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检索条件"任意字段=2024 International Conference on Computing, Machine Learning and Data Science, CMLDS 2024"

共 13410 条记录，以下是131-140 订阅

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Multi-temporal Domain Feature Adaptation Representation Framework for Website Fingerprinting 3rd

Multi-temporal Domain Feature Adaptation Representation Fram...

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3rd international conference on machine learning, Cloud computing and Intelligent Mining, MLCCIM 2024

作者： Lu, Zikui Song, Luona Han, Zhengxuan Ma, Zhixin Wang, Xiaojun Beijing University of Posts and Telecommunications Beijing100876 China Beijing Information Science and Technology University Beijing100192 China Dublin City University Dublin Ireland

ISBN: (纸本)9789819616978

The website fingerprinting (WF) classification focuses on identifying websites accessed by clients over encrypted network tools. By leveraging machine learning technology, methods have demonstrated high accuracy. However, many existing methods heavily rely on specific feature distributions, leading to poor performance on unseen data or data affected by concept drift. In this paper, we design a representation framework based on multi-temporal domain feature adaptation for website fingerprinting identifications, which consists of pre-training and fine-tuning stages. The framework extracts features from multi-temporal traffic data to generate robust representations and requires only a small amount of data for fine-tuning, making it applicable to data with different feature distributions. Experiments demonstrate that this approach effectively addresses concept drift. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： data accuracy

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4th international Workshop on Imaging and Treatment Challenges, SWITCH 2024, and 6th international Challenge on Ischemic Stroke Lesion Segmentation Challenge, ISLES 2024, Held in Conjunction with Medical Image computing and Computer Assisted Intervention, MICCAI 2024

4th International Workshop on Imaging and Treatment Challeng...

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4th international Workshop on Imaging and Treatment Challenges, SWITCH 2024, and 6th international Challenge on Ischemic Stroke Lesion Segmentation Challenge, ISLES 2024, Held in Conjunction with Medical Image computing and Computer Assisted Intervention, MICCAI 2024

ISBN: (纸本)9783031811005

The proceedings contain 12 papers. The special focus in this conference is on Imaging and Treatment Challenges and international Challenge on Ischemic Stroke Lesion Segmentation Challenge, Held in Conjunction with Medical Image computing and Computer Assisted Intervention. The topics include: Bayesian Uncertainty Estimation Improves nnU-Net Generalization to Unseen Sites for Stroke Lesion Segmentation;Usefulness of Monoenergetic Non-contrast CT and X-Map Images for Deep learning-Based Stroke Lesion Segmentation;improved Stroke Lesion Segmentation via Cross-Model Knowledge Distillation;Virtual DSA for learning Contrast Agent Dynamics in Projection Space;robust Feature Selection for Classifying Early Ischemic Changes in Posterior Stroke;ArterialGNet: Impossible Femoral Access Prediction in Stroke Mechanical Thrombectomy with Vascular Centerline Graph Embeddings;Spatio-Temporal Deep learning for Final Infarct Prediction Using Acute Stroke CT Perfusion data;Utilizing Baseline Infarct and Penumbra Masks to Improve NCCT and CTA Based Final Infarct Prediction;final Stroke Infarct Segmentation Using Deep Neural Networks;A Multi-modal Deep learning Framework for Final Infarct Prediction in Acute Ischemic Stroke: Combining CTA, NCCT, and Clinical data.

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Multi-view Contrastive learning for Next POI Recommendation 24

Multi-view Contrastive Learning for Next POI Recommendation

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8th international conference on computing and data Analysis, ICCDA 2024

作者： Zhang, Jin Zhu, Guixiang Chen, Yihan Lu, Yiheng Wang, Xinyi Feng, Shi Jiangsu Open University Jiang Su Nanjing China Nanjing University of Finance and Economics Jiang Su Nanjing China

ISBN: (纸本)9798400710445

Next POI recommendation aims to forecast their subsequent locations, which base been an important applications for Location-Based Services (LBS). Recently, there has been a growing interest in the investigation of POI recommendation using various deep learning techniques, and achieved great success. However, most of them only model the users preferences from a single view, such as the graph-structured features in the trajectory that indicate the preferences of individuals, or the dynamic change of personal preferences from trajectory sequences. Moreover, they ignore leverage the unique self-supervised signals to guide those two views of user preferences leaning perform better. To fill these critical voids, this paper proposes a Multi-View Contrastive learning for next POI recommendation named MVCL-POI. Specifically, MVCL-POI first exploits dynamic-aware GCN from trajectory graph to automatically extract graph-structured features with considerations of rich node connections. After that, MVCL-POI utilizes a temporal-aware transformer to capture users’ dynamic interest from their historical trajectory sequences. Finally, a multi-view contrastive learning is designed to maximize the agreement between two views of users’ representations. Experiments results on two real-life LBSN datasets show that MVCL-POI yields significant performance advantages over the competitive next POI recommendation methods. © 2024 Copyright held by the owner/author(s).

关键词： Contrastive learning

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When Uncertainty-Based Active learning May Fail? 27th

When Uncertainty-Based Active Learning May Fail?

