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

共 33664 条记录，以下是221-230 订阅

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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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Aggregation Strategy for Federated machine learning Algorithm 19th

Aggregation Strategy for Federated Machine Learning Algorit...

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19th international conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2024

作者： Erdei, Rudolf Delinschi, Daniela Bărăian, Iulia Matei, Oliviu Technical University of Cluj Napoca North University Centre of Baia Mare Baia Mare Romania Technical University of Cluj Napoca Cluj-Napoca Romania

ISBN: (纸本)9783031750090

This paper presents a novel approach for calculating average weights in Federated learning. This technique integrates data-related measures, such as data Quality, data Freshness, and data Importance. These changes provide a more accurate and pertinent averaging procedure, resulting in improved models and more relevant model output. This technology is tested and validated within an agricultural scenario, that employs a Low-Communication paradigm. Additionally, the metadata serves as a backup alerting mechanism to detect any issues inside the system. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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OpenAV: Bilingual dataset for Audio-Visual Voice Control of a Computer for Hand Disabled People 26th

OpenAV: Bilingual Dataset for Audio-Visual Voice Control of ...

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26th international conference on Speech and Computer

作者： Ivanko, Denis Ryumin, Dmitry Axyonov, Alexandr Kashevnik, Alexey Karpov, Alexey Russian Acad Sci St Petersburg Fed Res Ctr St Petersburg 199178 Russia

ISBN: (纸本)9783031779602;9783031779619

In recent years, audio-visual speech recognition (AVSR) assistance systems have gained increasing attention from researchers as an important part of human-computer interaction (HCI). The objective of this paper is to further advance the development of assistive technologies in the AVSR field by introducing a multi-modal OpenAV dataset, intended for state-of-the-art neural network model training. The OpenAV is designed to train AVSR models for assistance to persons without hands or with disabilities of their hands or arms in HCI. The dataset could also be useful for ordinary users at hands-free contactless HCI. The dataset currently includes the recordings in two languages (English and Russian) of 15 speakers with a minimum of 10 recording sessions for each. Along with this we provide a detailed description of the dataset and its collection pipeline. In addition, we evaluate state-of-the-art audio-visual (AV) speech recognition approach and present a baseline recognition results. We also describe the recording methodology, release the recording software to public, as well as open the access to the dataset https://***/OpenAV-dataset/.

关键词： Audio-Visual dataset Human-Computer Interaction Automatic Speech Recognition data Collection 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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Correlation Between Satellite Multispectral Imagery and Combine Harvester CANbus data for Corn Yield Assessment

Correlation Between Satellite Multispectral Imagery and Comb...

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international Mid-Term conference of the Italian Association of Agricultural engineering, MID-TERM AIIA 2024

作者： Miralles, Gica Stefanescu Biglia, Alessandro Mattetti, Michele Tortia, Cristina Gay, Paolo Comba, Lorenzo Università degli Studi di Torino Largo Paolo Braccini 2 TO Grugliasco10095 Italy Università degli Studi di Torino Via Pessinetto 12 TO Torino10149 Italy Department of Agricultural and Food Sciences Alma Mater Studiorum – University of Bologna Viale G. Fanin 50 BO Bologna40127 Italy

ISBN: (纸本)9783031842115

Adoption of digital tools has led to relevant advancements in the agricultural sector. In particular, precision agriculture techniques have shown effective applications in farming practices. Integrating multisource data is a crucial task for improving agricultural management, and in this context the reliable assessment of crop yield by remote sensed imagery plays a relevant role. In this work, the correlation between satellite multispectral imagery and corn yield was investigated. Sentinel-2 satellite mission was selected as source of multispectral data, with 10-m spatial resolution and 4–6 days revisit time. A corn field in Ferrara was considered as a case study, with the dataset consisting of 17 multispectral images acquired in days with cloud coverage under 7%. The reference yield map was computed using CANbus data from a combine harvester, and its correlation with NDVI and GRVI indices was explored throughout the whole corn life cycle. In addition, the effect of applying a gaussian filter in the raster CY spatial distribution was explored. Results showed an overall good correlation between remotely sensed tiles and in-field farm data. The Pearson correlation coefficients showed a sharp increase during the vegetative stage of the crop (May and June), followed by a slowly decreasing plateau in July and August dates. Imagery from early June provided the highest correlations. The application of a gaussian filter in the CY map revealed an enhance in correlation of more than 25%. These results pave the path to the development of machine learning based methods exploiting multi-band-multi-temporal data for CY estimation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： CANbus machine learning Precision Agriculture Remote sensing Yield assessment

