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arXiv 2024年

作者： Zhong, Luying Pi, Yueyang Chen, Zheyi Yu, Zhengxin Miao, Wang Chen, Xing Min, Geyong College of Computer and Data Science Fuzhou University China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University China Engineering Research Center of Big Data Intelligence Ministry of Education China School of Computing and Communications University of Lancaster United Kingdom School of Engineering Computing and Mathematics University of Plymouth United Kingdom Department of Computer Science University of Exeter United Kingdom

Federated Graph Learning (FGL) has garnered widespread attention by enabling collaborative training on multiple clients for semi-supervised classification tasks. However, most existing FGL studies do not well consider the missing inter-client topology information in real-world scenarios, causing insufficient feature aggregation of multi-hop neighbor clients during model training. Moreover, the classic FGL commonly adopts the FedAvg but neglects the high training costs when the number of clients expands, resulting in the overload of a single edge server. To address these important challenges, we propose a novel FGL framework, named SpreadFGL, to promote the information flow in edge-client collaboration and extract more generalized potential relationships between clients. In SpreadFGL, an adaptive graph imputation generator incorporated with a versatile assessor is first designed to exploit the potential links between subgraphs, without sharing raw data. Next, a new negative sampling mechanism is developed to make SpreadFGL concentrate on more refined information in downstream tasks. To facilitate load balancing at the edge layer, SpreadFGL follows a distributed training manner that enables fast model convergence. Using real-world testbed and benchmark graph datasets, extensive experiments demonstrate the effectiveness of the proposed SpreadFGL. The results show that SpreadFGL achieves higher accuracy and faster convergence against state-of-the-art algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph algorithms

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Like an Ophthalmologist: Dynamic Selection Driven Multi-View Learning for Diabetic Retinopathy Grading 39

Like an Ophthalmologist: Dynamic Selection Driven Multi-View...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Luo, Xiaoling Xu, Qihao Wu, Huisi Liu, Chengliang Lai, Zhihui Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen Key Laboratory of Visual Object Detection and Recognition Harbin Institute of Technology Shenzhen China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

ISBN: (纸本)157735897X

Diabetic retinopathy (DR), with its large patient population, has become a formidable threat to human visual health. In the clinical diagnosis of DR, multi-view fundus images are considered to be more suitable for DR diagnosis because of the wide coverage of the field of view. Therefore, different from the previous single-view DR grading methods, we design a dynamic selection-driven multi-view DR grading method to fit clinical scenarios better. Since lesion information plays a key role in DR diagnosis, previous methods usually boost the model performance by enhancing the lesion feature. However, during the actual diagnosis, ophthalmologists not only focus on the crucial parts, but also exclude irrelevant features to ensure the accuracy of judgment. To this end, we introduce the idea of dynamic selection and design a series of selection mechanisms from fine granularity to coarse granularity. In this work, we first introduce an Ophthalmic Image Reader (OIR) agent to provide the model with pixel-level prompts of suspected lesion areas. Moreover, we design a Multi-View Token Selection Module (MVTSM) that prunes redundant feature tokens and dynamically selects key information. In the final decision stage, we dynamically fuse multi-view features through the novel Multi-View Mixture of Experts Module (MVMoEM), to enhance key views and reduce the impact of conflicting views. Extensive experiments on a large multi-view fundus image dataset with 34,452 images prove that our method performs favorably against state-of-the-art models. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Ophthalmology

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Graceful Performance Degradation in Apache Storm 21st

Graceful Performance Degradation in Apache Storm

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21st International Conference on Parallel and Distributed computing, Applications, and Technologies, PDCAT 2020

作者： HoseinyFarahabady, Mohammad Reza Taheri, Javid Zomaya, Albert Y. Tari, Zahir School of Computer Science Center for Distributed and High Performance Computing The University of Sydney SydneyNSW Australia Department of Mathematics and Computer Science Karlstad University Karlstad Sweden School of Science RMIT University MelbourneVIC Australia

