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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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Hybrid Dipper Throated and Grey Wolf Optimization for Feature Selection Applied to Life Benchmark Datasets

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Computers, Materials & Continua 2023年第2期74卷 4531-4545页

作者： Doaa Sami Khafaga El-Sayed M.El-kenawy Faten Khalid Karim Mostafa Abotaleb Abdelhameed Ibrahim Abdelaziz A.Abdelhamid D.L.Elsheweikh Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of System Programming South Ural State UniversityChelyabinsk454080Russia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Science Faculty of Specific EducationMansoura UniversityEgypt

Selecting the most relevant subset of features from a dataset is a vital step in data mining and machine *** feature in a dataset has 2n possible subsets,making it challenging to select the optimum collection of features using typical *** a result,a new metaheuristicsbased feature selection method based on the dipper-throated and grey-wolf optimization(DTO-GW)algorithms has been developed in this *** can result when the selection of features is subject to metaheuristics,which can lead to a wide range of ***,we adopted hybrid optimization in our method of optimizing,which allowed us to better balance exploration and harvesting chores more *** propose utilizing the binary DTO-GW search approach we previously devised for selecting the optimal subset of *** the proposed method,the number of features selected is minimized,while classification accuracy is *** test the proposed method’s performance against eleven other state-of-theart approaches,eight datasets from the UCI repository were used,such as binary grey wolf search(bGWO),binary hybrid grey wolf,and particle swarm optimization(bGWO-PSO),bPSO,binary stochastic fractal search(bSFS),binary whale optimization algorithm(bWOA),binary modified grey wolf optimization(bMGWO),binary multiverse optimization(bMVO),binary bowerbird optimization(bSBO),binary hysteresis optimization(bHy),and binary hysteresis optimization(bHWO).The suggested method is superior 4532 CMC,2023,vol.74,no.2 and successful in handling the problem of feature selection,according to the results of the experiments.

关键词： Metaheuristics dipper throated optimization grey wolf optimization binary optimizer feature selection

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Implicit Supervision-Assisted Graph Collaborative Filtering for Third-Party Library Recommendation

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IEEE Transactions on Services Computing 2025年第3期18卷 1459-1471页

作者： Lianrong Chen Mingdong Tang Naidan Mei Fenfang Xie Guo Zhong Qiang He School of Information Science and Technology Guangdong University of Foreign Studies Guangzhou China Unicom (Guangdong) Industry Internet Company Guangzhou China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Department of Computing Technologies Swinburne University of Technology Melbourne VIC Australia

Third-party libraries (TPLs) play a crucial role in software development. Utilizing TPL recommender systems can aid software developers in promptly finding useful TPLs. A number of TPL recommendation approaches have been proposed and among them graph neural network (GNN)-based recommendation is attracting the most attention. However, GNN-based approaches generate node representations through multiple convolutional aggregations, which is prone to introducing noise, resulting in the over-smoothing issue. In addition, due to the high sparsity of labelled data, node representations may be biased in real-world scenarios. To address these issues, this paper presents a TPL recommendation method named Implicit Supervision-assisted Graph Collaborative Filtering (ISGCF). Specifically, it takes the App-TPL interaction relationships as input and employs a popularity-debiased method to generate denoised App and TPL graphs. This reduces the noise introduced during graph convolution and alleviates the over-smoothing issue. It also employs a novel implicitly-supervised loss function to exploit the labelled data to learn enhanced node representations. Extensive experiments on a large-scale real-world dataset demonstrate that ISGCF achieves a significant performance advantage over other state-of-the-art TPL recommendation methods in Recall, NDCG and MAP. The experiments also validate the superiority of ISGCF in mitigating the over-smoothing problem.

关键词： Vectors Noise Training Collaborative filtering Recommender systems Libraries Predictive models Graph neural networks Electronic mail Convolution

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Physically Based Bidirectional Path Tacing Algorithm on the Tianhe-2 Supercomputer 25

Physically Based Bidirectional Path Tacing Algorithm on the ...

