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Identifying the hierarchical emotional areas in the human brain through information fusion

INFORMATION FUSION 2025年 113卷

作者： Huang, Zhongyu Du, Changde Li, Chaozhuo Fu, Kaicheng He, Huiguang Chinese Acad Sci Inst Automat Key Lab Brain Cognit & Brain inspired Intelligence Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Beijing Univ Posts & Telecommun MoE Key Lab Trustworthy Distributed Comp & Serv Beijing 100876 Peoples R China

The brain basis of emotion has consistently received widespread attention, attracting a large number of studies to explore this cutting-edge topic. However, the methods employed in these studies typically only model the pairwise relationship between two brain regions, while neglecting the interactions and information fusion among multiple brain regions-one of the key ideas of the psychological constructionist hypothesis. To overcome the limitations of traditional methods, this study provides an in-depth theoretical analysis of how to maximize interactions and information fusion among brain regions. Building on the results of this analysis, we propose to identify the hierarchical emotional areas in the human brain through multi-source information fusion and graph machine learning methods. Comprehensive experiments reveal that the identified hierarchical emotional areas, from lower to higher levels, primarily facilitate the fundamental process of emotion perception, the construction of basic psychological operations, and the coordination and integration of these operations. Overall, our findings provide unique insights into the brain mechanisms underlying specific emotions based on the psychological constructionist hypothesis.

关键词： Emotion Human brain Hierarchical emotional areas Multi-source information fusion graph machine learning Psychological constructionism

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Solar Flare Prediction Using Multivariate Time Series of Photospheric Magnetic Field Parameters: A Comparative Analysis of Vector, Time Series, and graph Data Representations

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REMOTE SENSING 2025年第6期17卷 1075-1075页

作者： Vural, Onur Hamdi, Shah Muhammad Boubrahimi, Soukaina Filali Utah State Univ Dept Comp Sci Logan UT 84322 USA

The purpose of this study is to provide a comprehensive resource for the selection of data representations for machine learning-oriented models and components in solar flare prediction tasks. Major solar flares occurring in the solar corona and heliosphere can bring potential destructive consequences, posing significant risks to astronauts, space stations, electronics, communication systems, and numerous technological infrastructures. For this reason, the accurate detection of major flares is essential for mitigating these hazards and ensuring the safety of our technology-dependent society. In response, leveraging machine learning techniques for predicting solar flares has emerged as a significant application within the realm of data science, relying on sensor data collected from solar active region photospheric magnetic fields by space- and ground-based observatories. In this research, three distinct solar flare prediction strategies utilizing the photospheric magnetic field parameter-based multivariate time series dataset are evaluated, with a focus on data representation techniques. Specifically, we examine vector-based, time series-based, and graph-based approaches to identify the most effective data representation for capturing key characteristics of the dataset. The vector-based approach condenses multivariate time series into a compressed vector form, the time series representation leverages temporal patterns, and the graph-based method models interdependencies between magnetic field parameters. The results demonstrate that the vector representation approach exhibits exceptional robustness in predicting solar flares, consistently yielding strong and reliable classification outcomes by effectively encapsulating the intricate relationships within photospheric magnetic field data when coupled with appropriate downstream machine learning classifiers.

关键词： solar flare prediction multivariate time series classification representation learning graph machine learning time series features deep learning

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Three Decades of Low Power: From Watts to Wisdom

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IEEE ACCESS 2024年 12卷 19447-19458页

作者： Munir, Mustafa Modi, Saloni Cooper, Geffen Kim, Huntae Marculescu, Radu Univ Texas Austin Elect & Comp Engn Dept Austin TX 78653 USA Sungkyunkwan Univ Dept Comp Sci & Engn Seoul 16419 South Korea

Low power technologies have been critical to the advancement of many fields of research including computer architecture, analog, digital, and mixed signal circuit design, semiconductor technologies, wireless technologies, networks-on-chip, distributed computing, Internet-of-Things, and machine learning on mobile devices. Many of the research advancements in each of these low power fields seem independent of the other fields, but are actually inter-related through the flow of ideas between fields of research. Many ideas used in research seem novel within one field, but in actuality they are often borrowed from other fields showing the interdisciplinary nature of research with ideas transferring from applied physics and chemistry to areas in semiconductor technologies and computer engineering. In this paper, we first examine a few significant research ideas in the area of low power technologies and then explore the interdisciplinary nature, growth, and impact of low power technologies through a network science based analysis of research publications over the past 30 years.

