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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171-180 订阅

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CSClust: A Luxury Goods Image Authentication Algorithm Based on Contrastive Clustering 24

CSClust: A Luxury Goods Image Authentication Algorithm Based...

引用

8th international conference on Electronic Information Technology and Computer engineering, EITCE 2024

作者： Chao, Yaxin Yang, Yu Zhou, Linna Zou, Jixin Lei, Min School of Cyberspace Security Beijing University of Posts and Telecommunications Beijing China Institude of Forensic Science of China Beijing China

ISBN: (纸本)9798400710094

The aim of our paper is to introduce CSClust, a contrastive spectral clustering model for luxury goods image authentication. The approach involves fine-tuning a Vision Transformer (ViT) pretrained model to enhance representation learning. Utilizing a spectral clustering network, the model embeds latent representations into the feature space and performs subsequent clustering. The training process is conducted in an unsupervised manner on self-generated augmented data, incorporating both instance-level and cluster-level contrastive losses. Experiments have demonstrated that the proposed method exhibits superior accuracy and generalization capability in luxury goods image authentication, and has also achieved state-of-the-art results on the CIFAR-100, ImageNet-10, and ImageNet-dogs datasets. © 2024 Copyright held by the owner/author(s).

关键词： Contrastive learning

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Defending Against Poisoning Attacks in Federated Prototype learning on Non-IID data 18th

Defending Against Poisoning Attacks in Federated Prototype ...

引用

18th international conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Zhang, Jie Zhang, Hongliang Wang, Guijuan Dong, Anming Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan250353 China

ISBN: (纸本)9783031714665

Federated learning (FL) is an emerging distributed machine learning paradigm that enables participants to cooperatively train learning tasks without revealing the raw data. However, the distributed nature of FL makes it susceptible to poisoning attacks, especially when the local data of participants are non-independent and identically diatributed (non-IID). Although several defense methods have been proposed to mitigate poisoning attacks, their effectiveness is limited by the specific assumptions about the data distribution. In this work, we propose a new defense strategy, FedAPA (Federated Prototype learning Against Poisoning Attacks). Specifically, we use abstract class prototypes to communicate between the clients and server, thus effectively alleviating the impact of non-IID data. Moreover, we propose a new abnormal client detection method that aims to mitigate the impact of malicious clients while distinguishing between malicious and benign clients, thereby effectively defending against poisoning attacks. Extensive experiments on different datasets show that FedAPA can effectively resist the poisoning attacks under various data distributions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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Research on Vehicle Trajectory Prediction Based on Improved LSTM Model 11th

Research on Vehicle Trajectory Prediction Based on Improved ...

引用

11th international conference on Traffic and Transportation Studies, ICTTS 2024

作者： Li, Jiawei Wu, Xianyu School of Traffic and Transportation Beijing Jiaotong University Beijing100044 China Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Beijing Jiaotong University Beijing100044 China

ISBN: (纸本)9789819796397

The development of new detection equipment and vehicle networking technology has enabled the acquisition of a significant quantity of accurate realtime vehicle trajectory data on urban roads, which is being utilized in the field of urban transportation research. However, due to the existence of diverse driving scenarios and random driving behavior on real roads, simple mathematical and theoretical models are insufficient to simulate complex real driving scenarios. Therefore, in this paper we present a data-driven model for predicting vehicle trajectories by means of Long Short-Term Memory (LSTM) cells with attention mechanism. The model is trained and tested using high-precision trajectory data coming from intersections of Beijing, China. The results illustrate that the improved Long Short-Term Memory model exhibits a lower prediction error in comparison to the previous model and other machine learning methods. It is capable of accurately reflecting the trajectory trend in terms of longitude prediction. The results of this research offer a theoretical basis for the enhancement of assisted driving systems and the creation of vehicle warning systems. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Prediction models

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Improving Cloud Energy Efficiency through machine learning Models 33

Improving Cloud Energy Efficiency through Machine Learning M...

引用

33rd Euromicro international conference on Parallel, Distributed, and Network-Based Processing, PDP 2025

作者： Cesario, Eugenio Lindia, Paolo Lobello, Federica Vinci, Andrea Zarin, Shabnam Capalbo, Santina University of Calabria DICES Department Rende Italy University of Calabria DIMES Department Rende Italy CNRNational Research Council of Italy Rende Italy Monmouth University Computer Science Department West Long BranchNJ United States

ISBN: (纸本)9798331524937

The rapid growth of Cloud Computing and the expansion of large data centers have led to significant increases in energy consumption for hardware and cooling systems. This surge in power usage has raised operational costs, making energy efficiency a pressing issue for data center management. Virtual machines (VMs) consolidation is a widely studied strategy to mitigate these costs by minimizing the number of active physical servers while maintaining user Service Level Agreements (SLAs). However, the success of consolidation largely depends on accurately forecasting VM resource needs. This paper presents the design and development of an energy-aware VMs allocation system that leverages predictive machine learning models to forecast future computational demands (CPU) of each VM. By anticipating these needs, the system optimally allocates VMs to available servers, effectively balancing energy savings with performance. A preliminary experimental evaluation, conducted on synthetic data, leverages predictive modeling within a server simulation to assess CPU and energy usage, demonstrating the effectiveness of energy-aware VMs allocation in optimizing resource utilization and reducing energy consumption. © 2025 IEEE.