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25th IEEE International Conferences on High Performance Computing and Communications, 9th International Conference on Data science and systems, 21st IEEE International Conference on Smart City and 9th IEEE International Conference on Dependability in Sensor, Cloud and Big Data systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Wu, Yijia Zhang, Congpin Xu, Zhengwei Li, Ligang Chinese Academy of Sciences National Space Science Center Key Laboratory of Electronics and Information Technology for Space System Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences China School of Computer and Information Engineering Henan Normal University Henan Xinxiang China

ISBN: (纸本)9798350330014

Physically based ray tracing is a technique that involves creating a photo-realistic image from a 3D scene description by utilizing the laws of physics to simulate the behavior of light and matter. However, due to the random length of light samples and irregular memory access patterns, it can suffer from low scalability and severe load imbalance. As a result, the development of a framework for multi-node parallelism algorithm in physically based ray tracing is a significant challenge. To tackle these issues, this paper introduces a novel algorithm named distributed parallel physically based bidirectional path tacing algorithm (DP-BDPT). Through analyzing the physical essence of bidirectional path tracing algorithm and the light transport equation, we have devised a framework that carefully divides tasks between inter-compute nodes and intra-compute nodes. To support high scalability, we have developed a physically based architecture. Additionally, we have proposed a hybrid scheduling method to achieve load balancing. To further enhance performance, we have analyzed several key factors that affect rendering performance and implemented adaptive optimization techniques. The experiments conducted on the Tianhe-2 Supercomputer involving typical and challenging 3D scenes, namely the Cornell-box, Veach-bidir, and Teapot-full experiments, have illustrated that DP-BDPT demonstrate near-linear speedups as the number of CPU cores scales from 1200 to 9600, thereby demonstrating their efficiency when operating on a ten-thousand-core system. © 2023 IEEE.

关键词： Ray tracing

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Enhanced spectral splitting in a novel solar spectrum optical splitter based on one dimensional photonic crystal heterostructure

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Journal of Materiomics 2021年第3期7卷 648-655页

作者： Dong Wei Feng Cao Zuoxu Wu Yijie Liu Jian Wang Qingmei Wang Xingjun Liu Qian Zhang School of Science and Ministry of Industry and Information Technology Key Lab of Micro-Nano Optoelectronic Information SystemHarbin Institute of TechnologyShenzhenGuangdong518055China Department of Materials Science and Engineering Harbin Institute of TechnologyShenzhenGuangdong518055China

Controlling the distribution of solar spectrum in different bands would boost the energy harvesting efficiency and optimize the energy dispatchability.1D photonic crystal with intrinsic optical band gap can be used to split the solar spectrum for hybrid photovoltaic/thermal solar ***,we designed an efficient solar spectrum optical filter based on a cermet layer,Si/SiO_(2)1D photonic crystal,and top heterostructure *** with 1D photonic crystal structure,the 1D photonic crystal heterostructure with top YSZ layer can realize the reflectance of greater than 92%in PV band and the low average reflectance in two thermal bands by tuning the effective impedance of multilayer *** enhanced reflectance in PV band results from the huge mismatching of impedance between free space and the heterostructure *** top dielectric layer can also be extended to other oxides.

关键词： Solar spectral splitting 1D photonic crystal heterostructure Solar absorptance Effective impedance

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Ethereum smart contract security research:survey and future research opportunities

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Frontiers of Computer science 2021年第2期15卷 143-160页

作者： Zeli WANG Hai JIN Weiqi DAI Kim-Kwang Raymond CHOO Deqing ZOU National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabClusters and Grid Computing LabHubei Engineering Research Center on Big Data SecurityWuhan430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan430074China School of Cyber Science and Engineering Huazhong University of Science and TechnologyWuhan430074China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518057China Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249-0631USA

Blockchain has recently emerged as a research trend,with potential applications in a broad range of industries and *** particular successful Blockchain technology is smart contract,which is widely used in commercial settings(e.g.,high value financial transactions).This,however,has security implications due to the potential to financially benefit from a security incident(e.g.,identification and exploitation of a vulnerability in the smart contract or its implementation).Among,Ethereum is the most active and ***,in this paper,we systematically review existing research efforts on Ethereum smart contract security,published between 2015 and ***,we focus on how smart contracts can be maliciously exploited and targeted,such as security issues of contract program model,vulnerabilities in the program and safety consideration introduced by program execution *** also identify potential research opportunities and future research agenda.

关键词： smart contract security blockchain vulnerability unreliable data

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Deep Learning Based COVID-19 Detection from Chest X-ray Images 3

Deep Learning Based COVID-19 Detection from Chest X-ray Imag...

