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6th IEEE International Conference on Blockchain and Cryptocurrency, ICBC 2024

作者： Belchior, Rafael Scuri, Sabrina Nunes, Nuno Hardjono, Thomas Vasconcelos, Andre INESC-ID Lisbon Portugal Instituto Superior Técnico Universidade de Lisboa Department of Computer Science and Engineering Lisbon Portugal Politecnico di Milano Design Department Milan Italy Interactive Technologies Institute/LARsys Lisbon Portugal Massachusetts Institute of Technology Mit Connection Science & Engineering Cambridge United States

ISBN: (纸本)9798350316742

Decentralized ledger technology (DLT) is becoming ubiquitous in today's society. However, organizations need to connect their existing systems and processes to blockchains (centralized, decentralized) securely and reliably, sometimes also implying that they need to connect blockchains (decentralized, decentralized). This challenge is known as blockchain interoperability. We put the case forward that academia and industry must propose evaluation frameworks for blockchain interoperability solutions that address the three interoperability modes. Those are data transfers, asset transfers, and asset exchanges. In this position paper, we illustrate the remaining challenges of interoperability, focusing on the systematic evaluation of interoperability mechanisms based on the state of the art and our own experience. Our evaluation is a systematic online survey of 17 items targeting blockchain specialists. Our quantitative analysis shows that several interesting metrics can show promising directions for systematically evaluating integration solutions. © 2024 IEEE.

关键词： Blockchain

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GCD-Sampling: A General Cross-scale Decoupled Sampling for Point Cloud 39

GCD-Sampling: A General Cross-scale Decoupled Sampling for P...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Dai, Tao Wang, Yanzi Xiong, Jianyu Zha, Yaohua Xia, Shu-Tao Zhu, Zexuan College of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Tsinghua Shenzhen International Graduate School Tsinghua University China Shenzhen City Key Laboratory of Embedded System Design Shenzhen China

ISBN: (纸本)157735897X

Sampling strategy (e.g., fixed farthest point sampling) of point cloud has been an essential step for developing practical solutions in 3D computer vision tasks. Previous fixed sampling is simple, but suffer from suboptimal performance for downstream tasks. To adapt to target networks properly, adaptive sampling methods with trainable parameters have been recently developed to enhance the performance. However, existing adaptive sampling methods still suffer from the over-coupling problem of target network, and thus become model-specific, which limits their practical applications. To address this issue, we propose a novel general cross-scale decoupled sampling method (GCD-sampling) for point cloud, which consists of original feature cache, cross-scale feature fusion and convex combination learning for better feature extraction. To reduce the coupling relationship with the target task network, our method only utilizes the point cloud coordinates as the input and output of itself. Besides, we introduce an arbitrary scale structure to enable parameter sharing across multi-scale sampling in point cloud networks. Extensive experiments on different architectures demonstrate the effectiveness of our method over other existing adaptive sampling methods. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Feature extraction

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Railway Passenger Flow and Sales Prediction Using Advanced Machine Learning Techniques

Railway Passenger Flow and Sales Prediction Using Advanced M...

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2024 IEEE International BIT Conference, BITCON 2024

作者： Kumari, Nalli Vinaya Pradeep Ghantasala, G.S. Sharma R, Rajesh Vidyullatha, Pellakuri Ananthanagu, U. Bathool, Saliha JNTUH Malla Reddy Engineering College for Women Department of Computer Science and Engineering Hyderabad India Alliance University Alliance College of Engineering and Design Department of Computer Science and Engineering Bengaluru India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Guntur India

ISBN: (纸本)9798331518394

Effective passenger waft control is pivotal for the highest quality operation of railway structure, impacting each operational performance and passenger satisfaction. This study delves into the software of system gaining knowledge of strategies to investigate and forecast passenger waft and price tag income inside railway networks. Utilizing historic tour statistics, sensor statistics, and outside influences, we increase predictive fashions that offer treasured insights into passenger conduct and decorate operational decision-making. Our findings spotlight the extensive enhancements in prediction accuracy and operational performance that system gaining knowledge of can deliver, providing sizeable blessings for railway control. © 2024 IEEE.

关键词： Support vector machines

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Budget-constrained Emission Reduction in Economic and Environmental Dispatch

Budget-constrained Emission Reduction in Economic and Enviro...