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27th international conference on Pattern Recognition, ICPR 2024

作者： Rahmati, Amir Hossein Fan, Mingzhou Zhou, Ruida Urban, Nathan M. Yoon, Byung-Jun Qian, Xiaoning Texas A&M University College StationTX77843 United States University of California Los AngelesCA90095 United States Brookhaven National Laboratory UptonNY11973 United States

ISBN: (纸本)9783031781063

Instead of randomly acquiring training data, Uncertainty-based Active learning (UAL) selects pivotal samples from an unlabeled dataset based on the prediction uncertainty and queries their labels so that the labeling cost for model training can be minimized. As a result, the efficacy of UAL depends on the model capacity as well as the adopted uncertainty-based acquisition function. In this study, our analytical focus is directed toward comprehending how the capacity of the machine learning model may affect UAL efficacy. Through theoretical analysis and comprehensive simulation and empirical studies, we demonstrate that UAL can lead to worse performance compared to random sampling when the machine learning model class has low capacity and is unable to cover the underlying ground-truth. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Active learning

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Discovering Optimal Subspaces in Deep Architectures for Offline Signature Verification 24

Discovering Optimal Subspaces in Deep Architectures for Offl...

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13th international conference on computing and Pattern Recognition, ICCPR 2024

作者： Zhu, Qifeng Zhang, Benzhuang Yuan, Limengzi Ren, Yuanyuan Zhao, Qingzhan Zheng, Yuchen College of Information Science and Technology Shihezi University Shihezi China Dalian University of Technology Dalian China

ISBN: (纸本)9798400717482

In offline signature verification systems, deep learning-based feature extractors often fix the feature space for feature extraction, which may not obtain the best features for subsequent verification tasks. To address this issue, this paper proposes a novel framework that controls the dimensionality of features by stretching and reducing deep features to obtain the best features. Specifically, deep features can be stretched through a higher-dimensional projection matrix whose elements are randomly sampled from a standard normal distribution to obtain a more discriminative representation, and further reduced by Linear Discriminant Analysis (LDA) to more effectively capture the subtle differences between different signers. In this way, the learned features can be scaled to any optimal subspace. Extensive experimental results and discussions show that the proposed method improves the performance of deep learning-based offline signature verification systems and surpasses existing state-of-the-art verification systems on three benchmark datasets. © 2024 Copyright held by the owner/author(s).

关键词： Deep learning

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TKGR-GPRSCL: Enhance Temporal Knowledge Graph Reasoning with Graph Structure-Aware Path Representation and Supervised Contrastive learning 13th

TKGR-GPRSCL: Enhance Temporal Knowledge Graph Reasoning with...

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13th international conference on Natural Language Processing and Chinese computing

作者： Xiong, Lizhu Sheng, Yongpan He, Lirong Southwest Univ Coll Comp & Informat Sci Chongqing 400715 Peoples R China Chongqing Univ Sch Big Data & Software Engn Chongqing 401331 Peoples R China Chongqing Jiaotong Univ Sch Informat Sci & Engn Chongqing 400074 Peoples R China

ISBN: (纸本)9789819794331;9789819794348

Temporal knowledge graph reasoning (TKGR) aims to infer new temporal fact knowledge based on existing ones, which has become a major concern due to its wide demands, especially for time-sensitive modeling scenarios. However, existing temporal knowledge graph representation learning models usually have challenges in capturing complex graph structure-aware information and discriminating different relation expressions for unknown query prediction, which potentially limits the model performance on the TKGR task. In this paper, we propose a novel TKGR model named TKGR-GPRSCL. Specifically, we first introduce a maximum entropy-based random walk sampler, which can sample sufficient paths containing complicated graph structure-aware information in the TKG in form of path embedding. Then, we model the preference of different paths to the target entity, and the correlations between the target entity and its temporal property depending on transformations in the complex plane for encoding temporal fact. Finally, we advance supervised contrastive learning of relation patterns in light of the discrimination of different relation expressions to be preferably leveraged to predict entities for testing the queries, for those unknown queries in particular. We evaluate TKGR-GPRSCL on three TKGR benchmark datasets, and experimental results demonstrate that our model achieves superior performance compared to competitive baselines (All the data and codes are publicly available at https://***/shengyp/TKGR-GPRSCL).

关键词： Temporal knowledge graph reasoning Graph structure-aware path representation Supervised contrastive learning

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Adaptive Differentiable Singular Value Operator in Deep learning 24

Adaptive Differentiable Singular Value Operator in Deep Lear...

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3rd international conference on Algorithms, data Mining, and Information Technology, ADMIT 2024

作者： Wu, Hua Jia, Xixi Mei, Yu Wei, Hanjia Xidian University ShaanXi Xi'an China

ISBN: (纸本)9798400718120

Matrix decomposition has extensive application in the field of mathematics, involved in linear algebra, differential equations, statistics, and other fields. In recent years, it has also been widely used in deep learning and computer vision algorithms. The eigenvalue decomposition provides a powerful tool in theory, but in the practical application in the deep network is facing the challenge of value calculation. This is because in the process of backpropagation using singular value decomposition (SVD) or eigendecomposition (ED) for analytic calculation, because of the eigenvalue between close to each other, and build into numerical ill-condition problem of backpropagation, the numerical ill-condition problem affects the calculation of the gradient, thus affecting the depth of network training and convergence. In this article, we introduce a kind of numerical stable and differentiable method, least square model, which can solve the ill-conditioned problem from the root and make the calculation stable in the process of gradient backpropagation. Secondly, the least square regular method is improved into an adaptive method, which can according to the eigenvalue in the process of computing the size of the adaptive change regular items. Finally, its application in the experiments of image classification, in order to prove the feasibility and effectiveness of the method. © 2024 Copyright held by the owner/author(s).

