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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141-150 订阅

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Tracking Lesion Evolution Using a Boundary Enhanced Approach for MS Change Segmentation (BEAMS)

Tracking Lesion Evolution Using a Boundary Enhanced Approac...

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Workshop on Longitudinal Disease Tracking and Modeling with Medical Images and data, LDTM 2024, 5th international Workshop on Multiscale Multimodal Medical Imaging, MMMI 2024, 1st Workshop on machine learning for Multimodal/-sensor Healthcare data, ML4MHD2024 and Workshop on Multimodal learning and Fusion Across Scales for Clinical Decision Support, ML-CDS 2024 held in conjunction with the 27th international conference on Medical Image computing and Computer Assisted Intervention, MICCAI 2024

作者： Mathur, Prateek Kelly, Brendan S. Killeen, Ronan P Lawlor, Aonghus School of Computer Science University College Dublin Dublin Ireland School of Medicine University College Dublin Dublin Ireland Insight SFI Research Centre for Data Analytics Dublin Ireland St. Vincent’s University Hospital Dublin Ireland

ISBN: (纸本)9783031845246

Multiple sclerosis (MS) is a chronic disease of the Central Nervous System (CNS) primarily characterised on Magnetic Resonance Imaging (MRI) by hyper-intense lesions. Accurate and timely identification of new lesions or growth in existing lesions has a tremendous impact on treatment planning and patient care. Early detection can lead to prompt intervention possibly slowing the disease progression. However, this task is challenging, costly and often prone to observer bias. Our approach consists of three key components (1) redefining the lesion segmentation problem as a change detection challenge focusing only on new and enhancing lesions;(2) adding an additional boundary detection task to an existing Convolutional Neural Network (CNN) to provide detailed context regarding the shape, size, and evolution of enhancing lesions;(3) evaluating the Fβ loss objective as an alternative to traditional segmentation loss objectives, aiming to optimise the model’s sensitivity and specificity. Furthermore, we employ Gradient-weighted Class Activation Mapping (GradCAM) to visualise the network’s attention, aiding in the interpretation and validation of our approach. By providing an objective evaluation, our method aims to streamline clinical decision-making processes for improved patient outcomes. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image segmentation

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23rd IFIP TC 14 international conference on Entertainment computing, ICEC 2024

23rd IFIP TC 14 International Conference on Entertainment Co...

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23rd IFIP TC 14 international conference on Entertainment computing, ICEC 2024

ISBN: (纸本)9783031743528

The proceedings contain 38 papers. The special focus in this conference is on Entertainment computing. The topics include: Unveiling Computer Chess Evolution: Can machine learning Detect Historical Trends?;prototypical – A Framework for Board Game Creation;game Audio Description in Death of Internet: Insights from Players and Developers;ArtAI4DS: AI Art and Its Empowering Role in Digital Storytelling;Toward Scalable Content Generation for Gamified mHealth Interventions: The Evaluation of LLM-Generated Goals on User Engagement;the Effects of Roleplaying Orientations on Transformative learning and Perspective Taking in Virtual Reality;a Virtual Reality Simulator for Enhancing Marine Pollution Control Efforts;A Pattern-Oriented AI-Powered Approach to Story Composition;towards Game Design Elements on the Perception of a Fun Game: An Exergame Case Study;A Taxonomy of Collectible Card Games from a Game-Playing AI Perspective;Introducing Open-Sourced AI to Art and Design Education: A Gamified Course on LoRA Model Training;virtual Reality Visions from science Fiction Movies;accessible Heads-Up computing;automated Clustering of Video Games into Groups with Distinctive Names;Enhancing Orientation and Mobility Skills for Persons with Visual Impairments Through VR Games: Introducing the EdKit Unity Package;virtual Reality and Accessibility: What Users Need?;from Pixels to Feelings: Exploring Game Elements for Eliciting Empathy Toward Refugees;Harmony in Hierarchy: Mixed-Initiative Music Composition Inspired by WFC;DPGame: Game-Based learning for Dynamic Programming Algorithms;are Aesthetics and Accessibility Conflicting Aspects in Serious Games Design?;ethics of Mixed Reality Entertainment;challenges in data Visualization with Extended Reality Devices;game Accessibility Research Summit Workshop;Aesthetics and Empowerment: Exploring AI-Driven Creativity;dungeon Wipe: Exploring Dynamic Difficulty Adjustment with Power-Up Mechanics;answering Anya: A Virtual and Mixed Reality Game

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ABBA-VSM: Time Series Classification Using Symbolic Representation on the Edge 22nd

ABBA-VSM: Time Series Classification Using Symbolic Represen...

