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arXiv 2020年

作者： Fumanal-Idocin, J. Alonso-Betanzos, A. Cordón, O. Bustince, H. Minárová, M. Public University of Navarra Institute of Smart Cities Campus Arrosadia s/n Pamplona31006 Spain Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Slovak University of Technology in Bratislava Radlinského 11 Bratislava81005 Slovakia CITIC. University of A Coruña A Coruña15071 Spain Andalusian Research Institute DaSCI "Data Science and Computational Intelligence" University of Granada Granada18071 Spain

In this contribution we study social network modelling by using human interaction as a basis. To do so, we propose a new set of functions, affinities, designed to capture the nature of the local interactions among each pair of actors in a network. By using these functions, we develop a new community detection algorithm, the Borgia Clustering, where communities naturally arise from the multi-agent interaction in the network. We also discuss the effects of size and scale for communities regarding this case, as well as how we cope with the additional complexity present when big communities arise. Finally, we compare our community detection solution with other representative algorithms, finding favourable results. Copyright © 2020, The Authors. All rights reserved.

关键词： Multi agent systems

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Key-dependent Advanced Encryption Standard

Key-dependent Advanced Encryption Standard

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2018 International Conference on Computer, Control, Electrical, and Electronics Engineering, ICCCEEE 2018

作者： Altigani, Abdelrahman Hasan, Shafaatunnur Barry, Bazara Shamsuddin, Siti Mariyam UTM Big Data Centre Universiti Teknologi Malaysia Skudai Johor Malaysia Faculty of Computing Universiti Teknologi Malaysia Skudai Johor Malaysia Departement of Computer Science University of Khartoum Khartoum Sudan

ISBN: (纸本)9781538641231

Rijndael has been selected by the National Institute for Standards and Technology (NIST) as the Advanced Encryption Standard (AES). In compliance with Kerckhoffs's principle, and to preserve and promote interoperability, the design and internal operation of AES must remain open. However, we suggest that there is no need to retain the static nature of the design. Consequently, this research paper main objective is to introduce a dynamic algorithm, that determines the exact steps used for encrypting or decrypting payloads during execution time. This has been achieved by making AES parameters key dependent, rather than reuse fixed, pre-determined *** result is an armored version of the AES that changes its internal design with almost every new encryption key. The proposed design is provided and analyzed in terms of security, implementation cost and possibility for further improvements. © 2018 IEEE.

关键词： Cryptography

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Performance Analysis of Linear Quadratic Regulator Method in Microalga Growth with Control of Light Intensity

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Journal of Physics: Conference Series 2020年第1期1463卷

作者： Mardlijah A Fadhillah L Hanafi Department of Mathematics. Faculty of Mathematics Computing and Data Science. Institut Teknologi Sepuluh Nopember (ITS) Jl. Arief Rahman Hakim Surabaya 60111 Indonesia

Microalgae is an alternative energy source that considered capable of answering fuel oil scarcity problem. The growth of microalgae plays an important role in biomass production. One of the factors that influence the growth of microalgae is the control of light intensity so that it is necessary to adjust the intensity of the incoming light to produce maximum biomass. Control the intensity of light on the microalgae growth can be modelled in the form of differential equations and can be seen as a system. The control method chosen in this study was a linear quadratic regulator (LQR), as well as a comparison with the previous method, namely Pontryagin minimum principle (PMP). The simulation results show that at LQR an increase in the amount of biomass reaches 91.77% from initial condition. While the PMP has doubled from its initial condition.

