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2022 Chinese Automation Congress, CAC 2022

作者： Hou, Qing Liu, Tingqing Yang, Jing Ji, Xiaoyang Li, Qinglang Li, Jian Yin, Baofan Guizhou University School of Computer Science and Technology Guiyang China Guizhou University School of Computer Science and Technology Guizhou University State Key Laboratory of Public Big Data Guiyang China Guizhou University State Key Laboratory of Public Big Data Guizhou University School of Mechanical Engineering Guiyang China Guizhou University School of Mechanical Engineering Guiyang China

ISBN: (纸本)9781665465335

Tactile perception is indispensable for intelligent robots to interact intelligently like humans. Therefore, the effective use of deep learning methods to acquire tactile features has become an important focus of tactile perception research. Satisfactory time-driven characteristics and the ability to process spatiotemporal information efficiently of spiking neural networks are advantageous for event-based data. We apply a temporal spike sequence learning backpropagation method that can handle continuous spikes to improve the spike neural network for tactile object recognition based on event-driven data. We prove the effectiveness of the temporal spike sequence error backpropagation method in practical applications to address the problem of losing temporal information of data using approximate derivatives. In practical application, we have proved the validity of the back propagation method of temporal spike sequence error in solving the problem of losing temporal information of data using approximate derivatives © 2022 IEEE.

关键词： Backpropagation

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Self-supervised fast reconstruction method for diffusion MRI based on multi-directional assistance 17

Self-supervised fast reconstruction method for diffusion MRI...

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17th IEEE International Conference on Signal Processing, ICSP 2024

作者： Wu, Yuxin Zhang, Jian Wang, Lihui Cao, Ying Wu, Linhai Wu, Yunjia Ye, Chen Zhu, Yuemin Guizhou University Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang China Univ Lyon Insa Lyon Cnrs Inserm Creatis Umr 5220 U1206 France

ISBN: (纸本)9798350387384

Diffusion MRI (dMRI) is widely used for the detection and diagnosis of various pathologies due to its non-invasive nature. However, its signal acquisition process is relatively slow and susceptible to motions. In order to accelerate reconstruction process, many deep learning-based methods have been developed in recent years to achieve promising results, while most of them are supervised based methods and require fully sampled data which are difficult to obtain in practical applications to train the network. Self-supervised based methods were proposed to deal with this problem to a certain extent, however, they are devoted only to conventional MRI data, ignoring the correlation between the directions of dMRI data. To address this issue, we propose a self-supervised fast reconstruction method for dMRI based on multi-directional assistance. The method better utilizes the information between dMRI data directions by Partition-Replace operation, cleverly constructing data pairs and designing effective self-supervised strategy for bi-directional prediction, which can overcome the problem of data incompleteness and guide the model to reconstruct unsampled signals in spite of the difficulty of acquiring fully sampled data. Experimental results show that our method achieves better reconstruction in the absence of fully sampled data, even with an 8-fold acceleration factor. © 2024 IEEE.

关键词： Self-supervised learning

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Distilling Dynamic Spatial Relation Network for Human Pose Estimation 32

Distilling Dynamic Spatial Relation Network for Human Pose E...

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32nd British Machine Vision Conference, BMVC 2021

作者： Wu, Kewei Wang, Tao Xie, Zhao Guo, Dan Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

Human pose estimation is a challenging task that requires the comprehension of the pose structure. This work can refer to spatial relation inference in a pose structure model;how to model the dynamic spatial relation against various unreliable joints is critical. To this end, we propose a Distilling Dynamic Spatial Relation network (DDSR), which builds pose-based graph representation by exploiting the feature of spatial relation from the location distribution of joints. We use a dynamic message propagation mechanism to update the spatial relation on edges. Specifically, to filter out the noisy predictions, we select the joints with high confidence;to enhance the spatial relation in a large receptive field, we propagate multi-stage messages among joints. Besides, to reduce the computation cost of the multi-stage message propagation, we design a cross-resolution distillation framework. We use a new spatial distillation loss to verify the spatial relation between the teacher model and the student model. Experimental results on COCO and MPII datasets show that our method is superior to the state-of-the-art methods. The visualization results further verify the interpretability of our spatial relation. © 2021. The copyright of this document resides with its authors.

关键词： Distillation

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Comparative research on multi-source heterogeneous data fusion technologies

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Procedia Computer Science 2024年 242卷 1089-1095页

作者： Wen Long Jing Gao Man Guo School of Economics and Management University of Chinese Academy of Sciences Beijing 100090 China Research Center on Fictitious Economy & Data Science Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining & Knowledge Management Chinese Academy of Sciences Beijing 100190 China

Both structured market data and unstructured financial news data can significantly affect stock price fluctuations. Therefore, relying only on a single data source for stock price trend prediction may produce information bias. This article combines these two types of heterogeneous data and systematically compares the effectiveness of different data fusion technologies to establish the optimal data integration strategy, thereby improving the accuracy of stock price prediction. Specifically, this paper innovatively proposes a stock price trend prediction model named MVL-SVM. The model successfully combines multi-perspective learning with support vector machine, and realizes the effective fusion of stock market data and financial news data. It is worth mentioning that the prediction accuracy of MVL-SVM model using direct fusion method is more than 30% higher than that of index modeling method, which indicates that direct fusion method can fully learn effective information from multiple information sources, thus significantly improving the prediction effect.

