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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Jang, Yurim Namgung, Jong Young Park, Yeongjun Park, Bo-Yong Inha University Artificial Intelligence Convergence Research Center Incheon Korea Republic of Inha University Department of Data Science Incheon Korea Republic of Inha University Department of Computer Engineering Incheon Korea Republic of Center for Neuroscience Imaging Research Institute for Basic Science Suwon Korea Republic of

ISBN: (纸本)9798350371499

Brain state is dynamic in nature, and identifying the transitions between different brain states is crucial for understanding ongoing cognitive functions. Here, we opted for a transformer-based blood-oxygen-level-dependent transformer model (BolT) to investigate dynamic transitions of brain states across time. Using the given time series data, the model predicted subsequent time series. At the same time, it provided information about the contribution of each time point for the prediction with the importance weights. We extracted the time series corresponding to the top 10% and bottom 10% contribution of the importance weights. For each time series data, we constructed the connectivity matrix and calculated the degree centrality values. We compared the degree values between the top 10% and bottom 10% contribution data and found significant differences in the regions involved in the default mode network. Specifically, decreases in the degree values were observed at the highly contributed time points. Our findings indicate that the reorganization of connectome organization occurs in the default mode network during transitions in brain states. © 2024 IEEE.

关键词： Brain models

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Revealing the Development of Fish Species Identification Process through An Ontology Mapping Approach 10

Revealing the Development of Fish Species Identification Pro...

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10th International Conference on Computer, Control, Informatics and its applications, IC3INA 2023

作者： Kartika, Yulia A. Akbar, Zaenal Indrawati, Ariani Saleh, Dadan R. Haryono Yaman, Aris National Research and Innovation Agency Research Center for Data and Information Science Bandung Indonesia National Research and Innovation Agency Research Center for Biosystematics and Evolution Research Bandung Indonesia National Research and Innovation Agency Research Center for Artificial Intelligence and Cyber Security Bandung Indonesia

ISBN: (纸本)9798350394870

Ontology plays an essential role in biological research. It supports integrative analysis and interpretation by provisioning a standardized vocabulary, metadata, machine-readable axioms, and definitions. As a representation of knowledge within a domain, ontology must be adaptable to the domain changes. The fish species identification process has evolved as well. In this work, we would like to reveal the development of the process over time by using an ontology mapping approach. The approach relies on an ontology as a reference to be compared with the current requirements such that changes can be detected and revealed. This work utilizes Fish Ontology as the reference and database of Belida research Project as the current requirements. As a result, multiple patterns have been detected, grouped into two types: The BD contains more specific fields than the attributes in the FO, and the FO encompasses more detailed attributes than those in the BD. Consequently, the reference needs to be updated to represent current requirements in the species identification domain. © 2023 IEEE.

关键词： Biodiversity

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Construction and application of Digital Supply Chain Finance Platform in Oil and Gas Industry 2

Construction and Application of Digital Supply Chain Finance...

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2nd International Conference on Cyber-Energy Systems and Intelligent Energy, ICCSIE 2024

作者： Li, Yanmei Shi, Chaoyu Li, Xiaoxue Li, Yan Dai, Chunlin Wang, Linbo Kunlun Digital Technology Co. Ltd. Financial Business Department Beijing China Beijing Aerospace Data Stock Company Key Account Division Beijing China Artificial Intelligence Application Center China Electric Power Research Institute Beijing China

ISBN: (纸本)9798350373318

The oil and gas industry is an important part of the economy and energy strategy of countries, with the characteristics of large economic aggregate, long industrial chain, strong technical expertise and high complexity. Supply chain finance is an important tool to improve supply chain resilience, ease corporate financing constraints, and reduce financial transaction risks. In the context of the digital transformation of the industrial chain, in order to break the development problems such as closure and centralization under the traditional supply chain finance model, the digital supply chain finance platform has been created by using block chain, big data and Internet of things. The platform relies on the reconstruction of the bank's core business system to build a foundation for digital transformation, integrates the business flow, logistics, capital and information flow of the industrial chain at the base and application layer, and improves the supply chain financial product system based on the oil and gas industry, embedding bank financial services into all aspects of the oil and gas industry production and sales. This paper introduces the construction of digital supply chain finance platform. The application for the oil and gas industry shows that under the condition of data sharing and mutual trust, taking the digital supply chain financial service platform as the starting point can improve financing efficiency, reduce financing costs and risks, expand financing capacity, enhance supply chain value and competitiveness, and help the high-quality development of the oil and gas industry © 2024 IEEE.