关键词： Wireless communication Tag clouds Convolutional neural networks Communication system security Wireless sensor networks Transistors Performance evaluation graphical models Internet of Things Low-power electronics Semiconductor devices graph convolutional networks graph machine learning low power technologies network science semiconductor technologies wireless technologies

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BGAE: Auto-Encoding Multi-View Bipartite graph Clustering

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2024年第8期36卷 3682-3696页

作者： Li, Liang Pan, Yuangang Liu, Jie Liu, Yue Liu, Xinwang Li, Kenli Tsang, Ivor W. Li, Keqin Natl Univ Def Technol Sch Comp Changsha 410073 Peoples R China ASTAR Ctr Frontier AI Res Singapore 138632 Singapore Natl Univ Def Technol Sci & Technol & Parallel & Distributed Proc Lab Changsha 410073 Peoples R China Natl Univ Def Technol Lab Software Engn Complex Syst Changsha 410073 Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Changsha 410073 Peoples R China Hunan Univ Supercomp & Cloud Comp Inst Changsha 410073 Peoples R China SUNY Coll New Paltz Dept Comp Sci New Paltz NY 12561 USA

Unsupervised multi-view bipartite graph clustering (MVBGC) is a fast-growing research, due to promising scalability in large-scale tasks. Although many variants are proposed by various strategies, a common design is to construct the bipartite graph directly from the input data, i.e., only consider the unidirectional "encoding" process. However, "encoding-decoding" mechanism is a popular design for deep learning, the most representative one is auto-encoder (AE). Enlightened by this, this paper rethinks existing MVBGC paradigms and transfers the "encoding-decoding" design into graph machine learning, and proposes a novel framework termed auto-encoding multi-view bipartite graph clustering (BGAE), which integrates encoding, bipartite graph construction, and decoding modules in a self-supervised learning manner. The encoding module extracts a latent joint representation from the input data, the bipartite graph construction module learns a bipartite graph with connectivity constraint in latent semantic space, and the decoding module recreates the input data via the bipartite graph. Therefore, our novel BGAE combines representation learning, bipartite graph learning, reconstruction learning, and label inference into a unified framework. All the modules are seamlessly integrated and mutually reinforcing for clustering-friendly purposes. Extensive experiments verify the superiority of our novel design and the significance of "decoding" process. To the best of our knowledge, this is the first attempt to explore "encoding-decoding" design in traditional MVBGC.

关键词： Bipartite graph Decoding machine learning Encoding Synthetic data Matrix decomposition Image reconstruction Bipartite graph learning encoding-decoding graph machine learning multi-view clustering

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Decentralized Federated Recommendation with Privacy-aware Structured Client-level graph

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ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY 2024年第4期15卷 1-23页

作者： Li, Zhitao Lin, Zhaohao Liang, Feng Pan, Weike Yang, Qiang Ming, Zhong Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Hong Kong Peoples R China Shenzhen Univ Shenzhen Technol Univ Guangdong Lab Artificial Intelligence & Digital Ec Shenzhen Peoples R China

Recommendation models are deployed in a variety of commercial applications to provide personalized services for users. However, most of them rely on the users' original rating records that are often collected by a centralized server for model training, which may cause privacy issues. Recently, some centralized federated recommendation models are proposed for the protection of users' privacy, which however requires a server for coordination in the whole process of model training. As a response, we propose a novel privacy-aware decentralized federated recommendation (DFedRec) model, which is lossless compared with the traditional model in recommendation performance and is thus more accurate than other models in this line. Specifically, we design a privacy-aware structured client-level graph for the sharing of the model parameters in the process of model training, which is a one-stone-two-bird strategy, i.e., it protects users' privacy via some randomly sampled fake entries and reduces the communication cost by sharing the model parameters only with the related neighboring users. With the help of the privacy-aware structured client-level graph, we propose two novel collaborative training mechanisms in the setting without a server, including a batch algorithm DFedRec(b) and a stochastic one DFedRec(s), where the former requires the anonymity mechanism while the latter does not. They are both equivalent to probabilistic matrix factorization trained in a centralized server and are thus lossless. We then provide formal analysis of privacy guarantee of our methods and conduct extensive empirical studies on three public datasets with explicit feedback, which show the effectiveness of our DFedRec, i.e., it is privacy aware, communication efficient, and lossless.

