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5th International Conference on Video, Signal and Image Processing, VSIP 2023

作者： Hu, Zeyu Chen, Haisong Wei, Jingfang Zuo, Yunyi Xu, Kaiyang Iwahori, Yuji Wu, Haibin Wang, Aili Heilongjiang Province Key Laboratory Of Laser Spectroscopy Technology And Application Harbin University Of Science And Technology Harbin150080 China Shenzhen Polytechnic University ShenZhen518115 China Department Of Computer Science Chubu University Aichi Japan

ISBN: (纸本)9798400709272

With the rapid development of artificial intelligence, the emergence of intelligent vehicles has brought disruptive changes to traditional survey technology. In response to the limitations of geological environmental factors on the scope and search progress of field exploration, this article utilizes a modular design concept to develop a system structure framework and specific solutions for each functional module of a multi-functional intelligent survey vehicle. An intelligent control platform based on the Nvidia Jetson TX2 and ROS systems has been built, and software programs for eye movement control and 3D dense reconstruction have been written. A system that can assist motion control through line of sight has been designed, A small, low-cost intelligent survey vehicle that can simultaneously perform real-time scene 3D reconstruction. This survey vehicle combines IoT technology, and on the basis of traditional survey vehicle functions, it has functions such as eye tracking, scene 3D reconstruction, remote object detection, etc. The obtained information is transmitted back to the PC through remote communication for users to view the survey results in real-time. At the same time, the operation of the survey vehicle can be controlled according to the direction of the line of sight, providing intelligent interaction ability and multi-dimensional exploration results for exploration work. © 2023 ACM.

关键词： Vehicles

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Side-channel analysis attacks based on deep learning network

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Frontiers of computer Science 2022年第2期16卷 35-45页

作者： Yu OU Lang LI Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang Normal UniversityHengyang 421002China College of Information Science and Engineering Hunan Normal UniversityChangsha 410081China College of Computer Science and Technology Hengyang Normal UniversityHengyang 421002China

There has been a growing interest in the sidechannel analysis(SCA)field based on deep learning(DL)*** DL network or model has been developed to improve the efficiency of ***,few studies have investigated the impact of the different models on attack results and the exact relationship between power consumption traces and intermediate *** on the convolutional neural network and the autoencoder,this paper proposes a Template Analysis Pre-trained DL Classification model named TAPDC which contains three *** TAPDC model detects the periodicity of power trace,relating power to the intermediate values and mining the deeper features by the multi-layer convolutional *** implement the TAPDC model and compare it with two classical models in a fair *** evaluative results show that the TAPDC model with autoencoder and deep convolution feature extraction structure in SCA can more effectively extract information from power consumption ***,Using the classifier layer,this model links power information to the probability of intermediate *** completes the conversion from power trace to intermediate values and greatly improves the efficiency of the power attack.

关键词： side-channel analysis template attack machine learning deep learning

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Toward Intelligent Financial Advisors for Identifying Potential Clients:A Multitask Perspective

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Big Data Mining and Analytics 2022年第1期5卷 64-78页

作者： Qixiang Shao Runlong Yu Hongke Zhao Chunli Liu Mengyi Zhang Hongmei Song Qi Liu Department of Anhui Province Key Laboratory of Big Data Analysis and Application(BDAA) School of Computer Science and TechnologyUniversity of Science and Technology of China Department of College of Management and Economics Tianjin University School of Management Hefei University of Technology Department of Information Technology China Merchants Bank

Intelligent Financial Advisors(IFAs)in online financial applications(apps)have brought new life to personal investment by providing appropriate and high-quality portfolios for *** real-world scenarios,identifying potential clients is a crucial issue for IFAs,i.e.,identifying users who are willing to purchase the ***,extracting useful information from various characteristics of users and further predicting their purchase inclination are ***,two critical problems encountered in real practice make this prediction task challenging,i.e.,sample selection bias and data *** this study,we formalize a potential conversion relationship,i.e.,user→activated user→client and decompose this relationship into three related ***,we propose a Multitask Feature Extraction Model(MFEM),which can leverage useful information contained in these related tasks and learn them jointly,thereby solving the two problems *** addition,we design a two-stage feature selection algorithm to select highly relevant user features efficiently and accurately from an incredibly huge number of user feature ***,we conduct extensive experiments on a real-world dataset provided by a famous fintech *** results clearly demonstrate the effectiveness of MFEM.