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Pay attention to evolution: Time series forecasting with deep graph-evolution learning

arXiv

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arXiv 2020年

作者： Spadon, Gabriel Hong, Shenda Brandoli, Bruno Matwin, Stan Rodrigues, Jose F. Sun, Jimeng University of Sao Paulo Institute of Mathematics and Computer Sciences SP Sao Carlos13566-590 Brazil Georgia Institute of Technology College of Computing AtlantaGA30332-0365 United States Dalhousie University Institute for Big Data Analytics HalifaxNSB3H 1W5 Canada National Institute of Health Data Science Peking University Beijing100191 China Institute of Medical Technology Peking University Beijing100191 China Institute of Computer Science of the Polish Academy of Sciences Warsaw525-000-94-01 Poland Université Grenoble Alpes Faculty of Science Saint-Martin-d'Hères 38400 France University of Illinois Urbana-Champaign Department of Computer Science ChampaignIL61801-2302 United States

Time-series forecasting is one of the most active research topics in artificial intelligence. It has the power to bring light to problems in several areas of knowledge, such as epidemiological studies, healthcare inference, and climate change analysis. Applications in real-world time series should consider two factors for achieving reliable predictions: modeling dynamic dependencies among multiple variables and adjusting the model's intrinsic hyperparameters. An open gap in the literature is that statistical and ensemble learning approaches systematically present lower predictive performance than deep learning methods. The existing applications consistently disregard the data sequence aspect entangled with multivariate data represented in more than one time series. Conversely, this work presents a novel neural network architecture for time-series forecasting that combines the power of graph evolution with deep recurrent learning on distinct data distributions, named after Recurrent Graph Evolution Neural Network (REGENN). The idea is to infer multiple multivariate relationships between co-occurring time-series by assuming that the temporal data depends not only on inner variables and intra-temporal relationships (i.e., observations from itself) but also on outer variables and inter-temporal relationships (i.e., observations from other-selves). An extensive set of experiments was conducted comparing REGENN with tens of ensemble methods and classical statistical ones. The results outperformed both statistical and ensemble-learning approaches, showing an improvement of 64.87% over the competing algorithms on the SARS-CoV-2 dataset of the renowned John Hopkins University for 188 countries simultaneously. For further validation, we tested our architecture in two other public datasets of different domains, the PhysioNet computing in Cardiology Challenge 2012 and Brazilian Weather datasets. We also analyzed the Evolution Weights arising from the hidden layers of REGENN to

关键词： Time series

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Mechanistic Modeling and Multiscale Applications for Precision Medicine: Theory and Practice

Network and Systems Medicine

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Network and Systems Medicine 2020年第1期3卷 36-56页

作者： Stalidzans, Egils Zanin, Massimiliano Tieri, Paolo Castiglione, Filippo Polster, Annikka Scheiner, Stefan Pahle, Jürgen Stres, Blaž List, Markus Baumbach, Jan Lautizi, Manuela Van Steen, Kristel Schmidt, Harald H.H.W. Computational Systems Biology Group University of Latvia Riga Latvia Latvian Biomedical Reasearch and Study Centre Riga Latvia Centro de Tecnología Biomédica Universidad Politécnica de Madrid Pozuelo de Alarcón Spain CNR National Research Council IAC Institute for Applied Computing Rome Italy Oslo Norway Institute for Mechanics of Materials and Structures Vienna University of Technology Vienna Austria BioQuant Heidelberg University Heidelberg Germany Department of Animal Science University of Ljubljana Ljubljana Slovenia Faculty of Civil and Geodetic Engineering University of Ljubljana Ljubljana Slovenia Department of Automation Biocybernetics and Robotics Jozef Stefan Institute Ljubljana Slovenia Big Data in BioMedicine Research Group Chair of Experimental Bioinformatics TUM School of Weihenstephan Technical University of Munich Freising Germany Chair of Experimental Bioinformatics TUM School of Weihenstephan Technical University of Munich Freising Germany Computational Systems Medicine Research Group Chair of Experimental Bioinformatics TUM School of Weihenstephan Technical University of Munich Freising Germany BIO-Systems Genetics GIGA-R University of Liège Liège Belgium BIO3 - Systems Medicine Department of Human Genetics KU Leuven Leuven Belgium Department of Pharmacology and Personalised Medicine Faculty of Health Medicine and Life Science Maastricht University Maastricht Netherlands

