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Contrastive Prototype Network for Generative Zero-Shot learning 21st

Contrastive Prototype Network for Generative Zero-Shot Lear...

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21st Pacific Rim international conference on Artificial Intelligence, PRICAI 2024

作者： Luo, Xinxin Yin, Wei Li, Zhuang School of Cyber Science and Engineering Southeast University Nanjing China School of Economics and Management School of Cyber Science and Engineering Southeast University Nanjing China School of Foreign Languages Southeast University Nanjing China

ISBN: (纸本)9789819601189

In generative zero-shot learning, the visual distribution mismatch between synthetic and real samples is a challenge due to the lack of effective constraints for unseen class visual features. To address this, we propose the Contrastive Prototype Network (CPNet). CPNet uses prototype learning to determine the feature vector center for each category (the prototype) and classifies based on the similarity between test data and prototypes. Concurrently, contrastive learning ensures that representations of the same category are more compact, while those of different categories are more dispersed. Experimental results on three standard datasets confirm the effectiveness of our approach. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive learning

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HJCFL: Hashcash and Jaya-Based Communication Efficient Federated learning 21st

HJCFL: Hashcash and Jaya-Based Communication Efficient Feder...

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21st international conference on Distributed Computing and intelligent Technology

作者： Mondal, Sudipto Jana, Prasanta K. Indian Sch Mines Indian Inst Technol Dept Comp Sci & Engn Dhanbad Bihar India

ISBN: (纸本)9783031814037;9783031814044

Federated learning (FL) has emerged as an efficient technique to train machine learning (ML) models across decentralized devices without sharing any raw data for preserving privacy. However, it faces challenges in communication overhead and resource constraints, particularly in real-time applications of Internet of Things (IoT). To address these challenges, we propose a novel method called Hashcash and Jaya-based communication efficient Federated learning (HJCFL). It consists of three main phases: local aggregator selection, clustering of IoT devices and communication efficient FL process. It uses Hashcash for strategically selecting local aggregators and Jaya based algorithm for clustering IoT devices. The usage of Hashcash function has an added advantage over existing aggregator selection techniques because of it's simplicity, proof-of-work and adjustable difficulty level. To the best of our knowledge, none of the existing techniques uses Hashcash for the same. Moreover, unlike most of the existing schemes, the proposed method utilizes all the available devices and therefore it converges faster with significantly less number of global rounds than others. Through extensive simulations, we demonstrate the effectiveness of the proposed HJCFL as compared to two baseline techniques. The result's validity is further confirmed using a widely recognized statistical technique, i.e., analysis of variance (ANOVA), followed by a least significant difference (LSD) post-hoc analysis.

关键词： Federated learning Internet of Things Hashcash function Clustering Jaya Algorithm

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Review of Image Semantic Segmentation Research Based on Deep learning 21

Review of Image Semantic Segmentation Research Based on Deep...

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21st international Computer conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Yuting, Zeng Wenyu, Chen Yixin, Zhang School of Computer Science and Engineering School of Cybersecurity University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9798331519254

Image semantic segmentation based on deep learning has made great progress in recent years. This paper summarizes the application and development of convolutional neural network (CNN), full convolutional network (FCN), and U-Net, SegNet and DeepLab in semantic segmentation. Highlights of the advantages of recent models of Mask R-CNN, PSPNet, and HRN et in handling complex scenes, multiscale targets and detailed segmentation tasks. Deep learning improves the accuracy and robustness of semantic segmentation, but it still faces the challenges of high computational complexity and high dependence on annotation data. © 2024 IEEE.

关键词： Deep learning Image Segmentation

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Continuous Control Method for Lower Limb Rehabilitation Robot Based on Self-learning strategy 21

Continuous Control Method for Lower Limb Rehabilitation Robo...

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21st IEEE international conference on Mechatronics and Automation (IEEE ICMA)

作者： Lu, Yibo Guo, Shijie Wang, Mengge Chen, Lingling Hebei Univ Technol Sch Artificial Intelligence & Data Sci Tianjin Peoples R China Minist Educ Intelligent Rehabil Device & Detect Technol Engn Tianjin Peoples R China Hebei Univ Technol Sch Mech Engn Tianjin Peoples R China Hebei Univ Technol Sch Elect Engn Tianjin Peoples R China

ISBN: (纸本)9798350388084;9798350388077

Traditional lower limb rehabilitation robots employ discrete control methods, which are unable to decode human intention in real-time. To enhance human-machine interaction capabilities for patients with lower limb motor dysfunction, this study focuses on continuous control decoding of rehabilitation robots based on a self-learning strategy. An online classification model was developed, employing a multimodal brain-computer interface paradigm. The LGBM-LMS classification model was constructed to classify continuous control commands for the robot, converting them into control instructions for continuous control of the lower limb rehabilitation robot via electroencephalogram (EEG) signals. Experimental results indicate that this method reduces computational complexity and model redundancy, with an average recognition rate improvement of 17.82% per group.