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2023 IEEE International Conference on Power Electronics, Smart Grid, and Renewable Energy, PESGRE 2023

作者： Kurte, Kuldeep Ramchandra Raju, Mishma Mariyam Dongritot, Prathamesh Kulkarni, Kedar Jio Platforms Limited Ai Centre of Excellence India Indian Institute of Technology Dept. of Engineering Design Madras Chennai India National Institute of Technology Karnataka Dept. of Computer Science and Engineering Surathkal India

ISBN: (纸本)9798350310573

To mitigate the impact of climate change, industries worldwide are adopting renewable energy to meet power demand. However, optimizing power generation costs and reducing emissions by integrating renewables with conventional sources presents significant challenges. In this work, we present a multi-objective optimization approach to dynamically optimize both costs and emissions by accounting for fossil-fuel based generators, renewables such as wind and solar, as well as the uncertainty in the demand and renewable energy forecasts. The primary objective of this work is to demonstrate how a multi-objective optimization framework can be used to generate reliable recommendations for emission reduction with minimal impact on cost. Our study reveals that significant emission reduction (≈ 50%) is achievable with a slight increase in the cost budget (≈ 2%). © 2023 IEEE.

关键词： dynamic emission reduction multi-objective Optimal power dispatch renewable energy uncertainty

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Integrated IoT-Based Smart Home Security System: Real-Time Hazard Detection and Alerts Using Multi-Sensor Fusion

Integrated IoT-Based Smart Home Security System: Real-Time H...

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2024 International Conference on Intelligent and Innovative Practices in engineering and Management, IIPEM 2024

作者： Sushma, A.M.S.V. Aswini Priyanka, A. Lakshmi, A.N.G. Khatri, Narendra Sharma, Harish S R K R Engineering College Department of Electrical and Electronics Engineering Andhra Pradesh Bhimavaram India S R K R Engineering College Department of Computer Science and Design Engineering Andhra Pradesh Bhimavaram India Sir C.R. Reddy College Engineering Department of Electronics and Communication Engineering Andhra Pradesh Bhimavaram India Manipal Acacdemy of Higher Education Department of Mechatronics Manipal Institute of Technology Manipal India Manipal University Jaipur School of Computer Science and Engineering Department of Artificial Intelligence and Machine Learning Jaipur India

ISBN: (纸本)9798350390049

This paper presents an innovative IoT-based smart home security system that integrates multiple sensors to detect various hazards and provide real-time alerts. Unlike traditional single-function systems, this solution combines motion detection via a PIR sensor, gas leak detection using an MQ-2 sensor, and fire detection through an IR flame sensor into a single, affordable package. The Arduino UNO serves as the central controller, interfaced with a GSM module for instantaneous SMS notifications and supported by an on-site buzzer alarm. Extensive testing in diverse environments demonstrates the system's exceptional performance, detecting intrusions in under 2 seconds and reporting threats with approximately 89% accuracy, while minimizing false alarms. The integration of smart power management techniques reduces energy consumption compared to conventional systems. This approach not only enhances home security but also offers a scalable and easily deployable solution for both residential and commercial settings. This work addresses the urgent need for an affordable, multi-functional smart home security system and provides a robust framework for future IoT-driven safety solutions. © 2024 IEEE.

关键词： Smart homes

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Quantum-Enhanced Deep Learning for Predictive Diagnostics and Personalized Treatment in Smart Healthcare Systems

Quantum-Enhanced Deep Learning for Predictive Diagnostics an...

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Pervasive Computing and Social Networking (ICPCSN), International Conference on

作者： R. Rajesh P. Hemalatha R. Vijayabharathi S. Bargunan S. R. Ramprasad J. Raja Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science and Engineering R.M.K. Engineering College Chennai India Department of Computer Science and Design R.M.K. Engineering College Chennai India Department of Computer Science and Design Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798331535193