Drug research, therapy development, and other areas of pharmacology and medicine can benefit from simulations and optimization of mathematical models that contain a mathematical description of interactions between systems elements at the cellular, tissue, organ, body, and population level. This approach is the foundation of systems medicine and precision medicine. Here, simulated experiments are performed with computers (in silico) first, and they are then replicated through lab experiments (in vivo or in vitro) or clinical studies. In turn, these experiments and studies can be used to validate or improve the models. This iterative loop of dry and wet lab work is successful when biomedical researchers tightly collaborate with data scientists and modelers. From an educational point of view, the interdisciplinary research in systems biology can be sustained most effectively when specialists have been trained to have both a strong background in the disciplines of biology or modeling and strong communication skills, which make them able to communicate with other specialists. This overview addresses possible interdisciplinary communication gaps. Focusing our attention on biomedical researchers, we describe the reasons for using modeling and ways to collaborate with modelers, including their needs for specific biological expertise and data. This review includes an introduction to the principles of several widely used mechanistic modeling methods, focusing on their areas of applicability as well as their limitations. A potential complementary role of machine-learning methods in the development of mechanistic models is also discussed. The descriptions of the methods also include links to corresponding modeling software tools as well as practical examples of their application. Finally, we also explicitly address different aspects of multiscale modeling approaches that allow a more complete and holistic perspective of the human body. © Egils Stalidzans et al. 2020 Published b

关键词： Iterative methods

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Non-invasive biopsy diagnosis of diabetic kidney disease via deep learning applied to retinal images: a population-based study

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The Lancet Digital Health 2025年第5期7卷 100868页

作者： Meng, Ziyao Guan, Zhouyu Yu, Shujie Wu, Yilan Zhao, Yaoning Shen, Jie Lim, Cynthia Ciwei Chen, Tingli Yang, Dawei Ran, An Ran He, Feng Hamzah, Haslina Singh, Sarkaaj Abd Raof, Anis Syazwani Lee-Boey, Jian Wen Samuel Lim, Soo-Kun Sun, Xufang Ge, Shuwang Xu, Gang Su, Hua Cheng, Yang Lu, Feng Liao, Xiaofei Jin, Hai Deng, Chenxin Ruan, Lei Zhang, Cuntai Wu, Chan Dai, Rongping Jin, Yixiao Wang, Wenxiao Li, Tingyao Liu, Ruhan Li, Jiajia Shu, Jia Lu, Yuwei Wang, Xiangning Wu, Qiang Qin, Yiming Tang, Jin Sheng, Xiaohua Jiao, Qiong Yang, Xiaokang Guo, Minyi McKay, Gareth J Hogg, Ruth E Liew, Gerald Chee, Evelyn Yi Lyn Hsu, Wynne Lee, Mong Li Szeto, Simon Luk, Andrea O Y Chan, Juliana C N Cheung, Carol Y Tan, Gavin Siew Wei Tham, Yih-Chung Cheng, Ching-Yu Sabanayagam, Charumathi Lim, Lee-Ling Jia, Weiping Li, Huating Sheng, Bin Wong, Tien Yin Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Institute for Proactive Healthcare Shanghai Jiao Tong University Department of Endocrinology and Metabolism Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai Diabetes Institute Shanghai Clinical Centre for Diabetes Shanghai Key Laboratory of Diabetes Mellitus Shanghai China MOE Key Laboratory of AI School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Beijing Tsinghua Changgung Hospital Eye Center School of Clinical Medicine Tsinghua Medicine Tsinghua University Beijing China Medical Records and Statistics Office Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Renal Medicine Singapore General Hospital SingHealth-Duke Academic Medical Centre Singapore Singapore Department of Ophthalmology Shanghai Health and Medical Center Wuxi China Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong Hong Kong Special Administrative Region China Singapore Eye Research Institute Singapore National Eye Centre Singapore Department of Medicine Faculty of Medicine Universiti Malaya Kuala Lumpur Malaysia Department of Ophthalmology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Hubei Wuhan China Department of Nephrology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Department of Nephrology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Department of Ophthalmology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China National Engineering Research Centre for Big Dat