关键词： Intelligent Financial Advisor(IFA) potential client identification MultiTask Learning(MTL) feature selection

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Research on Deep Clustering Based on Image Data 13th

Research on Deep Clustering Based on Image Data

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13th International Conference on computer Engineering and Networks, CENet 2023

作者： Li, Xuanyu Yang, Houqun Zhang, Xiaoying Yang, Dangui Huang, Jianqiang Gan, Lin College of Computer Science and Technology Hainan University Hainan Haikou570228 China Haikou Key Laboratory of Deep Learning and Big Data Application Technology Hainan University Hainan Haikou570228 China Hainan Haikou China Elmore Family School of Computer and Electrical Engineering Purdue University West Lafayette United States

ISBN: (纸本)9789819992386

Clustering is an important branch of unsupervised tasks, aiming at mining deeper relationships and patterns in data. The quality of feature representation based on image datasets often determines the upper limit of clustering tasks. In recent years, the application of deep learning in deep clustering representation learning module has learned better feature representation, which gradually overcomes the limitation of traditional shallow clustering facing high-dimensional unstructured data. Deep clustering has become a popular research direction in the unsupervised field in recent years, and high clustering performance has been obtained in the continuous deepening research. The existing deep clustering research is mainly oriented to various fields of artificial intelligence, including natural language processing (NLP), speech recognition (ASR), computer vision (CV), etc. We take the field of computer vision as the entry point, analyze and study the research progress of deep clustering in computer vision (CV), and deconstruct the deep clustering model into two parts: a detailed classification of the feature extraction module from the perspective of network model architecture, while the data space of clustering is used as the entry point to divide the clustering module, and discuss the deep clustering. Finally, we analyze the datasets and evaluation metrics commonly used in experiments to study deep clustering architectures in computer vision. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Feature extraction

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HTDet:A Clustering Method Using Information Entropy for Hardware Trojan Detection

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Tsinghua Science and technology 2021年第1期26卷 48-61页

作者： Renjie Lu Haihua Shen Zhihua Feng Huawei Li Wei Zhao Xiaowei Li the University of Chinese Academy of Sciences Beijing 101408China Beijing Institute of Computer Technology and Application Beijing 100854China the State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China

Hardware Trojans(HTs)have drawn increasing attention in both academia and industry because of their significant potential *** this paper,we propose HTDet,a novel HT detection method using information entropybased *** maintain high concealment,HTs are usually inserted in the regions with low controllability and low observability,which will result in that Trojan logics have extremely low transitions during the *** implies that the regions with the low transitions will provide much more abundant and more important information for HT *** HTDet applies information theory technology and a density-based clustering algorithm called Density-Based Spatial Clustering of applications with Noise(DBSCAN)to detect all suspicious Trojan logics in the circuit under *** DBSCAN is an unsupervised learning algorithm,that can improve the applicability of *** addition,we develop a heuristic test pattern generation method using mutual information to increase the transitions of suspicious Trojan *** on circuit benchmarks demonstrate the effectiveness of HTDet.

关键词： Hardware Trojan(HT)detection information entropy Density-Based Spatial Clustering of applications with Noise(DBSCAN) unsupervised learning clustering mutual information test patterns generation

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Semi-Supervised Feature Selection With Multi-Scale Fuzzy Information Fusion: From Both Global and Local Perspectives

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IEEE Transactions on Fuzzy Systems 2025年第6期33卷 1825-1839页

作者： Zhou, Nan Liao, Shujiao Chen, Hongmei Ding, Weiping Lu, Yaqian Minnan Normal University School of Mathematics and Statistics Fujian Key Laboratory of Granular Computing and Applications Zhangzhou363000 China Southwest Jiaotong University School of Computing and Artificial Intelligence National Engineering Laboratory of Integrated Transportation Big Data Application Technology Chengdu611756 China Nantong University School of Artificial Intelligence and Computer Science Nantong226019 China City University of Macau Faculty of Data Science 999078 China

In reality, the laborious nature of label annotation leads to the widespread existence of limited labeled data. Moreover, multi-scale data have received widespread attention due to its rich knowledge representation. However, current research on multi-scale data primarily focuses on supervised learning environments, while semi-supervised feature selection for multi-scale data with limited labels remains inadequately explored. Meanwhile, existing studies on multi-scale data often emphasize selecting optimal scales and further conducting feature selection, but this strategy may result in losing potentially valuable information from other scales. To overcome these limitations, by adopting a multi-scale fuzzy information fusion mechanism, this article proposes two new semi-supervised feature selection approaches for multi-scale data with limited labels from both global and local perspectives. Initially, by fusing fuzzy information at various scales, a label learning method is proposed to convert the decision of missing labels into a fuzzy decision of label distribution. Subsequently, label-distributed multi-scale fuzzy global and local rough sets are constructed from global and local perspectives, respectively. Based on the two models, two semi-supervised feature selection algorithms are developed based on global and local multi-scale fuzzy information fusion. Experimental results demonstrate that, compared to other advanced feature selection algorithms, the two proposed algorithms can effectively handle multi-scale data with limited labels and exhibit superior classification performance, computational efficiency and robustness. © 1993-2012 IEEE.

