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Decentralized Defences from Federated Learning for Ethereum Phishing Detection 4th

Decentralized Defences from Federated Learning for Ethereum ...

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4th International Conference on Advanced Network Technologies and Intelligent Computing-ANTIC-Annual

作者： Ghosh, Piyush Kumar Bhushan, Aditya Kumar, Dharmendra Singh, Ashutosh Kumar United Coll Engn & Res Dept Comp Sci & Engn Prayagraj 211010 UP India Univ Allahabad Dept Comp Sci Allahabad Degree Prayagraj 211003 UP India

ISBN: (纸本)9783031837821;9783031837838

Phishing attacks in blockchain (Ethereum) create a notable security vulnerability for users to get exploited by fake actors who are trying to trick users into divulging sensitive information frequently resulting in substantial financial losses. Conventionally, phishing detection methods rely only on centralized systems, which fall short in terms of scalability and breach the characteristic nature of blockchain technology like privacy-preserving and decentralized approach. In this paper, we propose the use of Federated Learning (FL) for phishing detection in Ethereum which is a decentralized mechanism. By implementing an FL approach, the proposed model can allow multiple clients to train a global model collaboratively by using locally stored data. It not only ensures preserving the privacy of sensitive data but also enhances scalability. This model uses weighted federated averaging which results in an accuracy of 79.96% and F1-score of 79.94% in detecting phishing attacks. All this is achieved while preserving privacy and making it an acceptable solution for dynamic, decentralized environments like Ethereum. This work exemplifies the perspective of FL in elevating the security and privacy of blockchain networks.

关键词： Ethereum Phishing Detection Fedrated Learning Blockchain Security Decentralized Systems

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Adaptive Hierarchical Clustering Based Student Group Exercise Recommendation via Multi-objective Evolutionary Method 5th

Adaptive Hierarchical Clustering Based Student Group Exercis...

引用

5th International Conference on Neural Computing for Advanced Applications (NCAA)

作者： Wang, Ziang Sun, Yifei Cao, Yifei Yang, Jie Shi, Wenya Zhang, Ao Ju, Jiale Yin, Jihui Yan, Qiaosen Yang, Xinqi Shaanxi Normal Univ Sch Phys & Informat Technol Xian 710119 Peoples R China

ISBN: (纸本)9789819770007;9789819770014

Exercise recommendation is an important research direction in intelligent education. The most advanced exercise recommendation methods focus on the association between each student and each knowledge concept and exercise, and model the exercise recommendation as a Multi-Objective Optimization Problem (MOP). However, this method is based on each individual student to recommend exercises, which will cause the recommendation process to consume too much time when the number of students is large. One approach to solve this problem is to recommend exercises based on student clusters after clustering students with similar characteristics. According to the specific characteristics of the student group, this paper proposes an Adaptive Hierarchical Clustering algorithm with Ward's method. The algorithm can adaptively adjust the within-cluster variance threshold for different student sets to make appropriate clustering for them. After clustering, the exercise recommendation process, which is modeled as a MOP, will be carried out in clusters. The experimental results show that when using different Multi-Objective Evolutionary Algorithms (MOEAs) to solve the exercise recommendation process, the recommendation performance after clustering is improved compared with that before clustering, and the total recommendation time for the student group is greatly reduced.

关键词： Intelligent Education Exercise Recommendation Multi-Objective Optimization Hierarchical Clustering

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A Two-Stage Approach for Multimodal Emotion Cause Pair Extraction that is Based on Large Language Models 5th

A Two-Stage Approach for Multimodal Emotion Cause Pair Extra...

