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International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS)

作者： Faisal Asadi Joko Pebrianto Trinugroho Alam Ahmad Hidayat Reza Rahutomo Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Information System Department School of Information Systems Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9781665473286

Tropical disease is one of the infectious diseases that affect Indonesia. Many people die because of tropical diseases, such as dengue hemorrhagic fever (DHF), chikungunya, leprosy, lymphatic, and filariasis. The Indonesian government's lack of public policy is the main problem in treating and preventing tropical diseases. The paper aims to describe the analysis results of the incidence of DHF in Jakarta, Indonesia. The analysis used the DHF incidences data from the Jakarta Health service surveillance office representative and combined it with the meteorological parameters data. The meteorological parameters were collected from the Bureau of Meteorological Climatology and Geophysics (BMKG). The statistical analysis of this research used Pearson's correlation. The results show that the meteorological parameters affecting DHF incidences are temperature, humidity, and wind speed.

关键词： Correlation Statistical analysis Wind speed Surveillance Multimedia systems Sociology Humidity

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Organizational Intent, Organizational Structures, and Reviewer Mental Models Influence Mortality Review Processes

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Mayo Clinic Proceedings: Innovations, Quality and Outcomes 2023年第6期7卷 515-523页

作者： Khayal, Inas S. Butcher, Rebecca L. McLeish, Colin H. Shentu, Yujia Barnato, Amber E. Dartmouth Institute for Health Policy and Clinical Practice Geisel School of Medicine at Dartmouth Lebanon NH United States Department of Biomedical Data Science Geisel School of Medicine at Dartmouth Lebanon NH United States Center for Program Design and Evaluation Geisel School of Medicine at Dartmouth Lebanon NH United States Department of Computer Science Dartmouth College Hanover NH United States Section of Palliative Care Department of Medicine Dartmouth Hitchcock Medical Center Lebanon NH United States Geisel School of Medicine at Dartmouth Hanover NH United States Tuck School of Business Hanover NH United States Department of Neurosurgery The University of Texas McGovern Medical School Houston

Objective: To identify the factors that influence the mortality review process at health systems, including how mortality review is conducted, cases are adjudicated, and results are used. Methods: We conducted a qualitative analysis of the mortality review processes of 6 US health systems from February 1, 2021 to June 31, 2021. The data sources included individual and small-group semi-structured interviews with mortality review team members and a content analysis of site artifacts (eg, guiding principles, chart abstraction forms, review workflows, and clinical pathways developed from past mortality reviews). We analyzed each site's mortality review process, goals and incentives for mortality review, historical and evolving aspects of mortality review, personnel involved, and post-review use of findings. Results: Across the 6 systems, we interviewed a total of 24 mortality review experts and analyzed 26 site documents. We identified 3 thematic factors that influence mortality review processes: organizational intent, organizational structures for mortality review, and the mental models of individuals involved in the review process. Two subthemes emerged within organizational intent: (1) identifying preventable deaths to lower (clinical or financial) risk and (2) using death cases to guide system improvement. Sites varied in governance and decision rights concerning mortality review and adjudication, with 2 subthemes within organizational structures: (1) centralized-hierarchical and (2) decentralized or multidisciplinary. The analysis of mental models of participating reviewers revealed 2 themes: (1) confirmation of preventability and (2) identification of patterns or “signals.” Conclusion: Understanding the factors that influence mortality review allows health systems to better leverage mortality review for institutional improvement and to develop training that builds shared mental models to enhance the review process. © 2023 The Authors

关键词： AHRQ Agency for Healthcare Research and Quality COREQ COnsolidated criteria for REporting Qualitative research MR mortality review QI quality improvement

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Fusion of Mixture of Experts and Generative Artificial Intelligence in Mobile Edge Metaverse

arXiv

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arXiv 2024年

作者： Liu, Guangyuan Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui Jamalipour, Abbas Mao, Shiwen Kim, Dong In The College of Computing and Data Science The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The College of Computing and Data Science Nanyang Technological University Singapore The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The Department of Electrical and Computer Engineering Auburn University Auburn United States The School of Electrical and Computer Engineering University of Sydney Australia The Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of

In the digital transformation era, Metaverse offers a fusion of virtual reality (VR), augmented reality (AR), and web technologies to create immersive digital experiences. However, the evolution of the Metaverse is slowed down by the challenges of content creation, scalability, and dynamic user interaction. Our study investigates an integration of Mixture of Experts (MoE) models with Generative Artificial Intelligence (GAI) for mobile edge computing to revolutionize content creation and interaction in the Metaverse. Specifically, we harness an MoE model's ability to efficiently manage complex data and complex tasks by dynamically selecting the most relevant experts running various sub-models to enhance the capabilities of GAI. We then present a novel framework that improves video content generation quality and consistency, and demonstrate its application through case studies. Our findings underscore the efficacy of MoE and GAI integration to redefine virtual experiences by offering a scalable, efficient pathway to harvest the Metaverse's full potential. © 2024, CC BY.