关键词： Energy efficiency

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Improving the Robustness of Rumor Detection Models with Metadata-Augmented Evasive Rumor datasets 25th

Improving the Robustness of Rumor Detection Models with M...

引用

25th international conference on Web Information Systems engineering, WISE 2024

作者： Huynh, Larry Gansemer, Andrew Kim, Hyoungshick Hong, Jin B. The University of Western Australia Perth Australia Sungkyunkwan University Seoul Korea Republic of

ISBN: (纸本)9789819605750

Rumors on social media can cause serious harm. Advances in NLP enable deceptive rumors resembling real posts, necessitating more robust detection. One approach collects and augments a dataset with adversarial rumors meant to evade models. Understanding evasive rumors and adding them to a dataset improves model robustness. We demonstrate effective data augmentation that significantly improves detection models. State-of-the-art accuracy drops by up to 29.5% against evasive rumors, while our augmentation raises it by up to 14.62%. Results highlight data augmentation’s importance for robust detection models countering evasion. Our evaluation shows the value of augmentation for developing models robust against adversarial attacks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Deep learning method for precise fault location of multi-branch hybrid transmission lines

Deep learning method for precise fault location of multi-bra...

引用

2024 international conference on Physics, Photonics, and Optical engineering, ICPPOE 2024

作者： Li, Xinhai Fan, Dehe Xiao, Xing Zhou, Qiang Guangdong Zhongshan Power Supply Bureau China Southern Power Grid Corporation Guangdong Zhongshan528400 China College of Electrical and Electronic Engineering Shandong University of Technology Shandong Zibo255000 China

ISBN: (数字)9781510689121

ISBN: (纸本)9781510689114

Accurate fault distance measurement is crucial for maintaining the stability and reliability of power transmission lines. In multi-branch hybrid power transmission lines, where different types of lines are interconnected, fault distance measurement becomes challenging due to the complex network topology and diverse fault scenarios. Traditional fault distance measurement methods often rely on manual intervention and rule-based algorithms, which may lack the ability to adapt to the dynamic and complex nature of faults. To address these challenges, this paper proposes a novel deep learning method for precise fault distance measurement in multi-branch hybrid power transmission lines. The proposed method combines three key components to achieve accurate fault distance measurement. Firstly, the Convolutional Neural Network (CNN) is utilized to extract spatial features from the input power data. By applying convolutional filters, the CNN can effectively capture spatial patterns that are essential for fault distance estimation. Secondly, the Long Short-Term Memory (LSTM) network is employed to capture the temporal dependencies in the power data. The LSTM network's ability to model long-term dependencies allows it to learn the temporal patterns and trends exhibited by the power data, enabling accurate fault distance estimation. Finally, the attention mechanism is incorporated to assign different weights to crucial time steps, focusing on the most informative ones for fault distance estimation. © 2025 SPIE.

关键词： Convolution

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Efficient Federated learning with Cost-Adjustable Generative AI over Heterogeneous Edge Devices 20th

Efficient Federated Learning with Cost-Adjustable Generativ...

引用

20th IFIP WG 10.3 international conference on Network and Parallel Computing, NPC 2024

作者： Zhang, Hanwen Li, Peichun Kang, Jiawen Qian, Liping Wu, Yuan Niyato, Dusit State Key Laboratory of Internet of Things for Smart City University of Macau China School of Automation Guangdong University of Technology Guangzhou China College of Information Engineering Zhejiang University of Technology Hangzhou China Zhuhai UM Science and Technology Research Institute Zhuhai China Nanyang Technological University Block N4-02a-32 Nanyang Avenue Singapore Singapore

ISBN: (纸本)9789819628636

Federated learning (FL) on edge devices often faces critical challenges, such as slow convergence rates and suboptimal performance, largely attributed to limited local data availability and data heterogeneity across participating devices. Recent advancements in generative artificial intelligence (AI) have demonstrated exceptional proficiency in synthesizing realistic data, offering potential solutions to these issues. In this paper, we present an innovative approach that enables edge devices to enhance their datasets by leveraging generative AI models. Recognizing varying resource capacities among edge devices, we propose a flexible data synthesis strategy, allowing each device to customize the quantity and quality of generated data according to its specific resource conditions and operational requirements. Building on this strategy, we formulate an optimization problem to maximize training accuracy while meeting the local resource constraints and task completion timelines. Experimental results reveal that our model can effectively enhance global accuracy compared to conventional FL algorithms, with even more pronounced improvements observed under skewed data distributions. © IFIP international Federation for Information Processing 2025.