关键词： Federated recommendation Matrix factorization graph machine learning Explicit feedback

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HAZOPCT: A HAZOP analysis completeness tool based on knowledge graph reasoning

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PROCESS SAFETY AND ENVIRONMENTAL PROTECTION 2025年 197卷

作者： Li, Zhiyuan Zhao, Jinsong Tsinghua Univ Dept Chem Engn State Key Lab Chem Engn Beijing 100084 Peoples R China Tsinghua Univ Beijing Key Lab Ind Big Data Syst & Applicat Beijing 100084 Peoples R China

Hazard and Operability (HAZOP) analysis is widely recognized as a cornerstone of risk assessment in the chemical industry. However, the reliance on manual processes makes HAZOP analysis time-consuming, laborintensive, and subject to variability, leading to inconsistent report quality and increased accident risks. To address these challenges, this paper introduces a HAZOP analysis Completeness Tool (HAZOPCT), which helps to review process designs for enhancing the completeness of HAZOP analysis. Initially, a HAZOP knowledge graph is constructed automatically to integrate multi-source process safety information based on a predefined ontology. Subsequently, HAZOPCT is employed to perform reasoning over the knowledge graph, thereby generating recommended conclusions from similar cases for completing HAZOP analysis. A case study focusing on the sulfur recovery process demonstrates the ability of HAZOPCT to efficiently identify hazards and operability issues that may have been overlooked in original HAZOP analysis tables. Additionally, ablation studies validate the critical role of semantic information and graph attention mechanisms in improving reasoning performance. The generated attention weight network further enhances the interpretability of HAZOPCT, offering insights into the automated reasoning process and contributing to a more transparent and reliable HAZOP analysis.

关键词： Hazard and operability analysis Knowledge graph graph machine learning Process safety risk assessment Knowledge reasoning

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Forecasting Cyber Threats and Pertinent Mitigation Technologies

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TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE 2025年 210卷

作者： Almahmoud, Zaid Yoo, Paul D. Damiani, Ernesto Choo, Kim-Kwang Raymond Yeun, Chan Yeob Univ London Birkbeck Coll Sch Comp & Math Sci London WC1E 7HX England Univ Milan Dept Comp Sci I-20122 Milan Italy Univ Texas San Antonio Dept Informat Syst & Cyber Secur San Antonio TX 78249 USA Khalifa Univ Ctr Cyber Phys Syst C2PS Dept Elect Engn & Comp Sci Abu Dhabi 127788 U Arab Emirates

Geopolitical instability is exacerbating the risk of catastrophic cyber-attacks striking where defences are weak. Nevertheless, cyber-attack trend forecasting predominantly relies on human expertise, which is susceptible to subjectivity and potential bias. Asa solution, we have recently presented a novel study that harnesses machine learning for long-term cyber-attack forecasting. Building upon this groundwork, our research advances to the next level, by predicting the disparity between cyber-attack trends and the trend of the relevant alleviation technologies. The proposed approach applies key constructs of Protection Motivation Theory while introducing a proactive version of the theory. Our predictive analysis aims to offer strategic insights for the decision of investment in cyber security technologies. It also provides a sound foundation for the strategic decisions of national defence agencies. To achieve this objective, we have expanded our dataset, which now encompasses records spanning 42 distinct cyber-attack types and various related features, alongside data concerning the trends of 98 pertinent technologies, dating back to 2011. The dataset features were meticulously curated from diverse sources, including news articles, blogs, government advisories, as well as from platforms such as Elsevier, Twitter, and Python APIs. With our comprehensive dataset in place, we construct a graph that elucidates the intricate interplay between cyber threats and the development of pertinent alleviation technologies. To forecast the graph, we introduce a novel Bayesian adaptation of a recently proposed graph neural network model, which effectively captures and predicts these trends. We further demonstrate the efficacy of our proposed features in this context. Furthermore, our study extends its horizon by generating future data projections for the next three years, encompassing forecasts for the evolving graph, including predictions of the gap between cyber-attack trends and

关键词： Cyber threat trend forecasting Mitigation technology trend forecasting Alleviation technology trend forecasting Proactive approach Big data analytics graph machine learning Technology cycle

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A graph Attention Network Approach to Partitioned Scheduling in Real-Time Systems

