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ieee International Conference on High Performance Computing Workshops (HiPCW)

作者： Prasun Dewan Department of Computer Science University of North Carolina Chapel Hill USA

Today, AI tools are generally considered as education disrupters. In this paper, we put them in context with more traditional tools, showing how they complement the pedagogical potential of the former. We motivate a set of specific novel ways in which state-of-the-art tools, individually and together, can influence the teaching of concurrency. The pedagogy tasks we consider are illustrating concepts, creating motivating and debuggable assignments, assessing the runtime behavior and source code of solutions manually and automatically, generating model solutions for code and essay questions, discussing conceptual questions in class, and being aware of in-progress work. We use examples from past courses and training sessions in which we have been involved to illustrate the potential and actual influence of tools on these tasks. Some of the tools we consider are popular tools such as interactive programming environments and chat tools - we show novel uses of them. Some of the others such as testing and visualization tools are in-use novel tools - we discuss how they been used. The final group consists of AI tools such as ChatGPT 3.5 and 4.0 - we discuss their potential and how they can be integrated with traditional tools to realize this potential. We also show that version 4.0 has a better understanding of advanced concepts in synchronization and coordination than version 3.5, and both have a remarkable ability to understand concepts in concurrency, which can be expected to grow with advances in AI.

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Comparison of MIMIC-III and MIMIC-IV for big data analytics of health informatics

Comparison of MIMIC-III and MIMIC-IV for big data analytics ...

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ieee International Conference on Big data

作者： Dillon Chrimes Chanhee Kim School of Health Information Science University of Victoria Victoria Canada

The use of a health big data via Medical Information Mart for Intensive Care (MIMIC) data sets has significant advancements in health informatics and clinical research of electronic health records (EHRs) in hospital systems. MIMIC-III and MIMIC-IV data sets are the two latest publicly available iterations of data from electronic records that possess distinct features, variables, and structures in two separate large relational databases. This study aimed to provide big data analytics comparisons of MIMIC-III and MIMIC-IV for experiential learning of health informatics from *** data sets were quantitatively and qualitatively evaluated based on dataset properties (data, list of tables, timeline, patient encounters, software, system), data mining and visual categories (i.e., data size, data structure and schema, features/variables, data quality and completeness, clinical focus, access and use, dashboards, and visualization types), data usability heuristics and utilization (i.e., usability, complexity, granularity, applicability, and capacity). Results showed significant difference of 100 patients across 26 tables for MIMIC-III compared to 2,520 patients across 31 tables for MIMIC-IV data sets, respectively. There were 1716 diagnoses (ICD-9) and 503 procedures (ICD-9) for MIMIC-III. There were 262 diseases categories with >1000 disease instances and >2000 treatments for MIMIC-IV with no ICD codes. Moreover, these data sets contained large (big data) charting events in MIMIC-III of 758,356 rows, and MIMIC-IV eICU with charting events of 1,477,163 for nursing, and respiratory of 176,089 rows, respectively. The results suggest that MIMIC-III provided detailed information for retrospective clinical studies and operations in critical care with high data granularity in terms of re-admission (calculated fields from its admission table), length of stay, prescriptions, caregivers, and diagnosis and procedure (ICD-9). However, it lacked clinical capacity because of no diagno

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A Comparative Evaluation for Forecasting Diabetic Patients' Hospital Readmission within 30 Days through Machine Learning Techniques

A Comparative Evaluation for Forecasting Diabetic Patients' ...

