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BiMAConv: Bi-Modal Adaptive Convolution for Multispectral Point Cloud Segmentation

IEEE Geoscience and Remote Sensing Letters 2025年 22卷

作者： Zhang, Zheng Xu, Tingfa Lou, Peng Lv, Peng Tian, Tiehong Li, Jianan Beijing Institute of Technology Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Big Data and Artificial Intelligence Laboratory Chongqing401135 China Qingdao Agricultural University China Contemporary Amperex Technology Co. Ltd China Yunli Intelligence Technology Co. Ltd China

Multispectral point cloud segmentation, leveraging both spatial and spectral information to classify individual points, is crucial for applications such as remote sensing, autonomous driving, and urban planning. However, existing methods primarily focus on spatial information and merge it with spectral data without fully considering their differences, limiting the effective use of spectral information. In this paper, we introduce a novel approach, Bi-Modal Adaptive Convolution (BiMAConv), which fully exploits information from different modalities, based on the divide-and-conquer philosophy. Specifically, BiMAConv leverages the spectral features provided by the Spectral Information Divergence (SID) and the weight information provided by the Modal-Weight Block (MW-Block) module. The SID highlights slight differences in spectral information, providing the detailed differential feature information. The MW-Block module utilizes attention mechanism to combine generated features with the original point cloud, thereby generating weights to maintain learning balance sharply. Additionally, we reconstruct a large-scale urban point cloud dataset GRSS DFC 2018 3D based on dataset GRSS DFC 2018 to advance the field of multispectral remote sensing point cloud, with a greater number of categories, more precise annotations, and registered multispectral channels. BiMAConv is fundamentally plug-and-play and supports different shared-MLP methods with almost no architectural changes. Extensive experiments on GRSS DFC 2018 3D and Toronto-3D benchmarks demonstrate that our method significantly boosts the performance of popular detectors. © 2004-2012 IEEE.

关键词： Semantic Segmentation

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A Secure and Efficient Privacy data Aggregation Mechanism 18th

A Secure and Efficient Privacy Data Aggregation Mechanism

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18th International Conference on Wireless artificial Intelligent Computing Systems and applications, WASA 2024

作者： Ma, Wenshuo Liu, Xinru Yu, Kan Luo, Chuanwen Wang, Guopeng Liu, Xiaowu School of Artificial Intelligence and Big Data Zibo Vocational Institute Zibo China Weifang Engineering Vocational College Weifang China School of Computer Science and Engineering Macau University of Science and Technology Taipa China The Key Laboratory of Universal Wireless Communications Ministry of Education Beijing University of Posts and Telecommunications Beijing China School of Information Science and Technology Beijing Forestry University Beijing China School of Computer Science Qufu Normal University Rizhao China Zhejiang Ocean University Zhoushan China The Open University of China Beijing China

ISBN: (纸本)9783031714665

data aggregation (DA) plays an important role in conserving the limited resources and extending the network lifetime of Wireless Sensor Networks (WSNs). Privacy preservation has garnered considerable attention and is considered one of the most viable schemes to address issues such as communication eavesdropping, information leakage, and unauthorized access in WSNs employing DA. In this paper, we propose an improved Secure Privacy-preserving data Aggregation (iSECPDA) mechanism. It dynamically selects cluster heads through an enhanced Stable Election Protocol (SEP), which considers the residual energy of sensor nodes to enhance network lifetime. In our proposed SEPPDA, we integrate interference information into the sensing data and ensure data transmission privacy through a proposed slicing mechanism. Theoretical analysis and simulation experiments demonstrate that iSECPDA exhibits significant performance improvements in terms of communication overhead and privacy preservation levels. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： data aggregation

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Evaluating the Impact of Special Characters on Password Security: a Comparative Analysis

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IEEE Network 2025年

作者： He, Daojing Liu, Zhiyong Chan, Sammy Zhou, Beibei Guizani, Mohsen Harbin Institute of Technology School of Computer Science and Technology Shenzhen China Guizhou University State Key Laboratory of Public Big Data Guizhou China Research and Development Center Guangzhou Jingyuan Security Technology Co. Ltd Guangzhou China City University of Hong Kong Kowloon Department of Electrical Engineering Hong Kong Hong Kong Shanghai Urban Construction Vocational College School of Artificial Intelligence Application Shanghai China Machine Learning Department Abu Dhabi United Arab Emirates

Text-based password authentication remains essential for identity verification on internet platforms and blockchain systems. However, as computational power advances, security threats to user passwords have intensified, raising concerns about their effectiveness. While special characters (i.e., non-alphanumeric symbols, which include punctuation marks, mathematical symbols, and other non-letter/number characters like '.', '@', '#', '$', and similar) are often recommended to enhance password strength, the National institute of Standards and technology (NIST) prioritizes length over complexity, challenging their necessity. This study fills a critical gap in understanding user behavior regarding special character usage. By analyzing 15 datasets from Chinese, English, and German languages, we examined patterns of special character incorporation and their impact on password strength. Our findings indicate that users frequently employ predictable methods for including special characters, potentially compromising security, especially under stringent policies. We present three key contributions: (1) an analysis of special character usage patterns, (2) a quantitative assessment of strength differences using the PCFGv4.1 model, and (3) actionable recommendations for stakeholders to enhance password security practices. This research further advocates for aligning password policies with user behavior for more effective security. © 1986-2012 IEEE.

