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IEEE Transactions on Artificial Intelligence 2024年第11期5卷 5465-5477页

作者： Hayat, Malik Khizar Xue, Shan Wu, Jia Yang, Jian Macquarie University School of Computing Faculty of Science and Engineering SydneyNSW2113 Australia

Graphs are a foundational way to represent scenarios where objects interact in pairs. Recently, graph neural networks (GNNs) have become widely used for modeling simple graph structures, either in homogeneous or heterogeneous graphs, where edges represent pairwise relationships between nodes. However, many real-world situations involve more complex interactions where multiple nodes interact simultaneously, as observed in contexts such as social groups and gene-gene interactions. Traditional graph embeddings often fail to capture these multifaceted nonpairwise dynamics. A hypergraph, which generalizes a simple graph by connecting two or more nodes via a single hyperedge, offers a more efficient way to represent these interactions. While most existing research focuses on homogeneous and static hypergraph embeddings, many real-world networks are inherently heterogeneous and dynamic. To address this gap, we propose a GNN-based embedding for dynamic heterogeneous hypergraphs, specifically designed to capture nonpairwise interactions and their evolution over time. Unlike traditional embedding methods that rely on distance or meta-path-based strategies for node neighborhood aggregation, a k-hop neighborhood strategy is introduced to effectively encapsulate higher-order interactions in dynamic networks. Furthermore, the information aggregation process is enhanced by incorporating semantic hyperedges, further enriching hypergraph embeddings. Finally, embeddings learned from each timestamp are aggregated using a mean operation to derive the final node embeddings. Extensive experiments on five real-world datasets, along with comparisons against homogeneous, heterogeneous, and hypergraph-based baselines (both static and dynamic), demonstrate the robustness and superiority of our model. © 2024 IEEE.

关键词： Graph embeddings

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Research Progress in Solar Flare Prediction Methods

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Research in Astronomy and Astrophysics 2025年第3期25卷 280-309页

作者： Ke Han Zhen Liu Xian-Yi Zhao Yi-Fei Li De-Quan Zheng Jie Wan School of Computer and Information Engineering Harbin University of Commerce Faculty of Computing Harbin Institute of Technology School of Energy Science and Engineering Harbin Institute of Technology

Solar flares are one of the strongest outbursts of solar activity,posing a serious threat to Earth’s critical infrastructure,such as communications,navigation,power,and ***,it is essential to accurately predict solar flares in order to ensure the safety of human ***,the research focuses on two directions:first,identifying predictors with more physical information and higher prediction accuracy,and second,building flare prediction models that can effectively handle complex observational *** terms of flare observability and predictability,this paper analyses multiple dimensions of solar flare observability and evaluates the potential of observational parameters in *** flare prediction models,the paper focuses on data-driven models and physical models,with an emphasis on the advantages of deep learning techniques in dealing with complex and high-dimensional *** reviewing existing traditional machine learning,deep learning,and fusion methods,the key roles of these techniques in improving prediction accuracy and efficiency are *** prevailing challenges,this study discusses the main challenges currently faced in solar flare prediction,such as the complexity of flare samples,the multimodality of observational data,and the interpretability of *** conclusion summarizes these findings and proposes future research directions and potential technology advancement.

关键词： (Sun:) sunspots magnetohydrodynamics (MHD) Sun: activity Sun: flares Sun: magnetic fields

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Machine learning in concept drift detection using statistical measures

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International Journal of Computers and Applications 2024年第5期46卷 281-291页

作者： Ali Abdu, Nail Adeeb Basulaim, Khaled Omer Information Technology Department Faculty of Engineering & Computing University of Science and Technology Aden Yemen Information Technology Engineering Department Faculty of Engineering University of Aden Aden Yemen

In the data stream, the data has non-stationary quality because of continual and inconsistent change. This change is represented as the concept drift in the classifying process of the streaming data. Representing this data drift concept in data stream mining requires pre-labeled samples. However, labeling samples in real-time streaming (online) is not feasible due to resource utilization and time constraints. Therefore, this paper proposes the concept of Probabilistic Concept Drift Detection (PCDD) in the group classifier. PCDD relies on the data stream classification process and provides concept drift without labeled samples. The PCDD model is evaluated through an empirical study on a dataset called Poker-Hand. The study results show a high concept drift detection rate and a significant reduction in false alarms and missed detections compared to contemporary models. Hence, the results of the experimental study prove the accuracy and scalability of the PCDD model. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Concept drift data streams detection rate for concept drift Kolmogorov–Smirnov statistical test (K–S) statistical t-test Wilcoxon signed rank statistical test

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NSGAN:a non-dominant sorting optimisation-based generative adversarial design framework for alloy discovery

