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检索条件"任意字段=2022 International Conference on Machine Learning and Data Engineering, ICMLDE 2022"

共 33684 条记录，以下是201-210 订阅

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Method for Verifying Blockchain Transaction Amounts Based on Digital Signatures 30th

Method for Verifying Blockchain Transaction Amounts Based on...

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30th international conference on Computational and Experimental engineering and Sciences

作者： Jia, Xiangjuan Sun, Shuning Zhang, Yijie Zhang, Lu Chen, Jianghao Chen, Xiaofeng Hangzhou Qulian Technol Co Ltd Hangzhou Peoples R China Univ Chinese Acad Sci Hangzhou Inst Adv Study Hangzhou Peoples R China Hangzhou High Tech Zone Binjiang Inst Blockchain Hangzhou Peoples R China Zhejiang Univ State Key Lab Blockchain & Data Secur Hangzhou Peoples R China

ISBN: (纸本)9783031774911;9783031774898;9783031774881

In response to the potential issue of transaction amount leakage in current blockchain systems, we have explored an efficient, low computational complexity method for the secure verification of transaction amounts in blockchain, which also protects privacy. Utilizing homomorphic encryption and digital signature technology, we propose the use of the Paillier algorithm for the verification of transaction amounts in blockchain. This approach aims to protect the amounts sent and received in blockchain transactions and provides the functionality to update the balances of the transaction parties in an encrypted state. Through in-depth security analysis and experimental evaluation, this solution has been verified to not only meet security requirements but also achieve our performance expectations.

关键词： Privacy protection Blockchain Confidential transactions Digital signature Homomorphic encryption

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Privacy-Preserving in Federated learning: A Comparison between Differential Privacy and Homomorphic Encryption across Different Scenarios 18

Privacy-Preserving in Federated Learning: A Comparison betwe...

引用

18th IEEE international conference on Software Testing, Verification and Validation Workshops, ICSTW 2025

作者： Catalfamo, Alessio Fazio, Maria Celesti, Antonio Villari, Massimo University of Messina MIFT Department Messina Italy

ISBN: (纸本)9798331534677

Federated learning is a decentralized machine learning paradigm where multiple devices collaboratively train a model while keeping their training data local so to enhance data privacy and reduce communication costs. However, it is vulnerable to some privacy threats, such as model inversion and membership inference attacks, which can expose sensitive data in the devices. This paper explores the integration of different Privacy-Preserving approaches in FL, which are Differential Privacy and Homomorphic Encryption. We assess the impact of these techniques on model accuracy, training efficiency, and computational overhead through experimental evaluations across three different application use cases. Our evaluation results depict the trade-off between privacy enhancement and performance for different scenarios and models. They highlight the effectiveness of combining Federated learning with advanced cryptographic and privacy-preserving techniques to achieve secure, scalable, and privacy-aware distributed learning. © 2025 IEEE.

关键词： Differential privacy

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Beyond the Horizon: Using Mixture of Experts for Domain Agnostic Fake News Detection 27th

Beyond the Horizon: Using Mixture of Experts for Domain Agno...

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27th international conference on Discovery Science

作者： Comito, Carmela Guarascio, Massimo Liguori, Angelica Manco, Giuseppe Pisani, Francesco Sergio Inst High Performance Comp & Networking ICAR CNR Via Pietro Bucci 8-9C I-87036 Arcavacata Di Rende Italy

ISBN: (纸本)9783031789793;9783031789809

In recent years, social media have become one of the main means to quickly spread information worldwide, but this rapid dissemination also brings significant risks of misinformation and fake news, which can cause widespread confusion, erode public trust, and contribute to social and political instability. This scenario is further exacerbated by the fact that fake news can span various topics across different domains, making it impracticable for a single moderator to manage the massive quantity of data. The use of machine learning, particularly language models, is rising as an effective solution to mitigate the risk of misinformation. However, a single model cannot fully capture the complexity and variety of the information it needs to process, often failing to classify examples from new domains. In this work, the aforementioned challenges are addressed by leveraging a novel hierarchical deep-ensemble framework. This framework aims to integrate various domains to offer enhanced predictions for new ones. Specifically, the approach involves learning a distinct model for each domain and refining them through domain-specific adaptation procedures. The predictions of these refined models are hence blended using a Mixture of Experts approach, which allows for selecting the most reliable for predicting the new examples. The proposed approach is fully cross-domain and does not necessitate retraining or fine-tuning when encountering new domains, thus streamlining the adaptation process and ensuring scalability across diverse data landscapes. Experiments conducted on 5 real datasets demonstrate the robustness and effectiveness of our proposal.