ISBN: (纸本)9783030692438

The concept of stream data processing is becoming challenging in most business sectors where try to improve their operational efficiency by deriving valuable information from unstructured, yet, contentiously generated high volume raw data in an expected time spans. A modern streamlined data processing platform is required to execute analytical pipelines over a continues flow of data-items that might arrive in a high rate. In most cases, the platform is also expected to dynamically adapt to dynamic characteristics of the incoming traffic rates and the ever-changing condition of underlying computational resources while fulfill the tight latency constraints imposed by the end-users. Apache Storm has emerged as an important open source technology for performing stream processing with very tight latency constraints over a cluster of computing nodes. To increase the overall resource utilization, however, the service provider might be tempted to use a consolidation strategy to pack as many applications as possible in a (cloud-centric) cluster with limited number of working nodes. However, collocated applications can negatively compete with each other, for obtaining the resource capacity in a shared platform that, in turn, the result may lead to a severe performance degradation among all running applications. The main objective of this work is to develop an elastic solution in a modern stream processing ecosystem, for addressing the shared resource contention problem among collocated applications. We propose a mechanism, based on design principles of Model Predictive Control theory, for coping with the extreme conditions in which the collocated analytical applications have different quality of service (QoS) levels while the shared-resource interference is considered as a key performance limiting parameter. Experimental results confirm that the proposed controller can successfully enhance the p -99 latency of high priority applications by 67%, compared to the default round r

关键词： Storms

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Local Periodicity-Based Beat Tracking for Expressive Classical Piano Music

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IEEE/ACM Transactions on Audio Speech and Language Processing 2023年 31卷 2824-2835页

作者： Chiu, Ching-Yu Muller, Meinard Davies, Matthew E. P. Su, Alvin Wen-Yu Yang, Yi-Hsuan National Cheng Kung University Academia Sinica Graduate Program of Multimedia Systems and Intelligent Computing Tainan701401 Taiwan International Audio Laboratories Erlangen Erlangen91058 Germany University of Coimbra Department of Informatics Engineering Centre for Informatics and Systems Coimbra3004-531 Portugal National Cheng Kung University Department of Computer Science and Information Engineering Tainan701401 Taiwan Taiwan AI Labs Yating Music Team Taipei103622 Taiwan Academia Sinica Research Center for IT Innovation Taipei11529 Taiwan

To model the periodicity of beats, state-of-the-art beat tracking systems use 'post-processing trackers' (PPTs) that rely on several empirically determined global assumptions for tempo transition, which work well for music with a steady tempo. For expressive classical music, however, these assumptions can be too rigid. With two large datasets of Western classical piano music, namely the Aligned Scores and Performances (ASAP) dataset and a dataset of Chopin's Mazurkas (Maz-5), we report on experiments showing the failure of existing PPTs to cope with local tempo changes, thus calling for new methods. In this paper, we propose a new local periodicity-based PPT, called predominant local pulse-based dynamic programming (PLPDP) tracking, that allows for more flexible tempo transitions. Specifically, the new PPT incorporates a method called 'predominant local pulses' (PLP) in combination with a dynamic programming (DP) component to jointly consider the locally detected periodicity and beat activation strength at each time instant. Accordingly, PLPDP accounts for the local periodicity, rather than relying on a global tempo assumption. Compared to existing PPTs, PLPDP particularly enhances the recall values at the cost of a lower precision, resulting in an overall improvement of F1-score for beat tracking in ASAP (from 0.473 to 0.493) and Maz-5 (from 0.595 to 0.838). © 2014 IEEE.

关键词： Hidden Markov models

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Uncovering neural scaling laws in molecular representation learning 23

Uncovering neural scaling laws in molecular representation l...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Dingshuo Chen Yanqiao Zhu Jieyu Zhang Yuanqi Du Zhixun Li Qiang Liu Shu Wu Liang Wang Center for Research on Intelligent Perception and Computing Institute of Automation Chinese Academy of Sciences Department of Computer Science University of California Los Angeles The Paul G. Allen School of Computer Science and Engineering University of Washington Department of Computer Science Cornell University Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong

Molecular Representation Learning (MRL) has emerged as a powerful tool for drug and materials discovery in a variety of tasks such as virtual screening and inverse design. While there has been a surge of interest in advancing model-centric techniques, the influence of both data quantity and quality on molecular representations is not yet clearly understood within this field. In this paper, we delve into the neural scaling behaviors of MRL from a data-centric viewpoint, examining four key dimensions: (1) data modalities, (2) dataset splitting, (3) the role of pre-training, and (4) model capacity. Our empirical studies confirm a consistent power-law relationship between data volume and MRL performance across these dimensions. Additionally, through detailed analysis, we identify potential avenues for improving learning efficiency. To challenge these scaling laws, we adapt seven popular data pruning strategies to molecular data and benchmark their performance. Our findings underline the importance of data-centric MRL and highlight possible directions for future research.

