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4th IEEE International Conference on Information Technology, Big data and Artificial Intelligence, ICIBA 2024

作者： Xiao, Hengjiang Liang, Zhihong Huang, Yuxiang Qin, Mingming Xiong, Fei Liu, Mengxin Southwest Forestry University School of Big Data and Intelligent Engineering Kunming China Southwest Forestry University Key Laboratory of National Forestry and Grassland Administration on Forestry and Ecological Big Data China Southwest Forestry University School of Economics and Management Kunming China

ISBN: (纸本)9798350363586

With the rapid development of the biomedical field, stem cell therapy has received widespread attention as an innovative therapy with great potential. However, privacy protection and secure sharing of stem cell testing data are still major challenges. In this paper, we propose a homomorphic encryption-based data sharing scheme for stem cell testing, which achieves efficient and secure data sharing with data privacy by designing a system architecture covering data acquisition, encryption, transmission, decryption and analysis. Experimental results show that the scheme can not only effectively protect data privacy, but also support efficient data processing with less impact on system performance. The main contributions of this paper include designing and implementing a stem cell detection data sharing system based on homomorphic encryption, proposing a homomorphic encryption algorithm optimised for the high-dimensional characteristics of stem cell data, and experimentally verifying the security and effectiveness of the system. Future research directions include optimising homomorphic encryption algorithms, expanding application scenarios, integrating multiple privacy protection techniques, and optimising system performance. © 2024 IEEE.

关键词： data assimilation

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SS-MAF: Semi-Supervised Echocardiographic Segmentation Using Multi-Atlas Fusion 8

SS-MAF: Semi-Supervised Echocardiographic Segmentation Using...

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8th International Conference on Communication, Image and Signal Processing, CCISP 2023

作者： Yang, Shuaihua Zheng, Caixia College of Information Science and Technology Northeast Normal University Changchun130117 China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun130012 China

ISBN: (纸本)9798350305838

Left ventricle (LV) segmentation in echocardiography is of paramount significance in cardiac function analysis. Recently, semi-supervised segmentation has garnered considerable attention due to its potential mitigation of labeling costs. Despite this, most semi-supervised methodologies tend to overlook the specialized anatomical priors specific to echocardiography, often yielding suboptimal outcomes. Simultaneously, atlas priors have been validated on other organs and imaging modalities. However, their direct application in echocardiography is fraught with challenges, primarily attributed to the indistinct boundaries of the LV and the presence of fan-shaped image edges, which impede atlas image registration. This paper presents a new method of semi-supervised segmentation using multi-atlas fusion, named SS- MAF, which is designed to incorporate atlas priors tailored for echocardiography while circumventing the intricate image registration process in traditional atlas-based methods. The SS-MAF comprises two components: a Multi-Task Learning (MTL) module and a Multi-Atlas Fusion (MAF) module. The MTL module serves precise landmark localization and segmentation estimation. The MAF module is primarily focused on the generation of probabilistic atlases, utilizing annotated atlas data with the guidance of key landmarks to facilitate the automatic annotation of unlabeled data, avoiding the traditional image-level registration, thus enhancing the performance of the MTL module. Our SS- MAF is rigorously evaluated on the publicly available CAMUS dataset, with results demonstrating its superiority over other state-of-the-art semi-supervised segmentation techniques in echocardiography. © 2023 IEEE.

关键词： Image registration

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PS3: Precise Patch Presence Test Based on Semantic Symbolic Signature 24

PS3: Precise Patch Presence Test Based on Semantic Symbolic ...

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44th ACM/IEEE International Conference on Software engineering, ICSE 2024

作者： Zhan, Qi Hu, Xing Li, Zhiyang Xia, Xin Lo, David Li, Shanping Zhejiang University The State Key Laboratory of Blockchain and Data Security Hangzhou China Zhejiang University Hangzhou China Software Engineering Application Technology Lab Huawei China Singapore Management University Singapore