关键词： Gas industry

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The Assessment of Clustering on Weighted Network with R Package clustAnalytics 24

The Assessment of Clustering on Weighted Network with R Pack...

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24th International Conference of the Catalan Association for artificial intelligence, CCIA 2022

作者： Arratia, Argimiro Renedo-Mirambell, Martí Intelligent Data Science and Artificial Intelligence Research Center Department of Computer Sciences Polytechnical University of Catalonia Barcelona Spain

ISBN: (纸本)9781643683263

We present clustAnalytics, an R package available now on CRAN, which provides methods to validate the results of clustering algorithms on unweighted and weighted networks, particularly for the cases where the existence of a community structure is unknown. clustAnalytics comprises a set of criteria for assessing the significance and stability of a clustering. To evaluate clusters' significance, clustAnalytics provides a set of community scoring functions, and systematically compares their values to those of a suitable null model. For this it employs a switching model to produce randomized graphs with weighted edges. To test for clusters' stability, a non parametric bootstrap method is used, together with similarity metrics derived from information theory and combinatorics. In order to assess the effectiveness of our clustering quality evaluation methods, we provide methods to synthetically generate networks (weighted or not) with a ground truth community structure based on the stochastic block model construction, as well as on a preferential attachment model, the latter producing networks with communities and scale-free degree distribution. © 2022 The authors and IOS Press.

关键词： Information theory

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Association between Serum Uric Acid and the Early Marker of Kidney Function Decline among Chinese Middle-Aged and Older Population:Evidence from the China Health and Retirement Longitudinal Study

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Biomedical and Environmental sciences 2023年第3期36卷 231-240页

作者： TANG Xu XU Lu MENG Ruo Gu DU Yi Qing LIU Shi Jun ZHAN Si Yan XU Tao Department of Urology Peking University People’s HospitalBeijing 100044China Research Center of Clinical Epidemiology Peking University Third HospitalBeijing 100191China National Institute of Health Data Science Center for Data Science in Health and MedicinePeking UniversityBeijing 100191China Department of Epidemiology and Biostatistics School of Public HealthPeking UniversityBeijing 100191China Center for Intelligent Public Health Institute for Artificial IntelligencePeking UniversityBeijing 100191China

Objective To evaluate the association between serum uric acid(SUA)and kidney function *** data was obtained from the China Health and Retirement Longitudinal Study on the Chinese middle-aged and older population for *** kidney function decline was defined as an annual estimated glomerular filtration rate(e GFR)decrease by>3 mL/min per 1.73 m^(2).Multivariable logistic regression was applied to determine the association between SUA and kidney function *** shape of the association was investigated by restricted cubic *** A total of 7,346 participants were included,of which 1,004 individuals(13.67%)developed kidney function decline during the follow-up of 4 years.A significant dose-response relation was recorded between SUA and the kidney function decline(OR 1.14,95%CI 1.03-1.27),as the risk of kidney function decline increased by 14%per 1 mg/d L increase in *** the subgroup analyses,such a relation was only recorded among women(OR 1.22,95%CI 1.03-1.45),those aged<60 years(OR 1.22,95%CI 1.05-1.42),and those without hypertension and without diabetes(OR 1.22,95%CI 1.06-1.41).Although the dose-response relation was not observed in men,the high level of SUA was related to kidney function decline(OR 1.83,95%CI 1.05-3.17).The restricted cubic spline analysis indicated that SUA>5 mg/dL was associated with a significantly higher risk of kidney function *** The SUA level was associated with kidney function *** elevation of SUA should therefore be addressed to prevent possible kidney impairment and dysfunction.

