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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111-120 订阅

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A data Model of a data Lineage Management System for database Repair and Simulation 26th

A Data Model of a Data Lineage Management System for Data...

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26th international conference on Information Integration and Web Intelligence, iiWAS 2024

作者： Wong, Wei Jun Yasuda, Kyoko Chang, Qiong Miyazaki, Jun Department of Computer Science School of Computing Tokyo Institute of Technology 2 Chome-12-1 Ookayama Tokyo Meguro152-8550 Japan

ISBN: (纸本)9783031780929

In this paper, we propose a new data model that uses data lineage, which represents relationships between data and their histories, for the verification of data. In recent years, although the verifiability of processes in many industries for their data is required, current data management systems are inadequate, as they do not distinguish between data flow and data dependencies, and also offer little support for machine learning models. Our data model for managing data lineage metadata is based on the property graph model, and supports not only machine learning models which must be treated as both operations to data as well as the data derived from training datasets, but also database repairs and database simulations, which are for maintaining the integrity of data and leveraging past data for future use. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： data integrity

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An Autoencoder Framework for Few-Shot Human Activity Recognition with Sensor data 3rd

An Autoencoder Framework for Few-Shot Human Activity Recogni...

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

作者： Han, Zhengxuan He, Mingshu School of International School Beijing University of Posts and Telecommunications Beijing China School of Cyberspace Security Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9789819624676

As the training sample data in deep learning is not always easy to obtain, and the labeling task of sample data is very labor-intensive, weakly supervised learning method as a branch of machine learning has increasingly garnered interest. The scale of training datasets for different actions in sensor-based human activity recognition (HAR) often varies widely. For some action categories with little sample data, it is difficult for conventional learning methods to learn their features, which will lead to poor model performance in practical applications. In weakly supervised learning methods, the model can still achieve excellent performance without the need for a good deal of various label data in the training stage. Therefore, this study will use weakly supervised learning methods to improve low model performance caused by unbalanced samples. Based on autoencoder, which is a weakly supervised learning method, this research proposed a framework for sensor-based HAR to ameliorate the negative effects of the unbalanced distribution of training samples on the model. Results from experiments on open datasets demonstrated that the proposed framework enhances the original model's low accuracy caused by sample imbalance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Self-supervised learning

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Hybrid Quantum Solvers in Production: How to Succeed in the NISQ Era? 25th

Hybrid Quantum Solvers in Production: How to Succeed in the ...

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25th international conference on Intelligent data Engineering and Automated learning

作者： Osaba, Eneko Villar-Rodriguez, Esther Gomez-Tejedor, Aitor Oregi, Izaskun TECNALIA Basque Res & Technol Alliance BRTA Derio 48160 Spain Univ Basque Country UPV EHU Leioa 48940 Spain European Univ Gasteiz EUNEIZ Vitoria 01013 Spain

ISBN: (纸本)9783031777370;9783031777387

Hybrid quantum computing is considered the present and the future within the field of quantum computing. Far from being a passing fad, this trend cannot be considered just a stopgap to address the limitations of NISQ-era devices. The foundations linking both computing paradigms will remain robust over time. The contribution of this work is twofold: first, we describe and categorize some of the most frequently used hybrid solvers, resorting to two different taxonomies recently published in the literature. Secondly, we put a special focus on two solvers that are currently deployed in real production and that have demonstrated to be near the real industry. These solvers are the LeapHybridBQMSampler contained in D-Wave's Hybrid Solver Service and Quantagonia's Hybrid Solver. We analyze the performance of both methods using as benchmarks four combinatorial optimization problems.

关键词： Hybrid Quantum-Classical computing Combinatorial Optimization Quantagonia D-Wave Quantum computing

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MedPromptX: Grounded Multimodal Prompting for Chest X-Ray Diagnosis

MedPromptX: Grounded Multimodal Prompting for Chest X-Ray D...

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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： Shaaban, Mai A. Khan, Adnan Yaqub, Mohammad Mohamed Bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates Department of Mathematics and Computer Science Faculty of Science Alexandria University EG Alexandria Egypt School of Computer Science Carleton University OttawaCA Canada