关键词： Authentication Protocol

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Visual Servoing under Limited Field of View (FOV) for Lumbar Vertebrae Positioning

Visual Servoing under Limited Field of View (FOV) for Lumbar...

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IEEE Information technology and Mechatronics Engineering Conference (ITOEC)

作者： Peng Xiao Chi Xiong Yu Wang Lingming Kong Bin Cai Zhiyong Sun Le Zou Xiaofeng Wang Bo Song School of Artificial Intelligence and Big Data Hefei University Hefei China Chinese Academy of Sciences Institute of Intelligent Machines HFIPS Hefei China University of Science and Technology of China Hefei China Chimed Intelligent Machinery Co. Ltd Hefei China Intelligent Manufacturing Multimodal Data Fusion Engineering Research Center China

ISBN: (数字)9798331529482

ISBN: (纸本)9798331529499

X-ray machines are vital in medical imaging for viewing internal body structures. However, in lumbar vertebrae surgery, the X-ray machine has to move and position frequently and manually, which risks infection, misalignment, and overexposure to radiation. There’s a need for mobile X-ray machines with autonomous recognition and positioning. Although visual servoing suitable for X-ray positioning naturally, it still experiencing challenges like limited Field-of-View (FOV) and the structural similarity among vertebrae. In this paper, a Bayesian Estimation based approach is developed to improve the detection accuracy in case of vertebrae positions outside the FOV. Furthermore, a visual servoing method is proposed for X-ray positioning, considering their mechanical structure and imaging characteristics. The experiment results indicates that the proposed approach improves lumbar vertebrae detection accuracy significantly. This approach facilitates precise positioning in X-ray imaging and enhances the safety and effectiveness of lumbar vertebrae surgery.

关键词： Accuracy Mechatronics Surgery Estimation Visual servoing Safety Bayes methods Information technology X-ray imaging Biomedical imaging

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Decouple-and-Couple Learning in Multi-Modal Brain Tumor Segmentation

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Xiao, Fuan Ji, Chaojie Zhang, Zheng Wang, Ruxin Guangzhou University Institute of Artificial Intelligence and Blockchain Guangzhou510006 China The University of British Columbia Department of Mathematics VancouverV6T 1Z2 Canada Renmin University of China Institute of Statistics and Big Data Beijing100872 China Chinese Academy of Sciences Shenzhen Institutes of Advanced Technology Shenzhen518055 China

Exploiting multi-modal magnetic resonance imaging complementary information for brain tumor segmentation is still a challenging task. Existing methods are usually inclined to learn the joint representation of all tumor regions indiscriminately, thus salient sub-region or healthy tissue would be dominant during the training procedure, which leads to a biased and limited representation performance. In this study, a novel transformer-based multi-modal brain tumor segmentation approach is developed by decoupling and coupling strategy. First, Anatomy-induced Region Decoupler decouples the representation of the tumor scattered in different semantic sub-regions following anatomical view, which forces the model to fully learn intra-region representation separately with multiple modalities context. Additionally, we introduce the collaborative decoupling of the corresponding sub-region edge to serve auxiliary cues. We then design the Edge-supported Intra-region Coupler to separately couple edge and object learning within each anatomical sub-region structure. Lastly, the Mutual Cross-region Coupler is further applied to implement mutual improvement by coupling complementary gains among the above decoupled sub-regions. Extensive experiments clearly demonstrate that our method outperforms current state-of-the-arts for brain tumor segmentation on BRATS2018, BRATS2020, MSD, and BRATS2021 benchmarks while retaining high efficiency in the learning procedure. © 2013 IEEE.