关键词： Federated learning

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Leveraging machine learning to Assess Factors Influencing Undernourishment in Nigeria 1st

Leveraging Machine Learning to Assess Factors Influencing Un...

引用

1st international conference on Intelligent Systems, Blockchain, and Communication Technologies, ISBCom 2024

作者： Mobark, Nada Elfiky, Noha Ahmedin, Ahmed Ibrahim, Ameera Abdel-Aziz, M. Hamad, Safwat Faculty of Computers and Information South Valley University Qena83523 Egypt Business Analytics and Data Science Department School of Economics and Business Administration Saint Mary’s College of California MoragaCA94575 United States Department of Artificial Intelligence Engineering Faculty of Computer Science and Engineering King Salman International University South Sinai46511 Egypt

ISBN: (纸本)9783031823763

Notwithstanding economic progress, Nigeria continues to countenance significant food security issues. This study investigates the root causes of this problem and attempts to develop predictive models to address hunger in Nigeria. The research covering data from 2000 to 2022, sourced primarily from the World Bank and other institutions. The trend analysis, along with the linear model, indicates that investment in farming is crucial for reducing undernourishment. Additionally, there is a strong relationship between agricultural expenditure, exports, access to electricity, and unemployment in relation to undernourishment. In this paper, a linear model, correlation analysis, ARIMA, and trend analysis are utilized to demonstrate how undernourishment, representing hunger and food insecurity, is influenced by other factors. As a result, it is recommended to maintain the unemployment rate between 3.5% and 3.7%, while ensuring that exceeds 5% of government spending is recommended. In other words, to combat hunger in Nigeria, it is important to focus on investing in agriculture, creating jobs, and improving infrastructure. This aligns with our findings and with another research around the world. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： ARIMA Nigeria predictive model Undernourishment

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Augmenting Knowledge Tracing: Personalized Modeling by Considering Forgetting Behavior in learning Process 7th

Augmenting Knowledge Tracing: Personalized Modeling by Consi...

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8th Asia Pacific Web and Web-Age Information Management Joint international conference on Web and Big data

作者： Yang, Hongxin Du, Yuefeng Liu, Tingting Ding, Linlin Liaoning Univ Shenyang 110036 Liaoning Peoples R China

ISBN: (纸本)9789819600540;9789819600557

With the development of internet online education system, knowledge tracking (KT) is becoming more and more widely used. This technology can accurately model students' learning process, so as to provide students with a personalized knowledge push. However, most previous KT methods don't take into account the learning ability (LA) and forgetting behavior (FB) of student when assessing the state of knowledge. In fact, each student has their own LA and FB, which plays an important role in the KT process. However, at present, the personalized LA and FB of different students are not given in advance, which makes it more challenging to predict the learning status of each student. To address students' challenges in modeling KT, we design augmenting knowledge tracing (AKT), which first uses concept-wised percent correct (CPC) to describe students' overall mastery of knowledge, and builds an individualized forgetting rate (IFR) to describe the degree of forgetting during student learning process. The relationship between student history learning situation and time is considered, moreover, the knowledge mastery and FBare measured from the interaction with the topic during student learning process. The final experimental results show that the performance of proposed model is better than that of traditional methods.

关键词： knowledge tracking augmenting knowledge tracing concept-wised percent correct individualized forgetting rate

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Disentangling Hierarchical Features for Anomalous Sound Detection Under Domain Shift

Disentangling Hierarchical Features for Anomalous Sound Dete...

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

作者： Guan, Jian Tian, Jiantong Zhu, Qiaoxi Xiao, Feiyang Zhang, Hejing Liu, Xubo Group of Intelligent Signal Processing College of Computer Science and Technology Harbin Engineering University Harbin150001 China Acoustics Lab University of Technology Sydney UltimoNSW2007 Australia Centre for Vision Speech and Signal Processing University of Surrey GuildfordGU2 7XH United Kingdom

ISBN: (纸本)9798350368741

Anomalous sound detection (ASD) encounters difficulties with domain shift, where the sounds of machines in target domains differ significantly from those in source domains due to varying operating conditions. Existing methods typically employ domain classifiers to enhance detection performance, but they often overlook the influence of domain-unrelated information. This oversight can hinder the model's ability to clearly distinguish between domains, thereby weakening its capacity to differentiate normal from abnormal sounds. In this paper, we propose a Gradient Reversal-based Hierarchical feature Disentanglement (GRHD) method to address the above challenge. GRHD uses gradient reversal to separate domain-related features from domain-unrelated ones, resulting in more robust feature representations. Additionally, the method employs a hierarchical structure to guide the learning of fine-grained, domain-specific features by leveraging available metadata, such as section IDs and machine sound attributes. Experimental results on the DCASE 2022 Challenge Task 2 dataset demonstrate that the proposed method significantly improves ASD performance under domain shift. © 2025 IEEE.

关键词： Anomalous sound detection domain shift feature learning gradient reversal

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