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npj Computational Materials 2024年第1期10卷 2087-2101页

作者： Z.Li N.Birbilis College of Engineering Computing and CyberneticsThe Australian National UniversityActonACTAustralia Faculty of Science Engineeringand Built EnvironmentDeakin UniversityWaurn PondsVICAustralia

The design and discovery of new materials is fundamental to advancing scientific and technological *** recent emergence of the materials genome concept holds great promise in revolutionising materials science by enabling the systematic utilisation of data for efficient prediction and optimisation of‘superior’***,the materials genome approach can be stymied by the vast complexity of design spaces,which often demand substantial computational resources and sophisticated data processing *** address these challenges,this work introduces a generative design framework called the non-dominant sorting optimisation-based generative adversarial networks(NSGAN).Capitalising on the synergies of genetic algorithms(GA)and generative adversarial networks(GANs),NSGAN provides a robust and efficient approach for tackling high-dimensional multi-objective optimisation design *** validate the efficacy of the proposed framework,we applied the model to a comprehensive dataset of aluminium ***,an online tool was created as a supplementary resource,offering a brief introduction to this innovative method for the wider scientific *** study explores the potential of a predictive and data-driven approach in material design,indicating a promising pathway for widespread applications in the field of materials science.

关键词： alloy offering holds

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Optimal Voltage Regulator Coordination via Cuckoo Search Algorithm in Distributed Generation System to Enhance Grid Stability

Optimal Voltage Regulator Coordination via Cuckoo Search Alg...

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2024 IEEE Sustainable Power and Energy Conference, iSPEC 2024

作者： Kamal, Hamid Ali Afrouzi, Hadi Nabipour Jayamani, Elammaran Ektesabi, Mehran Siang, Chua Hong En, Lee Hui Swinburne University of Technology Faculty of Engineering Computing and Science Sarawak Malaysia Swinburne University of Technology Faculty of Engineering Computing and Science Melbourne Australia

ISBN: (纸本)9798350395075

Hybrid power systems need frequent voltage regulation to handle fluctuations in load profile and renewable energy output, ensuring effective voltage management. This increased need for regulation extends the operating time of the tap changer in transformers, accelerating the degradation of the on-load tap changer (OLTC) device, which happens to be the most costly component of the transformer. In this paper, the optimal coordinated operations among distribution static compensator (DSTATCOM), distributed generation (DGs), and OLTC are proposed using the Cuckoo Search algorithm (CSA). The aim is to optimize voltage deviations, OLTC usage, and power losses. The effectiveness of this approach is evaluated across two realistic scenarios using 32 distribution test systems. The simulation outcomes demonstrate how important coordinated management is in ensuring that voltage standards are met at each node while lowering power losses. © 2024 IEEE.

关键词： Distribution transformers

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Optimal aeration management strategy for a small-scale food waste composting

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Carbon Resources Conversion 2024年第1期7卷 79-88页

作者： Jia Chi Lai Yi Lung Then Siaw San Hwang Chung Sien Lee School of Engineering and Science Faculty of EngineeringComputing and ScienceSwinburne University of Technology93350 KuchingSarawakMalaysia Department of Electrical and Electronic Engineering Faculty of EngineeringUniversiti Malaysia Sarawak94300 Kota SamarahanSarawakMalaysia

Food waste is a significant contributor to greenhouse gas emissions when it ends up in *** turns out to be a sustainable solution to this problem,but it requires controlled and continuous airflow for optimal *** study focused on the effect of aeration rates and airflow directions on food waste composting using a closed system with forced *** was entered into the composting vessel in three directions,which were upward,downward,and a combination of both *** direction was run at aeration rates of 0.1,0.4,and 0.7 L/*** findings showed that the compost pile aerated at 0.4 L/min by using two-directional airflow can reach the thermophilic temperature within half of the *** compost pile achieved temperature of 40.94℃ after 10.5 *** the compost experienced slightly high in moisture loss(4.3%),the compost still attained the standard values for *** compost produced from food waste could be applied in soil to improve its fertility.