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3rd International Conference on Computer, Control and Robotics, ICCCR 2023

作者： Wan, Pian Lu, Zhiwei Chen, Chen Teoh, Teik Toe School of Electronic Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China Nanyang Technological University NTU Business AI Lab Singapore

ISBN: (纸本)9781665492126

The Coronavirus Disease 2019 (COVID-19) pandemic has dramatically affected the healthcare systems and economy over the world. To help reduce the negative effects of such a contagious malady, the detection time of COVID-19 becomes crucial. While one of the most promising methods to detect whether a patient is infected with this virus is to track his or her lung tissue with chest x-ray (CXR) images, it is considerably time-consuming if all the images are examined by professional radiologists. Besides, the accuracy of detection is determined mainly by the status and stress of the specific radiologist. To tackle these issues, we present a convolutional neural network that automatically detects infections from CXR images. Due to the limited amount of training data, data augmentation methods are used to reduce overfitting. Besides, training improvements such as warmup, cosine scheduler, and label smoothing are also applied during training. The proposed method is validated on the COVID-QU-Ex dataset and has an accuracy of 95.18% and an F1 score of 95.11 % on the validation set, showing its powerful potential in real-world applications for medical diagnostic systems. © 2023 IEEE.

关键词： COVID-19

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A Robust Camera-LiDAR Fusion Framework for 3D Object Detection in High-Dust Environments 22

A Robust Camera-LiDAR Fusion Framework for 3D Object Detecti...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Wang, Mingyuan Liu, Wentao Zhou, Bin Wang, Zhangyu Liu, Runsen Wang, Hanyu School of Transportation Science and Engineering Ministry of Industry and Information Technology Beihang University Beijing China Sate Key Lab of Intelligent Transportation System Ministry of Industry and Information Technology Beihang University Beijing China The Key Laboratory of Autonomous Transportation Technology for Special Vehickes Ministry of Industry and Information Technology Beihang University Beijing China Technology and Development Center of Hefei Metro Research Institute Co. Ltd. Hefei Metro Research Institute Co. Ltd Hefei China

ISBN: (纸本)9798331527471

The fusion of camera and LiDAR features in the Bird's Eye View (BEV) perspective has become a prevalent solution for 3D detection in autonomous driving due to its simplicity and efficiency. However, in challenging environments like mining areas with dusty roads, existing BEV-based fusion networks struggle due to dust occlusion and misidentification of dust as obstacles. To address this, we propose a robust fusion framework that integrates fine-grained depth supervision and channel-wise attention mechanisms. Combined with a temporal multi-frame mechanism, our framework effectively mitigates issues caused by dust occlusion and misidentification. We validated our method's detection accuracy on a self-constructed mining road dataset, achieving 89.6% mAP, surpassing BEVFusion's 88.3% mAP. Tests on sensor occlusion and failure further demonstrated its robustness in adverse conditions typical of unstructured road scenarios. © 2024 IEEE.

关键词： Mine dust

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A multi-source signals separation algorithm for identifying the threatening signals applied in Fiber-Optic Distributed Acoustic Sensor

A multi-source signals separation algorithm for identifying ...

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2023 Optical Fiber Communications Conference and Exhibition, OFC 2023

作者： He, Tao Zhang, Shixiong Li, Hao Yan, Zhijun Liu, Deming Sun, Qizhen School of Cyber Science and Engineering Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Wuhan430074 China Optics Valley Laboratory Huazhong University of Science and Technology School of Optical and Electronic Information National Laboratory for Optoelectronics National Engineering Laboratory for Next Generation Internet Access System Wuhan430074 China

A multi-source signals separation algorithm is proposed to identify the potential threatening signals submerged in the strong background noises. Finally, the recognition rate of the mixed signals is improved from 62.8... 详细信息

ISBN: (纸本)9781957171180

关键词： Source separation

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A Data-Efficient Erbium-Doped Fiber Amplifier Model under Partial Channel Loadings

A Data-Efficient Erbium-Doped Fiber Amplifier Model under Pa...

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2023 Asia Communications and Photonics Conference/2023 International Photonics and Optoelectronics Meetings, ACP/POEM 2023

作者： Li, Yuqi Zhang, Mingming Hu, Zihe Song, Zhuoxuan Yan, Siqi Tang, Ming School of Optical and Electronic Information Huazhong University of Science and Technology Wuhan National Laboratory for Optoelectronics Optics Valley Laboratory Wuhan China School of Optical and Electronic Information Huazhong University of Science and Technology National Engineering Laboratory for Next Generation Internet Access System Wuhan China

We proposed a modified spectral gain model for the Erbium-doped fiber amplifier based on singular value decomposition. More than 93% of predicted loading channel gain errors are less than 0.2 dB under partial channel ... 详细信息

ISBN: (纸本)9798350312614

关键词： Broadband Amplification EDFA Modeling Partial Channel Loadings Singular Value Decomposition

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