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Self-supervised 3D Point Cloud Completion via Multi-view Adversarial Learning

arXiv

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arXiv 2024年

作者： Wu, Lintai Cheng, Xianjing Xu, Yong Zeng, Huanqiang Hou, Junhui Bio-Computing Research Center Harbin Institute of Technology Shenzhen Guangdong Shenzhen518055 China The Department of Computer Science City University of Hong Kong Hong Kong School of Computer Science and Technology Harbin Institute of Technology Shenzhen Guangdong Shenzhen518055 China Department of Computer Science City University of Hong Kong Hong Kong Shenzhen Key Laboratory of Visual Object Detection and Recognition Guangdong Shenzhen518055 China School of Engineering Huaqiao University Quanzhou362021 China School of Information Science and Engineering Huaqiao University Xiamen361021 China

In real-world scenarios, scanned point clouds are often incomplete due to occlusion issues. The task of self-supervised point cloud completion involves reconstructing missing regions of these incomplete objects without the supervision of complete ground truth. Current self-supervised methods either rely on multiple views of partial observations for supervision or overlook the intrinsic geometric similarity that can be identified and utilized from the given partial point clouds. In this paper, we propose MAL-SPC, a framework that effectively leverages both region-level and category-specific geometric similarities to complete missing structures. Our MAL-SPC does not require any 3D complete supervision and only necessitates a single partial point cloud for each object. Specifically, we first introduce a Pattern Retrieval Network to retrieve similar position and curvature patterns between the partial input and the predicted shape, then leverage these similarities to densify and refine the reconstructed results. Additionally, we render the reconstructed complete shape into multi-view depth maps and design an adversarial learning module to learn the geometry of the target shape from category-specific single-view depth images. To achieve anisotropic rendering, we design a density-aware radius estimation algorithm to improve the quality of the rendered images. Our MAL-SPC yields the best results compared to current state-of-the-art methods. We will make the source code publicly available at https://***/ltwu6/malspc. Copyright © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning

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Deep learning for brain age estimation: A systematic review

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Information Fusion 2023年 96卷 130-143页

作者： Tanveer, M. Ganaie, M.A. Beheshti, Iman Goel, Tripti Ahmad, Nehal Lai, Kuan-Ting Huang, Kaizhu Zhang, Yu-Dong Del Ser, Javier Lin, Chin-Teng Department of Mathematics Indian Institute of Technology Indore Simrol Indore453552 India Department of Robotics University of Michigan Ann ArborMI48109 United States Department of Human Anatomy and Cell Science Rady Faculty of Health Sciences Max Rady College of Medicine University of Manitoba WinnipegMB Canada Biomedical Imaging Lab National Institute of Technology Silchar Assam 788010 India Department of Electrical Engineering and Computer Science National Taipei University of Technology Taipei Taiwan Department of Electronic Engineering National Taipei University of Technology Taipei Taiwan Data Science Research Center Duke Kunshan University China School of Computing and Mathematical Sciences University of Leicester LeicesterLE1 7RH United Kingdom Derio48160 Spain Bilbao48013 Spain School of Computer Science Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia

Over the years, Machine Learning models have been successfully employed on neuroimaging data for accurately predicting brain age. Deviations from the healthy brain aging pattern are associated with the accelerated brain aging and brain abnormalities. Hence, efficient and accurate diagnosis techniques are required to elicit accurate brain age estimations. Several contributions have been reported in the past for this purpose, resorting to different data-driven modeling methods. Recently, deep neural networks (also referred to as deep learning) have become prevalent in manifold neuroimaging studies, including brain age estimation. In this review, we offer a comprehensive analysis of the literature related to the adoption of deep learning for brain age estimation with neuroimaging data. We detail and analyze different deep learning architectures used for this application, pausing at research works published to date quantitatively exploring their application. We also examine different brain age estimation frameworks, comparatively exposing their advantages and weaknesses. Finally, the review concludes with an outlook towards future directions that should be followed by prospective studies. The ultimate goal of this paper is to establish a common and informed reference for newcomers and experienced researchers willing to approach brain age estimation by using deep learning models. © 2023 Elsevier B.V.

关键词： Deep neural networks

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Evaluate the Performance Parameters for Web browsers Empowered with Fuzzy logic

Evaluate the Performance Parameters for Web browsers Empower...

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Business Analytics for Technology and Security (ICBATS), International Conference on

作者： Faiza Khaliq Farheen Afzal Abd Ur Rehman Nauman Aziz Dr Fareeha Zafar Riphah School of Computing & Innovation Riphah International University Lahore Pakistan School of Computer Science NCBA&E Lahore Pakistan Riphah School of Computing Riphah Interntional University Lahore Pakistan School of Computer Science NCBA &E Lahore Pakistan Applied Science Research Center Applied Science Private University Amman Jordan Department of Computer Science GC University Lahore

In recent years, the Web browser achieved huge popularity in academic as well as industrial circles. A large number of web services and applications are trying to make their performance efficient. Decreasing the waiting of fetch Web browser has become a real challenge. This paper presents a parameter estimation methodology and model that measure the performance of Web browser. A system that displays a wide and natural manner of thinking based on a Multiple Criteria Decision-Making process. In this research paper, the proposed system uses different kinds of parameters to estimate the efficiency of the browser empowered with the Fuzzy logic to elaborate on this problem.

