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9th ieee Smart World Congress, SWC 2023

作者： Kim, Jong Bum Wang, Fang Khanna, Sanjeev Balakrishnan, Bimal Uddin, Meftah Aman, Jayedi Reddy Thipparthi, Vivek Vardhan University of Missouri Columbia Architectural Studies ColumbiaMO United States University of Missouri Columbia Electrical Engineering and Computer Science ColumbiaMO United States University of Missouri Columbia Mechanical and Aerospace Engineering ColumbiaMO United States Mississippi State University Mississippi State College of Architecture Art + Design MO United States

ISBN: (纸本)9798350319804

Building sectors have significant footprints in carbon emissions. However, monitoring and reducing carbon emissions in buildings are still challenging due to the siloed workflow throughout the building lifecycle. The research proposed a digital twin framework to facilitate communication between real-time building use/operation and energy simulations to reduce greenhouse gas emissions. A digital twin is a virtual representation of a physical object or process, widely studied in product design and system engineering. Digital twins in building research focus on a virtual building model that can monitor real building use and produce feedback to improve building operation and *** paper presents the testbed development using a university campus building and the digital twin creation connected with parametric BIM, CO2 emission simulation, and ML-based prediction. The testbed is located in Stanley Hall of University of Missouri. The 30m x 8m space has sensor-equipped lighting systems and a data gateway to transfer sensor data to the data server. The digital twin was built on parametric BIM with the building configuration and material information for simulation and visualization. We created the software prototypes for the physics-based energy simulation and ML predictions. The system interface expeditiously displayed the benchmark data of energy use and gas emissions with the web-based dashboard. The paper describes the novel findings, challenges, limitations, and recommendations for future digital twin research for smart campuses. © 2023 ieee.

关键词： Building Information Modeling Digital Twin Energy Simulation Greenhouse Gas Emission ML-Prediction Smart Campus

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Surface Sampling, Vertex Manipulation and Surface Generation Based on WebGL Technologies 7

Surface Sampling, Vertex Manipulation and Surface Generation...

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7th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2023

作者： Runchevski, Danail Rechkoska-Shikoska, Ustijana Salkoski, Rasim University for Information Science and Technology 'St. Paul the Apostle' Faculty of Communication Networks and Security Resen Macedonia University for Information Science and Technology 'St. Paul the Apostle' Faculty of Computer Science and Engineering Ohrid Macedonia University for Information Science and Technology 'St. Paul the Apostle' Faculty of Information Systems Visualisation Multimedia and Animation Ohrid Macedonia

ISBN: (纸本)9798350342154

Surface sampling is a powerful technique used in computer graphics and visualization to gather data from 3D surfaces. Surface sampling is a process of collecting visual and tactile information from real world surfaces to serve as references for artistic creations. It allows artists to study and analyze the intricate details of surfaces, such as the roughness of tree bark, the smoothness of a pebble, or the subtle variations in fabric patterns. By examining surfaces up close, artists gain insights into the nuances that make each surface visually and texturally distinct. The solution we've developed operates seamlessly on both desktop and mobile devices, utilizing the functionalities of widely used web browsers. It eliminates the need for any software installations. Our achievement is founded on fully open-source solutions, allowing users to explore 3D models without any barriers. © 2023 ieee.

关键词： Web browsers

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Situational Awareness and Feature Extraction for Indoor Building Navigation using Mixed Reality

Situational Awareness and Feature Extraction for Indoor Buil...

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International Conference on Computational science and Computational Intelligence (CSCI)

作者： Pesaladirme, Rishitha Reddy Chellatore, Maruthi Prasanna Dronavalli, Sri Chandra Sharma, Sharad Univ North Texas Dept Comp Sci Denton TX 76207 USA Univ North Texas Dept Informat Sci Denton TX 76207 USA