Background: Improving the accessibility of screening diabetic kidney disease (DKD) and differentiating isolated diabetic nephropathy from non-diabetic kidney disease (NDKD) are two major challenges in the field of diabetes care. We aimed to develop and validate an artificial intelligence (AI) deep learning system to detect DKD and isolated diabetic nephropathy from retinal fundus images. Methods: In this population-based study, we developed a retinal image-based AI-deep learning system, DeepDKD, pretrained using 734 084 retinal fundus images. First, for DKD detection, we used 486 312 retinal images from 121 578 participants in the Shanghai Integrated Diabetes Prevention and Care System for development and internal validation, and ten multi-ethnic datasets from China, Singapore, Malaysia, Australia, and the UK (65 406 participants) for external validation. Second, to differentiate isolated diabetic nephropathy from NDKD, we used 1068 retinal images from 267 participants for development and internal validation, and three multi-ethnic datasets from China, Malaysia, and the UK (244 participants) for external validation. Finally, we conducted two proof-of-concept studies: a prospective real-world study with 3 months' follow-up to evaluate the effectiveness of DeepDKD in screening DKD;and a longitudinal analysis of the effectiveness of DeepDKD in differentiating isolated diabetic nephropathy from NDKD on renal function changes with 4·6 years' follow-up. Findings: For detecting DKD, DeepDKD achieved an area under the receiver operating characteristic curve (AUC) of 0·842 (95% CI 0·838–0·846) on the internal validation dataset and AUCs of 0·791–0·826 across external validation datasets. For differentiating isolated diabetic nephropathy from NDKD, DeepDKD achieved an AUC of 0·906 (0·825–0·966) on the internal validation dataset and AUCs of 0·733–0·844 across external validation datasets. In the prospective study, compared with the metadata model, DeepDKD could detect DKD wit

关键词： Deep learning

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Detecting anomalous citation groups in journal networks

arXiv

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arXiv 2020年

作者： Kojaku, Sadamori Livan, Giacomo Masuda, Naoki Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN47408 United States Department of Computer Science University College London LondonWC1E 6EA United Kingdom Systemic Risk Centre London School of Economics and Political Science LondonWC2A 2AE United Kingdom Department of Mathematics University at Buffalo State University of New York BuffaloNY United States Computational and Data-Enabled Science and Engineering Program University at Buffalo State University of New York BuffaloNY14260-2900 United States Faculty of Science and Engineering Waseda University Tokyo169-8555 Japan

The ever-increasing competitiveness in the academic publishing market incentivizes journal editors to pursue higher impact factors. This translates into journals becoming more selective, and, ultimately, into higher publication standards. However, the fixation on higher impact factors leads some journals to artificially boost impact factors through the coordinated effort of a "citation cartel" of journals. "Citation cartel" behavior has become increasingly common in recent years, with several instances being reported. Here, we propose an algorithm—-named CIDRE—to detect anomalous groups of journals that exchange citations at excessively high rates when compared against a null model that accounts for scientific communities and journal size. CIDRE detects more than half of the journals suspended from Journal Citation Reports due to anomalous citation behavior in the year of suspension or in advance. Furthermore, CIDRE detects many new anomalous groups, where the impact factors of the member journals are lifted substantially higher by the citations from other member journals. We describe a number of such examples in detail and discuss the implications of our findings with regard to the current academic climate. Copyright © 2020, The Authors. All rights reserved.

关键词： Publishing

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GPPT: Graph pyramid pooling transformer for visual scene

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Neurocomputing 2025年 647卷

作者： Li, Zhi-Peng Liu, Wen-Jian Sun, Xin Pan, Yi-Jie Gribova, Valeriya Filaretov, Vladimir Fedorovich Cohn, Anthony G. Huang, De-Shuang Ningbo Institute of Digital Twin Eastern Institute of Technology Tongxin Road No. 568 Zhejiang Ningbo315201 China Faculty of Data Science City University of Macau 999078 China MEGAROBO Technologies Linglong Street No. 88 Jiangsu Suzhou215128 China College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Jiangsu Nanjing211106 China Institute of Automation and Control Processes Far Eastern Branch of Russian Academy of Sciences Russia School of Computing University of Leeds LeedsLS2 9JT United Kingdom Institute for Regenerative Medicine Medical Innovation Center and State Key Laboratory of Cardiology Shanghai East Hospital School of Life Sciences and Technology Tongji University Shanghai200123 China