关键词： Multi-view Learning MVL-SVM Market data Financial news data Heterogeneous data fusion

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DarkSAM: Fooling Segment Anything Model to Segment Nothing 38

DarkSAM: Fooling Segment Anything Model to Segment Nothing

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zhou, Ziqi Song, Yufei Li, Minghui Hu, Shengshan Wang, Xianlong Zhang, Leo Yu Yao, Dezhong Jin, Hai National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Cluster and Grid Computing Lab China Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China School of Computer Science and Technology Huazhong University of Science and Technology China School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia

Segment Anything Model (SAM) has recently gained much attention for its outstanding generalization to unseen data and tasks. Despite its promising prospect, the vulnerabilities of SAM, especially to universal adversarial perturbation (UAP) have not been thoroughly investigated yet. In this paper, we propose DarkSAM, the first prompt-free universal attack framework against SAM, including a semantic decoupling-based spatial attack and a texture distortion-based frequency attack. We first divide the output of SAM into foreground and background. Then, we design a shadow target strategy to obtain the semantic blueprint of the image as the attack target. DarkSAM is dedicated to fooling SAM by extracting and destroying crucial object features from images in both spatial and frequency domains. In the spatial domain, we disrupt the semantics of both the foreground and background in the image to confuse SAM. In the frequency domain, we further enhance the attack effectiveness by distorting the high-frequency components (i.e., texture information) of the image. Consequently, with a single UAP, DarkSAM renders SAM incapable of segmenting objects across diverse images with varying prompts. Experimental results on four datasets for SAM and its two variant models demonstrate the powerful attack capability and transferability of DarkSAM. Our codes are available at: https://***/CGCL-codes/DarkSAM. © 2024 Neural information processing systems foundation. All rights reserved.

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Semi-supervised Multi-Label Learning with Missing Labels via Correlation Information

Semi-supervised Multi-Label Learning with Missing Labels via...

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International Joint Conference on Neural Networks (IJCNN)

作者： Zexian Xie Peipei Li Jinling Jiang Xindong Wu Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) Hefei University of Technology School of Computer Science and Information Engineering Hefei University of Technology Hefei Anhui China Knowledge Engineering Research Center Zhejiang Lab Hangzhou Zhejiang China

In multi-label learning, each instance is associated with a set of labels simultaneously. Most existing studies assume that the set of labels for each instance is complete. However, it is generally difficult to obtain all the relevant labels of each instance, and only a partial or even empty set of relevant labels is available, which is called semi-supervised multi-label learning with missing labels. To tackle this problem, we propose a novel framework that considers label correlations and instance correlations to recover the missing labels and utilizes a large amount of unlabeled data simultaneously to improve the classification performance. Specifically, a new supplementary label matrix is firstly obtained by learning the label correlation. Secondly, considering each class label may be decided by some specific characteristics of its own, a label-specific data representation is hence learned for each class label. Thirdly, instance correlations are utilized not only to recover the missing labels, but also to propagate the supervision information from labeled instances to unlabeled ones. In addition, a united objective function is designed to facilitate the above processing and an accelerated proximal gradient method is adopted to solve the optimization problem. Finally, extensive experimental results conducted on several benchmark datasets demonstrate the effectiveness of the proposed method compared to competing ones.

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Mining Top-k Frequent Patterns over Streaming Graphs 28th

Mining Top-k Frequent Patterns over Streaming Graphs

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28th International Conference on database Systems for Advanced Applications, DASFAA 2023

作者： Wang, Xi Zhang, Qianzhen Guo, Deke Zhao, Xiang Institute for Quantum Information and State Key Laboratory of High Performance Computing National University of Defense Technology Changsha China Science and Technology on Information Systems Engineering Laboratory National University of Defense Technology Changsha China Laboratory for Big Data and Decision National University of Defense Technology Changsha China

ISBN: (纸本)9783031306747

Mining top-k frequent patterns is an important operation on graphs, which is defined as finding k interesting subgraphs with the highest frequency. Most existing work assumes a static graph. However, graphs are dynamic in nature, which is described as streaming graphs. Mining top-k frequent patterns in streaming graphs is challenging due to the streaming nature of the input and the exponential time complexity of the problem. A naive solution is to calculate approximations of the frequent patterns in the streaming graph and then find the top-k answers, which is a memory- and time-consuming method. In this paper, we design a novel auxiliary data structure, FPC, to detect valid subgraph patterns and their frequency in real-time. We first convert each newly produced subgraph into a sequence and then map it into corresponding tracks in FPC based on hash functions. We theoretically prove that FPC can provide unbiased estimation and then give an error bound of our algorithm. In addition, we propose a vertical hashing and candidate buckets sampling technique to further improve FPC with higher space utilization and higher accuracy. Extensive experiments confirm that our approach generates high-quality results compared to the baseline method. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Hash functions