ISBN: (纸本)9798350361513;9798350372304

Indoor navigation in complex building environments poses significant challenges, particularly for individuals who are unfamiliar with their surroundings. Mixed reality (MR) technologies have emerged as a promising solution to enhance situational awareness and facilitate navigation within indoor spaces. However, there is a lack of spatial data for indoor environments, including outdated floor plans and limited real-time operational data. This paper presents the development of a mixed-reality application for indoor building navigation and evacuation. The application uses feature extraction for location sensing and situational awareness to provide accurate and reliable navigation in any indoor environment using Microsoft HoloLens. The application can track the user's position and orientation and give the user-specific information on how to evacuate the building. This information is then used to generate navigation instructions for the user. We demonstrate how this mixed reality HoloLens application can provide spatially contextualized 3D visualizations that promote spatial knowledge acquisition and situational awareness. These 3D visualizations are developed as an emergency evacuation and navigation tool to aid the building occupants in safe and quick evacuation. Experimental results demonstrate the effectiveness of the application, providing 3D visualizations of multilevel spaces and aiding individuals in understanding their position and evacuation path during emergencies. We believe that adopting mixed reality technologies, such as the HoloLens, can greatly enhance individuals' ability to navigate large-scale environments during emergencies by promoting spatial knowledge acquisition and supporting cognitive mapping.

关键词： Mixed Reality Situational Awareness Spatial data Feature Extraction visualization

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LongArms: Fraud Prediction in Online Lending Services Using Sparse Knowledge Graph

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ieee TRANSACTIONS ON BIG data 2023年第2期9卷 758-772页

作者： Wang, Cheng Zhu, Hangyu Hu, Ruixin Li, Rui Jiang, Changjun Shanghai Artificial Intelligence Lab Shanghai 200030 Peoples R China Tongji Univ Dept Comp Sci & Engn Shanghai 200070 Peoples R China Minist Educ Serv Comp Key Lab Embedded Syst Shanghai 200070 Peoples R China Xidian Univ Sch Comp Sci & Technol Xian 710071 Peoples R China

Gang fraud, the major and primary security issue in online lending services, can be efficiently solved by the data-driven paradigm that is recognized as a promising solution for online lending gang fraud prediction. However, it is challenging that such predictions need to detect evolving and increasingly impalpable fraud patterns based on low-quality data, i.e., very preliminary and coarse applicant information. The technical difficulty mainly stems from two factors: the extreme deficiency of information associations and weakness of data labels. In this work, we mainly address the challenges by enhancing the utility of associations (i.e., recovering missing associations and mining underlying associations) on a knowledge graph. Specifically, we first propose an efficient method of Chinese address disambiguation to recover some critical associations that are broken by the ambiguity of applicant information, e.g., address related information. Then, to mine the implicit associations, we design a novel association representation method, called Adaptive Connected Component Embedding Simplification Scheme (ACCESS), which can adaptively implement embedding for different connected components depending on their sizes. Finally, we adopt the graph clustering algorithms and devised predicting schemes based on the above enhanced associations to predict gang fraud in the case of weakness of data labels. Moreover, we propose a framework called RMCP by integrating the above techniques, which is consists of four steps: Recovering, Mining, Clustering, and Predicting, for efficiently predicting gang fraud. The good performance is validated by the experiments on a real-world dataset from a commercial lending company. Meanwhile, we provide a visual decision support system named LongArms over the RMCP framework.

关键词： Prediction algorithms Clustering algorithms Expert systems Supervised learning data models Companies visualization Gang fraud prediction online lending services chinese address disambiguation network embedding unsupervised learning community detection

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Assessment of Typhoon Disaster Development Situation based on Online Social Media data 6

Assessment of Typhoon Disaster Development Situation based o...

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6th International Conference on Artificial Intelligence and Big data, ICAIBD 2023

作者： Huixian, Jiang Lijing, Chen Yao, Chen Fujian Normal University Fujian Provincial Engineering Research Center for Monitoring and Assessing Terrestrial Disasters Institute of Geograph Fuzhou China Fujian Normal University School of Mathematics and Statisice Fuzhou China Fujian Normal University Institute of Geograph Fuzhou China

ISBN: (纸本)9781665491259

Social media, an emerging source of disaster data, provides a new information channel for the assessment of disaster development situation. Aiming at the assessment of typhoon disaster development situation, this paper creates a typhoon disaster dictionary based on topic identification and disaster classification knowledge base, by integrating the natural language processing technology and information characteristics of typhoon disasters, and mining the information in microblog texts of typhoon disaster that can reflect people's sentiment, discussions about disaster, user information, location, etc. on the basis of social media microblog data. Besides, it also determines the disaster - sentiment indexes, and combines the typhoon disaster - sentiment indexes and multi-source data to design a method of assessing the typhoon disaster development situation based on the microblog data. Meanwhile, it further develops a typhoon disaster situation awareness system, and realizes the process of disaster information and visualization of disaster development, so as to help disaster emergency managers to grasp the spatial distribution pattern of disaster development situation and make reasonable relief strategies. © 2023 ieee.