关键词： Cross-Domain Fake News Detection Deep Ensemble learning Language Models Mixture of Experts

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5th international conference on Applied machine learning and data Analytics, AMLDA 2022

5th International Conference on Applied Machine Learning and...

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5th international conference on Applied machine learning and data Analytics, AMLDA 2022

ISBN: (纸本)9783031342219

The proceedings contain 20 papers. The special focus in this conference is on Applied machine learning and data Analytics. The topics include: RU-Net: A Novel Approach for Gastro-Intestinal Tract Image Segmentation Using Convolutional Neural Network;a Credit Card Fraud Detection Model Using machine learning Methods with a Hybrid of Undersampling and Oversampling for Handling Imbalanced datasets for High Scores;Implementation of YOLOv7 for Pest Detection;online Grocery Shopping: - Key Factors to Understand Shopping Behavior from data Analytics Perspective;a Novel Approach: Semantic Web Enabled Subject Searching for Union Catalogue;how People in South America is Facing Monkeypox Outbreak?;multilevel Classification of Satellite Images Using Pretrained AlexNet Architecture;handwriting Recognition for Predicting Gender and Handedness Using Deep learning;retrieval of Weighted Lexicons Based on Supervised learning Method;univariate Feature Fitness Measures for Classification Problems: An Empirical Assessment;performance Evaluation of Smart Flower Optimization Algorithm Over Industrial Non-convex Constrained Optimization Problems;securing Advanced Metering Infrastructure Using Blockchain for Effective Energy Trading;keratoconus Classification Using Feature Selection and machine learning Approach;Semantic Segmentation of the Lung to Examine the Effect of COVID-19 Using UNET Model;SMDKGG: A Socially Aware Metadata Driven Knowledge Graph Generation for Disaster Tweets;Brain MRI Image Classification Using Deep learning;a Real-Time Face Recognition Attendance Using machine learning;towards Abalone Differentiation Through machine learning.

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HyperDiff: Masked Diffusion Model with High-efficient Transformer for Hyperspectral Image Cross-Scene Classification

HyperDiff: Masked Diffusion Model with High-efficient Transf...

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2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhang, Pei Wang, Dong Wu, Chanyue Yang, Jing Kang, Lei Bai, Zongwen Li, Ying Shen, Qiang School of Computer Science Northwestern Polytechnical University Xi'an China Shaanxi Key Laboratory of Intelligent Processing for Big Energy Data Yan'an University Yan'an China School of Automation and Software Engineering Shanxi University Taiyuan China Qingdao Topscomm Communication Co. Ltd. Qingdao China Department of Computer Science Aberystwyth University Aberystwyth United Kingdom

ISBN: (纸本)9798350368741

Hyperspectral Image (HSI) cross-scene classification is a challenging task in remote sensing, particularly when real-time processing of Target Domain (TD) HSI is required, and data cannot be reused for training. While deep learning methods have shown promising results, the generalization ability of HSI representations remains limited, mainly due to class label imbalance. This paper introduces a dual-stage learning framework based on transfer learning to enhance classification accuracy in the TD. The framework includes a self-supervised learning stage and a supervised fine-tuning stage. The self-supervised stage focuses on learning robust representations by leveraging inherent structures within HSI data, while the fine-tuning stage uses training labels to extract semantic information. A masked diffusion model predicts masked tokens from unmasked ones, capturing both high-level structures and fine details in HSI data. An efficient spatiospectral Transformer, which removes self-attention from the decoder, is proposed to enhance the self-supervised process. This design allows mask tokens to obtain information from visible tokens without interacting with each other, reducing sequence length and computational costs. By decoding each mask token conditionally independently, only a subset of masked tokens is processed. Extensive experiments on two public HSI datasets demonstrate that the proposed method outperforms state-of-the-art techniques. © 2025 IEEE.

关键词： Cross-scene classification Diffusion model Hyperspectral image Transformer

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Partially Supervised Unpaired Multi-modal learning for Label-Efficient Medical Image Segmentation 15th

Partially Supervised Unpaired Multi-modal Learning for Label...