ISBN: (纸本)9798400702174

During software development, vulnerabilities have posed a significant threat to users. Patches are the most effective way to combat vulnerabilities. In a large-scale software system, testing the presence of a security patch in every affected binary is crucial to ensure system security. Identifying whether a binary has been patched for a known vulnerability is challenging, as there may only be small differences between patched and vulnerable versions. Existing approaches mainly focus on detecting patches that are compiled in the same compiler options. However, it is common for developers to compile programs with very different compiler options in different situations, which causes inaccuracy for existing methods. In this paper, we propose a new approach named PS3, referring to precise patch presence test based on semantic-level symbolic signature. PS3 exploits symbolic emulation to extract signatures that are stable under different compiler options. Then PS3 can precisely test the presence of the patch by comparing the signatures between the reference and the target at semantic level. To evaluate the effectiveness of our approach, we constructed a dataset consisting of 3,631 (CVE, binary) pairs of 62 recent CVEs in four C/C++ projects. The experimental results show that PS3 achieves scores of 0.82, 0.97, and 0.89 in terms of precision, recall, and F1 score, respectively. PS3 outperforms the state-of-the-art baselines by improving 33% in terms of F1 score and remains stable in different compiler options. © 2024 ACM.

关键词： Software design

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A topic detection method based on KM-LSH Fusion algorithm and improved BTM model

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Soft Computing 2024年第19期28卷 11421-11438页

作者： Liu, Wenjun Guo, Huan Gan, Jiaxin Wang, Hai Wang, Hailan Zhang, Chao Peng, Qingcheng Sun, Yuyan Yu, Bao Hou, Mengshu Li, Bo Li, Xiaolei School of Computer and Software Engineering XiHua University Chengdu610039 China Chengdu611731 China Intelligent Policing Key Laboratory of Sichuan Province Sichuan Police College Luzhou646000 China School of Big Data and Artificial Intelligence Chengdu Technological University Chengdu611730 China Sichuan Provincial Engineering Research Center of Hydroelectric Energy Power Equipment Technology Chengdu610039 China

Topic detection is an information processing technology designed to help people deal with the growing problem of data information on the Internet. In the research literature, topic detection methods are used for topic classification through word embedding, supervised-based and unsupervised-based approaches. However, most methods for topic detection only address the problem of clustering and do not focus on the problem of topic detection accuracy reduction due to the cohesiveness of topics. Also, the sequence of biterm during topic detection can cause substantial deviations in the detected topic content. To solve the above problems, this paper proposes a topic detection method based on KM-LSH fusion algorithm and improved BTM model. KM-LSH fusion algorithm is a fusion algorithm that combines K-means clustering and LSH refinement clustering. The proposed method can solve the problem of cohesiveness of topic detection, and the improved BTM model can solve the influence of the sequence of biterm on topic detection. First, the text vector is constructed by processing the collected set of microblog texts using text preprocessing methods. Secondly, the KM-LSH fusion algorithm is used to calculate text similarity and perform topic clustering and refinement. Finally, the improved BTM model is used to model the texts, which is combined with the word position and the improved TF-IDF weight calculation algorithm to adjust the microblogging texts in clustering. The experiment results indicate that the proposed KM-LSH-IBTM method improves the evaluation indexes compared with the other three topic detection methods. In conclusion, the proposed KM-LSH-IBTM method promotes the processing capability of topic detection in terms of cohesiveness and the sequence of biterm. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： K-means clustering

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Evolutionary Algorithms with Heuristic Gradient-based Repair for Constrained Optimization

Evolutionary Algorithms with Heuristic Gradient-based Repair...

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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Du, Jiacheng Bu, Chenyang Liu, Yuxin Liu, Fei Luo, Wenjian Hefei University of Technology Ministry of Education Key Laboratory of Knowledge Engineering with Big Data School of Computer Science and Information Engineering Anhui Hefei230027 China Harbin Institute of Technology School of Computer Science and Technology Guangdong Shenzhen518000 China

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Gradient-based repair aims to repair infeasible solutions to feasible ones using the gradient information of the constraints. As an effective constraint handling method, gradientbased repair has received extensive attention and has been applied in various evolutionary algorithms (EAs). Nevertheless, due to the complexity of constraints in practical problems, a single infeasible solution often needs to be repaired multiple times until it becomes a feasible solution or reaches the maximum number of repairs. As far as we know, existing related research on gradient-based repair mainly applies this method directly to EAs, while there is little work in the evolutionary computing community on how to improve gradient-based repair. Currently, the multiple repairs for a single individual are independent. That is, the current repair does not consider the previous repair experience. However, only using gradient information to repair infeasible individuals may result in oscillations in the search process. Therefore, in this paper, we propose a heuristic gradient-based repair method (HGR) which exploits the previous repair information of an individual to alleviate this issue. Experimental results on several benchmarks demonstrate the effectiveness of the proposed method. The source code is available at https://***/DMiC-Lab-HFUT/HGR-SMC2022. © 2022 IEEE.