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ieee Region 10 Humanitarian Technology Conference (R10-HTC)

作者： Tania Sultana Sheila Nusrat Jahan Nishat Aklima Haque Tanjila Jannatul Nuri Tahsina Farah Sanam Sayma Alam Suha Department of CSE Bangladesh Army International University of Science and Technology Cumilla Bangladesh Institute of Appropriate Technology Bangladesh University of Engineering & Technology Dhaka Bangladesh

Readmission to the hospital can place a heavy burden on patients and healthcare institutions with limited resources. Early intervention and better healthcare service quality can be achieved by identifying which patients are most likely to require readmission. The prevalence of diabetes among hospitalized patients is significantly increasing and so predictive analytics through machine learning methods to forecast hospital readmission, particularly for diabetic patients can be an excellent solution in this circumstance. Therefore, in this research, the electronic health record data from a hospital system has been used to predict readmissions of diabetic patients within 30 days of discharge using machine learning classification algorithms. The study has analyzed the significance o f various a ttributes, including commodities, demographic data, and prior hospitalization experience, with extensive data analysis and visualization. Several classification models 0 f machine1 earning, such a s gradient boosting, adaptive boosting, decision trees, random forests, K-Nearest Neighbours and logistic regression, have been trained and tested to make predictions. A comparative performance analysis with different types of parameters has been conducted, which reveals that the Adaboost classification technique provides the best accuracy with 88.3 % accuracy for predicting the readmission of diabetic patients.

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Cloud-Based Processing on Drone Imaging for Precision Agriculture

Cloud-Based Processing on Drone Imaging for Precision Agricu...

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ieee International Conference on Software Engineering and Service sciences (ICSESS)

作者： Joseph Ramses Méndez Cam Eulogio Guillermo Santos de la Cruz Félix Melchor Santos López Víctor Genaro Rosales Urbano School of Science and Engineering Pontifical Catholic University of Peru Lima Peru Faculty of Industrial Engineering National University of San Marcos Lima Peru Department of Engineering Pontifical Catholic University of Peru Lima Peru

The agricultural industry plays a crucial role in society and consistently invests large quantities of money. This is because the process of growing crops involves varied processes that are often time-consuming. In most countries, manual labor is still a key component of agriculture. However, with current technologies, many processes in precision agriculture can be automated or improved. Among these is traditional plant health inspection, which is often time-consuming and inefficient. The research gap this paper is trying to handle is about the difficult task of continuously monitoring crops. This paper aimed to address this challenge by proposing an innovative solution for assessing plant health, which applied cloud-based processing for aerial images. It was inspired by existing drone projects, as well as the Internet of Things technology. It used data captured from drone sensors and processed it automatically in the cloud. The software architecture used to achieve this was guided by the Attribute-Driven Design (ADD) methodology. According to the findings of the study, 430 ms was the average response time for plant state classification. Moreover, the cloud architecture was capable of sending an alarm if an unusual state was reached. Finally, a colored map was created to enable better visualization.

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Design and Development of Student Digital Health Profile 15

Design and Development of Student Digital Health Profile

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ieee 15th International Conference on Application of Information and Communication Technologies (AICT)

作者： Mansurova, Madina Sarsenova, Lazzat Kadyrbek, Nurgali Sarsembayeva, Talshyn Tyulepberdinova, Gulnur Sailau, Bekasyl Al Farabi Kazakh Natl Univ Dept Artificial Intelligence & Big Data Alma Ata Kazakhstan Al Farabi Kazakh Natl Univ Dept Fundamental Med Alma Ata Kazakhstan Al Farabi Kazakh Natl Univ Dept Comp Sci Alma Ata Kazakhstan Al Farabi Kazakh Natl Univ Alma Ata Kazakhstan

ISBN: (纸本)9781665436403

The aim of the paper is to design and develop an information system for assessing student health to form a set of measures to prevent disease using artificial intelligence algorithms. Within the framework of this scientific problem, a whole range of tasks related to the analysis of medical data, identification of patterns, search for patterns, and visualization of results using artificial intelligence methods is solved. This study is particularly relevant for Kazakhstan's educational institutions. The authors of the study expect to form a scientific and technological basis for promoting the idea and principles of "Healthy University - Healthy nation" among educational organizations of the Republic of Kazakhstan. The information system with visualization of the results of aggregation and analysis of data on health indicators and indicators related to health can be used by various groups of internal and external stakeholders as a regularly updated information resource for the development of various social or medical support programs.

