. Thus, ANG-LapPVP simultaneously learns adaptive similarity matrices and a pair of projection vectors with an iterative process. Experimental results on an artificial and real-world benchmark datasets show the superiority of ANG-LapPVP compared to the related methods. Thus, ANG-LapPVP is promising in semi-supervised learning. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Iterative methods

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Unsupervised Nonlinear Adaptive Manifold learning for Global and Local Information

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Tsinghua Science and Technology 2021年第2期26卷 163-171页

作者： Jiajun Gao Fanzhang Li Bangjun Wang Helan Liang School of Computer Science and Technology Soochow UniversitySuzhou 215006China Joint International Research Laboratory of Machine Learning and Neuromorphic Computing Provincial Key Laboratory for Computer Information Processing TechnologySoochow UniversitySuzhou 215006China

In this paper,we propose an Unsupervised Nonlinear Adaptive Manifold learning method(UNAML)that considers both global and local *** this approach,we apply unlabeled training samples to study nonlinear manifold features,while considering global pairwise distances and maintaining local topology *** method aims at minimizing global pairwise data distance errors as well as local structural *** order to enable our UNAML to be more efficient and to extract manifold features from the external source of new data,we add a feature approximate error that can be used to learn a linear ***,we add a feature approximate error that can be used to learn a linear *** addition,we use a method of adaptive neighbor selection to calculate local structural *** paper uses the kernel matrix method to optimize the original *** algorithm proves to be more effective when compared with the experimental results of other feature extraction methods on real face-data sets and object data sets.

关键词： unsupervised manifold learning global and local information,adaptive neighbor selection method kernel matrix

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A Neurobehavioral Evaluation of the Efficacy of 1mA Longitudinal, Anodal TDCS on Multitasking and Transfer Performance

A Neurobehavioral Evaluation of the Efficacy of 1mA Longitud...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Rao, Akash K Uttrani, Shashank Shah, Darshil Menon, Vishnu K Bhavsar, Arnav Roy Chowdhury, Shubhajit Negi, Ramsingh Dutt, Varun Manipal Academy of Higher Education India Indian Institute of Technology Applied Cognitive Science Laboratory Mandi India School of Computing and Electrical Engineering Indian Institute of Technology Mandi India Cognitive Control and Machine Learning Group The Institute of Nuclear Medicine and Allied Sciences Defence Research and Development Organization India

ISBN: (纸本)9781665410205

Multitasking requires rapid switching of attention and cognitive resources between different tasks in a dynamic environment, relying on cognitive processes, such as working memory, executive control, and selective attention. Although studies have investigated the efficacy of various neurobehavioral interventions in improving multitasking capabilities, the effects of longitudinal anodal transcranial direct current stimulation (tDCS) in enhancing multitasking performance have not been investigated. This research investigated the efficacy of 1mA anodal tDCS administered longitudinally on multitasking performance. 42 participants were randomly and equally divided into the experimental and placebo control conditions in this study conducted for 10 days. All participants executed two multitasking tasks on day 1 and received 1mA anodal/placebo tDCS during task training from day 2 to day 8. Various behavioral and neurophysiological measures have been measured. The findings revealed that tDCS had the propensity to augment multitasking capabilities in the trained task but had limited transfer capabilities. EEG-based brain connectivity analysis also revealed the formation of network hubs in the prefrontal and frontal regions, indicating enhanced cortical activation in the beta band. We intend to use these findings to design interventional frameworks to enhance multitasking performance using tDCS. © 2024 IEEE.

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Probabilistic and Semantic Descriptions of Image Manifolds and Their Applications

arXiv

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arXiv 2023年

作者： Tu, Peter Yang, Zhaoyuan Hartley, Richard Xu, Zhiwei Zhang, Jing Fu, Yiwei Campbell, Dylan Singh, Jaskirat Wang, Tianyu Computer Vision and Machine Learning Laboratory General Electric Research NiskayunaNY United States School of Computing College of Engineering Computing and Cybernetics Australian National University Canberra Australia

This paper begins with a description of methods for estimating probability density functions for images that reflects the observation that such data is usually constrained to lie in restricted regions of the high-dimensional image space — not every pattern of pixels is an image. It is common to say that images lie on a lower-dimensional manifold in the high-dimensional space. However, although images may lie on such lower-dimensional manifolds, it is not the case that all points on the manifold have an equal probability of being images. Images are unevenly distributed on the manifold, and our task is to devise ways to model this distribution as a probability distribution. In pursuing this goal, we consider generative models that are popular in AI and computer vision community. For our purposes, generative/probabilistic models should have the properties of 1) sample generation: it should be possible to sample from this distribution according to the modelled density function, and 2) probability computation: given a previously unseen sample from the dataset of interest, one should be able to compute the probability of the sample, at least up to a normalising constant. To this end, we investigate the use of methods such as normalising flow and diffusion models. We then show how semantic interpretations are used to describe points on the manifold. To achieve this, we consider an emergent language framework that makes use of variational encoders to produce a disentangled representation of points that reside on a given manifold. Trajectories between points on a manifold can then be described in terms of evolving semantic descriptions. In addition to describing the manifold in terms of density and semantic disentanglement, we also show that such probabilistic descriptions (bounded) can be used to improve semantic consistency by constructing defences against adversarial attacks. We evaluate our methods on CelebA and point samples for likelihood estimation with improved seman