关键词： Multimodal brain-computer interface EEG decoding self-learning strategy continuous control lower limb rehabilitation robot

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Prediction of Hardness of Cast Iron Using intelligent Tools 21st

Prediction of Hardness of Cast Iron Using Intelligent Tools

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21st ISME international conference on Advances in Mechanical engineering, ISME 2023

作者： Sridhar, K.S. Madabhushi, Abhilash Viswanath, Ganapathi Sudheer, Nirmal Umesh, Sabrish PES University Karnataka Bengaluru560085 India

ISBN: (纸本)9789819736539

The objective of this research paper is to introduce a machine learning and artificial intelligence-based approach for predicting the hardness of cast iron. The hardness of cast iron is a significant factor in determining its strength and performance for different applications. The hardness is primarily tested using experimental methods and is majorly done using testing materials. It is observed that the Brinell Hardness testing method can be time-consuming. In this paper, the authors have made an attempt to use an Artificial Intelligence and Machine learning (AI/ML) approach that can provide an efficient and accurate analysis of Brinell hardness test data, allowing for improved measurement and prediction of material hardness values. By processing large amounts of data and identifying complex patterns, AI/ML algorithms can enhance the accuracy and reliability of Brinell Hardness measurements, making them valuable tools for materials science research. The model is trained using diverse data sources, including experimental data, published results, and foundry data. Subsequently, the trained model is employed to accurately predict the Brinell Hardness Number (BHN) of cast iron. The model's predictions are verified against experimental methods to validate the accuracy of the proposed approach. This method offers an effective and efficient means of predicting the BHN of cast iron by optimizing its process parameters, making it useful for a variety of applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cast iron

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Customer Segmentation Using Credit Card data Analysis 21

Customer Segmentation Using Credit Card Data Analysis

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21st IEEE/ACIS international conference on Software engineering Research, Management and Applications, SERA 2023

作者： Raj, Saikat Roy, Santanu Jana, Surajit Roy, Soumyadip Goto, Takaaki Sen, Soumya Kolkata India Technology University of Calcutta A.K. Choudhury School of Information Kolkata India Toyo University Faculty of Information Sciences and Arts Japan

ISBN: (纸本)9798350345889

Customer segmentation is a separation of a market into multiple distinct groups of consumers who share the similar characteristics. Segmentation of market is an effective way to define and meet Customer needs and also to identify the future business plan. Unsupervised machine learning algorithms are suitable to analyze and identify the possible set of customers when the labeled data about the customers are no available. In this research work the spending of different customers who have credit cards are analyzed to segment them into different clusters and also to plan further business improvements based on the different characteristics of these identified clusters. © 2023 IEEE.

关键词： K-means clustering

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21st international Symposium on Web and Wireless Geographical Information Systems, W2GIS 2024

21st International Symposium on Web and Wireless Geographica...

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21st international Symposium on Web and Wireless Geographical Information Systems, W2GIS 2024

ISBN: (纸本)9783031607950

The proceedings contain 16 papers. The special focus in this conference is on Web and Wireless Geographical Information Systems. The topics include: Exploring Spatio-temporal Dynamics: A Historical Analysis of Missing Persons data in Mexico, Revealing Patterns and Trends;Assessing and Managing Soil Quality with Geodata: The IQS Project;Publication of Satellite Earth Observations in the Linked Open data Cloud: Experiment Through the TRACES Project;geospatial Webservices and Reproducibility of Research: Challenges and Needs;TAME II: A Modern Geographic Text Annotation Tool;Towards OGC API - Features Centric GIS Applications Controlled by Object Relational Mapping;smooth Building Footprint Aggregation with Alpha Shapes;in Situ Visualization of 6DoF Georeferenced Historical Photographs in Location-Based Augmented Reality;Can Large Language Models Automatically Generate GIS Reports?;A Digital Twin Architecture for intelligent Public Transportation Systems: A FIWARE-Based Solution;an Analysis of Container Transportation Multiplex Networks from the Perspective of Shipping Company;a Spatial Interaction Model for the Identification of Urban Functional Regions;Enhancing Efficiency and Privacy of intelligent Public Transportation Systems Through Federated learning and EdgeAI;towards a Framework for Personalising Leisure Walking Route Recommendations.