关键词： digital health profile machine learning data mining information technology in medicine

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Outlining the Open Digital Twin Platform

Outlining the Open Digital Twin Platform

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Smart World Congress (SWC), ieee

作者： Jascha Grübel Carlos Vivar Rios Chenyu Zuo Sabrina Ossey Robin M. Franken Milos Balac Yanan Xin Kay W. Axhausen Martin Raubal Oksana Riba-Grognuz Center for Sustainable Future Mobility ETH Zurich Zurich Switzerland Swiss Data Science Center EPFL Lausanne Switzerland Transport Planning Group ETH Zurich Zurich Switzerland Geoinformation Engineering Group ETH Zurich Zurich Switzerland

Complex simulations and machine-learning models increase in application in research, industry, and governance. However, applying these systems with reasonable accuracy and efficiency requires large-scale efforts of data collection, data transformation, data analysis, and data visualization. At the same time, maintaining the required infrastructure, software, and personnel skyrockets making these tools unavailable to many potential users. The paradigm of the digital twin offers a novel perspective on how to manage the data efficiently and make these systems available more steadily at a lower cost. We introduce the first prototype of the Open Digital Twin Platform (ODTP) that is designed to be openly available to all interested parties to enable a common framework and baseline for digital twin based research. ODTP uses containerization, loose coupling, and micro-services to provide dynamically composable digital twins. ODTP also provides tools for licensing resolution, privacy and access control, and reproducibility. In its first iteration presented here, ODTP implements a common mobility research pipeline of the eqasim pipeline for MATSim. These kind of programs are usually difficult to assemble and use, thus leading to dangerous versions of "never change a running system". ODTP converts them into an easy-to-use version making it possible to initiate mobility simulations with one click. ODTP enables the quick adding of relevant data sources and analytical pipelines related to any topic and make them easily usable, accessible and shareable to research, industry, and governance. Thus, ODTP expands the FAIR principle from data to the complete data life cycle.

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Investigation of Thermal Perception and Emotional Response in Augmented Reality using Digital Biomarkers: A Pilot Study

Investigation of Thermal Perception and Emotional Response i...

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Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), ieee Conference on

作者： Sangjun Eom Seijung Kim Yihang Jiang Ryan Jay Chen Ali R. Roghanizad M. Zachary Rosenthal Jessilyn Dunn Maria Gorlatova Department of Electrical and Computer Engineering Duke University Department of Computer Science Duke University Department of Biomedical Engineering Duke University Department of Psychiatry and Behavioral Sciences Department of Psychology and Neuroscience Duke University

Dialectical behavior therapy (DBT) is an evidence-based psychotherapy that helps patients learn skills to regulate emotions as a central strategy to improve life functioning. However, DBT skills require a long-term and consistent commitment, typically to group therapy over the course of months. Patients who might benefit may find this approach undesirable; it can be challenging to transfer learning from therapy sessions to daily life, and there is no way to personalize skills learning based on individualized needs. In this paper we propose the use of Augmented Reality (AR) and digital biomarkers to enhance DBT skill exercises to be more immersive and personalized by using physiological data as real-time feedback. To explore the feasibility of AR-based DBT skill implementation, we developed AR-based DBT skill exercises that manipulate the user's thermal perception by visualizing different thermal information in holograms. We conducted a user study to evaluate the impact of AR in changing the thermal perception and emotional states of the user with an analysis of physiological data collected from wearable devices.

关键词： Wearable computers Transfer learning Medical treatment data visualization Biomarkers Thermal conductivity Physiology

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MDSFE: Multiscale Deep Stacking Fusion Enhancer Network for Visual data Enhancement

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ieee TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2023年 72卷 1页

作者： Guo, Yu Lu, Yuxu Qu, Jingxiang Liu, Ryan Wen Ren, Wenqi Wuhan Univ Technol Sch Nav Wuhan 430063 Peoples R China Sun Yat Sen Univ Sch Cyber Sci & Technol Shenzhen Campus Shenzhen 518107 Peoples R China