关键词： Small-scale composting Two-directional airflow Intermittent aeration Food waste

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Robotic Automation System for Indoor Greenhouse Applications 12

Robotic Automation System for Indoor Greenhouse Applications

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12th IEEE Region 10 Humanitarian Technology Conference, R10-HTC 2024

作者： Qi, Josiah Yang Hung, Lai Chean Jo, Hudyjaya Siswoyo Kiat, Justin Sia Wei Ting, Evon Lim Wan Dunn, Michelle Swinburne University of Technology Faculty of Engineering Computing and Science Kuching Malaysia Swinburne University of Technology Faculty of Engineering Computing and Science Melbourne Australia

ISBN: (纸本)9798350340204

As the world advances, the global population is projected to reach an estimated 9 billion by 2050, leading to a higher demand for food. To boost agricultural production without harming the environment, new and sustainable technologies are essential for improving efficiency and output. Precision farming has existed for years, but as its efficiency improves, scalability on large farms has emerged as a significant challenge. This project proposes using a mobile robotic system that can be deployed in existing greenhouses with minor changes and little additional infrastructure to assist current laborers in the agricultural sector. This paper would focus on the development and early onsite testing of the proposed robot in an indoor greenhouse environment. The robot successfully interacted with plants in 105 out of 135 test runs in a laboratory setting giving a success rate of 75.5%. Onsite testing where done with similar configurations to the laboratory settings to identify potential problems in a different environment. © 2024 IEEE.

关键词： Greenhouse Automation Greenhouses Object Detection Precision Agriculture Precision Farming Robotics Visual Control Feedback

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A Review of Sustainable Farming with Robotics in Indoor Greenhouse Environments 12

A Review of Sustainable Farming with Robotics in Indoor Gree...

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12th IEEE Region 10 Humanitarian Technology Conference, R10-HTC 2024

ISBN: (纸本)9798350340204

Current trend projected the global population to increase to 9 billion by the year 2050 which will increase the demand of food production by around 70%. The agricultural sector also supplies produce to bio-based products such as bioenergy and bioplastics which is also increasing the demand for crops. However, the situation is not as simple as planting more crops, as current agricultural practices are not sustainable. Environmental impact and climate changes could be reduced with the utilization of indoor farming could bring about a more sustainable farming solution. Combined with the utilization of precision farming solutions such as IoT based sensors and robotic systems indoor farming could become the next step in sustainable farming. The implementation of precision farming in indoor farming comes with its problems which may cause a challenge in the development and deployment period. This paper serves as the preliminary research for the development of a robotic system to be deployed at Sarawak's Digital Economy Corporation (SDEC)'s greenhouse. © 2024 IEEE.

关键词： Sustainable agriculture

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The Security of Using Large Language Models:A Survey With Emphasis on ChatGPT

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 1-26页

作者： Wei Zhou Xiaogang Zhu Qing-Long Han Lin Li Xiao Chen Sheng Wen Yang Xiang the School of Science Computing and Engineering Technologies Swinburne University of Technology IEEE the School of Computer and Mathematical Sciences the University of Adelaide Adelaide the Faculty of Science and Engineering Southern Cross University the School of Information and Physical Sciences the University of Newcastle

ChatGPT is a powerful artificial intelligence(AI)language model that has demonstrated significant improvements in various natural language processing(NLP) tasks. However, like any technology, it presents potential security risks that need to be carefully evaluated and addressed. In this survey, we provide an overview of the current state of research on security of using ChatGPT, with aspects of bias, disinformation, ethics, misuse,attacks and privacy. We review and discuss the literature on these topics and highlight open research questions and future *** this survey, we aim to contribute to the academic discourse on AI security, enriching the understanding of potential risks and mitigations. We anticipate that this survey will be valuable for various stakeholders involved in AI development and usage, including AI researchers, developers, policy makers, and end-users.

关键词： Artificial intelligence (AI) ChatGPT large language models (LLMs) security

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A new perspective exploration of machine learning algorithms for defending Side-Channel attacks

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Multimedia Tools and Applications 2024年第16期84卷 1-20页

作者： Ramakrishnan, Vedhavathy Thoguluva Seethapathy, Murugaanandam Sundar, Ramesh Dhandapani, Saveetha Munusamy, Sundarrajan Balasubrahmaniam, Lakshmi Dhevi Department of Networking and Communications School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur India

Machine learning algorithms are used in various real-time applications, where security is one of the major problems. Security is applied in various aspects of the application in cloud computing. One of the security issues is a side-channel attack, which is not a common problem that can be solved by a point solution. The emerging computing industries need a better solution for side-channel attack detection since the amount of data used in communication and sharing on the Internet is enormous. Several old research works have concluded that machine learning algorithms are highly suitable for data analytics. Hence, this paper has aimed to propose a better machine-learning model for side-channel attack detection. This paper explains some machine learning algorithms from a technical point of view. The explanation identifies that the Convolution Neural Network algorithm is selected as a better algorithm for side-channel attack detection. The proposed CNN model has experimented with Python software to verify the results. Since CNN is more efficient in data analytics, the analysis result produces more accurate predictions than earlier algorithms. The performance of the proposed CNN is compared with the earlier algorithms for evaluating it. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Data Analytics

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