关键词： Disasters

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Coarse-Refined Consistency Learning Using Pixel-Level Features for Semi-Supervised Medical Image Segmentation

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ieee JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2023年第8期27卷 3970-3981页

作者： Du, Jie Zhang, Xiaoci Liu, Peng Wang, Tianfu Shenzhen Univ Shenzhen Univ Med Sch Sch Biomed Engn Natl Reg Key Technol Engn Lab Med UltrasoundGuang Shenzhen 518060 Peoples R China Univ Macau Dept Comp & Informat Sci Taipa 999078 Macau Peoples R China

Pixel-level annotations are extremely expensive for medical image segmentation tasks as both expertise and time are needed to generate accurate annotations. Semi-supervised learning (SSL) for medical image segmentation has recently attracted growing attention because it can alleviate the exhausting manual annotations for clinicians by leveraging unlabeled data. However, most of the existing SSL methods do not take pixel-level information (e.g., pixel-level features) of labeled data into account, i.e., the labeled data are underutilized. Hence, in this work, an innovative Coarse-Refined Network with pixel-wise Intra-patch ranked loss and patch-wise Inter-patch ranked loss (CRII-Net) is proposed. It provides three advantages: i) it can produce stable targets for unlabeled data, as a simple yet effective coarse-refined consistency constraint is designed;ii) it is very effective for the extreme case where very scarce labeled data are available, as the pixel-level and patch-level features are extracted by our CRII-Net;and iii) it can output fine-grained segmentation results for hard regions (e.g., blurred object boundaries and low-contrast lesions), as the proposed Intra-Patch Ranked Loss (Intra-PRL) focuses on object boundaries and Inter-Patch Ranked loss (Inter-PRL) mitigates the adverse impact of low-contrast lesions. Experimental results on two common SSL tasks for medical image segmentation demonstrate the superiority of our CRII-Net. Specifically, when there are only 4% labeled data, our CRII-Net improves the Dice similarity coefficient (DSC) score by at least 7.49% when compared to five classical or state-of-the-art (SOTA) SSL methods. For hard samples/regions, our CRII-Net also significantly outperforms other compared methods in both quantitative and visualization results.

关键词： Image segmentation Head Predictive models Task analysis data models Annotations Lesions Semi-supervised medical image segmentation coarse-refined consistency learning pixel-level features ranked loss function

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Cognitive-Based Crack Detection for Road Maintenance: An Integrated System in Cyber-Physical-Social Systems

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ieee TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2023年第6期53卷 3485-3500页

作者： Fan, Lili Cao, Dongpu Zeng, Changxian Li, Bai Li, Yunjie Wang, Fei-Yue Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Tsinghua Univ Sch Vehicle & Mobil Beijing 116600 Peoples R China Dalian Nationalities Univ Sch Sci Dalian 116600 Peoples R China Hunan Univ Coll Mech & Vehicle Engn State Key Lab Adv Design & Mfg Vehicle Body Changsha 410082 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Effective road maintenance can not only achieve a balance between limited resources and long-term high-efficiency performance of road but also reduce the loss of life and property caused by road damage to vehicles and pedestrians. Due to the lack of a multidimensional dynamic monitoring system and enough extremely special data, the existing road maintenance system cannot accurately assess the road surface condition and provide timely early warning of sudden road damage. In this article, the M-RM system is proposed, that is, a metaverse-enabled road maintenance system based on cyber-physical-social systems (CPSSs), which fully utilizes the social and artificial system information of CPSS, as well as the simulation, monitoring, diagnosis and prediction functions of road systems in the virtual world of the metaverse. Then, in the road damage detection of system model in the virtual world, for the virtual data of the core assets of the metaverse, we propose an adaptive and information-preserving data augmentation (AIDA) algorithm-based nonclassical receptive field suppression and enhancement, an algorithm developed from human visual cognition. This algorithm enables the generation of a large amount of scarce fidelity data and avoids the introduced noise from impairing the performance of nonaugmented data. Finally, a crack detection algorithm named pay attention twice (PAT) is proposed, which uses the generated virtual data for training, and achieves secondary attention to high-frequency targets by fusing frequency-division convolution and mixed-domain attention mechanism. The detection performance of small targets in uncertain environments is enhanced. The metaverse system built in the current research can not only be used for road maintenance but also empower the traffic metaverse by using the traffic flow prediction module embedded in the algorithm. Experimental results demonstrate that the proposed algorithm can be applied to the road damage detection task under diff

关键词： Roads Maintenance engineering Metaverse visualization Real-time systems Monitoring Safety Brain inspired crack detection data augmentation metaverse road maintenance visual cognition

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A Comparative Evaluation for Forecasting Diabetic Patients' Hospital Readmission within 30 Days through Machine Learning Techniques 11

A Comparative Evaluation for Forecasting Diabetic Patients' ...