ISBN: (纸本)9798331535209

The synergy between quantum computing and deep learning holds revolutionary promise in facilitating smart healthcare by addressing problems in predictive diagnosis, realtime patient tracking, and personalized therapy. Deep learning algorithms such as CNNs and RNNs have scalability and efficiency limitations in processing clinical data with high dimensionality. Their limitations can be addressed by quantum computing through an inbuilt parallelism. This work suggests that Quantum-Enhanced Deep Learning for Smart Healthcare (QEDL-Health) should marry classical deep learning with quantum algorithms to revamp paradigms in healthcare. QEDL-Health applies quantum neural networks (QNNs) with quantum gates (Pauli-X, Hadamard, CNOT) to perform encoding and feature extraction efficiently. Model parameters are trained by quantum variational circuits (QVCs), while quantum support vector machines (QSVMs) diagnose diseases with better accuracy. IoT-generated vitals in real-time tracking are processed by quantum principal component analysis (QPCA), while personalized treatment is created by quantum approximate optimization algorithms (QAOAs). The system applies quantum Fourier transforms (QFTs) and Grover’s search algorithm to process efficiently. Experimental results in MIMIC-III, TCGA, and COVID-19 datasets confirm dominance by improving diagnostic correctness by 15% while computation time is decreased by 40% in genomic data processing compared to classical methods. Experimental results confirm that comparative evaluation establishes that conventional methods in deep learning have scalability limitations in efficiency and performance in intricate tasks in disease detection (such as cancer) and in tracking in real-time. This work bridges quantum computing with AI to lay the foundations for developing efficient, scalable, and intelligent healthcare systems to detect diseases earlier in time to reduce costs and improve patient outcomes.

关键词： Deep learning Support vector machines Quantum computing Accuracy Quantum algorithm Scalability Neural networks Approximation algorithms Real-time systems Optimization

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LanCOPE: Language-Guided Category-Level Object Pose Estimation from a Single RGB Image

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IEEE Robotics and Automation Letters 2025年第7期10卷 7555-7562页

作者： Yang, Hui Sun, Wei Liu, Jian Zheng, Jin Dai, Zhenqi Mian, Ajmal Hunan University National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Changsha 410082 China Central South University School of Architecture and Art Changsha 410082 China The University of Western Australia Department of Computer Science and Software Engineering 6009 WA Australia

Monocular RGB-based category-level object pose estimation is more practical and cost-effective for robotics. However, existing methods do not fully exploit the rich semantic and contextual information in multimodal data (e.g. language) that provides additional object attributes to guide the model in extracting category features more reliably. We propose a language-guided category-level object pose estimation method (LanCOPE), taking a single RGB image as input. Our method uses DINOv2 to recover depth from a single RGB image and converts it into point cloud to perceive the object's geometry. We then introduce language descriptions for the RGB image, estimated point cloud and overall scene to better guide the point cloud encoder and image encoder in learning category features. We develop a cross-modal differential perception feature fusion network to fuse multimodal features. This network employs a differential perception module to eliminate redundant information across different modalities, highlighting signifcant semantic differences and similarities. Furthermore, it uses a cross-attention mechanism to fuse the semantic information of the language and vision features, improving the overall perception. Finally, we design a denoising network based on the skip fusion transformer to recover the object pose accurately. Extensive experiments on REAL275 and Wild6D datasets show that LanCOPE achieves state-of-the-art performance. Our code is available at LanCOPE. © 2016 IEEE.

关键词： category-level object pose estimation languag RGB image

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A Global Visual Information Intervention Model for Medical Visual Question Answering

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computers in Biology and Medicine 2025年第Pt A期192卷 110195-110195页

作者： Peng, Peixi Fan, Wanshu Shen, Yue Yang, Xin Zhou, Dongsheng National and Local Joint Engineering Laboratory of Computer-Aided Design School of Software Engineering Dalian University Dalian116622 China School of Computer Science and Technology Dalian University of Technology Dalian116024 China

Medical Visual Question Answering (Med-VQA) aims to furnish precise responses to clinical queries related to medical imagery. While its transformative potential in healthcare is undeniable, current solutions remain nascent and are yet to see widespread clinical adoption. Med-VQA presents heightened complexities compared to standard visual question answering (VQA) tasks due to the myriad of clinical scenarios and the scarcity of labeled medical imagery. This often culminates in language biases and overfitting vulnerabilities. In light of these challenges, this study introduces Global Visual Information Intervention (GVII), an innovative Med-VQA model designed to mitigate language biases and improve model generalizability. GVII is centered on two key branches: the Global Visual Information Branch (GVIB), which extracts and filters holistic visual data to amplify the image's contribution and reduce question dominance, and the Forward Compensation Branch (FCB), which refines multimodal features to counterbalance disruptions introduced by GVIB. These branches work in tandem to enhance predictive accuracy and robustness. Furthermore, a multi-branch fusion mechanism ensures cohesive integration of features and losses across the model. Experimental results demonstrate that the proposed model outperforms existing state-of-the-art models, achieving a 2.6% improvement in accuracy on the PathVQA dataset. In conclusion, the GVII-based Med-VQA model not only successfully mitigates prevalent language biases and overfitting issues but also significantly improves diagnostic precision, offering a considerable stride toward robust, clinically applicable VQA systems. © 2025