In the field of computer vision, network architectures are critical to the performance of tasks. Vision Graph Neural Network (ViG) has shown remarkable results in handling various vision tasks with their unique characteristics. However, the lack of multi-scale information in ViG limits its expressive capability. To address this challenge, we propose a Graph Pyramid Pooling Transformer (GPPT), which aims to enhance the performance of the model by introducing multi-scale feature learning. The core advantage of GPPT is its ability to effectively capture and fuse feature information at different scales. Specifically, it first generates multi-level pooled graphs using a graph pyramid pooling structure. Next, it encodes features at each scale using a weight-shared Graph Convolutional Neural Network (GCN). Then, it enhances information exchange across scales through a cross-scale feature fusion mechanism. Finally, it captures long-range node dependencies using a transformer module. The experimental results demonstrate that GPPT achieves exceptional performance across various visual scenes, including image classification, and object detection, highlighting its generality and validity. © 2025 Elsevier B.V.

关键词： Convolutional neural networks

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Determination factors of internet advertising affecting internet users’ behavior using canonical correlation analysis

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AIP Conference Proceedings 2024年第1期2895卷

作者： Farah Nabilah Zabidi Nurain Ibrahim Nur Izzah Atiqah Nurul Athirah Norzaidi Salsabilla Kamaludin Adina Najwa Kamarudin School of Mathematical Sciences College of Computing Informatics and Media Universiti Teknologi MARA 40450 Shah Alam Selangor Malaysia Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) Kompleks Al-Khawarizmi Universiti Teknologi MARA 40450 Shah Alam Selangor Malaysia Regional Department Suu Balm Malaysia Livemed Sdn Bhd Aurora Place Plaza No 1 Persiaran Bukit Jalil 57000 Kuala Lumpur Malaysia Federal Government Administrative Centre Block C6 Complex C Lebuh Perdana Selatan Presint 1 62514 Putrajaya Wilayah Persekutuan Putrajaya Malaysia Department of Mathematical Sciences Faculty of Science Universiti Teknologi Malaysia 81310 Johor Bahru Malaysia

One of the most important and fundamental parts of today’s business world is advertising. Advertising is necessary for both business and consumers. We can now observe that the advertising industry has experienced substantial changes and is now employed globally. An advertising company has found this platform to be quite useful for increasing revenue by influencing the online users’ behaviour, which may change in either a positive or bad way. Therefore, the primary topic of this study paper is on how internet advertising affects consumers’ online behaviour in UiTM Shah Alam. Through surveys, we gathered information about two sets of variables: Internet Users’ Behaviour (Purchase Intention, Effective Medium, Frequency of Browsing, and Demographic Variable), and sets of variables Internet Advertising (Webpage Content, Informativeness and Interactivity). To determine the aspects of internet advertising influencing internet users’ behaviour, Canonical Correlation Analysis was applied to the data that was gathered. Internet advertising and internet users’ behaviour are significantly correlated, according to the results analysis. As a result, Internet advertising plays a big role in influencing consumers’ behaviour because of its close connection to their purchasing decisions. Based on findings, it can be concluded that internet advertising plays significant roles in examining internet users’ behaviour, which may be advantageous for many industries globally, particularly in current era of digitalization. With information on consumer behaviour both now and in the future, these findings can assist marketers and advertisers in developing more effective online advertising strategies that better engage young adults’ attention and influence their behaviour, particularly with regard to purchase intention, leading to a misalignment. It will then be able to create more successful online advertising.