关键词： Semantics

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WiViPose: A Video-aided Wi-Fi Framework for Environment-Independent 3D Human Pose Estimation

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IEEE Transactions on Multimedia 2025年

作者： Zhang, Lei Ning, Haoran Tang, Jiaxin Chen, Zhenxiang Zhong, Yaping Han, Yahong Tianjin University College of Intelligence and Computing the Tianjin Key Laboratory of Advanced Network Technology and Application Tianjin300050 China Key Laboratory of Computing Power Network and Information Security Ministry of Education China University of Jinan Shandong Provincial Key Laboratory of Ubiquitous Intelligent Computing the School of Information Science and Engineering Jinan250022 China Wuhan Sports University Sports Big-data Research Center Wuhan430079 China Tianjin University College of Intelligence and Computing the Tianjin Key Laboratory of Machine Learning Tianjin300350 China

The inherent complexity of Wi-Fi signals makes video-aided Wi-Fi 3D pose estimation difficult. The challenges include the limited generalizability of the task across diverse environments, its significant signal heterogeneity, and its inadequate ability to analyze local and geometric information. To overcome these challenges, we introduce WiViPose, a video-aided Wi-Fi framework for 3D pose estimation, which attains enhanced cross-environment generalization through cross-layer optimization. Bilinear temporal-spectral fusion (BTSF) is initially used to fuse the time-domain and frequency-domain features derived from Wi-Fi. Video features are derived from a multiresolution convolutional pose machine and enhanced by local self-attention. Cross-modality data fusion is facilitated through an attention-based transformer, with the process further refined under a supervisory mechanism. WiViPose demonstrates effectiveness by achieving an average percentage of correct keypoints (PCK)@50 of 91.01% across three typical indoor environments. © 2025 IEEE.

关键词： Frequency domain analysis

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ContinUNet: fast deep radio image segmentation in the Square Kilometre Array era with U-Net

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RAS Techniques and Instruments 2024年第1期3卷 315-332页

作者： Stewart, Hattie Birkinshaw, Mark Yeung, Siu-Lun Maddox, Natasha Maughan, Ben Thiyagalingam, Jeyan School of Physics University of Bristol HH Wills Physics Laboratory SciML Scientific Computing Department Research Complex at Harwell Rutherford Appleton Laboratory UKRI Centre for Doctoral Training in Artificial Intelligence Machine Learning & Advanced Computing Department of Physics Vivian Tower Swansea University

We present a new machine learning (ML)-driven source-finding tool for next-generation radio surveys that performs fast source extraction on a range of source morphologies at large dynamic ranges with minimal parameter tuning and post-processing. The construction of the Square Kilometre Array (SKA) radio telescope will revolutionize the field of radio astronomy. However, accurate and automated source-finding techniques are required to reach SKA science goals. We have developed a novel source-finding method, ContinUNet, powered by an ML segmentation algorithm, U-Net, that has proven highly effective and efficient when tested on SKA precursor data sets. Our model was trained and tested on simulated radio continuum data from SKA Science Data Challenge 1 and proved comparable with the state-of-the-art source-finding methods, PyBDSF and ProFound. ContinUNet was then tested on the MeerKAT International GHz Tiered Extragalactic Exploration Early Science data without retraining and was able to extract point-like and extended sources with equal ease;processing a 1.6 deg² field in <13 s on a supercomputer and ≈2 min on a personal laptop. We were able to associate components of extended sources without manual intervention with the powerful inference capabilities learnt within the network, making ContinUNet a promising tool for enabling science in the upcoming SKA era. © 2024 The Author(s).

关键词： Data Methods Evolution Galaxies Galaxies machine learning Radio Continuum Software

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External Joint Calibration of A Novel Multi-Modal Perception System

External Joint Calibration of A Novel Multi-Modal Perception...

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IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems

作者： Xiao Han Hongpeng Wang Chongshan Fan Yaojing Li Research Laboratory of Machine Learning and Pervasive Computing Nankai University Tianjin

ISBN: (数字)9781728190105

ISBN: (纸本)9781728190112

We design a new multi-modal perception fusion system for the whole scene, which can be divided into two parts. The first part is lidar, rgbamera1, rgbamera2. The second part is RGB camera 3, RGB camera 4, depth camera. The combination of these three modes provides a close range (0.3-10 m) of environmental perception, and provides the texture and distance information of the environment in the cone-shaped blind area of lidar. Our main work is to design the experimental equipment of the proposed multi-modal perceptual fusion system, and to push forward the optimization theory with the re projection error as the objective function and analyze the multi-modal fusion sensing system. The graph structure is established, and the relative pose transformation between various modes is analyzed. In the back-end optimization part, MCPE (Minimally connected pose estimation) and PSE(Pose and structure estimation)are used to analyze and compare the system. In the experimental part, we use matlab to calibrate the binocular camera by minimizing the error of re projection. The lidar, RGB camera and depth camera are calibrated with the help of the multi-modal calibration tool (binding on ROS) proposed. It provides a solid foundation for the future experiments in perception of self-driving, UAV perception, medical field and military field.

关键词： Cameras Sensors Calibration Laser radar Sensor systems Multimodal sensors Optimization

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