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2023 International Conference on computer Application and Information Security, ICCAIS 2023

作者： Wang, Mini Han Fang, Xiaoxiao Lin, Zhiyuan Zeng, Peijin Yang, Yu Liu, Yunxiao Liu, Haoyang Hu, Wenhan Li, Xinyue Jiang, Xudong Chen, Guangshun Hou, Guanghui Chong, Kelvin K.L. Fang, Junbin The Faculty of Medicine Chinese University of Hong Kong 999077 Hong Kong City University of Macao The faculty of Data Sciences 999078 China Radiology and Imaging Department Zhuhai People's Hospital Zhuhai Hospital Affiliated with Jinan University Guangdong Zhuhai519000 China AI and big data R&D department Zhuhai Institute of Advanced Technology Chinese Academy of Sciences Guangdong Zhuhai519000 China AI and Ophthalmology R&D Laboratory Perspective Technology Group Guangdong Zhuhai519000 China The department of Dry Eye Disease Zhuhai Aier Eye Hospital Guangdong Zhuhai519000 China Beijing Institute of Technology Guangdong Zhuhai5190000 China School of Biomedical Engineering Guangdong Medical University Guangdong Dongguan523000 China Faculty of Science and Technology Beijing Normal University-Hong Kong Baptist University United International College Guangdong Zhuhai519000 China School of Optometry and Ophthalmology Tianjin Medical University Tianjin300000 China The School of Computer Zhuhai College of Science and Technology Guangdong Zhuhai519000 China Department of Optoelectronic Engineering Jinan University Guangdong Guangzhou510000 China

ISBN: (纸本)9781510675032

This study conducts a rigorous comparative assessment of YOLOv5 and YOLOv8 for the detection of Demodex mites in microscopic examination images, leveraging crucial metrics such as accuracy, precision, recall, and F1-score. The investigation reveals the unequivocal superiority of YOLOv8, not only in quantitative measures but also substantiated by visual evidence, showcasing its applicability for real-time scenarios. YOLOv8 exhibits exceptional accuracy in overall detection and introduces a novel functionality for quantitative assessment of individual mites, providing essential granularity for precise diagnoses and therapeutic planning within dermatological and ophthalmological contexts. Positioned as a substantial advancement in object detection methodologies, YOLOv8 holds promise for significantly improving both accuracy and granularity in Demodex mite detection within microscopic examination images. While acknowledging potential limitations associated with dataset-specific considerations, this research underscores the imperative for further validation across diverse clinical scenarios. Computational considerations for real-time processing prompt future investigations to explore optimization strategies, particularly in resource-constrained environments. These findings position YOLOv8 as a valuable tool for clinicians and researchers engaged in dermatological and ophthalmological studies, offering heightened accuracy and nuanced insights. Ongoing research, encompassing clinical validations and comparative assessments with other state-of-the-art models, is anticipated to contribute to a more exhaustive understanding of YOLOv8's potential and limitations in real-world applications based on microscopic examination images. © 2024 SPIE.

关键词： Object detection

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Enhancing Knee Contact Force Prediction Using Transformer Networks: A Comparative Study with Lstm and Mlp

SSRN

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SSRN 2025年

作者： Xu, Fashu Wang, Junqing Tong, Bingkui Fang, Xuyi Zhang, Hui Deng, Tao Wei, Qu Nie, Yong Li, Kang Department of Orthopedic Surgery West China Biomedical Big Data Center West China Hospital Sichuan University Sichuan Chengdu China Department of Orthopedic Surgery Orthopedic Research Institute West China Hospital Sichuan University Sichuan Chengdu China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Liaoning Shenyang China College of Computer Science Sichuan University Sichuan Chengdu China College of Electrical Engineering Sichuan University Sichuan Chengdu China School of Mechanical Engineering Sichuan University Sichuan Chengdu China