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15th international Workshop on machine learning in Medical Imaging

作者： Zhu, Lei Xu, Yanyu Fu, Huazhu Xu, Xinxing Goh, Rick Siow Mong Liu, Yong ASTAR Inst High Performance Comp IHPC 1 Fusionopolis Way16-16 Connexis Singapore 138632 Singapore Shandong Univ Joint SDU NTU Ctr Artificial Intelligence Res C F Jinan Shandong Peoples R China

ISBN: (纸本)9783031732928;9783031732904

Unpaired Multi-Modal learning (UMML) which leverages unpaired multi-modal data to boost model performance on each individual modality has attracted a lot of research interests in medical image analysis. However, existing UMML methods require multi-modal datasets to be fully labeled, which incurs tremendous annotation cost. In this paper, we investigate the use of partially labeled data for label-efficient unpaired multi-modal learning, which can reduce the annotation cost by up to one half. We term the new learning paradigm as Partially Supervised Unpaired Multi-Modal learning (PSUMML) and propose a novel Decomposed partial class adaptation with snapshot Ensembled Self-Training (DEST) framework for it. Specifically, our framework consists of a compact segmentation network with modality specific normalization layers for learning with partially labeled unpaired multi-modal data. The key challenge in PSUMML lies in the complex partial class distribution discrepancy due to partial class annotation, which hinders effective knowledge transfer across modalities. We theoretically analyze this phenomenon with a decomposition theorem and propose a decomposed partial class adaptation technique to precisely align the partially labeled classes across modalities to reduce the distribution discrepancy. We further propose a snapshot ensembled self-training technique to leverage the valuable snapshot models during training to assign pseudo-labels to partially labeled pixels for self-training to boost model performance. We perform extensive experiments under different scenarios of PSUMML for two medical image segmentation tasks, namely cardiac substructure segmentation and abdominal multi-organ segmentation. Our framework outperforms existing methods significantly.

关键词： Unpaired Multi-Modal learning Partially Supervised learning Segmentation

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Onboard Detection Method of Metro Rail Corrugation Based on Unsupervised learning

Onboard Detection Method of Metro Rail Corrugation Based on ...

引用

international conference on Applied Nonlinear Dynamics, Vibration and Control, ICANDVC 2024

作者： Wang, Yang Xiao, Hong Zhang, Zhihai Chi, Yihao Zhao, Weize School of Civil Engineering Beijing Jiaotong University Beijing100044 China Beijing Key Laboratory of Track Engineering Beijing Jiaotong University Beijing100044 China

ISBN: (纸本)9789819633166

The onboard high-speed detection is a critical development direction for the operation and maintenance of railway networks. Rail corrugation is the most common track irregularity in metro systems, causing a series of environmental vibration and noise issues. Current onboard detection of rail corrugation primarily relies on axle box acceleration data and depends on data labeling and supervised learning frameworks. To facilitate data acquisition and reduce labeling costs, an unsupervised learning framework based on train body vertical vibration acceleration signals is proposed. First, the train body vertical vibration acceleration signals were transformed into time-frequency spectrogram datasets using Synchrosqueezed Wave Packet Transform (SSWPT). Then, an unsupervised learning framework was established based on momentum contrastive learning and trained on the unlabeled time-frequency spectrogram datasets. Finally, the pretrained model was fine-tuned for downstream tasks, including corrugation wavelength classification and amplitude assessment. Results indicate that the model achieved optimal performance after 430,000 iterations. For the binary classification of corrugation presence, the accuracy of the wavelength classification task ranged between 95% and 100%, while the amplitude assessment task achieved over 95% accuracy. Field application results demonstrate that the proposed model possesses strong generalization capabilities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Unsupervised learning

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Interpretable machine learning for Oral Lesion Diagnosis Through Prototypical Instances Identification 27th

Interpretable Machine Learning for Oral Lesion Diagnosis Thr...

