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32nd ACM International Conference on Multimedia, MM 2024

作者： Yin, Wen Zhu, Bin Benjamin Xie, Yulai Zhou, Pan Feng, Dan School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Microsoft Corporation Beijing China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9798400706868

RGB-Thermal Salient Object Detection (RGBT-SOD) plays a critical role in complex scene recognition applications, such as autonomous driving. However, security research in this domain is still in its infancy. This paper presents the first backdoor attack on RGBT-SOD systems, generating saliency maps on triggered inputs that depict non-existent salient objects chosen by the attacker or falsely mark an entire image as fully salient or entirely non-salient. We uncover that triggers have an influence range for generating non-existent salient objects, supported by a theoretical analysis. Extensive experiments show the effectiveness of our attack in both digital and physical-world scenarios. Notably, our dual-modality backdoor attack achieves an Attack Success Rate (ASR) of 86.72% with only five pairs of poisoned images in model training. After investigating potential countermeasures, we find them inadequate in mitigating our attacks, highlighting the urgent need for robust defenses against sophisticated backdoor attacks in RGBT-SOD systems. © 2024 ACM.

关键词： Object detection

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Characterizing the Performance of Deep Learning Inference for Edge Video Analytics 9th

Characterizing the Performance of Deep Learning Inference fo...

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9th CCF Conference on big data, bigdata 2021

作者： Luo, Zhenxiao Wang, Xinglong Zhang, Yuxiao Wang, Zelong Hu, Miao Wu, Di School of Computer Science and Engineering Sun Yat-Sen University Guangzhou510006 China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510006 China

ISBN: (纸本)9789811697081

Real-time video analytics is a killer application for edge computing, however, it has not been fully understood how much edge resource is required to support compute-intensive video analytics tasks at the edge. In this paper, we conduct an in-depth measurement study to unveil the resource requirement of deep learning (DL) inference for edge video analytics (EVA). We measure the DL inference time under various resource combinations, with different physical resources (e.g., processor types, core numbers, memory), video resolutions, and DL inference models. It is observed that the relationship between DL inference time and resource configuration is complicated, which cannot be captured by a simple model. Considering the coupling effects, we model the resource requirements of the DL inference by utilizing tensor factorization (TF) method. Our TF-based DL inference model can match well with the observations from field measurements. Different from previous models, our model is completely explainable and all the components have their physical meanings. Especially, it is possible to match the top three components extracted from the TF-based model with the practical DL functional layers, namely, convolutional layer, pooling layer, and fully-connected layer. The measurement results show that the top three components contribute approximately 50%, 25%, and 10% time for DL inference execution, which provides clear instruction on fine-grained DL task scheduling for edge video analytics applications. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Deep learning

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A Framework based on Deep Neural Network for Ranking-oriented Software Defect Prediction 23

A Framework based on Deep Neural Network for Ranking-oriente...

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23rd IEEE International Conference on Software Quality, Reliability, and Security, QRS 2023

作者： Dai, Jiapeng Yang, Xiaoxing Huang, Bingding Lu, Xiaofen Shenzhen Technology University College of Big Data and Internet Shenzhen518118 China Southern University of Science and Technology Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Shenzhen518055 China

ISBN: (纸本)9798350319583

Software systems are getting larger and more complex than ever before. In order to improve software reliability, software defect prediction is applied to assist developers in bug discovery. The ranking-oriented software defect prediction aims to rank software modules according to the predicted defect counts. However, existing ranking-oriented defect prediction models are constructed based on traditional hand-crafted features, which might overlook the rich syntactic information buried inside the source codes. In this paper, we propose a universal deep learning-based framework called United Deep Network for ranking-oriented software defect prediction. This framework utilizes deep neural networks to automatically generate features from source code with the syntactic and structural information preserved, and it can combine extracted features with traditional hand-crafted features in order to take advantage of both kinds of features to construct prediction models. Experimental results over 29 sets of data show the good performance of the proposed framework for building ranking-oriented defect prediction models. © 2023 IEEE.

关键词： Deep neural networks

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Real-time Dense 3D Semantic Mapping Using RGB-D Camera

Real-time Dense 3D Semantic Mapping Using RGB-D Camera

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第42届中国控制会议

作者： Guanci Yang Xiaoyuan Wang Dongying Zhu Yang Li School of Mechanical Engineering Guizhou University State Key Laboratory of Public Big Data Guizhou University

Focusing on the poor real-time performance of 3D semantic mapping using RGB-D cameras in indoor environments,this paper investigates the Real-time Dense 3D Semantic Mapping(RD3DSM) using RGB-D camera. Firstly, a lightweight semantic point cloud acquisition algorithm based on keyframe is designed, which is used to segment scene images and obtain the semantic point cloud real-timely. Secondly, to address the loss of inter-frame tracking in the semantic mapping process,ORB-SLAM3 system is employed as the underlying architecture of the visual 3D semantic mapping system to provide stable camera pose. Thirdly, an information fusion mechanism of the visual 3D semantic mapping system is designed, and the OctoMap 3D scene reconstruction module is adopted to implement dense 3D map representation with semantic information, and then a visual 3D semantic mapping algorithm based on ORB-SLAM3 is proposed. Finally, the designed RD3DSM system is integrated into a robot platform. The tests were conducted on the TUM dataset and natural indoor scenes. The results show the effectiveness and real-time performance of the RD3DSM system.