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Scientific satellite events held in conjunction with the 21st international conference on Service-Oriented Computing, ICSOC 2023

Scientific satellite events held in conjunction with the 21s...

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Scientific satellite events held in conjunction with the 21st international conference on Service-Oriented Computing, ICSOC 2023

ISBN: (纸本)9789819709885

The proceedings contain 31 papers. The special focus in this conference is on Service-Oriented Computing. The topics include: Towards Improving Insurance Processes: A Time Series Analysis of Psychosocial Recovery After Workplace Injury Across Legislative Environments;Uncovering LLMs for Service-Composition: Challenges and Opportunities;transformative Predictive Modelling in the Business of Health: Harnessing Decision Trees for strategic Insights and Enhanced Operational Efficiency;breaking Boundaries: Can a Unified Hardware Abstraction Layer Simplify Transformer Deployments on Edge Devices?;Improving Deep learning Transparency: Leveraging the Power of LIME Heatmap;non-expert Level Analysis of Self-adaptive Systems;federated data Products: A Confluence of data Mesh and Gaia-X for data Sharing;XPS++: A Publish/Subscribe System with Built-In Security and Privacy by Design;on Rounding Errors in the Simulation of Quantum Circuits;linear structure of Training Samples in Quantum Neural Network Applications;towards Higher Abstraction Levels in Quantum Computing;hybrid data Management Architecture for Present Quantum Computing;quantum Block-Matching Algorithm Using Dissimilarity Measure;some Initial Guidelines for Building Reusable Quantum Oracles;Carbon-Awareness in CI/CD;smart Public Transport with Be-in/Be-out System Supported by iBeacon Devices;towards a Systematic Comparison Framework for Cloud Services Customer Agreements;formalizing Microservices Patterns with Event-B: The Case of Service Registry;privacy engineering in the data Mesh: Towards a Decentralized data Privacy Governance Framework;towards a Taxonomy and Software Architecture for data Processing and Contextualization for the Internet of Things;advanced Serverless Edge Computing;immersive 3D Simulator for Drone-as-a-Service;SLA-Wizard - automated Configuration of REstful API Gateways Based on SLAs;The IDL Tool Suite: Inter-parameter Dependency Management in Web APIs;smelling Homemade Crypto Code in Microservi

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Revolutionizing the Present Education System Through AI-Driven learning Platform with Hybrid Techniques 4

Revolutionizing the Present Education System Through AI-Driv...

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4th IEEE international conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Yunusaliyev, Muhammadqodir Yuldashev, Ibrokhim Yusupov, Ardasher Fergana State University Fergana Uzbekistan Auezov South Kazakhstan University Shymkent Kazakhstan

ISBN: (纸本)9798350360165

Enter artificial intelligence (AI) technology, and it begins to change the landscape in higher education. This paper describes the developing paradigm of AI-driven learning environments and their revolutionary effects on 21st-century higher education. As personalized and adaptive learning experiences gain further ground, these new opportunities enable AI-driven systems to provide revolutionized pedagogical practices, improved student engagement, and boosted learning outcomes. The nature of AI in learning environments is customization: to refine the learning experience to every single, individual need, and preference of the student. The AI algorithms, capable of handling a huge amount of data, can make a proper evaluation of students, including their strengths, weaknesses, learning preferences, and others, for the improvement in learning and thus cater to them with personalized content and recommendations. This does set up a rich and transparent learning environment but also allows students to insist on owning their own learning. That democratization of education would therefore be paramount not just in handling disparities in access, but also encouraging a diverse and inclusive learning community. © 2024 IEEE.

关键词： Federated learning

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Transferable Deep Reinforce learning With Limited Target data 21

Transferable Deep Reinforce Learning With Limited Target Dat...

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21st international Computer conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Xiaoqin, Liang Xingyu, Chen Zheng, Wang Wenyi, Xu Shijiao, Han School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China Chengdu Aircraft Design and Research Institude Chengdu610091 China School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9798331519254

While there has been significant progress in the application of transfer learning within reinforcement learning, most existing research primarily focuses on online reinforcement learning, with few studies addressing the optimization of offline reinforcement learning. This paper proposes a novel transferable deep reinforcement learning method designed to enhance limited offline samples in the target domain by utilizing online interactive samples from a similar source domain. The method is versatile and can be integrated with any offline reinforcement learning algorithm. To validate its effectiveness, extensive evaluations were conducted across multiple Mujoco environments and D4RL datasets. The results indicate that by employing the proposed method, the algorithm's performance can approach that of using 100% of the target domain data, even when only 10% of the data is available. © 2024 IEEE.

关键词： Adversarial machine learning

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