关键词： Augmented reality

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A deep learning system for predicting time to progression of diabetic retinopathy

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NATURE MEDICINE 2024年第2期30卷 358-359页

作者： [Anonymous] Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering 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 Center for Diabetes Shanghai China MOE Key Laboratory of AI School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Department of Ophthalmology Huadong Sanatorium Wuxi China Department of Ophthalmology Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong Hong Kong China Singapore Eye Research Institute Singapore National Eye Centre Singapore Singapore Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong China Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology Hong Kong China State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China Department of Ophthalmology Peking Union Medical College Hospital Peking Union Medical College Chinese Academy of Medical Sciences 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 Geriatrics Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Tech

We developed and validated a deep learning system (termed DeepDR Plus) in a diverse, multiethnic, multi-country dataset to predict personalized risk and time to progression of diabetic retinopathy. We show that DeepDR Plus can be integrated into the clinical workflow to promote individualized intervention strategies for the management of diabetic retinopathy.

关键词： Diabetes complications Machine learning Predictive markers

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A Design of Childhood Stunting Assessment Feature with Agile UX Approach

A Design of Childhood Stunting Assessment Feature with Agile...

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HCI and UX Conference in Indonesia (CHIuXiD), International

作者： Reza Rahutomo Anzaludin Samsinga Perbangsa Faisal Asadi Rudi Nirwantono Bens Pardamean Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9781665476652

multidisciplinary collaboration between public health, system engineering, and UX is able to generate a solution in healthcare problem like stunting. The principle of Agile UX gathers requirements to generate an application design. Currently, various website and mobile applications are available to be utilized for stunting research. StuntingDB is one of many web-based applications that specialized in stunting data management. The application required several types of users to operate hierarchically. This research focused on Public Health Officers’ workflow that require a solution to in the form of assessment module for stunting DBMS by using Agile UX principle. The method collaborates agile team and UX team to solve Public Health Officer's difficulty in changing stunting status at pre-saved child patient's profile. Collaboration of both agile team and UX team successfully proposed an application design that system design.

关键词： Human computer interaction Pediatrics Collaboration Companies Mobile applications Public healthcare System analysis and design

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Scalable Gaussian-process regression and variable selection using Vecchia approximations

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The Journal of Machine Learning Research 2022年第1期23卷 15799-15828页

作者： Jian Cao Joseph Guinness Marc G. Genton Matthias Katzfuss Department of Statistics and Institute of Data Science Texas A&M University College Station TX Department of Statistics Cornell University Ithaca NY Statistics Program King Abdullah University of Science and Technology Thuwal Saudi Arabia Department of Statistics Texas A&M University College Station TX

Gaussian process (GP) regression is a flexible, nonparametric approach to regression that naturally quantifies uncertainty. In many applications, the number of responses and covariates are both large, and a goal is to select covariates that are related to the response. For this setting, we propose a novel, scalable algorithm, coined VGPR, which optimizes a penalized GP log-likelihood based on the Vecchia GP approximation, an ordered conditional approximation from spatial statistics that implies a sparse Cholesky factor of the precision matrix. We traverse the regularization path from strong to weak penalization, sequentially adding candidate covariates based on the gradient of the log-likelihood and deselecting irrelevant covariates via a new quadratic constrained coordinate descent algorithm. We propose Vecchia-based mini-batch subsampling, which provides unbiased gradient estimators. The resulting procedure is scalable to millions of responses and thousands of covariates. Theoretical analysis and numerical studies demonstrate the improved scalability and accuracy relative to existing methods.

关键词： adaptive bridge penalty gradient-based variable selection mini-batch subsampling ordered conditional approximation penalized Gaussian regression

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The Technology Behind Genomic database

The Technology Behind Genomic Database

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Joko Pebrianto Trinugroho Anzaludin Samsinga Perbangsa Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9781665453967

In this digital era, we are exposed to a large amount of data. This includes biological data, which stores information about living organisms, including Deoxyribonucleic acid (DNA), genes, and proteins. With the development of information technology and information system, most of available biological data are stored in an online public database. Many of the databases are free-access and easily used, which helps the users, especially researchers, to make use of the data. Among the known public biological databases are the University of California Santa Cruz (UCSC) Genome Browser database and the Rat Genome database (RGD). These two databases provide access to the biological data from different organisms. This paper aims to describe the technology of public biological databases. Also elucidated in this paper are the differences features between UCSC Genome Browser database and the RGD. Our results showed that the UCSC contains much more biological data and features than the RGD. However, the genome browser of UCSC has a complex display, while the RGD has a simple display. Overall, both databases give the users the option to choose the most suitable database for them.