关键词： Fuzzy logic Parameter estimation Web services Decision making Browsers Security Business

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CLIP-SLA: Parameter-Efficient CLIP Adaptation for Continuous Sign Language Recognition

arXiv

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arXiv 2025年

作者： Alyami, Sarah Luqman, Hamzah Information and Computer Science Department King Fahd University of Petroleum and Minerals Saudi Arabia Computing Department Imam Abdulrahman Bin Faisal University Saudi Arabia SDAIA–KFUPM Joint Research Center for Artificial Intelligence Saudi Arabia

Continuous sign language recognition (CSLR) focuses on interpreting and transcribing sequences of sign language gestures in videos. In this work, we propose CLIP sign language adaptation (CLIP-SLA), a novel CSLR framework that leverages the powerful pre-trained visual encoder from the CLIP model to sign language tasks through parameter-efficient fine-tuning (PEFT). We introduce two variants, SLA-Adapter and SLA-LoRA, which integrate PEFT modules into the CLIP visual encoder, enabling fine-tuning with minimal trainable parameters. The effectiveness of the proposed frameworks is validated on four datasets: Phoenix2014, Phoenix2014-T, CSL-Daily, and Isharah-500, where both CLIP-SLA variants outperformed several SOTA models with fewer trainable parameters. Extensive ablation studies emphasize the effectiveness and flexibility of the proposed methods with different vision-language models for CSLR. These findings showcase the potential of adapting large-scale pre-trained models for scalable and efficient CSLR, which pave the way for future advancements in sign language understanding. Code is available at https://***/snalyami/CLIP-SLA. © 2025, CC BY.

关键词： visual languages

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MediGuard:A Survey on Security Attacks in Blockchain-IoT Ecosystems for e-Healthcare Applications

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computers, Materials & Continua 2025年第6期83卷 3975-4029页

作者： Shrabani Sutradhar Rajesh Bose Sudipta Majumder Arfat Ahmad Khan Sandip Roy Fasee Ullah Deepak Prashar Institute of Engineering and Technology Dibrugarh UniversityDibrugarh786004AssamIndia Departments of Computational Sciences Brainware UniversityKolkata700125West BengalIndia Department of Computer Science&Engineering JIS UniversityKolkata700109West BengalIndia Department of Computer Science College of ComputingKhon Kaen UniversityKhon Kaen40002Thailand Department of Computer and Information Sciences Universiti Teknologi PETRONASSeri Iskandar32610Perak Darul RidzuanMalaysia Department of Computer Science and Engineering(CSE) Lovely Professional UniversityPhagwara144411PunjabIndia Jadara University Research Center Jadara UniversityIrbid21110Jordan

Cloud-based setups are intertwined with the Internet of Things and advanced,and technologies such as blockchain revolutionize conventional healthcare *** digitization has major advantages,mainly enhancing the security barriers of the green tree *** this study,we conducted a systematic review of over 150 articles that focused exclusively on blockchain-based healthcare systems,security vulnerabilities,cyberattacks,and system *** addition,we considered several solutions proposed by thousands of researchers *** results mostly delineate sustained threats and security concerns in blockchain-based medical health infrastructures for data management,transmission,and ***,we describe 17 security threats that violate the privacy and data integrity of a system,over 21 cyber-attacks on security and QoS,and some system implementation problems such as node compromise,scalability,efficiency,regulatory issues,computation speed,and power *** propose a multi-layered architecture for the future healthcare ***,we classify all threats and security concerns based on these layers and assess suggested solutions in terms of these *** thorough theoretical examination of several performance criteria—including confidentiality,access control,interoperability problems,and energy efficiency—as well as mathematical verifications establishes the superiority of security,privacy maintenance,reliability,and efficiency over conventional *** conducted in-depth comparative studies on different interoperability parameters in the blockchain *** research justifies the use of various positive protocols and optimization methods to improve the quality of services in e-healthcare and overcome problems arising fromlaws and *** the theoretical aspects,their scope,and future expectations encourages us to design reliable,secure,and privacy-preserving systems.

关键词： Blockchain internet of medical things cloud infrastructure cyber-attacks privacy issues

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