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IEEE EMBEDDED SYSTEMS LETTERS 2024年第4期16卷 457-460页

作者： Lee, Seunghoon Lee, Jinkyu Sungkyunkwan Univ Dept Comp Sci & Engn Suwon 16419 South Korea

machine learning methods have been used to solve real-time scheduling problems but none has yet made an architecture that utilizes influences between real-time tasks as input features. This letter proposes a novel approach to partitioned scheduling in real-time systems using graph machine learning. We present a graph representation of real-time task sets that enable graph machine-learning schemes to capture the influence between real-time tasks. By using a graph attention network (GAT) with this method, our model successfully partitioned-schedule task sets that were previously deemed unschedulable by state-of-the-art partitioned scheduling algorithms. The GAT is used to establish relationships between nodes in the graph, which represent real-time tasks, and to learn how these relationships affect the schedulability of the system.

关键词： Task analysis Real-time systems Program processors Processor scheduling machine learning Job shop scheduling Neural networks graph machine learning real-time scheduling

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Knowledge graphs in Pharmacovigilance: A Scoping Review

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CLINICAL THERAPEUTICS 2024年第7期46卷 544-554页

作者： Hauben, Manfred Rafi, Mazin Abdelaziz, Ibrahim Hassanzadeh, Oktie New York Med Coll Dept Family & Community Med Valhalla NY USA Truliant Consulting Baltimore MD USA Rutgers State Univ Dept Stat Piscataway NJ 98854 USA IBM Res Yorktown Hts Yorktown Hts NY USA

Purpose: To critically assess the role and added value of knowledge graphs in pharmacovigilance, focusing on their ability to predict adverse drug reactions. Methods: A systematic scoping review was conducted in which detailed information, including objectives, technology, data sources, methodology, and performance metrics, were extracted from a set of peer-reviewed publications reporting the use of knowledge graphs to support pharmacovigilance signal detection. Findings: The review, which included 47 peer-reviewed articles, found knowledge graphs were utilized for detecting/predicting single-drug adverse reactions and drug-drug interactions, with variable reported performance and sparse comparisons to legacy methods. Implications: Research to date suggests that knowledge graphs have the potential to augment predictive signal detection in pharmacovigilance, but further research using more reliable reference sets of adverse drug reactions and comparison with legacy pharmacovigilance methods are needed to more clearly define best practices and to establish their place in holistic pharmacovigilance systems.

关键词： Adverse drug reactions Drug safety graph machine learning Knowledge graphs Pharmacovigilance Scoping review

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MHNF: Multi-Hop Heterogeneous Neighborhood Information Fusion graph Representation learning

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2023年第7期35卷 7192-7205页

作者： Sun, Yundong Zhu, Dongjie Du, Haiwen Tian, Zhaoshuo Harbin Inst Technol Sch Astronaut Harbin 150001 Heilongjiang Peoples R China Harbin Inst Technol Sch Comp Sci & Technol Weihai 264209 Shandong Peoples R China

The attention mechanism enables graph neural networks (GNNs) to learn the attention weights between the target node and its one-hop neighbors, thereby improving the performance further. However, most existing GNNs are oriented toward homogeneous graphs, and in which each layer can only aggregate the information of one-hop neighbors. Stacking multilayer networks introduces considerable noise and easily leads to over smoothing. We propose here a multihop heterogeneous neighborhood information fusion graph representation learning method (MHNF). Specifically, we propose a hybrid metapath autonomous extraction model to efficiently extract multihop hybrid neighbors. Then, we formulate a hop-level heterogeneous information aggregation model, which selectively aggregates different-hop neighborhood information within the same hybrid metapath. Finally, a hierarchical semantic attention fusion model (HSAF) is constructed, which can efficiently integrate different-hop and different-path neighborhood information. In this fashion, this paper solves the problem of aggregating multihop neighborhood information and learning hybrid metapaths for target tasks. This mitigates the limitation of manually specifying metapaths. In addition, HSAF can extract the internal node information of the metapaths and better integrate the semantic information present at different levels. Experimental results on real datasets show that MHNF achieves the best or competitive performance against state-of-the-art baselines with only a fraction of 1/10 similar to 1/100 parameters and computational budgets. Our code is publicly available at https://***/PHD-lanyu/MHNF.

关键词： graph machine learning heterogeneous graph graph represent learning graph neural networks metapath

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