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11th ieee Region 10 Humanitarian Technology Conference, R10-HTC 2023

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

ISBN: (纸本)9798350326147

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. © 2023 ieee.

关键词： Hospitals

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Interactive visualization and Portable Image Blending of Massive Aerial Image Mosaics

Interactive Visualization and Portable Image Blending of Mas...

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2023 ieee International Conference on Big data, Bigdata 2023

作者： Petruzza, Steve Summa, Brian Gooch, Amy Laney, Christine M. Goulden, Tristan Schreiner, John Callahan, Steven Pascucci, Valerio Utah State University United States Tulane University United States ViSOAR Llc United States Battelle Neon United States Zero-Epsilon Llc United States University of Utah United States

ISBN: (纸本)9798350324457

Processing, managing and publishing the substantial volume of data collected through modern remote sensing technologies in a format that is easy for researchers - across broad skill levels and scientific domains - to view and use presents a formidable challenge. As a prime example, the massive scale of image mosaics produced by NEON's Airborne Observation Platform (AOP), often several to hundreds of gigabytes in volume, demands efficient data management strategies. Additionally, these aerial mosaics frequently exhibit seams due to variations in lighting conditions during the data acquisition process. These seams undermine the integrity of subsequent scientific analyses, introducing distortions that hinder accurate interpretation of ecological patterns. Finally, one of NEON's core objectives is to make these data broadly accessible to users, including those who are not yet versed in working with remote sensing data or who wish to view the datasets without needing to download and process *** response to these challenges, we have developed a comprehensive data management pipeline that enables interactive access for analysis and visualization of NEON's aerial mosaic collection. This pipeline automates data ingestion, conversion, and publication in a streamable format, facilitating seamless user interaction through web viewers and programming APIs. Moreover, we have implemented a portable blending algorithm aimed at eliminating these problematic seams from large aerial mosaics. This algorithm, grounded in the Conjugate Gradient (CG) method, has been implemented both in CUDA and using the modern SYCL programming model for enhanced portability across diverse computing *** results demonstrate scalable performance across both CPU and GPU architectures. This work not only addresses the challenges of large aerial data management and seam removal but also opens avenues for more accurate and comprehensive scientific investigations within the NEON ecosystem

关键词： aerial imagery Color blending data streaming interactive visualization remote sensing

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Sensitive data Classification of Imbalanced Short Text Based on Probability Distribution BERT in Electric power industry

Sensitive Data Classification of Imbalanced Short Text Based...

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2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms, PRMVIA 2023

作者： Zhang, Wensi Liang, Xiao Zhang, Yifang Su, Hanchen State Grid Smart Grid Research Institute Co.Ltd. Artificial Intelligence on Electric Power System State Grid Corporation Joint Laboratory Beijing China

ISBN: (纸本)9798350346596

The exploitation of big data in industrial fields faces several challenges, such as data privacy and security, data integration and interoperability, and data analysis and visualization. data privacy and security is a major concern, as the data collected from industrial fields often contain sensitive information. Due to the particularity of the industrial field, there are challenges in the utilization of big data. 1. The distribution of different categories data is extremely uneven;2. There are a large number of industry terms in the short texts that constitute the metadata, which makes semantic representation difficult. These two challenges have a large impact on the application performance of existing models. In order to resolve the problems above, this paper proposes a pre-training model based on probability distribution, which for the classification of sensitive data in the power industry. The model consists of three modules: 1. The data enhancement module adopts the technology of synonym expansion and noise introduction, so that the model can extract the classification features of sensitive data with a small proportion;2. The pre-training module adopts the BERT model, which can obtain the semantics of industry terms in short texts;3. The probability prediction module is used to regularize the distribution of test data to meet the training data. Compared with the traditional classification model and the classification model based on deep learning, the F1-score can be improved by 36.68% and 6.39%. © 2023 ieee.

关键词： Probability distributions

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