Background and objectiveKnee contact force (KCF) is pivotal in both the study and degenerative pathologies like Knee Osteoarthritis. Traditionally, they are primarily estimated using musculoskeletal modeling and machine learning predictive algorithms. However, musculoskeletal modeling is limited by intricate experimental protocols, prohibitive equipment cost, and scalability challenges. While machine learning methods, though computationally efficient, exhibit suboptimal predictive accuracy. This study innovatively developed a Transformer model for predicting KCF, benchmarked against long short-term memory (LSTM) and multilayer perceptron (MLP) *** comprehensive database was constructed with gait data from 513 participants, incorporating three degrees of freedom of hip, knee flexion/extension, and ankle dorsiflexion/plantarflexion, constituting four input combinations for the models. Contact forces derived from OpenSim served as the ground truth. Root mean square error (RMSE) and coefficient of determination (R2) were used to evaluate the performance of *** results indicated that the Transformer model demonstrated superior performance, achieving a significantly lower mean root mean square error of 0.02 body weight (BW) compared to MLP (0.54 BW) and LSTM (0.45 BW). Furthermore, the mean R2 of the Transformer network (0.99) was markedly superior to that of MLP (0.45) and LSTM (0.58).ConclusionsThe application of Transformer networks significantly improved the accuracy of KCF prediction, addressing the limitations of traditional musculoskeletal modeling and existing machine learning approaches, which was of paramount importance in both clinical practice and biomechanical research. © 2025, The Authors. All rights reserved.

关键词： Mean square error

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Transformer-Based Hierarchical Dynamic Decoders for Salient Object Detection

SSRN

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SSRN 2023年

作者： Zheng, Qingping Zheng, Ling Deng, Jiankang Li, Ying Shang, Changjing Shen, Qiang School of Computer Science National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Provincial Key Laboratory of Speech & Image Information Processing Northewestern Polytechnical University Shaanxi Xi’an710129 China Fuzhou Institute of Data Technology Fujian350200 China Department of Computing Imperial College London LondonSW7 2AZ United Kingdom

HighlightsTransformer-Based Hierarchical Dynamic Decoders for Salient Object DetectionQingping Zheng, Ling Zheng, Jiankang Deng, Ying Li, Changjing Shang, Qiang Shen• T-HDDNet employs dynamic upsampling and fusion for data-driven salient object detection.• T-HDDNet uses a spatially sensitive loss to learn salient object structures, yielding a high-resolution map.• Our method exceeds benchmarks at 22.19G MACs and 27M Params, reducing costs compared to the top model. © 2023, The Authors. All rights reserved.

关键词： Object detection

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Digital Twin-Assisted Space-Air-Ground Integrated Multi-Access Edge Computing for Low-Altitude Economy: An Online Decentralized Optimization Approach

arXiv

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arXiv 2024年

作者： He, Long Sun, Geng Sun, Zemin Wang, Jiacheng Du, Hongyang Niyato, Dusit Liu, Jiangchuan Leung, Victor C.M. College of Computer Science and Technology Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Department of Electrical and Electronic Engineering University of Hong Kong Pok Fu Lam Hong Kong School of Computing Science Simon Fraser University BurnabyBCV5A 1S6 Canada R&D Department Jiangxing Intelligence Inc. Nanjing210000 China Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen518115 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering The University of British Columbia VancouverV6T 1Z4 Canada

The emergence of space-air-ground integrated multi-access edge computing (SAGIMEC) networks opens a significant opportunity for the rapidly growing low altitude economy (LAE), facilitating the development of various applications by offering efficient communication and computing services. However, the heterogeneous nature of SAGIMEC networks, coupled with the stringent computational and communication requirements of diverse applications in the LAE, introduces considerable challenges in integrating SAGIMEC into the LAE. In this work, we first present a digital twin-assisted SAGIMEC paradigm for LAE, where digital twin enables reliable network monitoring and management, while SAGIMEC provides efficient computing offloading services for Internet of Things sensor devices (ISDs). Then, a joint satellite selection, computation offloading, communication resource allocation, computation resource allocation and UAV trajectory control optimization problem (JSC4OP) is formulated to maximize the quality of service (QoS) of ISDs. Given the complexity of JSC4OP, we propose an online decentralized optimization approach (ODOA) to address the problem. Specifically, JSC4OP is first transformed into a real-time decision-making optimization problem (RDOP) by leveraging Lyapunov optimization. Then, to solve the RDOP, we introduce an online learning-based latency prediction method to predict the uncertain system environment and a game theoretic decision-making method to make real-time decisions. Finally, theoretical analysis confirms the effectiveness of the ODOA, while the simulation results demonstrate that the proposed ODOA outperforms other alternative approaches in terms of overall system performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Quality of service