关键词： Singular value decomposition

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11th international Workshop on Ophthalmic Medical Image Analysis, OMIA-XI 2024 was held in conjunction with the 27th international conference on Medical Image computing and Computer-Assisted Intervention, MICCAI 2024

11th International Workshop on Ophthalmic Medical Image Anal...

引用

11th international Workshop on Ophthalmic Medical Image Analysis, OMIA-XI 2024 was held in conjunction with the 27th international conference on Medical Image computing and Computer-Assisted Intervention, MICCAI 2024

ISBN: (纸本)9783031731181

The proceedings contain 16 papers. The special focus in this conference is on Ophthalmic Medical Image Analysis. The topics include: VNR-AV: Structural Post-processing for Retinal Arteries and Veins Segmentation;wavelet Deep learning Network for Objective Retinal Functional Estimation from Multimodal Retinal Imaging;inter-frame Sclera Vessel Rotation Tracking for Toric Intraocular Lens Implantation Navigation;data Heterogeneity-Aware Personalized Federated learning for Diagnosis;MM-UNet: A Mixed MLP Architecture for Improved Ophthalmic Image Segmentation;Coral-CVDs: A Consistent Ordinal Regression Model for Cardiovascular Diseases Grading;affordable Deep learning for Diagnosing Inherited and Common Retinal Diseases via Color Fundus Photography;Comparative Analysis of data Augmentation for Retinal OCT Biomarker Segmentation;advanced Diabetic Retinopathy Classification: Integrating Pathological Indicators Segmentation and Morphological Feature Analysis;Masked Image Modelling for Retinal OCT Understanding;A Dual-Stream Network for Langerhans’ Cells Segmentation in CCM Images;formula-Driven data Augmentation and Partial Retinal Layer Copying for Retinal Layer Segmentation;Enhancing Community Vision Screening: AI-Driven Retinal Photography for Early Disease Detection and Patient Trust;enhancing Large Foundation Models to Identify Fundus Diseases Based on Contrastive Enhanced Low-Rank Adaptation Prompt.

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Queueing Analysis of an Ensemble machine learning System 28th

Queueing Analysis of an Ensemble Machine Learning System

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28th international conference on Analytical & Stochastic Modeling Techniques & Applications

作者： Tsutsumi, Keishin Phung-Duc, Tuan Truong, Hong-Linh Univ Tsukuba Degree Programs Syst & Informat Engn I-1-1 Tennodai Tsukuba Ibaraki 3058573 Japan Univ Tsukuba Inst Syst & Informat Engn I-1-1 Tennodai Tsukuba Ibaraki 3058573 Japan Aalto Univ Dept Comp Sci Espoo Finland

ISBN: (纸本)9783031707520;9783031707537

Recent advances in AI/ML technologies have accelerated the development of various ML applications. One of the major trends in AI/ML application development is the increasing use of multiple ML models to support high-accuracy inference in a complex end-to-end ML serving. However, testing the right configuration of multiple ML models is expensive, and the application requirements for ML inferences are highly dependent on various factors like the quality of ML models, computing resource performance, and data quality. In this context, techniques and methods that help to emulate and analyze ML inference characteristics using queueing theory can reduce the development effort and cost for ML services encapsulating ML models but also the entire ML system. In this paper, we modeled and analyzed a queueing model for an ML system that uses ensemble learning as an inference method with a new rule and clarified the impacts of model design in ensemble learning on the system's performance. As a result, we demonstrate the usefulness of the analysis for understanding possible configurations and their efficiency in the ML system through queueing analysis and simulation.

关键词： Queueing theory

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ITiCSE WGR 2024 - Publication of the 2024 Working Group Reports on Innovation and Technology in Computer science Education

ITiCSE WGR 2024 - Publication of the 2024 Working Group Repo...

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29th Working Group Reports on Innovation and Technology in Computer science Education, ITiCSE WGR 2024

ISBN: (纸本)9798400712081

The proceedings contain 10 papers. The topics discussed include: computing education in African countries: a literature review and contextualized learning materials;AI integration in the IT professional workplace: a scoping review and interview study with implications for education and professional competencies;instructors’ perspectives on capstone courses in computing fields: a mixed-methods study;data systems education: curriculum recommendations, course syllabi, and industry needs;an international examination of non-technical skills and professional dispositions in computing – identifying the present day academia-industry gap;diversity, equity, and inclusion in computing science: culture is the key, curriculum contributes;what we talk about when we talk about K-12 computing education;and a pedagogical framework for developing abstraction skills.

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