ISBN: (纸本)9783031845246

Chest X-ray images are commonly used for predicting acute and chronic cardiopulmonary conditions, but efforts to integrate them with structured clinical data face challenges due to incomplete electronic health records (EHR). This paper introduces MedPromptX, the first clinical decision support system that integrates multimodal large language models (MLLMs), few-shot prompting (FP) and visual grounding (VG) to combine imagery with EHR data for chest X-ray diagnosis. A pre-trained MLLM is utilized to complement the missing EHR information, providing a comprehensive understanding of patients’ medical history. Additionally, FP reduces the necessity for extensive training of MLLMs while effectively tackling the issue of hallucination. Nevertheless, the process of determining the optimal number of few-shot examples and selecting high-quality candidates can be burdensome, yet it profoundly influences model performance. Hence, we propose a new technique that dynamically refines few-shot data for real-time adjustment to new patient scenarios. Moreover, VG narrows the search area in X-ray images, thereby enhancing the identification of abnormalities. We also release MedPromptX-VQA, a new in-context visual question answering dataset encompassing interleaved images and EHR data derived from MIMIC-IV and MIMIC-CXR-JPG databases. Results demonstrate the SOTA performance of MedPromptX, achieving an 11% improvement in F1-score compared to the baselines. Code and data are publicly available on https://***/BioMedIA-MBZUAI/MedPromptX. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Electronic health record

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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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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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Performance Evaluation of Intrusion Detection Systems on the TON_IoT datasets Using a Feature Selection Method 24

Performance Evaluation of Intrusion Detection Systems on the...

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8th international conference on Computer science and Artificial Intelligence, CSAI 2024

作者： Maseno, Elijah Mwandata Wang, Zenghui Sun, Yanxia Department of Computing and Information Technology Mama Ngina University College Gatundu Nairobi Kenya Department of Electrical and Smart Systems Engineering Centre for Augmented Intelligence and Data Science University of South Africa Florida Gauteng South Africa Department of Electrical and Electronic Engineering Science University of Johannesburg Gauteng Johannesburg South Africa

ISBN: (纸本)9798400718182

As Internet of Things (IoT) technology develops so quickly, security issues with IoT devices have come to light. IoT is an array of intelligent devices connected via a network to provide various services. The amount of data generated by these devices has an impact on how well the current intrusion detection systems (IDS) function. The generated dataset consists of irrelevant features which reduces the performance of IDS, making IoT ecosystem vulnerable to cyberattacks. The researchers have suggested the feature reduction technique as a potential solution to the current problem. The proposed method seeks to reduce the feature count by removing the redundant feature subset. Several machine learning methods have been successfully implemented in this discipline. This study proposed the application of hybrid feature reduction technique. The research combined Convolutional neural network (CNN) and Long short-term memory (LSTM);CNN extracted local features and decreased dimensionality, while LSTM identified long-term relationships in the data. SVM and Random Forest classifiers models were used to classify the chosen feature subset. This study employed the TON_IoT datasets, an up-to-date dataset, to evaluate the model. During data preprocessing, the study applied SMOTETomek data pre-processing technique to address class imbalance in the dataset. With the decreased feature subset, the classification models fared reasonably well;RF had a 98% accuracy rate while SVM had a 91% accuracy rate, showcasing the suggested methodology’s potential for creating efficient IDS. © 2024 Copyright held by the owner/author(s).

关键词： Convolutional neural networks

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HyperMM: Robust Multimodal learning with Varying-Sized Inputs

HyperMM: Robust Multimodal Learning with Varying-Sized Inpu...

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作者： Chaptoukaev, Hava Marcianó, Vincenzo Galati, Francesco Zuluaga, Maria A. Data Science Department EURECOM Biot France School of Biomedical Engineering and Imaging Sciences Kings College London London United Kingdom

ISBN: (纸本)9783031845246

Combining multiple modalities carrying complementary information through multimodal learning (MML) has shown considerable benefits for diagnosing multiple pathologies. However, the robustness of multimodal models to missing modalities is often overlooked. Most works assume modality completeness in the input data, while in clinical practice, it is common to have incomplete modalities. Existing solutions that address this issue rely on modality imputation strategies before using supervised learning models. These strategies, however, are complex, computationally costly and can strongly impact subsequent prediction models. Hence, they should be used with parsimony in sensitive applications such as healthcare. We propose HyperMM, an end-to-end framework designed for learning with varying-sized inputs. Specifically, we focus on the task of supervised MML with missing imaging modalities without using imputation before training. We introduce a novel strategy for training a universal feature extractor using a conditional hypernetwork, and propose a permutation-invariant neural network that can handle inputs of varying dimensions to process the extracted features, in a two-phase task-agnostic framework. We experimentally demonstrate the advantages of our method in two tasks: Alzheimer’s disease detection and breast cancer classification. We demonstrate that our strategy is robust to high rates of missing data and that its flexibility allows it to handle varying-sized datasets beyond the scenario of missing modalities. We make all our code and experiments available at ***/robustml-eurecom/hyperMM. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semi-supervised learning

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Bias-Rectified Multi-way learning with data Augmentation for Implicit Discourse Relation Recognition 13th

Bias-Rectified Multi-way Learning with Data Augmentation for...