With the rapid development of visual sensors and artificial intelligence (AI), video/image data has increased dramatically, especially in the era of AI-enabled intelligent transportation. In low-light imaging conditions, however, the camera only captures weak scene-reflected light. The visual data is thus inevitably affected by noise, low contrast, and poor brightness, and so on. It will have a negative influence on the development of vision-based traffic situational awareness, traffic safety management, and automatic/autonomous vehicles. To guarantee high-quality visual data, the multiscale deep stacking fusion enhancer (termed MDSFE) is proposed to enhance a low-light image. In particular, our MDSFE consists of four components, i.e., coarse extraction module (C-EM), coarse attention fusion module (C-AFM), multiscale dense enhancement module (M-DEM), and fine encoder-decoder fusion module (F-EDFM). The combination of these modules is capable of enhancing the abilities of feature mapping and expression. Experimental results on both synthetic and real-world scenarios have illustrated that the proposed method can provide superior enhancement results under different imaging conditions. It also has the capacity of improving the detection precision under low-light conditions.

关键词： Feature extraction Image enhancement Lighting visualization Image color analysis Image restoration Brightness intelligent transportation low-light images multiscale network visual sensors

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Exploratory data Analysis in Electronic Health Records Graphs: Intuitive Features and visualization Tools

Exploratory Data Analysis in Electronic Health Records Graph...

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Annual ieee Symposium on Computer-Based Medical Systems

作者： Mirela T. Cazzolato Marco Antonio Gutierrez Caetano Traina Christos Faloutsos Agma J.M. Traina Institute of Mathematics and Computer Science - University of São Paulo (ICMC-USP) São Carlos Brazil The Heart Institute (InCor) - Clinical Hospital of Faculty of Medicine University of São Paulo (HC-FMUSP) São Paulo Brazil Carnegie Mellon University (CMU) Pittsburgh USA

Given a large, unlabeled set of Electronic Health Records (EHRs) acquired from multiple hospitals, how can we analyze the available entities and identify relationships in the data? Also, how can we perform Exploratory data Analysis (EDA) over such EHR data? Many medical institutions generate EHRs as tabular data with entities and attributes in common. However, due to a large number of records, attributes, and high cardinality, exploring the different datasets and finding patterns and insights become laborious and prone to errors. In this work, we propose GraF- Eda for EDA over EHR data from different institutions. GraF-EDA models EHRs as time-evolving graphs, allowing the interoperability of such data into a single representation. We extract meaningful features from the graph nodes and provide intuitive visualizations to improve data explainability. We evaluate GraF-EDA with four COVID-19 datasets from hospitals of the São Paulo state, Brazil, resulting in million-scale graphs. Our method identified correlations, similarities and dissimilarities among medical treatments, exams, clinics, and outcomes. With the visual tools provided by GraF-EDA, we were able to spot cases of interest and check more details about them. Our results indicate that GraF-EDA is a fast, effective, open-sourced tool for EDA of EHRs from multiple institutions.

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Seafloor Classification Based on an AUV Based Sub-Bottom Acoustic Probe data for Mn-Crust Survey

Seafloor Classification Based on an AUV Based Sub-Bottom Aco...

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OCEANS

作者： Umesh Neettiyath Harumi Sugimatsu Blair Thornton Institute of Industrial Science The University of Tokyo Komaba Japan Southampton Marine and Maritime Institute University of Southampton Southampton UK

ISBN: (数字)9798218142186

ISBN: (纸本)9798350310528

The possibility of automatically classifying high frequency sub-bottom acoustic reflections collected from an Autonomous Underwater Robot is investigated in this paper. In field surveys of Cobalt-rich Manganese Crusts (Mn-crusts), existing methods relies on visual confirmation of seafloor from images and thickness measurements using the sub-bottom probe. Using these visual classification results as ground truth, an autoencoder is trained to extract latent features from bundled acoustic reflections. A Support Vector Machine classifier is then trained to classify the latent space to idetify seafloor classes. Results from data collected from seafloor at 1500m deep regions of Mn-crust showed an accuracy of about 70%.

关键词： Surveys visualization Three-dimensional displays Sea floor Feature extraction Acoustics Sediments

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