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22nd international conference on Service Oriented computing

作者： Kanatbekova, Meerzhan Ilager, Shashikant Brandic, Ivona TU Wien Vienna Austria

ISBN: (纸本)9789819608041;9789819608058

In recent years, Edge AI has become more prevalent with applications across various industries, from environmental monitoring to smart city management. Edge AI facilitates the processing of Internet of Things (IoT) data and provides privacy-enabled and latency-sensitive services to application users using machine learning (ML) algorithms, e.g., Time Series Classification (TSC). However, existing TSC algorithms require access to full raw data and demand substantial computing resources to train and use them effectively in runtime. This makes them impractical for deployment in resource-constrained Edge environments. To address this, in this paper, we propose an Adaptive Brownian Bridge-based Symbolic Aggregation Vector Space Model (ABBA-VSM). It is a new TSC model designed for classification services on Edge. Here, we first adaptively compress the raw time series into symbolic representations, thus capturing the changing trends of data. Subsequently, we train the classification model directly on these symbols. ABBA-VSM reduces communication data between IoT and Edge devices, as well as computation cycles, in the development of resource-efficient TSC services on Edge. We evaluate our solution with extensive experiments using datasets from the UCR time series classification archive. The results demonstrate that the ABBA-VSM achieves up to 80% compression ratio and 90-100% accuracy for binary classification. Whereas, for non-binary classification, it achieves an average compression ratio of 60% and accuracy ranging from 60-80%.

关键词： Edge computing EdgeAI Time Series Classification data Compression Symbolic Representation

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Parkinson’s Disease Detection from Resting State EEG Using Multi-head Graph Structure learning with Gradient Weighted Graph Attention Explanations 7th

Parkinson’s Disease Detection from Resting State EEG Using...

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7th international Workshop on machine learning in Clinical Neuroimaging, MLCN 2024, Held in Conjunction with 27th international conference on Medical Image computing and Computer-Assisted Intervention, MICCAI 2024

作者： Neves, Christopher Zeng, Yong Xiao, Yiming Department of Computer Science and Software Engineering Concordia University MontrealQC Canada Concordia Institute for Information Systems Engineering Concordia University MontrealQC Canada

ISBN: (纸本)9783031787607

Parkinson’s disease (PD) is a debilitating neurodegenerative disease that has severe impacts on an individual’s quality of life. Compared with structural and functional MRI-based biomarkers for the disease, electroencephalography (EEG) can provide more accessible alternatives for clinical insights. While deep learning (DL) techniques have provided excellent outcomes, many techniques fail to model spatial information and dynamic brain connectivity, and face challenges in robust feature learning, limited data sizes, and poor explainability. To address these issues, we proposed a novel graph neural network (GNN) technique for explainable PD detection using resting state EEG. Specifically, we employ structured global convolutions with contrastive learning to better model complex features with limited data, a novel multi-head graph structure learner to capture the non-Euclidean structure of EEG data, and a head-wise gradient-weighted graph attention explainer to offer neural connectivity insights. We developed and evaluated our method using the UC San Diego Parkinson’s disease EEG dataset, and achieved 69.40% detection accuracy in subject-wise leave-one-out cross-validation while generating intuitive explanations for the learnt graph topology. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive 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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MultiAICL: Multi-task Tuning for Augmented In-Context learning in Text Style Transfer 13th

MultiAICL: Multi-task Tuning for Augmented In-Context Learni...

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13th international conference on Natural Language Processing and Chinese computing

作者： Zhu, Linan Zhou, Zehai Chen, Xiangfan Guo, Xiaolei Kong, Xiangjie Zhejiang Univ Technol Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9789819794362;9789819794379

In-context learning (ICL) enhances the performance of large language models (LLMs) across various natural language process (NLP) tasks by simply demonstrating a few-shot of examples or instructions during inference. However, ICL still encounters significant challenges on the text style transfer (TST) tasks, which require high levels of model reasoning. The existing ICL ability has not been further developed because LLMs lack the process of training and learning in context. To address these issues, we introduce Multi-Task Tuning for Augmented In-Context learning (MultiAICL), a framework designed to enhance model ICL ability by simulating LLM's supervised fine-tuning steps. MultiAICL contains three main components: firstly, we construct example instructions for multiple tasks from the text corpus, where these examples are in the form of text-label pairs;secondly, we propose the Multi-Task Tuning (MTT) module, which tunes the model by randomly combining example instructions;and thirdly, we design the Augmented In-context learning (AICL) module, which incorporates different tasks into example templates to infer the model. MultiAICL improves the ICL ability of LLMs while maintaining their generalization across multiple tasks, thus encouraging models to generate high-quality text. Extensive experiments show that MultiAICL achieves excellent results on all 6 TST tasks, even outperforming larger LLMs. The code and data are available at https://***/fuz999/NLPCC-2024- MultiAICL.