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Environment Scan of Generative AI Infrastructure for Clinical and Translational science

arXiv

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arXiv 2024年

作者： Idnay, Betina Xu, Zihan Adams, William G. Adibuzzaman, Mohammad Anderson, Nicholas R. Bahroos, Neil Bell, Douglas S. Bumgardner, Cody Campion, Thomas Castro, Mario Cimino, James J. Cohen, I. Glenn Dorr, David Elkin, Peter L. Fan, Jungwei W. Ferris, Todd Foran, David J. Hanauer, David Hogarth, Mike Huang, Kun Kalpathy-Cramer, Jayashree Kandpal, Manoj Karnik, Niranjan S. Katoch, Avnish Lai, Albert M. Lambert, Christophe G. Li, Lang Lindsell, Christopher Liu, Jinze Lu, Zhiyong Luo, Yuan McGarvey, Peter Mendonca, Eneida A. Mirhaji, Parsa Murphy, Shawn Osborne, John D. Paschalidis, Ioannis C. Harris, Paul A. Prior, Fred Shaheen, Nicholas J. Shara, Nawar Sim, Ida Tachinardi, Umberto Waitman, Lemuel R. Wright, Rosalind J. Zai, Adrian H. Zheng, Kai Lee, Sandra Soo-Jin Malin, Bradley A. Natarajan, Karthik Price, W. Nicholson Zhang, Rui Zhang, Yiye Xu, Hua Bian, Jiang Weng, Chunhua Peng, Yifan Department of Biomedical Informatics Columbia University Irving Medical Center New YorkNY United States Department of Population Health Sciences Weill Cornell Medicine New YorkNY United States Department of Pediatrics Boston Medical Center BostonMA United States Chobanian & Avedisian School of Medicine Boston University BostonMA United States Oregon Clinical and Translational Research Institute Oregon Health and Science University PortlandOR United States Department of Public Health Sciences University of California Davis DavisCA United States Keck School of Medicine University of Southern California Los AngelesCA United States Department of Medicine David Geffen School of Medicine University of California Los Angeles Los AngelesCA United States Department of Pathology and Laboratory Medicine University of Kentucky College of Medicine LexingtonKY United States Division of Pulmonary Critical Care and Sleep Medicine University of Kansas School of Medicine Kansas CityKS United States Department of Biomedical Informatics and Data Science Heersink School of Medicine University of Alabama BirminghamAL United States Harvard Law School Petrie-Flom Center for Health Law Policy Biotechnology and Bioethics Harvard University CambridgeMA United States Department of Biomedical Informatics University at Buffalo BuffaloNY United States Center for Clinical and Translational Science Mayo Clinic RochesterMN United States Technology and Digital Solutions Stanford Medicine Stanford University StanfordCA United States Center for Biomedical Informatics Rutgers Cancer Institute New BrunswickNJ United States Department of Learning Health Sciences University of Michigan Medical School Ann ArborMI United States University of California San Diego La Jolla CA United States Department of Biostatistics and Health Data Science School of Medicine Indiana University IndianapolisIN United States Division of Artificial Medical Intelligence in Ophthalmology Univ

This study reports a comprehensive environmental scan of the generative AI (GenAI) infrastructure in the national network for clinical and translational science across 36 institutions supported by the Clinical and Translational science Award (CTSA) Program led by the National Center for Advancing Translational sciences (NCATS) of the National Institutes of Health (NIH) at the United States. With the rapid advancement of GenAI technologies, including large language models (LLMs), healthcare institutions face unprecedented opportunities and challenges. This research explores the current status of GenAI integration, focusing on stakeholder roles, governance structures, and ethical considerations by administering a survey among leaders of health institutions (i.e., representing academic medical centers and health systems) to assess the institutional readiness and approach towards GenAI adoption. Key findings indicate a diverse range of institutional strategies, with most organizations in the experimental phase of GenAI deployment. The study highlights significant variations in governance models, with a strong preference for centralized decision-making but notable gaps in workforce training and ethical oversight. Moreover, the results underscore the need for a more coordinated approach to GenAI governance, emphasizing collaboration among senior leaders, clinicians, information technology staff, and researchers. Our analysis also reveals concerns regarding GenAI bias, data security, and stakeholder trust, which must be addressed to ensure the ethical and effective implementation of GenAI technologies. This study offers valuable insights into the challenges and opportunities of GenAI integration in healthcare, providing a roadmap for institutions aiming to leverage GenAI for improved quality of care and operational efficiency. © 2024, CC BY.

关键词： Electronic health record

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