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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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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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Applying machine learning Tools for Urban Resilience Against Floods 5

Applying Machine Learning Tools for Urban Resilience Against...

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5th IEEE international conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Pour, Mahla Ardebili Ghiasi, Mohammad B. Karkehabadi, Ali University of California Davis Davis United States University of California Davis Davis United States

ISBN: (纸本)9798350356236

Floods are among the most prevalent and destructive natural disasters, often leading to severe social and economic impacts in urban areas due to the high concentration of assets and population density. In Iran, particularly in Tehran, recurring flood events underscore the urgent need for robust urban resilience strategies. This paper explores flood resilience models to identify the most effective approach for District 6 in Tehran. Through an extensive literature review, various resilience models were analyzed, with the Climate Disaster Resilience Index (CDRI) emerging as the most suitable model for this district due to its comprehensive resilience dimensions: Physical, Social, Economic, Organizational, and Natural/Health resilience. Although the CDRI model provides a structured approach to resilience measurement, it remains a static model focused on spatial characteristics and lacks temporal adaptability. An extensive literature review enhances the CDRI model by integrating data from 2013 to 2022 in three-year intervals and applying machine learning techniques to predict resilience dimensions for 2025. This integration enables a dynamic resilience model that can accommodate temporal changes, providing a more adaptable and data-driven foundation for urban flood resilience planning. By employing artificial intelligence to reflect evolving urban conditions, this model offers valuable insights for policymakers and urban planners to enhance flood resilience in Tehran's district 6. © 2025 IEEE.

关键词： Urban planning

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2nd international conference on data Science, machine learning and Applications, ICDSMLA 2020

2nd International Conference on Data Science, Machine Learni...

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2nd international conference on data Science, machine learning and Applications, ICDSMLA 2020

ISBN: (纸本)9789811636899

The proceedings contain 157 papers. The special focus in this conference is on data Science, machine learning and Applications. The topics include: Twitter Spam Review Detection Using Hybrid machine learning Techniques;throughput Improvement in Energy Efficient Heterogeneous Wireless Sensor Network;predicting Expected Time of Arrival of Shipments Through Multiple Linear Regression;learning via Long Short-Term Memory (LSTM) Network for Predicting Strains in Railway Bridge Members Under Train Induced Vibration;smart Transportation Using Fog Computing;detection of learning Disability: A Survey;EEG Based Emotion Investigation from Various Brain Region Using Deep learning Algorithm;electric Motor Drive Using Single Input Fuzzy Logic Controller for Husk Extraction in Rice Mill Industry;states Categorization in India Based on Health Security Capacity with machine learning Technique;CovidSORT: Detection of Novel COVID-19 in Chest X-ray Images by Leveraging Deep Transfer learning Models;a Real Time Radio Spectrum Measurement Campaign for machine learning Based Spectrum Inference in Cognitive Radio Network;application of Disruptive Technologies in Intelligent Transportation System;deep learning Models for Rubik’s Cube with Entropy Modelling;performance Analysis of Software Enabled Accelerator Library for Intel Architecture;blind Descent: A Prequel to Gradient Descent;a Survey: Modelling Strategies for Predictive Maintenance;abstract and Image Analysis of High-Temperature Materials from Scientific Journals Using Deep learning and Rule-Based machine learning Approaches;a Time Dependent Epidemiology Model for Hospital Resource Management in Usual Scenarios and Pandemic;Load Forecasting Using LSTM and Its Comparison with Other machine learning Techniques and Their Implementation;application of machine learning to Detect Neuroticism in Individuals Using Handwriting Analysis;preface.

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