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Impact of the China Railway Express on Employment Structure - Evidence from Urban Economic data 11

Impact of the China Railway Express on Employment Structure ...

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11th International Conference on Information Technology and Quantitative Management, ITQM 2024

作者： Yan, Bochao Jiang, Hailiang Wu, Junfeng Tian, Xin China Agricultural University Beijing100083 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China Shanghai WinJoin Information Technology Co. Ltd. Shanghai200126 China

As the main international trade channel of China's "westward development", the opening of China Railway Express has significantly improved the trade facilitation of countries along the route. This paper investigates the impact of the opening of China Railway Express sites on the employment choices of individuals by using the difference in difference method based on the economic panel data of China's prefecture-level cities and the data on the opening of China Railway Express during the period of 2007~2019, which are matched by the correlation. The results of the study show that the opening of the China Railway Express increases the probability of individuals within the radiation area to enter the private sector for employment. The empirical results provide a new explanatory mechanism for the opening of the China Railway Express on economic growth, which has certain theoretical and policy value. © 2024 The Authors.

关键词： Railroads

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Increase in global per capita cropland imbalance across countries from 1985 to 2022:A threat to achieving Sustainable Development Goals

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Geography and Sustainability 2025年第2期6卷 253-264页

作者： Tingting Zhao Xiao Zhang Wendi Liu Jinqing Wang Zhehua Li Liangyun Liu School of Geography and Ocean Science Nanjing UniversityNanjing 210023China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China Key Laboratory of Digital Earth Science Aerospace Information Research InstituteChinese Academy of SciencesBeijing 100094China School of Electronic Electrical and Communication EngineeringUniversity of Chinese Academy of SciencesBeijing 100049China

Sustainable Development Goal 2(SDG 2,zero hunger)highlights that global hunger and food insecurity have worsened since 2015,driven in part by growing *** the challenge of achieving SDG 2 in the face of rapid global population growth requires sustained attention to global and national cropland *** quantifying the correlation between population and cropland area(i.e.,SDG 2.4.1 per capita cropland)and analyzing the trends of global cropland imbalance are essential for a comprehensive understanding of SDG *** this study,we utilized a new global 30 m land-cover dynamic dataset(GLC_FCS30D)to analyze cropland dynamics,quantify per capita cropland and its changes across various countries and levels of *** results indicate that the global cropland area expanded by 0.944 million km^(2)from 1985 to 2022,with an average expansion rate of 2.42×10^(4)km^(2)/***,the global per capita cropland area decreased from 0.347 ha in 1985 to 0.217 ha in 2022,mainly due to a higher population increase of nearly 65%in the same *** the context of globalization,cropland expansion and per capita cropland exhibited spatial imbalances globally,particularly in developing *** countries saw an increase in total cropland area by 7.09%but a significant decrease in per capita cropland area by 37.38%.From a temporal perspective,the global imbalance has been steadily increasing with the Gini index rising from 0.895 in 1985 to 0.909 in ***,this study reveals an increasing imbalance of global per capita cropland across various countries,which threatens the attainment of the targets of SDG 2.

关键词： Sustainable Development Goals GLC_FCS30D Cropland changes Population Imbalance

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Multi-Pyramid Dehazing Network with Residue Channel Enhancement 2

Multi-Pyramid Dehazing Network with Residue Channel Enhancem...

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2022 2nd International Conference on Artificial Intelligence, big data and Algorithms, CAIBDA 2022

作者： Hang, Ruishan Gu, Shenming Tang, Zhibo Tan, Xiaoqiu School of Information Engineering Zhejiang Ocean University Zhoushan316022 China Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province Zhoushan316022 China

ISBN: (纸本)9783800760268

Single image dehazing is to remove haze from degraded images and recover clean scenes. The researches of dehazing have academic significance and application value. In this paper, we design a Multi-Pyramid Dehazing Network (MPDN) with Residue Channel Enhancement (RCE). A RCE module was designed to help the network model remove a large amount of homogeneous haze. To further improve the effect of eliminating heterogeneous haze, the Pyramid Feature Extraction (PFE) module is used. The two modules complement each other to achieve excellent haze removal effect. Finally, we do a lot of experiments to verify the model, and compare it with the state-of-the-art (SOTA) method on the benchmark datasets. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Demulsification

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Remote Sensing Image River Segmentation Method Based on U-Net 8

Remote Sensing Image River Segmentation Method Based on U-Ne...