关键词： Uric acid Glomerular filtration rate Kidney function decline

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Orbit Weighting Scheme in the Context of Vector Space Information Retrieval

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Computers, Materials & Continua 2024年第7期80卷 1347-1379页

作者： Ahmad Ababneh Yousef Sanjalawe Salam Fraihat Salam Al-E’mari Hamzah Alqudah Department of Computer Science Faculty of Information TechnologyAmerican University of MadabaAmman11821Jordan Department of Cybersecurity Faculty of Information TechnologyAmerican University of MadabaAmman11821Jordan Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityP.O.Box 346Ajman13306United Arab Emirates Information Security Department Faculty of Information TechnologyUniversity of PetraAmman11196Jordan Department of Data Science and Artificial Intelligence Faculty of Information TechnologyAmerican University of MadabaAmman11821Jordan

This study introduces the Orbit Weighting Scheme(OWS),a novel approach aimed at enhancing the precision and efficiency of Vector Space information retrieval(IR)models,which have traditionally relied on weighting schemes like tf-idf and *** conventional methods often struggle with accurately capturing document relevance,leading to inefficiencies in both retrieval performance and index size *** proposes a dynamic weighting mechanism that evaluates the significance of terms based on their orbital position within the vector space,emphasizing term relationships and distribution patterns overlooked by existing *** research focuses on evaluating OWS’s impact on model accuracy using Information Retrieval metrics like Recall,Precision,InterpolatedAverage Precision(IAP),andMeanAverage Precision(MAP).Additionally,we assessOWS’s effectiveness in reducing the inverted index size,crucial for model *** compare OWS-based retrieval models against others using different schemes,including tf-idf variations and *** reveal OWS’s superiority,achieving a 54%Recall and 81%MAP,and a notable 38%reduction in the inverted index *** highlights OWS’s potential in optimizing retrieval processes and underscores the need for further research in this underrepresented area to fully leverage OWS’s capabilities in information retrieval methodologies.

关键词： Information retrieval orbit weighting scheme semantic text analysis Tf-Idf weighting scheme vector space model

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Review on application progress of federated learning model and security hazard protection

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Digital Communications and Networks 2023年第1期9卷 146-158页

作者： Aimin Yang Zezhong Ma Chunying Zhang Yang Han Zhibin Hu Wei Zhang Xiangdong Huang Yafeng Wu Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes North China University of Science and TechnologyTangshanHebeiChina Hebei Key Laboratory of Data Science and Application North China University of Science and TechnologyTangshanHebeiChina The Key Laboratory of Engineering Computing in Tangshan City North China University of Science and TechnologyTangshanHebeiChina Tangshan Intelligent Industry and Image Processing Technology Innovation Center North China University of Science and TechnologyTangshanHebeiChina College of Science North China University of Science and TechnologyTangshanHebeiChina College of Artificial Intelligence North China University of Science and TechnologyTangshanHebeiChina

Federated learning is a new type of distributed learning framework that allows multiple participants to share training results without revealing their data *** data privacy becomes more important,it becomes difficult to collect data from multiple data owners to make machine learning predictions due to the lack of data *** is forced to be stored independently between companies,creating“data silos”.With the goal of safeguarding data privacy and security,the federated learning framework greatly expands the amount of training data,effectively improving the shortcomings of traditional machine learning and deep learning,and bringing AI algorithms closer to our *** the context of the current international data security issues,federated learning is developing rapidly and has gradually moved from the theoretical to the applied *** paper first introduces the federated learning framework,analyzes its advantages,reviews the results of federated learning applications in industries such as communication and healthcare,then analyzes the pitfalls of federated learning and discusses the security issues that should be considered in applications,and finally looks into the future of federated learning and the application layer.

关键词： data silos Machine learning Federated learning Privacy protection Learning framework

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Harmful Algal Blooms Prediction Model: Dealing with Limited datasets 10

Harmful Algal Blooms Prediction Model: Dealing with Limited ...