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13th international conference on Natural Language Processing and Chinese computing

作者： Zheng, Ziwei Liang, Chao Xiang, Wei Wang, Bang Huazhong Univ Sci & Technol Sch Elect Informat & Commun Hubei Key Lab Smart Internet Technol Luoyu Rd 1037 Wuhan 430074 Hubei Peoples R China Cent China Normal Univ Fac Artificial Intelligence Educ Wuhan Peoples R China

ISBN: (纸本)9789819794300;9789819794317

Implicit Discourse Relation Recognition (IDRR) is a challenging but vital task in discourse analysis that focuses on identifying and classifying the relation between two arguments without explicit connectives. Previous research has focused on constructing sophisticated argument representations or utilizing labels' hierarchical information while neglecting the intrinsic prior relational bias, where some relation categories are significantly less frequent than others due to inherent linguistic properties. Furthermore, some works amalgamate Explicit Discourse Relation Recognition (EDRR) data with IDRR data to achieve data augmentation;however, the linguistic discrepancies between EDRR and IDRR data may mislead the relation recognition process. To address the prior relation bias and misleading from linguistic discrepancies, we propose a novel Bias-REctified Multi-way learning (BREM) model, which incorporates prior probability knowledge to assist in understanding the discourse relations and employs a PLM-based method for pre-training on the EDRR dataset to achieve data augmentation without introducing misleading effects. Experiments on the PDTB 3.0 corpus show that BREM outperforms previous models, especially in identifying less frequent relation senses, highlighting the effectiveness of our proposed model. The source code of our proposed model is publicly available at https://***/zzzziwy/NLPCC2024-BREM_for_IDRR.

关键词： Implicit Discourse Relation Recognition prior relation bias data Augmentation

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COCOWEARS: A framework for COntinuum computing WEARable Systems 6

COCOWEARS: A framework for COntinuum COmputing WEARable Syst...

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6th international conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Aloi, Gianluca Frustaci, Fabio Gravina, Raffaele Guerrieri, Antonio Pio, Gianvito Savaglio, Claudio DIMES Dept. University of Calabria CS Rende87036 Italy ICAR-CNR CS Rende87036 Italy Dept. of Computer Science University of Bari Aldo Moro Via Orabona 4 BA Bari70125 Italy

The amount and diversity of data generated by the sensors available on current Wearable computing Systems (WCS) impose the adoption of advanced, efficient (and often customized) data analysis methods, including machine learning (ML) / Artificial Intelligence (AI) and multi-sensor information fusion. With the diffusion of Cloud computing and ARM/MCU architectures in the last decade, commercial WCS have gradually been introduced into the market, resulting in vertical solutions with poor pro-grammability, privacy, and smartness. To make WCS truly smart and Cloud/smartphone independent, we propose COCOWEARS: a framework for COntinuum computing WEARable Systems. COCOWEARS relies on (i) sound theoretical foundations with a hardware/software system co-design methodology based on simulations, (ii) tailored AI/ML algorithms, and (iii) development tools to enable the engineering of next-generation WCS. A use case about the monitoring of operators' activities within the smart factory is provided to exemplify our approach, which is, to our knowledge, the first solution fully exploiting AI technologies at different levels of granularity, complexity, and user proximity, able to push towards massive adoption of WCS in the immediate future. © 2024 The Authors.

关键词： Wearable computers

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Multimodality Frequency Feature Customized learning for Pediatric Ventricular Septal Defects Identification

Multimodality Frequency Feature Customized Learning for Pedi...

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作者： Jin, Feifei Zhao, Cheng Yang, Peng Xiang, Zhuo Chen, Xunyi Zhang, Yu Fan, Shumin Zhou, Luyao Chen, Weiling Wang, Tianfu Lei, Baiying School of Biomedical Engineering Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging Shenzhen University National-Regional Key Technology Engineering Laboratory for Medical Ultrasound Shenzhen China Ultrasound Department Shenzhen Children Hospital Shenzhen China

ISBN: (纸本)9783031845246

Ventricular septal defects (VSD) can be more effectively identified by combining anatomical structural features from 2D grayscale images and blood flow information from Doppler images. Most current algorithms only perform the same operation on features and do not take into account that different frequency features focus on the expression of different information. Starting from the perspective of frequency and multi-modality, this paper designs a method called multimodality frequency feature customized learning (MFCL) for the identification of VSD. Specifically, this paper first constructs a frequency decomposition module (FD) based on the Fourier transform to extract high-frequency and low-frequency features corresponding to different modes. Secondly, this paper designs a cross-pooling fusion Transformer (CPFT) module tailored for multimodality low-frequency features, which can achieve the fusion of multi-modality global information while reducing computational costs. This paper designs a cross-convolution fusion (CCF) module tailored for multi-modality high-frequency features to achieve the fusion of anatomical structure detail features of 2D grayscale images and blood flow information of color Doppler images. Experimental results show that the proposed algorithm is superior to the comparison methods for VSD identification. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image coding

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