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MCRformer: Morphological Constraint Reticular Transformer for 3D Medical Image Segmentation

SSRN

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SSRN 2022年

作者： Li, Jun Chen, Nan Zhou, Han Lai, Taotao Dong, Heng Feng, Chunhui Chen, Riqing Yang, Changcai Cai, Fanggang Wei, Lifang Digital Fujian Research Institute of Big Data for Agriculture and Forestry College of Computer and Information Science Fujian Agriculture and Forestry University Fuzhou350002 China College of Computer and Control Engineering Minjiang University Fuzhou350108 China Department of Vascular Surgery The First Affiliated Hospital Fujian Medical University Fuzhou350005 China

Background and objective: Medical image segmentation is essential in medical image analysis since it can provide reliable assistance in computer-aided clinical diagnosis, treatment planning, and intervention. Although deep learning algorithms based on CNNs and Transformers have made notable progress in medical image segmentation, it is still challenging owing to the objects with complex structures, low discrimination and differences between individuals. Methods: To alleviate the problems, we propose a novel 3D medical image segmentation network based on Transformers and CNNs combining morphological information and reticular mechanism. Firstly, the morphological constraint stream is designed to learn the prior shape information based on the CNN model for enhancing the interpretability of the ultimate trained model and accelerating the convergence. Secondly, the Reticular Transformer is utilized to obtain multi-scale information based on the Transformer, which can bind the local texture information and underlying semantic information to further acquire the feature maps with sufffficient details and receptive field. Results: The experiments demonstrate that our proposed method outperforms many existing segmentation models in terms of the performance in metrics DSC and HD (80.46% in DSC on the Synapse dataset and 90.83% in DSC on the ACDC dataset). The code will be released at https://***/rocklijun/MCRformer. Conclusions: Our proposed method can not only achieve superior performance compared with most of the current state-of-the-art methods, but also enhance the robustness and interpretability of the model. Furthermore, the proposed morphological constraint stream has the potential to be transferred to other frameworks for different medical image analysis tasks. © 2022, The Authors. All rights reserved.

关键词： Semantics

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Predicting crash fault residence via simplified deep forest based on a reduced feature set

arXiv

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arXiv 2021年

作者： Zhao, Kunsong Liu, Jin Xu, Zhou Li, Li Yan, Meng Yu, Jiaojiao Zhou, Yuxuan School of Computer Science Wuhan University Wuhan China School of Big Data and Software Engineering Chongqing University Chongqing China Faculty of Information Technology Monash University Melbourne Australia College of Engineering and Computer Science Syracuse University Syracuse United States

The software inevitably encounters the crash, which will take developers a large amount of effort to find the fault causing the crash (short for crashing fault). Developing automatic methods to identify the residence of the crashing fault is a crucial activity for software quality assurance. Researchers have proposed methods to predict whether the crashing fault resides in the stack trace based on the features collected from the stack trace and faulty code, aiming at saving the debugging effort for developers. However, previous work usually neglected the feature preprocessing operation towards the crash data and only used traditional classification models. In this paper, we propose a novel crashing fault residence prediction framework, called ConDF, which consists of a consistency based feature subset selection method and a state-of-the-art deep forest model. More specifically, first, the feature selection method is used to obtain an optimal feature subset and reduce the feature dimension by reserving the representative features. Then, a simplified deep forest model is employed to build the classification model on the reduced feature set. The experiments on seven open source software projects show that our ConDF method performs significantly better than 17 baseline methods on three performance indicators. Copyright © 2021, The Authors. All rights reserved.