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8th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2022

作者： Cai, Qiang Wan, Ruyi Li, Haisheng Wang, Chen Chang, Haodong School of Computer Science and Engineering Beijing Technology and Business University Beijing100048 China Beijing Key Laboratory of Big Data Technology for Food Safety China National Engineering Laboratory for Agri-product Quality Traceability

ISBN: (纸本)9781665477352

River segmentation based on remote sensing images plays an important role in water conservancy business work, water wading monitoring work, and flood disaster prevention. In actual remote sensing images of rivers, most of the backgrounds are complex, and there is no public remote sensing image dataset specifically for the study of river segmentation. The traditional river segmentation methods have rough edge information and serious noise. To solve the above problems, this paper firstly preprocesses the Gaofen Image dataset (GID) and Remote Sensing Image Block Segmentation dataset (BDCI), and creates two datasets for river segmentation in high-resolution remote sensing images respectively (GID-river and BDCI-river) and then proposed a river segmentation method based on U-Net. On the basis of the original U-Net, the ResNet34 and VGG16 structures were combined to strengthen the feature extraction ability of the network, so as to achieve more accurate river edge details. The experimental results shows the mIoU of the ResNet34-UNet network on the GID-river dataset reaches 93.6%, and the mPA of the VGG16-UNet network on the BDCI-river dataset reaches 82.1%. © 2022 IEEE.

关键词： Remote sensing

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Multi-Label Text Classification for Judicial Texts via Dual Graph and Label Feature Fusion 10

Multi-Label Text Classification for Judicial Texts via Dual ...

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10th IEEE Smart World Congress, SWC 2024

作者： Gu, Qiliang Lu, Qin Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan China

ISBN: (纸本)9798331520861

The legal judgement prediction (LJP) of judicial texts represents a multi-label text classification (MLTC) problem, which in turn involves three distinct tasks: the prediction of charges, legal articles, and terms of penalty. Nevertheless, extant multi-label text classification models tend to eschew the consideration of multiple correlations and semantic information between labels when predicting judicial texts, which may result in the loss of pertinent information. Furthermore, these models do not take full advantage of the local and overall information of the labels in the process of selecting appropriate labels for the text using the multi-head attention mechanism. To address these issues, we propose a new model, BGFLFF, which explores label correlation and semantics among the three tasks by employing Graph Convolutional Network (GCN) and multi-head attention mechanisms. In particular, we propose a Bi-Graph Fusion GCN (BGF-GCN), which fuses the co-occurrence matrix of labels with the cosine similarity matrix, thereby fully exploiting the multiple correlations between labels. Furthermore, prior to embedding the tags, the interpretations of the labels are obtained via Google in order to enhance the semantic information between the labels. To further reinforce the interconnections between labels and text and more effectively comprehend the interrelationships between the three tasks, we propose a Multi-Head Label Feature Fusion Attentional Mechanism (MH-LFFAtt), which assigns distinct weights to the pertinent label information in the text by fusing the local and overall features of the labels. The experimental results demonstrate that the F1 score exhibits an improvement of up to 2. 2 8 % and a minimum of 1. 2 9 % across the various tasks of the two datasets. This evidence substantiates the assertion that BGFLFF outperforms the existing baseline model. © 2024 IEEE.

关键词： Semantics

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Integrated Building Information Modelling and 3D Printing for Prefabricated Building

Integrated Building Information Modelling and 3D Printing fo...

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第35届中国控制与决策会议

作者： Yukang Wang Yu Zhou Xiaoping Zhou Junjun Han Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture

The prefabricated components can be connected to form buildings in prefabricated building based on the design *** production of components in the factory is not standardized and accurate,which leads to difficulty in connecting prefabricated components and low efficiency in *** construction materials are piled up on the construction site and these wastes have caused a lot of pollution to the *** proposes to integrating Building Information Modelling(BIM) and 3 D printing for prefabricated *** of BIM models is *** components information is extracted by Industry Foundation Class(IFC) files so that BIM can provide prefabricated component ***,the conversion relationship between BIM and 3 D printing is ***,the conversion rules and methods for converting BIM data into STL files are developed so that the 3 D printer can print the prefabricated components according to the corresponding ***,testing the data format conversion on BIM models shows that our presented approach enables us to convert a BIM model into a data format recognizable for 3 D *** BIM and 3 D print in prefabricated buildings will boost the fineness of components and the efficiency of construction and have good performance in decreasing engineering waste.

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