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5th China Intelligent Robotics Academic Conference

作者： Li, Jinyu Chen, Fei Xu, Longmei Dalian Maritime Univ Dalian 116026 Liaoning Peoples R China

ISBN: (纸本)9789819616138;9789819616145

Emotions play a crucial role in human communication;however, past research in emotion analysis has focused primarily on the text modality, neglecting the importance of audio and visual modalities in emotion recognition. To address this issue, we propose an innovative multimodal emotion-cause pair net (MECPN) framework that captures emotions and their causes in conversations more comprehensively by integrating information from text, audio, and visual modalities, thereby achieving more accurate emotion analysis. The MECPN framework consists of two key modules: the multimodal emotion recognition (MER) module and the multimodal cause extraction(MCE) module. TheMERmodule usesRoBERTa, OpenSMILE, and VisExtNet to extract text, audio, and visual features, effectively integrating multimodal information through the MultiAttn multimodal fusion network. TheMCEmodule uses the MiniGPT-v2 model to integrate visual and textual information, generate natural language responses, and extract emotions that cause utterances through similarity matching. The experimental results on the ECF2.0 dataset show that the F1 score of the MECPN framework in the emotional cause extraction task is 0.2692, and the weighted average F1 score is 0.2574. Ablation experiments confirmed the importance of the VisExtNet, DialogueRNN, and MultiAttn modules in the model, demonstrating the effectiveness of these components in processing multimodal information and capturing the dialog context.

关键词： Emotion cause pair extraction Multimodal fusion Multimodal emotion recognition Large language models Promotion of learning

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AI-Driven Smart Air Conditioning System for a Sustainable and Energy-Efficient Industrial Future 5th

AI-Driven Smart Air Conditioning System for a Sustainable an...

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5th International Conference on Innovative Intelligent Industrial Production and Logistics

作者： Nakkach, Cherifa Picaud, Yvan Sofrecom Tunisia Orange Innovat Tunisia Tunis Tunisia Orange Innovat Lannion France

ISBN: (纸本)9783031807596;9783031807602

This paper examines short-term predictions of industrial air-conditioning loads using the data from smart meters. Using IoT, data can be collected from sensors that measure temperature, humidity, and other relevant parameters. This enables real-time monitoring of environmental conditions. We present a comprehensive architecture for an energy-efficient and sustainable solar air conditioning system for an efficient industrial future. To optimize operational efficiency and reduce energy costs, this system must predict energy consumption. Various machine learning models were explored and tested, including Convolutional Neural Networks - Long Short-Term Memory (CNN-LSTM), Recurrent Neural Networks (RNN), Gated Recurrent Units (GRU) and TimeGPT to identify the most effective approach for energy prediction. RMSE and MAPE metrics showed that the TimeGPT model outperformed all other models in terms of accuracy and reliability in forecasting energy consumption. TimeGPT's results provide evidence that industrial environments can improve their energy efficiency and sustainability by using the TimeGPT model. In addition to better energy management, this approach reduces costs and reduces carbon footprints. Energy consumption can is therefore predicted to optimize air conditioner operation, plan preventive maintenance, detect potential problems, and take corrective action to reduce the unnecessary consumption of energy.

关键词： Energy consumption prediction Industry Air conditioner RNN CNN-LSTM GRU TimeGPT

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information-Retrieval System of Rock Pictures of Different Countries and Continents 2nd

Information-Retrieval System of Rock Pictures of Different C...

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2nd International Conference on information Technologies and Their Applications

作者： Kazim-zada, Aydin Huseynova, Hayat Azerbaijan Univ Architecture & Construct Ayna Sultanova St 5 AZ-1073 Baku Azerbaijan

ISBN: (纸本)9783031734199;9783031734205

This article narrates about petroglyphs of the Azerbaijan Republic, countries of Central and Middle Asia, and other countries of the world, representing the cultural heritage of ancient generations. The article touches on the issues concerning the necessity of preserving the rock pictures for future generations using all modern available means (printed, editions, electronic media of information, articles etc.). The article contains examples of rock pictures on which our ancestors depicted people, animals, hunting scenes, the history of their daily life, various kinds of festivities, sacred ceremonies etc. with the use of tools they had at hand. As the said petroglyphs are of different geometrical dimensions (ranging from some centimeters to some meters) one of the methods of normalizing the rock pictures through the placement of the pictures within the circle area is considered. The coordinates of the coincidence of drawing points with the circle points complement a list of informative indicators. The presence of symmetry and asymmetry in the rock drawings is treated as other informative indicators. A number of rock pictures of deer and goats of different countries were studied and their graphs of distribution density along both the OX and OY axes are given. These graphs also are one of the additional informative features of the rock pictures. Correlation analysis of the pictures against both coordinate axes has been made. A structure of the developed information retrieval system of petroglyphs of the world countries is demonstrated.