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27th international conference on Discovery Science

作者： Cascione, Alessio Setzu, Mattia Galatolo, Federico A. Cimino, Mario G. C. A. Guidotti, Riccardo Univ Pisa Largo Bruno Pontecorvo 3 I-56127 Pisa PI Italy ISTI CNR KDD Lab Via G Moruzzi 1 I-56124 Pisa PI Italy

ISBN: (纸本)9783031789793;9783031789809

Decision-making processes in healthcare can be highly complex and challenging. machine learning tools offer significant potential to assist in these processes. However, many current methodologies rely on complex models that are not easily interpretable by experts. This underscores the need to develop interpretable models that can provide meaningful support in clinical decision-making. When approaching such tasks, humans typically compare the situation at hand to a few key examples and representative cases imprinted in their memory. Using an approach which selects such exemplary cases and grounds its predictions on them could contribute to obtaining high-performing interpretable solutions to such problems. To this end, we evaluate PivotTree, an interpretable prototype selection model, on an oral lesion detection problem. We demonstrate the efficacy of using such method in terms of performance and offer a qualitative and quantitative comparison between exemplary cases and ground-truth prototypes selected by experts.

关键词： Interpretable machine learning Explainable AI Instance-based Approach Pivotal Instances Transparent Model Dental Health AI Oral Disease Prediction

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1st international conference of Urban Climate, Sustainability, and Urban Design, UCSUD 2023

1st International Conference of Urban Climate, Sustainabilit...

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1st international conference of Urban Climate, Sustainability, and Urban Design, UCSUD 2023

ISBN: (纸本)9789819784004

The proceedings contain 70 papers. The special focus in this conference is on Urban Climate, Sustainability, and Urban Design. The topics include: Impact of Climate Change and Human Activities on Ecosystem Health in the Poyang Lake City Group, China;application Method of Multi-model Fusion for Heavy Rainfall Forecast;evaluation of High-Resolution Model Heavy Rainfall Forecast Under Different Weather Systems;comparative Analysis of the Causes of Two Sudden Mountainous Rainstorms Occurred in Mianyang in 2022;characteristics of Climate Element Changes and Mango Planting Climate Suitability Changes in Panxi Region Under Global Warming;risk Analysis of Blueberry Gale Disaster in Liaodong Green Economic Zone Based on machine learning;spatial Distribution and Determinants of Convalescence Climate Tourist Attraions in Sichuan, China;rainstorm Waterlogging Simulation and Risk Assessment in Central Urban Area of Chengdu, China;study on Carbon Emission data of Urban Buildings;climate Adaptation in Carbon Neutral Cities: The Role of Clustered Buildings for Enhancing Energy Efficiency and Decarbonization in West Asian Cities;impact Mechanism of Residential Area Patterns Based on Carbon Emission Measurement on Residents’ Travel: A Case Study of Mianyang, China;Analysis of Carbon Emission Impact Factors and Trend Prediction Based on LMDI and ARIMA Models: A Case Study of Zhejiang Province;technical Strategies for Low-Cost Adaptive Renovation of Traditional Dong Ethnic Group Residences in Southeast Guizhou;energy Saving of City Road Tunnel Lighting Based on Optimal Luminaire Installation Method;enhancing Facade Design to Improve Energy Efficiency of Office Towers in the Hot and Humid Climate Region (Case Study: Bandung, Indonesia).

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learning Non-cooperative Driving Practices and its Impact on Road Traffic Dynamics 17

Learning Non-cooperative Driving Practices and its Impact on...

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17th international conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Osho Chakraborty, Suchetana Indian Institute of Technology Department of Computer Science and Engineering Jodhpur India

ISBN: (纸本)9798331531195

Cooperative driving that prioritizes global safety and efficiency over self-interest is crucial in ensuring smooth traffic flow and enhanced road safety. However, experiencing noncooperative driving is common under heterogeneous and chaotic on-road traffic conditions. While non-cooperativeness is often context-dependent and assessed during direct vehicle interactions, it can be inferred through longitudinal analysis of driving patterns from vehicular kinematics data. This behavior not only adversely affects the vehicle itself but also has a cascading impact on nearby vehicles, ultimately disrupting overall traffic flow. In this study, we quantify a vehicle's non-cooperativeness using kinematic data and further model its cascading effects on nearby cooperative vehicles. Experimental analysis was conducted using the SUMO traffic simulator and the TraCI interface to assess the impact of non-cooperativeness on overall traffic dynamics. This research is among the first to examine the cascading effects of a vehicle's non-cooperativeness on its cooperative nearby vehicles. © 2025 IEEE.

关键词： Motor transportation

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