关键词： Proteins databases Genomics DNA Rats Biology Organisms

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Accuracy of a one-dimensional reduction of dynamical systems on networks

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Physical Review E 2022年第2期105卷 024305-024305页

作者： Prosenjit Kundu Hiroshi Kori Naoki Masuda Department of Mathematics State University of New York at Buffalo Buffalo New York 14260-2900 USA Department of Complexity Science and Engineering The University of Tokyo Chiba 277-8561 Japan Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo Buffalo New York 14260-5030 USA

Resilience is an ability of a system with which the system can adjust its activity to maintain its functionality when it is perturbed. To study resilience of dynamics on networks, Gao et al. [Nature (London) 530, 307 (2016)] proposed a theoretical framework to reduce dynamical systems on networks, which are high dimensional in general, to one-dimensional dynamical systems. The accuracy of this one-dimensional reduction relies on three approximations in addition to the assumption that the network has a negligible degree correlation. In the present study, we analyze the accuracy of the one-dimensional reduction assuming networks without degree correlation. We do so mainly through examining the validity of the individual assumptions underlying the method. Across five dynamical system models, we find that the accuracy of the one-dimensional reduction hinges on the spread of the equilibrium value of the state variable across the nodes in most cases. Specifically, the one-dimensional reduction tends to be accurate when the dispersion of the node's state is small. We also find that the correlation between the node's state and the node's degree, which is common for various dynamical systems on networks, is unrelated to the accuracy of the one-dimensional reduction.

关键词： Complex systems Dynamics of networks Network resilience

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Research on the Spillover Effects of Digital Transformation on the Sustainable Growth of Green Schools

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Proceedings of Business and Economic Studies 2023年第6期6卷 16-23页

作者： Yiting Qiu Qiuhua Chen Poh Soon Joseph Ng Zhejiang Technical Institute of Economics Hangzhou 3018Zhejiang ProvinceChina Innovation and Technology Program Engineering and Quantity SurveyingINTI International UniversityNilai 71800Negeri SembilanMalaysia Faculty of Data Science and Information Technology INTI International UniversityNilai 71800Negeri SembilanMalaysia

The development of society and the advancement of science and technology have led to the widespread integration of digital transformation in the field of ***,the current establishment of green schools faces various challenges,including non-environmental building facilities,high renovation costs,low organizational management efficiency,high energy consumption,outdated office tools,and insufficient environmental awareness among teachers and *** thorough research and analysis,it becomes evident that digital technology can play a pivotal role in addressing these challenges and contribute to all aspects of green school *** incorporation of digital thinking concepts is essential for the construction of ecologically civilized campuses and inclusive *** process of digital design and transformation proves instrumental in optimizing both software and hardware facilities within the campus,thereby reducing energy ***,comprehensive digital teaching management enhances overall efficiency in management and service *** digital teaching and learning models emerge as transformative tools,providing new avenues to create low-carbon,green classrooms for both teachers and *** exploring the application of digital transformation in establishing green schools and examining the resulting spillover effects,valuable insights can be *** insights,in turn,serve as reference points for building diversified digital technology paths on campus and fostering the creation of green schools.

关键词： Digital transformation Green schools Spillover effect

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Robust two-layer partition clustering of sparse multivariate functional data

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Econometrics and Statistics 2023年

作者： Qu, Zhuo Dai, Wenlin Genton, Marc G. Statistics Program King Abdullah University of Science and Technology Thuwal 23955 Saudi Arabia Center for Applied Statistics Institute of Statistics and Big Data Renmin University of China Beijing 100872 China

A novel elastic time distance for sparse multivariate functional data is proposed and used to develop a robust distance-based two-layer partition clustering method. With this proposed distance, the new approach not only can detect correct clusters for sparse multivariate functional data under outlier settings but also can detect those outliers that do not belong to any clusters. Classical distance-based clustering methods such as density-based spatial clustering of applications with noise (DBSCAN), agglomerative hierarchical clustering, and K-medoids are extended to the sparse multivariate functional case based on the newly-proposed distance. Numerical experiments on simulated data highlight that the performance of the proposed algorithm is superior to the performances of existing model-based and extended distance-based methods. The effectiveness of the proposed approach is demonstrated using Northwest Pacific cyclone tracks data as an example. © 2023 EcoSta Econometrics and Statistics

关键词： Cyclone data Elastic time distance Multivariate functional data Outliers Robust clustering Sparse data

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