关键词： Constrained optimization

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Using Depth-Enhanced Spatial Transformation for Student Gaze Target Estimation in Dual-View Classroom Images

Using Depth-Enhanced Spatial Transformation for Student Gaze...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Miao, Haonan Zhao, Peizheng Sun, Yuqi Nan, Fang Zhang, Xiaolong Wu, Yaqiang Tian, Feng School of Computer Science and Technology Xi'an Jiaotong University Xi'an710049 China Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi'an Jiaotong University Xi'an710049 China School of Advanced Technology Xi'an Jiaotong-Liverpool University Suzhou215123 China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi'an Jiaotong University Xi'an710049 China

ISBN: (纸本)9798350368741

Dual-view gaze target estimation in classroom environments has not been thoroughly explored. Existing methods lack consideration of depth information, primarily focusing on 2D image information and neglecting the latent 3D spatial context, which could lead to suboptimal transformation and cause the gaze cone to intersect with an incorrect object. This paper introduces a novel dual-view gaze target estimation method tailored for classroom settings, leveraging depth-enhanced spatial transformations. By formulating a depth-enhanced 2D space, our method uses depth-enhanced spatial transformation to accurately project students' gaze cones to the teacher-oriented image. Additionally, we collected a dataset named DVSGE, specifically for student gaze target estimation in dual-view classroom images. Experimental results demonstrate significant performance improvements of 9.8% in AUC and 19.9% in L2-Distance for our method, surpassing existing methods. © 2025 IEEE.

关键词： classroom depth-enhanced 2D space dual-view gaze target estimation spatial transformation

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FedLMT: Tackling System Heterogeneity of Federated Learning via Low-Rank Model Training with Theoretical Guarantees 41

FedLMT: Tackling System Heterogeneity of Federated Learning ...

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41st International Conference on Machine Learning, ICML 2024

作者： Liu, Jiahao Zhou, Yipeng Wu, Di Hu, Miao Guizani, Mohsen Sheng, Quan Z. School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China School of Computing Faculty of Science and Engineering Macquarie University Sydney Australia Abu Dhabi United Arab Emirates

Federated learning (FL) is an emerging machine learning paradigm for preserving data privacy. However, diverse client hardware often has varying computation resources. Such system heterogeneity limits the participation of resource-constrained clients in FL, and hence degrades the global model accuracy. To enable heterogeneous clients to participate in and contribute to FL training, previous works tackle this problem by assigning customized sub-models to individual clients with model pruning, distillation, or low-rank based techniques. Unfortunately, the global model trained by these methods still encounters performance degradation due to heterogeneous sub-model aggregation. Besides, most methods are heuristic-based and lack convergence analysis. In this work, we propose the FedLMT framework to bridge the performance gap, by assigning clients with a homogeneous pre-factorized low-rank model to substantially reduce resource consumption without conducting heterogeneous aggregation. We theoretically prove that the convergence of the low-rank model can guarantee the convergence of the original full model. To further meet clients' personalized resource needs, we extend FedLMT to pFedLMT, by separating model parameters into common and custom ones. Finally, extensive experiments are conducted to verify our theoretical analysis and show that FedLMT and pFedLMT outperform other baselines with much less communication and computation costs. Copyright 2024 by the author(s)

关键词： Federated learning

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A multi-target UAV fault diagnosis method based on feature discriminant dimension reduction under small samples 4

A multi-target UAV fault diagnosis method based on feature d...

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4th International Symposium on Computer Technology and Information Science, ISCTIS 2024

作者： An, Xue Zhang, Yizong Li, Shaobo Xiong, Xuan Guizhou University School of Mechanical Engineering Guiyang China Guizhou University State Key Laboratory of Public Big Data Guiyang China Guizhou University College of Computer Science and Technology Guiyang China

ISBN: (纸本)9798350354560

In recent years, deep learning has provided many efficient and accurate solutions to UAV fault diagnosis, but most of the current deep learning can only deal with a single task, whereas the UAV system is a complex coupled system, which needs to perform several different fault position diagnostics (FPD) and fault degree diagnostics (FDD) at the same time. Therefore, this paper proposed a multi-target UAV fault diagnosis method based on feature discriminant dimensionality reduction (MFD-FDDR) under small samples. Firstly, We connect specific features of different tasks with the same spatial definition through the channel dimension. Then, we perform feature discriminant dimensionality reduction (FDDR) for each task feature, and the output characteristics satisfy the input size of the following layer of LSTM. Finally, we validate the effectiveness of our proposed method with extensive experiments on real UAV flight data. © 2024 IEEE.