关键词： Self-supervised learning

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SCENERY:a lightweight block cipher based on Feistel structure

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Frontiers of computer Science 2022年第3期16卷 193-202页

作者： Jingya FENG Lang LI Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang Normal UniversityHengyang 421002China College of Information Science and Engineering Hunan Normal UniversityChangsha 410081China College of Computer Science and Technology Hengyang Normal UniversityHengyang 421002China

In this paper,we propose a new lightweight block cipher called *** main purpose of SCENERY design applies to hardware and software *** is a 64-bit block cipher supporting 80-bit keys,and its data processing consists of 28 *** round function of SCENERY consists of 84×4 S-boxes in parallel and a 32× 32 binary matrix,and we can implement SCENERY with some basic logic *** hardware implementation of SCENERY only requires 1438 GE based on 0.18 um CMOS technology,and the software implementation of encrypting or decrypting a block takes approximately 1516 clock cycles 0118-bit microcontrollers and 364 clock cycles on 64-bit *** with other encryption algorithms,the performance of SCENERY is well balanced for both hardware and *** the security analyses,SCENERY can achieve enough security margin against known attacks,such as differential cryptanalysis,linear cryptanalysis,impossible differential cryptanalysis and related-key attacks.

关键词： lightweight block cipher feistel structure cryptanalysis internet of things

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Prediction of rainfall-type debris flow in Jiangjiagou based on LSTM-Attention 3

Prediction of rainfall-type debris flow in Jiangjiagou based...

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3rd International Conference on computer Vision, Image and Deep Learning and International Conference on computer Engineering and applications, CVIDL and ICCEA 2022

作者： Gao, Yuan Li, Yingna Kunming University of Science and Technology College of Faculty of Information Engineering and Automation Kunming China Key Laboratory of Computer Technology Application of Yunnan Province Kunming China

ISBN: (纸本)9781665459112

Through the neural network model, the rainfall-type debris flow in Jiangjiagou is predicted. Based on seven meteorological factors with time series characteristics, such as daily rainfall, previous effective rainfall, ground temperature, and humidity, which have been screened by features, the Jiangjiagou rainfall debris flow prediction and early warning model based on LSTM-Attention is constructed. The results show that: (1) LSTM-Attention model is 0.02 higher than that of the Attention-LSTM model, and the prediction effect is better. (2) The prediction accuracy of LSTM-Attention model for Jiangjiagou rainfall-type debris flow can reach 0.88, which is 0.05, 0.07, 0.04, 0.25, 0.13 higher than that of LSTM model, RNN model, RNN-Attention model, SVM model, and BP model, respectively. Research results: It provides a new idea and method for the prediction and research of rainfall-type debris flow, which can be referenced by scholars. © 2022 IEEE.

关键词： Long short-term memory

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Adaptive Feature Exchange Network with Complementary Advantages for Cooperative Classification of Hyperspectral and Multispectral Imagery

Adaptive Feature Exchange Network with Complementary Advanta...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Qingwang Wang Xingxing Fan Jiangbo Huang Yuanqin Meng Chengbiao Fu Tao Shen Kunming University of Science and Technology Kunming China Yunnan Key Laboratory of Computer Technologies Application Kunming China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

With the development of remote sensing technology for earth observation, the collaborative utilization of Hyperspectral images (HSI) and multispectral images (MSI) has received increasing attention in terrestrial observation. HSI and MSI represent two typical types of optical remote sensing data and can provide rich complementary information. However, the paradoxical problem of high spatial resolution and high spectral resolution leads to difficulties in extracting complementary information. In this paper, we propose an Adaptive Feature Exchange network with Complementary Advantages (AFECAnet). Specifically, we enhance the discriminative feature extraction of HSI-MSI by introducing a spectral-spatial feature enhancement module based on a dual attention mechanism. Subsequently, in order to reduce redundant information, we design an adaptive feature interaction strategy based on batch normalization privatization factors. This strategy helps to accurately replace redundant information and reduces the computational burden on the network. Experimental results demonstrate that the proposed AFECAnet has a significant improvement in HSI-MSI collaborative classification.

关键词： Earth Adaptive systems Privatization Redundancy Collaboration Feature extraction Optical imaging

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Graph Convolutional Network with Local Topology and Spectral Feature Representation for Multispectral Point Cloud Classification

Graph Convolutional Network with Local Topology and Spectral...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Qingwang Wang Xueqian Chen Mingye Wang Chengbiao Fu Tao Shen Kunming University of Science and Technology Kunming China Yunnan Key Laboratory of Computer Technologies Application Kunming China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Multispectral LiDAR contributes to the rapid acquisition of 3D spatial and spectral information of land covers, providing more comprehensive features for classification. Despite the impressive performance of existing Graph Neural Networks (GNNs) in point cloud classification, extracting local features with discriminative ability remains challenging in multispectral LiDAR scenes due to the uneven distribution of geometric and spectral information. To enhance the local representation of spectral features, we propose a novel Graph Convolutional Network with Local Topology and Spectral Feature Representation (GCN-LTSFR). The network constructs optimal local topological graphs of corresponding scales based on the feature distribution density of the point cloud to enhance local spectral features. Experimental results demonstrate that the proposed GCN-LTSFR outperforms several state-of-the-art methods on a real multispectral point cloud.

关键词： Point cloud compression Laser radar Three-dimensional displays Network topology Graph convolutional networks Land surface Geoscience and remote sensing

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