关键词： informative features petroglyphs rock pictures information retrieval system distribution density correlation analysis recognition identification

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The Role of Social Media in Fostering Social Movements: Assessing User Experience (UX) Elements for Effective Mobilization in Bangladeshi Digital Activism 1

引用

15th Indian Conference on Human-computer Interaction Design and Research

作者： Billah, Md Arif Rahman, Rifat Jatiya Kabi Kazi Nazrul Islam Univ Mymensingh 2224 Bangladesh Bangladesh Univ Engn & Technol Dhaka Bangladesh

ISBN: (数字)9783031808326

ISBN: (纸本)9783031808319;9783031808326

In today's era of contemporary activism, social media (SM) platforms are becoming an essential medium for fostering social movements and activism, especially in growing regions like Bangladesh, where digital connectivity is rapidly expanding. This study explores the intricate relationship between various user experience (UX) elements, including hashtags, banners, information cards, reels, videos, captions, and live streams, and their efficacy in driving social change through digital activist campaigns on social media in Bangladesh. By leveraging both online and offline surveys, this research employs a quantitative approach to examine the behavior of regular social media users in Bangladesh. Key findings from this study suggest that strategically designed visual UX elements, such as videos or reels and social media banners, significantly influence the efficacy of activist campaigns on social media, demonstrating their ability to stimulate social change. Efficient user experience (UX) can help accomplish social change goals by enhancing user engagement and disseminating information. This study offers valuable insights for UX practitioners in Bangladesh who wish to utilize social media as an opportunity to facilitate constructive social change.

关键词： User experience Social media Digital activism Social change Bangladesh

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An Inventory Management Support Tool Through Indirect Q-Value Estimation: A Combined Optimization and Forecasting Approach 5th

An Inventory Management Support Tool Through Indirect Q-Valu...

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5th International Conference on Innovative Intelligent Industrial Production and Logistics

作者： Delfiol, Amanda Rodrigues Dadouchi, Camelia Agard, Bruno St-Aubin, Philippe Ecole Polytech Montreal Lab Intelligence Donnees Montreal PQ Canada Logist Unicorp Inc St Jean PQ Canada

ISBN: (纸本)9783031807596;9783031807602

Effective inventory management is crucial in manufacturing and wholesale businesses to reduce operation costs and meet service level guarantees. Due to the continuous increase in product catalogues and highly volatile demand, inventory management complexity continues to grow. This paper introduces a decision support tool designed to aid in inventory management through an indirect Q-value estimator technique. The proposed tool employs simulation, optimization and forecasting techniques to enable purchase actions evaluation for large horizons. By integrating both simulation and optimization into a supervised learning algorithm, the tool provides an easy to interpret cost estimation that can directly be used to make informed procurement decisions. A case study in the textile industry demonstrates its use and its performance in a single-echelon supply chain setting. This research presents a comprehensive step by step framework to support the creation of a decision support tool that can offer valuable aid for decision-making processes across different supply management contexts.

关键词： Inventory management Supervised learning Decision making Supply chain management

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Democratizing AI in Africa: Federated Learning for Low-Resource Edge Devices 1st

Democratizing AI in Africa: Federated Learning for Low-Resou...