关键词： Magnetic resonance imaging

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Cross-Modal Causal Representation Learning for Radiology Report Generation

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IEEE transactions on image processing : a publication of the IEEE Signal Processing Society 2025年 PP卷 PP页

作者： Weixing Chen Yang Liu Ce Wang Jiarui Zhu Guanbin Li Cheng-Lin Liu Liang Lin 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 Science Sun Yat-sen University Shenzhen China Department of Health Technology and Informatics The Hong Kong Polytechnic University Hung Hom Hong Kong State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

Radiology Report Generation (RRG) is essential for computer-aided diagnosis and medication guidance, which can relieve the heavy burden of radiologists by automatically generating the corresponding radiology reports according to the given radiology image. However, generating accurate lesion descriptions remains challenging due to spurious correlations from visual-linguistic biases and inherent limitations of radiological imaging, such as low resolution and noise interference. To address these issues, we propose a two-stage framework named Cross-Modal Causal Representation Learning (CMCRL), consisting of the Radiological Cross-modal Alignment and Reconstruction Enhanced (RadCARE) pre-training and the Visual-Linguistic Causal Intervention (VLCI) fine-tuning. In the pre-training stage, RadCARE introduces a degradation-aware masked image restoration strategy tailored for radiological images, which reconstructs high-resolution patches from low-resolution inputs to mitigate noise and detail loss. Combined with a multiway architecture and four adaptive training strategies (e.g., text postfix generation with degraded images and text prefixes), RadCARE establishes robust cross-modal correlations even with incomplete data. In the VLCI phase, we deploy causal front-door intervention through two modules: the Visual Deconfounding Module (VDM) disentangles local-global features without fine-grained annotations, while the Linguistic Deconfounding Module (LDM) eliminates context bias without external terminology databases. Experiments on IU-Xray and MIMIC-CXR show that our CMCRL pipeline significantly outperforms state-of-the-art methods, with ablation studies confirming the necessity of both stages. Code and models are available at.

关键词： Visualization Linguistics Radiology Heart Training Correlation Feature extraction Lesions Accuracy Lungs

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Trends of Candida species causing bloodstream infections in South-Eastern Asia:A systematic review and meta-analysis

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Asian Pacific Journal of Tropical Medicine 2025年第2期18卷 51-66页

作者： Dina Yamin Abubakar Muhammad Wakil Mohammed Dauda Goni Ahmad Adebayo Irekeola Khalid Hajissa Department of Medical Laboratory Sciences School of ScienceThe University of JordanAmman 11942Jordan Faculty of Veterinary Medicine Universiti Malaysia KelantanPengkalan Chepa16100Kota BharuKelantanMalaysia Institute for Artificial Intelligence and Big Data Universiti Malaysia KelantanCity CampusPengkalan Chepa16100Kota BharuKelantanMalaysia Department of Medical Microbiology and Parasitology School of Medical SciencesUniversiti Sains MalaysiaKubang KerianKelantan 16150Malaysia Microbiology Unit Department of Biological SciencesCollege of Natural and Applied f SciencesSummit University OffaOffa PMB 4412KwaraNigeria Department of Zoology Faculty of Science and TechnologyOmdurman Islamic University14415OmdurmanSudan

A reliable estimation of Candida bloodstream infection prevalence is increasingly important to track changes in Candida species distribution and define burden of ongoing candidemia.A systematic review and meta-analysis were conducted to estimate candidemia prevalence and identify patterns of Candida species in South-eastern *** electronic-databases literature search was performed on published studies recorded candidemia prevalence in South-Eastern *** meta-analysis of proportions,the overall pooled prevalences of candidemia by Candida(C.)albicans,***,*** and *** were calculated as 28.4%(95%CI 24.9-31.8),29.2%(95%CI 24.7-33.7),19.1%(95%CI 14.8-23.4)and 14.0%(95%CI 10.4-17.5),*** on publication year and country,subgroup analyses were conducted on Candida species to determine heterogeneity *** findings may not precisely reflect true candidemia prevalence in different ***,it highlights continuous need to conduct prevalence studies,assess and monitor growing burden,control effect of potential risk factors and implement regional surveillance programs to prevent further rise.

关键词： Candida species Candidemia Bloodstream infections Prevalence South-Eastern Asia Systematic review and meta-analysis

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Cloud-magnetic resonance imaging system:In the era of 6G and artificial intelligence

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Magnetic Resonance Letters 2025年第1期5卷 52-63页

作者： Yirong Zhou Yanhuang Wu Yuhan Su Jing Li Jianyu Cai Yongfu You Jianjun Zhou Di Guo Xiaobo Qu Department of Electronic Science Intelligent Medical Imaging R&D CenterFujian Provincial Key Laboratory of Plasma and Magnetic ResonanceNational Institute for Data Science in Health and MedicineXiamen UniversityXiamen361104China Department of Electronic Science Key Laboratory of Digital Fujian on IoT CommunicationXiamen UniversityXiamen361104China Shanghai Electric Group Co. LtdShanghai200002China China Telecom Group Quanzhou362018China China Mobile Group Xiamen361009China Department of Radiology Zhongshan Hospital(Xiamen)Fudan UniversityXiamen Municipal Clinical Research Center for Medical ImagingFujian Province Key Clinical Specialty Construction Project(Medical Imaging Department)Xiamen Key Laboratory of Clinical Transformation of Imaging Big Data and Artificial IntelligenceXiamen361006China School of Computer and Information Engineering Xiamen University of TechnologyXiamen361024China