关键词： Forecasting

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Downlink Channel Covariance Matrix Reconstruction for FDD Massive MIMO Systems with Limited Feedback

arXiv

引用

arXiv 2022年

作者： Li, Kai Li, Ying Cheng, Lei Shi, Qingjiang Luo, Zhi-Quan The Chinese University of Hong Kong Shenzhen518172 China Shenzhen Research Institute of Big Data Shenzhen518172 China The College of Information Science and Electronic Engineering Zhejiang University Hangzhou310058 China The School of Software Engineering Tongji University Shanghai201804 China Pazhou Lab Guangzhou510310 China

The downlink channel covariance matrix (CCM) acquisition is the key step for the practical performance of massive multiple-input and multiple-output (MIMO) systems, including beamforming, channel tracking, and user scheduling. However, this task is challenging in the popular frequency division duplex massive MIMO systems with Type I codebook due to the limited channel information feedback. In this paper, we propose a novel formulation that leverages the structure of the codebook and feedback values for an accurate estimation of the downlink CCM. Then, we design a cutting plane algorithm to consecutively shrink the feasible set containing the downlink CCM, enabled by the careful design of pilot weighting matrices. Theoretical analysis shows that as the number of communication rounds increases, the proposed cutting plane algorithm can recover the ground-truth CCM. Numerical results are presented to demonstrate the superior performance of the proposed algorithm over the existing benchmark in CCM reconstruction. Copyright © 2022, The Authors. All rights reserved.

关键词： Covariance matrix

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Selection for Cheaper Amino Acids Drives Nucleotide Usage at the Start of Translation in Eukaryotic Genes

引用

Genomics, Proteomics & Bioinformatics 2021年第6期19卷 949-957页

作者： Na L.Gao Zilong He Qianhui Zhu Puzi Jiang Songnian Hu Wei-Hua Chen Key Laboratory of Molecular Biophysics of the Ministry of Education Hubei Key Laboratory of Bioinformatics and Molecular-imagingDepartment of Bioinformatics and Systems BiologyCollege of Life Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Institute for Computer Science and Cluster of Excellence on Plant Sciences Heinrich Heine UniversityD-40225 DuesseldorfGermany CAS Key Laboratory of Genome Sciences and Information Beijing Institute of GenomicsChinese Academy of SciencesBeijing 100101China State Key Laboratory of Microbial Resources Institute of MicrobiologyChinese Academy of SciencesBeijing 100101China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Interdisciplinary Innovation Institute of Medicine and EngineeringBeihang UniversityBeijing 100191China University of Chinese Academy of Sciences Beijing 100049China

Coding regions have complex interactions among multiple selective forces,which are manifested as biases in nucleotide *** studies have revealed a decreasing GC gradient from the 5′-end to 3′-end of coding regions in various *** confirmed that this gradient is universal in eukaryotic genes,but the decrease only starts from the~25th *** trend is mostly found in nonsynonymous(ns)sites at which the GC gradient is universal across the eukaryotic *** GC contents at ns sites result in cheaper amino acids,indicating a universal selection for energy efficiency toward the N-termini of encoded *** a genome,the decreasing GC gradient is intensified from lowly to highly expressed genes(more and more protein products),further supporting this *** reveals a conserved selective constraint for cheaper amino acids at the translation start that drives the increased GC contents at ns *** GC contents can facilitate transcription but result in a more stable local secondary structure around the start codon and subsequently impede translation ***,the GC gradients at four-fold and two-fold synonymous sites vary across *** could decrease or increase,suggesting different constraints acting at the GC contents of different codon sites in different *** study reveals that the overall GC contents at the translation start are consequences of complex interactions among several major biological processes that shape the nucleotide sequences,especially efficient energy usage.