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10th International Conference on Computer, Control, Informatics and its applications, IC3INA 2023

作者： Yaman, Aris Puspasari, Reny Akbar, Zaenal Indrawati, Ariani Kartika, Yulia Aris Manik, Lindung Parningotan Triharyuni, Setiya Albasri, Hatim Wibowo, Sandi Pardede, Hilman F. Research Center for Artificial Intelligence and Cyber Security Bandung Indonesia Research Center for Fisheries Bandung Indonesia Research Center for Data and Information Science National Research and Innovation Agency Bandung Indonesia Center for Fisheries Research Ministry of Marine Affairs and Fisheries of the Republic of Indonesia Bandung Indonesia

ISBN: (纸本)9798350394870

HABs pose serious threats to natural aquatic systems, such as mass mortality of aquatic organisms, degradation of water quality, and human poisoning from consuming aquatic organisms exposed to HABs. Monitoring water quality and weather data through buoy data loggers is very useful nowadays. Through these buoys, environmental data can be accessed in real time. Various technical constraints on these buoys will directly and indirectly result in missing values (limited data sets). It often happens that one has a good idea of a learning model, but due to its complexity and smaller number of data sets, the model performs far below expectations. Transfer learning approaches and data synthesis with the CT GAN algorithm have been applied to deal with modelling on limited datasets. The transfer learning model gives better results. It can be seen from the value of model evaluation parameters (AUC and MSE) that the transfer learning model provides better results than other models (model-based CTGAN and deep learning model without transfer learning). The process of adapting (fine-tuning) knowledge is an important process in improving the performance of the model in the transfer learning procedure. © 2023 IEEE.

关键词： Deep learning

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Joint Pre-Encoding Representation and Sturcture Embedding for Efficient and Low-Resource Knowledge Graph Completion

Joint Pre-Encoding Representation and Sturcture Embedding fo...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Qiu, Chenyu Qian, Pengjiang Wang, Chuang Yao, Jian Liu, Li Fang, Wei Ng, Eddie-Yin-Kwee School of Artificial Intelligence and Computer Science Jiangnan University China Engineering Research Center of Intelligent Technology for Healthcare Ministry of Education Jiangnan University China Big Data Center Affiliated Hospital of Jiangnan University China Nanyang Technological University Singapore

ISBN: (纸本)9798891761643

Knowledge graph completion (KGC) aims to infer missing or incomplete parts in knowledge graph. The existing models are generally divided into structure-based and description-based models, among description-based models often require longer training and inference times as well as increased memory usage. In this paper, we propose Pre-Encoded Masked Language Model (PEMLM) to efficiently solve KGC problem. By encoding textual descriptions into semantic representations before training, the necessary resources are significantly reduced. Furthermore, we introduce a straightforward but effective fusion framework to integrate structural embedding with pre-encoded semantic description, which enhances the model's prediction performance on 1-N relations. The experimental results demonstrate that our proposed strategy attains state-of-the-art performance on the WN18RR (MRR+5.4% and Hits@1+6.4%) and UMLS datasets. Compared to existing models, we have increased inference speed by 30x and reduced training memory by approximately 60%. © 2024 Association for Computational Linguistics.

关键词： Knowledge graph

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Towards a Cyber Information Ontology

Towards a Cyber Information Ontology

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2024 Joint Ontology Workshops - Episode X: The Tukker Zomer of Ontology, and Satellite Events, JOWO 2024

作者： Limbaugh, David Jensen, Mark Beverley, John National Center for Ontological Research Brazil U.S. Customs & Border Protection United States University at Buffalo Department of Philosophy BuffaloNY United States Institute for Artificial Intelligence and Data Science United States

This paper introduces a set of ontology terms that are intended to act as an interface between cyber ontologies, such as a file system ontology or a data fusion ontology, and top- and mid-level Basic Formal Ontology and Common Core Ontologies. These terms center on what makes cyber information management unique: numerous acts of copying items of information, the aggregates of copies that result from those acts, and the faithful members of those aggregates that represent all other members. © 2024 Copyright for this paper by its authors.

关键词： data assimilation

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