关键词： discriminant dimensionality reduction fault diagnosis multi-task learning small samples

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Evolution of vegetation phenology and its response to urbanisation in Shaanxi province,China:A fine-scale perspective

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Journal of Geographical Sciences 2025年第5期35卷 1024-1048页

作者： GAO Meiling MA Jingjing LI Zhenhong YANG Guijun College of Geological Engineering and Geomatics Chang'an UniversityXi'an 710054China State Key Laboratory of Loess Science Xian 710054China Key Laboratory of Ecological Geology and Disaster Prevention Ministry of Natural ResourcesXi'an 710054China Big Data Center for Geosciences and Satellites Xi'an 710054China

Shaanxi province in China is rich in vegetation resources,making the study of vegetation phenology crucial for understanding climate change and ecological *** studies on Shaanxi have primarily focused on coarse-scale phenology,which results in the loss of essential spatial details due to the lack of finer-scale ***,the impact of rapid urbanisation on the phenology of various vegetation types in this region remains *** address these gaps,we integrated Landsat and MODIS data to generate a daily 30 m NDVI *** phenology across Shaanxi from 2000 to 2020 was then extracted and analysed,and the responses of different vegetation types to urbanisation were *** indicated that accurate vegetation phenology can be obtained at a 30 m resolution,which is more precise than conventional coarse-scale *** Start of Season(SOS)and End of Season(EOS)in the province advanced by-0.56 and-0.08 day/year,respectively,from 2000 to *** general,the SOS gradually delays,and the EOS progressively advances with increasing ***,anomalies were identified in regions south of 32.64°N and across the zone from 34.04°N to 37.56°N,which are primarily due to the effects of high elevation and rapid urbanisation,*** investigation into phenological differences among various vegetation types revealed that forests exhibit the earliest SOS,while grasslands present the *** addition,a significant negative correlation was found between the SOS difference and air temperature difference along the urban-rural gradient,with urbanisation having the most significant impact on cropland *** study provides valuable insights into vegetation phenology and its response to *** insights offer crucial information for ecological conservation and management strategies.

关键词： vegetation phenology gap filling and Savitzky-Golay filtering Google Earth Engine spatiotemporal changes urbanisation

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Realizing n-type CdSb with promising thermoelectric performance

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Journal of Materials Science & Technology 2023年第13期144卷 54-61页

作者： Peng Zhao Honghao Yao Shizhen Zhi Xiaojing Ma Zuoxu Wu Yijie Liu Xinyu Wang Li Yin Zongwei Zhang Shuaihang Hou Xiaodong Wang Siliang Chen Chen Chen Xi Lin Haoliang Liu Xingjun Liu Feng Cao Qian Zhang Jun Mao School of Materials Science and Engineering and Institute of Materials Genome&Big DataHarbin Institute of Technology(Shenzhen)Shenzhen 518055China School of Science Harbin Institute of Technology(Shenzhen)Shenzhen 518055China State Key Laboratory of Advanced Welding and Joining Harbin Institute of TechnologyHarbin 150001China

Realizing high performance in both n-type and p-type materials is essential for designing efficient ther-moelectric ***,the doping bottleneck is often encountered,i.e.,only one type of conduction can be *** one example,p-type CdSb with high thermoelectric performance has been discovered for several decades,while its n-type counterpart has rarely been *** this work,the calculated band structure of CdSb demonstrates that the valley degeneracy is as large as ten for the conduction band,and it is only two for the valence ***,the n-type CdSb can potentially realize an ex-ceptional thermoelectric ***,the n-type conduction has been successfully real-ized by tuning the stoichiometry of *** further doping indium at the Cd site,an improved room-temperature electron concentration has been *** modeling predicts an optimal electron con-centration of∼2.0×1019 cm−3,which is higher than the current experimental ***,future optimization of the n-type CdSb should mainly focus on identifying practical approaches to optimize the electron concentration.

关键词： Thermoelectric materials n-type CdSb Indium doping Band degeneracy

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