Magnetic resonance imaging(MRI)plays an important role in medical diagnosis,generating petabytes of image data annually in large *** voluminous data stream requires a significant amount of network bandwidth and extensive storage ***,local data processing demands substantial manpower and hardware *** isolation across different healthcare institutions hinders crossinstitutional collaboration in clinics and *** this work,we anticipate an innovative MRI system and its four generations that integrate emerging distributed cloud computing,6G bandwidth,edge computing,federated learning,and blockchain *** system is called Cloud-MRI,aiming at solving the problems of MRI data storage security,transmission speed,artificial intelligence(AI)algorithm maintenance,hardware upgrading,and collaborative *** workflow commences with the transformation of k-space raw data into the standardized Imaging Society for Magnetic Resonance in Medicine Raw data(ISMRMRD)***,the data are uploaded to the cloud or edge nodes for fast image reconstruction,neural network training,and automatic ***,the outcomes are seamlessly transmitted to clinics or research institutes for diagnosis and other *** Cloud-MRI system will save the raw imaging data,reduce the risk of data loss,facilitate inter-institutional medical collaboration,and finally improve diagnostic accuracy and work efficiency.

关键词： Magnetic resonance imaging Cloud computing 6G bandwidth artificial intelligence Edge computing Federated learning Blockchain

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Federated Services: A Smart Service Ecology with Federated Security for Aligned data Supply and Scenario-Oriented Demands

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IEEE/CAA Journal of Automatica Sinica 2025年第5期12卷 925-936页

作者： Jia, Xiaofeng Li, Juanjuan Wang, Shouwen Qi, Hongwei Wang, Fei-Yue Qin, Rui Zhang, Min Liang, Xiaolong Beijing Big Data Centre Beijing100101 China Institute of Automation Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing100190 China Technology Company Ltd. Beijing100190 China Chinese Academy of Sciences State Key Laboratory for Management and Control of Complex Systems Beijing100190 China Macau University of Science and Technology Faculty of Innovation Engineering Macao999078 China

This paper introduces federated services as a smart service ecology with federated security to align distributed data supply with diversified service demands spanning digital and societal contexts. It presents the comprehensive researches on the theoretical foundation and technical system of federated services, aiming at advancing our understanding and implementation of this novel service paradigm. First, a thorough examination of the characteristics of federated security within federated services is conducted. Then, a five-layer technical framework is formulated under a decentralized intelligent architecture, ensuring secure, agile, and adaptable service provision. On this basis, the operational mechanisms underlying data federation and service confederation is analyzed, with emphasis on the smart supply-demand matching model. Furthermore, a scenario-oriented taxonomy of federated services accompanied by illustrative examples is proposed. Our work offers actionable insights and roadmap for realizing and advancing federated services, contributing to the refinement and wider adoption of this transformative service paradigm in the digital era. © 2014 Chinese Association of Automation.

关键词： Decentralized autonomous organizations and operations decentralized physical infrastructure networks federated security federated services multimodal large language models smart contracts

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Perpendicular-Cutdepth:Perpendicular Direction Depth Cutting data Augmentation Method

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Computers, Materials & Continua 2024年第4期79卷 927-941页

作者： Le Zou Linsong Hu Yifan Wang Zhize Wu Xiaofeng Wang Anhui Provincial Engineering Laboratory of Big Data Technology Application forUrban Infrastructure School ofArtificial Intelligence and Big DataHefei UniversityHefei230601China Institute of Applied Optimization School of Artificial Intelligence and Big DataHefei UniversityHefei230601China

Depth estimation is an important task in computer *** data at scale for monocular depth estimation is challenging,as this task requires simultaneously capturing RGB images and depth ***,data augmentation is crucial for this *** data augmentationmethods often employ pixel-wise transformations,whichmay inadvertently disrupt edge *** this paper,we propose a data augmentationmethod formonocular depth estimation,which we refer to as the Perpendicular-Cutdepth *** method involves cutting realworld depth maps along perpendicular directions and pasting them onto input images,thereby diversifying the data without compromising edge *** validate the effectiveness of the algorithm,we compared it with existing convolutional neural network(CNN)against the current mainstream data augmentation ***,to verify the algorithm’s applicability to Transformer networks,we designed an encoder-decoder network structure based on Transformer to assess the generalization of our proposed *** results demonstrate that,in the field of monocular depth estimation,our proposed method,Perpendicular-Cutdepth,outperforms traditional data *** the indoor dataset NYU,our method increases accuracy from0.900 to 0.907 and reduces the error rate from0.357 to *** the outdoor dataset KITTI,our method improves accuracy from 0.9638 to 0.9642 and decreases the error rate from 0.060 to 0.0598.

关键词： Perpendicular depth estimation data augmentation

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