关键词： Question answering

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Online Learning in Blockchain-based Energy Trading Systems 24

Online Learning in Blockchain-based Energy Trading Systems

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2024 International Symposium on Theory, Algorithmic Foundations, and Protocol design for Mobile Networks and Mobile Computing, MobiHoc 2024

作者： Liu, Yunshu Cheung, Man Hon Huang, Jianwei Pillar of Engineering Systems and Design Singapore University of Technology and Design Singapore Singapore Department of Computer Science City University of Hong Kong Hong Kong School of Science and Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen Key Laboratory of Crowd Intelligence Empowered Low-Carbon Energy Network CSIJRI Joint Research Centre on Smart Energy Storage The Chinese University of HongKong Guangdong Shenzhen518172 China

ISBN: (纸本)9798400705212

In this paper, we consider a blockchain-based energy trading (BBET) system with the proof-of-stake (PoS) protocol. The system designer aims to minimize system cost by considering the prosumers’ strategic token allocation between blockchain staking and energy purchase for their applications. This is challenging as the system designer does not know prosumers’ private information of impatience levels towards different applications. To this end, we propose an online learning mechanism (OLM), which includes incentive mechanisms to guide both prosumers’ private information reporting and staking decisions in two phases. In the exploration phase, we design a randomized staking reward to encourage prosumers’ truthful reporting of their private information for the learning of impatience level distributions. Based on the threshold structure of the prosumers’ equilibrium staking strategies, in the exploitation phase, we propose a learning-error-based reward to minimize the system cost considering the finite-sample bias. By characterizing the optimal exploration duration, we prove that OLM achieves an asymptotic zero-regret against the complete information benchmark, with the regret bounded by O(3√lnT/T) when operating for T time slots. We implement the corresponding smart contract in Ethereum to demonstrate the feasibility of our approach. Experiment results show that our mechanism reduces regret by an average of 74% compared to the state-of-art mechanism. © 2024 Copyright held by the owner/author(s).

关键词： Blockchain

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When Graph Meets Retrieval Augmented Generation for Wireless Networks: A Tutorial and Case Study

arXiv

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arXiv 2024年

作者： Xiong, Yang Zhang, Ruichen Liu, Yinqiu Niyato, Dusit Xiong, Zehui Liang, Ying-Chang Mao, Shiwen Engineering Systems and Design Pillar Singapore University of Technology and Design Singapore College of Computing and Data Science Nanyang Technological University Singapore Computer Science and Design Pillar University of Technology and Design Singapore Chengdu611731 China Department of Electrical and Computer Engineering Auburn University AuburnAL36849 United States

The rapid development of next-generation networking technologies underscores their transformative role in revolutionizing modern communication systems, enabling faster, more reliable, and highly interconnected solutions. However, such development has also brought challenges to network optimizations. Thanks to the emergence of Large Language Models (LLMs) in recent years, tools including Retrieval Augmented Generation (RAG) have been developed and applied in various fields including networking, and have shown their effectiveness. Taking one step further, the integration of knowledge graphs into RAG frameworks further enhanced the performance of RAG in networking applications such as Intent-Driven Networks (IDNs) and spectrum knowledge maps by providing more contextually relevant responses through more accurate retrieval of related network information. This paper introduces the RAG framework that integrates knowledge graphs in its database and explores such framework’s application in networking. We begin by exploring RAG’s applications in networking and the limitations of conventional RAG and present the advantages that knowledge graphs’ structured knowledge representation brings to the retrieval and generation processes. Next, we propose a detailed GraphRAG-based framework for networking, including a step-by-step tutorial on its construction. Our evaluation through a case study on channel gain prediction demonstrates GraphRAG’s enhanced capability in generating accurate, contextually rich responses, surpassing traditional RAG models. Finally, we discuss key future directions for applying knowledge-graphs-empowered RAG frameworks in networking, including robust updates, mitigation of hallucination, and enhanced security measures for networking applications. © 2024, CC BY.

关键词： Knowledge graph

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