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Multiferroic oxide BFCNT/BFCO heterojunction black silicon photovoltaic devices

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Light(Science & Applications) 2021年第11期10卷 2153-2160页

作者： Kaixin Guo Xu Wang Rongfen Zhang Zhao Fu Liangyu Zhang Guobin Ma Chaoyong Deng Key Laboratory of Electronic Composites of Guizhou Province College of Big Data and Information EngineeringGuizhou UniversityGuiyang550025GuizhouChina Guizhou College of Electronic Science and Technology Guiyang561113GuizhouChina

Multiferroics are being studied increasingly in applications of photovoltaic devices for the carrier separation driven by polarization and *** this work,textured black silicon photovoltaic devices are fabricated with Bi_(6)Fe_(1.6)Co_(0.2)Ni_(0.2)Ti_(3)O_(18)/Bi_(2)FeCrO_(6)(BFCNT/BFCO)multiferroic heterojunction as an absorber and graphene as an *** structural and optical analyses showed that the bandgap of Aurivillius-typed BFCNT and double perovskite BFCO are 1.62±0.04 eV and 1.74±0.04 eV respectively,meeting the requirements for the active layer in solar *** the simulated AM 1.5 G illumination,the black silicon photovoltaic devices delivered a photoconversion efficiency(η)of 3.9%with open-circuit voltage(Voc),short-circuit current density(Jsc),and fill factor(FF)of 0.75 V,10.8 mA cm^(-2),and 48.3%,*** of modulation of an applied electric and magnetic field on the photovoltaic properties revealed that both polarization and magnetization of multiferroics play an important role in tuning the built-in electric field and the transport mechanisms of charge carriers,thus providing a new idea for the design of future high-performance multiferroic oxide photovoltaic devices.

关键词： illumination absorber perovskite

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ZnIn_(2)S_(4)双空位调控极化电场用以提升光催化产氢及协同氧化苄胺

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Science China Materials 2023年第6期66卷 2299-2307页

作者：邓晓婿陈鹏王旭崔瑞瑞邓朝勇 School of Electronics and Information Engineering Guiyang UniversityGuiyang 550005China Key Laboratory of Electronic Composites Materials of Guizhou Province College of Big Data and Information EngineeringGuizhou UniversityGuiyang 550025China Key Laboratory of Green Chemical and Clean Energy Technology of Guizhou Provincial School of Chemistry and Chemical EngineeringGuizhou UniversityGuiyang 550025China

光生载流子无定向的迁移及高的反应能垒,严重地抑制了光催化产氢及协同苄胺氧化的效率.本文利用简单的水热与碱刻蚀法制备了富含锌、硫双空位的硫铟化锌.在无共催化剂的条件下,该催化剂展现出优异的光催化产氢性能与苄胺转化速率.实验... 详细信息

光生载流子无定向的迁移及高的反应能垒,严重地抑制了光催化产氢及协同苄胺氧化的效率.本文利用简单的水热与碱刻蚀法制备了富含锌、硫双空位的硫铟化锌.在无共催化剂的条件下,该催化剂展现出优异的光催化产氢性能与苄胺转化速率.实验数据及理论计算表明,双空位重置了硫铟化锌的电子结构,并增强了其极化电场.这些调控将有助于激子的分离及载流子的定向迁移.另外,Zn与S空位充当了氢离子与苄胺吸附的活性位点,极大地提高了其催化活性.因此,本文不仅提供了一种调控极化电场的方法,也为苄胺氧化与协同产氢提供了更深入的见解.

关键词：光生载流子协同氧化氢离子催化活性双空位实验数据极化电场激子

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Judgment Model of Cock Reproductive Performance based on Vison Transformer 5

Judgment Model of Cock Reproductive Performance based on Vis...

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5th International Conference on Sensors, Signal and Image Processing, SSIP 2022

作者： Lin, Xuhong Yan, Qian Wu, Caicong Chen, Yifei College of Information and Electrical Engineering China Agricultural University China College of Information and Electrical Engineering China Agricultural University Key Laboratory of Agricultural Machinery Monitoring and Big Data Applications China

ISBN: (纸本)9781450397124

With the improvement of people's living standards, the demand for poultry has further increased. The screening of cock reproductive performance according to semen quality has become one of the attention directions. It is time-consuming to screen the breeding performance of cocks based on human vision, and there will be recognition errors. In this study, combined with the hypothesis that there is a correlation between cockscomb characteristics and semen quality, a scheme based on computer vision is proposed to avoid this problem. The collected cockscomb data are input into our model, which can automatically judge the breeding performance of cocks. We use transfer learning and change the weight ratio of the latest fine-grained visual classification algorithm Transfg at different depths to improve the accuracy of our data set. The average accuracy of the model in the test set is 44.6% (the data set contains 2053 pictures in the training set and 505 pictures in the test set), which is 1% better than the original vision transformer and 1.3% better than the convolution network model. © 2022 ACM.

关键词： Statistical tests

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