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1st MICCAI Meets Africa Workshop

作者： Fabila, Jorge Campello, Ictor M. Isla, Carlos Martin Obungoloch, Johnes Leo, Kinyera Ronald, Amodoi Lekadir, Karim Univ Barcelona Dept Matemat Informat Barcelona Spain Mbarara Univ Sci & Technol Mbarara Uganda Inst Catalana Recerca & Estudis Avancats ICREA Barcelona Spain

ISBN: (纸本)9783031791024;9783031791031

Africa faces significant challenges in healthcare delivery due to limited infrastructure and access to advanced medical technologies. This study explores the use of federated learning to overcome these barriers, focusing on perinatal health. We trained a fetal plane classifier using perinatal data from five African countries: Algeria, Ghana, Egypt, Malawi, and Uganda, along with data from Spanish hospitals. To incorporate the lack of computational resources in the analysis, we considered a heterogeneous set of devices, including a Raspberry Pi and several laptops, for model training. We demonstrate comparative performance between a centralized and a federated model, despite the compute limitations, and a significant improvement in model generalizability when compared to models trained only locally. These results show the potential for a future implementation at a large scale of a federated learning platform to bridge the accessibility gap and improve model generalizability with very little requirements.

关键词： Federated Learning Low resources Edge devices Fetal ultrasound

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A MSARM-Based EIT Image Reconstruction Method 5th

A MSARM-Based EIT Image Reconstruction Method

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5th International Conference on Neural Computing for Advanced Applications (NCAA)

作者： Dong, Qinghe Wang, Xichan He, Qian Xu, Chuanpei Guilin Univ Elect Technol Sch Elect Engn & Automat Guilin 541004 Peoples R China Guilin Univ Elect Technol Sch Comp Sci & Informat Secur Guilin 541004 Peoples R China

ISBN: (纸本)9789819770007;9789819770014

Electrical Impedance Tomography (EIT) image reconstruction is the process of generating visual images that represent the electrical conductivity or impedance distribution within a body or object, based on external electrical measurements. Existing EIT image reconstruction methods suffer from low image resolution and unclear boundary problems due to its nonlinear, under-determined and ill-posed nature. To address the limitations of existing works, we propose a novel MSARM-based EIT image reconstruction method, which aims to improve the quality of reconstructed image. Our method consists of three modules: pre-mapping, encoder and decoder modules. Furthermore, we introduce multi-scale feature extraction, Sim-AM attention mechanism and residual blocks, which can extract more features and fuse contextual information discriminatively. We evaluate our proposed method on a simulated dataset generated by EIDORS. The experimental results demonstrate that our proposed MSARM method have achieved state-of-the-art performance on image reconstruction tasks. To be specific, compared with existing multi-scale U-Net module, our method has the 1.43% improvement in CC and 13.43% reduction in RE, showing the effectiveness of our proposed method.

关键词： Electrical impedance tomography Image reconstruction Multi-scale fusion SimAM attention

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Towards Multi-stakeholder Evaluation of ML Models: A Crowdsourcing Study on Metric Preferences in Job-Matching System 1

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8th International Conference on computer-Human Interaction Research and Applications-CHIRA-Annual

作者： Yokota, Takuya Nakao, Yuri Fujitsu Ltd Kawasaki Kanagawa Japan

ISBN: (数字)9783031826337

ISBN: (纸本)9783031826320;9783031826337

While machine learning (ML) technology affects diverse stakeholders, there is no one-size-fits-all metric to evaluate the quality of outputs, including performance and fairness. Using predetermined metrics without soliciting stakeholder opinions is problematic because it leads to an unfair disregard for stakeholders in the ML pipeline. In this study, to establish practical ways to incorporate diverse stakeholder opinions into the selection of metrics for ML, we investigate participants' preferences for different metrics by using crowdsourcing. We ask 837 participants to choose a better model from two hypothetical ML models in a hypothetical job-matching system twenty times and calculate their utility values for seven metrics. To examine the participants' feedback in detail, we divide them into five clusters based on their utility values and analyze the tendencies of each cluster, including their preferences for metrics and common attributes. Based on the results, we discuss the points that should be considered when selecting appropriate metrics and evaluating ML models with multiple stakeholders.

关键词： Machine learning Multi-stakeholder Fairness Crowdsourcing Job-matching system

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