关键词： In-Context learning Text Style Transfer Large Language Models

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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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Enhanced Stroke Prediction Through Integrated Classification and Hybrid Sampling Techniques 3rd

Enhanced Stroke Prediction Through Integrated Classification...

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

作者： Peng, Guangxi Liu, Jie Guangdong University of Science and Technology Dongguan China Baise University Baise China

ISBN: (纸本)9789819624676

This paper introduces a integrated machine learning model designed to predict stroke risk by analyzing various contributing factors. The model was rigorously trained and validated on an imbalanced dataset, encompassing numerous features associated with stroke risk. To enhance the model's performance, we applied hybrid sampling techniques to address data imbalance and used Grid Search to precisely identify the optimal parameters for our five foundational models. Among these, the Integrated Classification Method achieved remarkable precision, recall, accuracy, and F1-score, ranging from 94% to 95%. This high level of performance underscores the model's robustness and its potential to significantly aid clinical decision-making in stroke risk prediction. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Prediction models

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A Few-Shot Image Classification Algorithm Combining Graph Neural Network and Attention Mechanism 24

A Few-Shot Image Classification Algorithm Combining Graph Ne...

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

作者： Zhou, Jie Qian, Jinfeng Guo, Qinghua Zhao, Yong School of Mathematical Sciences Ocean University of China Qingdao Shandong China Sinopec Geophysical Research Institute Co. Ltd Nanjing Jiangsu China

ISBN: (纸本)9798400717482

Deep learning has achieved success in various applications, depending on the abundance of training data. However, in practical applications, it is challenging to gather a substantial number of training samples. We employ few-shot learning algorithm to tackle this issue. Graph neural network can effectively capture the intra-class similarities and inter-class differences, which is highly beneficial for few-shot learning. This paper introduces an enhanced architecture based on graph neural network. Initially, we utilize a pre-trained residual network to extract features, which serve as the initial node features. And the similarity information between node features is defined as edge features. Subsequently, the graph neural network iteratively updates node features and edge features. Finally, we incorporate an attention mechanism into the metric network to compute the similarity of node features, which is used for classifying test samples. The attention mechanism can mitigate the interference from irrelevant information of neighbouring nodes, thereby improving the robustness and performance of the algorithm. Experiments demonstrate that our algorithm exhibits excellent performance in few-shot image classification. © 2024 Copyright held by the owner/author(s).

关键词： Zero-shot learning

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Multi-keyword Searchable data Auditing for Cloud-Based machine learning 6th

Multi-keyword Searchable Data Auditing for Cloud-Based Mach...

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6th international conference on machine learning for Cyber Security, ML4CS 2024

作者： Yu, Haiyan Ma, Qingru Zhu, Yilu Cui, Yuxin Zhejiang Sci-Tech University Hangzhou310018 China Zhejiang Key Laboratory of Digital Fashion and Data Governance Zhejiang Sci-Tech University Hangzhou310018 China Zhejiang Provincial International Cooperation Base for Science and Technology on Cloud Computing Security and Data Aggregation Zhejiang Sci-Tech University Hangzhou310018 China Zhejiang Sci-Tech University Hangzhou310018 China

ISBN: (纸本)9789819645657

machine learning (ML) relies on large amounts of data for training, which often requires outsourcing data to cloud servers. This raises concerns about data integrity and security. Preventing data from being tampered with or lost during storage and transmission has become a critical issue in the ML process. This paper presents a novel data auditing scheme for cloud-based ML, which supports multi-keyword search functionality. It allows searching one or more keywords to ensure the integrity of data in model training. To avoid data privacy leakage, the keywords are encrypted during the auditing process. Only the encrypted keywords of the data required for model training need to be provided to the third-party auditor (TPA). Subsequently, TPA can check and ensure the integrity of the relevant data. Through security analysis and performance evaluations, the results indicate that this scheme can effectively and reliably audit outsourced data while ensuring the security and privacy of model training data. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： data integrity

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