关键词： Macroevolution Prioritization of selective forces Energy efficiency Transcription Translation initiation

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Survey on data Analysis in Social Media:A Practical Application Aspect

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big data Mining and Analytics 2020年第4期3卷 259-279页

作者： Qixuan Hou Meng Han Zhipeng Cai the College of Computing Georgia Institute of TechnologyAtlantaGA 30332USA Data-driven Intelligence Research(DIR)Lab of College of Computing and Software Engineering Kennesaw State UniversityKennesawGA 30060USA the Department of Computer Science Georgia State UniversityAtlantaGA 30303USA

Social media has more than three billion users sharing events,comments,and feelings throughout the *** serves as a critical information source with large volumes,high velocity,and a wide variety of *** previous studies on information spreading,relationship analyzing,and individual modeling,etc.,have been heavily conducted to explore the tremendous social and commercial values of social media *** survey studies the previous literature and the existing applications from a practical *** outline a commonly used pipeline in building social media-based applications and focus on discussing available analysis techniques,such as topic analysis,time series analysis,sentiment analysis,and network *** that,we present the impacts of such applications in three different areas,including disaster management,healthcare,and ***,we list existing challenges and suggest promising future research directions in terms of data privacy,5 G wireless network,and multilingual support.

关键词： social media topic analysis time series analysis sentiment analysis network analysis disaster management bio-surveillance business intelligence

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NuSegDG: Integration of heterogeneous space and Gaussian kernel for domain-generalized nuclei segmentation

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Knowledge-Based Systems 2025年 322卷

作者： Lou, Zhenye Xu, Qing Jiang, Zekun He, Xiangjian Li, Chenxin Chen, Zhen Wang, Yi He, Maggie M. Duan, Wenting Sichuan University Pittsburgh Institute Sichuan University Chengdu China School of Computer Science Computer Vision and Intellifent Perception Laboratory and Municipal Digital Port Technology Key Laboratory University of Nottingham Ningbo Zhejiang China West China Biomedical Big Data Center West China Hospital Sichuan University Chengdu China Hong Kong Institute of Science & Innovation Chinese Academy of Sciences Hong Kong School of Software Dalian University of Technology Dalian116600 China Department of Electronic Engineering The Chinese University of Hong Kong 999077 Hong Kong Department of Cardiology Gold Coast University Hospital QLD Australia School of Computer Science University of Lincoln LincolnLN6 7TS United Kingdom

Domain-generalized nuclei segmentation refers to the generalizability of models to unseen domains based on knowledge learned from source domains and is challenged by various image conditions, cell types, and stain strategies. Recently, the Segment Anything Model (SAM) has made great success in universal image segmentation by interactive prompt modes (e.g., point and box). Despite its strengths, the original SAM presents limited adaptation to medical images. Moreover, SAM requires providing manual bounding box prompts for each object to produce satisfactory segmentation masks, so it is laborious in nuclei segmentation scenarios. To address these limitations, we propose a domain-generalizable framework for nuclei image segmentation, abbreviated to NuSegDG. Specifically, we first devise a Heterogeneous Space Adapter (HS-Adapter) to learn multi-dimensional feature representations of different nuclei domains by injecting a small number of trainable parameters into the image encoder of SAM. To alleviate the labour-intensive requirement of manual prompts, we introduce a Gaussian-Kernel Prompt Encoder (GKP-Encoder) to generate density maps driven by a single point, which guides segmentation predictions by mixing position prompts and semantic prompts. Furthermore, we present a Two-Stage Mask Decoder (TSM-Decoder) to effectively convert semantic masks to instance maps without the manual demand for morphological shape refinement. Based on our experimental evaluations, the proposed NuSegDG demonstrates state-of-the-art performance in nuclei semantic and instance segmentation, exhibiting superior domain generalization capabilities. The source code is available at https://***/xq141839/NuSegDG. © 2025 Elsevier B.V.

关键词： Semantic Segmentation

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