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ExteriorTag: Automatic Semantic Annotation of BIM Building Exterior Via Voxel Index analysis

ieee COMPUTER GRAPHICS AND APPLICATIONS 2021年第3期41卷 48-58页

作者： Liu, Xiaojun He, Changyan Zhao, Hantao Jia, Jinyuan Liu, Chang Jiaxing Univ Nanhu Coll Jiaxing 314001 Peoples R China Southeast Univ Nanjing 210096 Peoples R China Tongji Univ Shanghai 200092 Peoples R China Nanchang Hangkong Univ Nanchang 330063 Jiangxi Peoples R China

The growing demand for building information modeling (BIM) data and ubiquitous applications make it increasingly necessary to establish a reliable way to share the models on lightweight devices. Building scenes have strong occlusion features and the building exterior plays an important role in digital devices with limited computational resources. This allows the possibility to reduce the resource consumption while roaming in outdoor scenes by culling away the interior building data. This article addresses the task of automatic annotation of BIM building exterior via voxel index analysis. We showcase the research of using industry foundation classes (IFC) and other mainstream formats as our input data and proposed an automatic algorithm for annotating the building exterior. Afterward, a practical and accurate voxel index analysis procedure is designed for frequently flawed models. The annotation can be added directly into the original data file under the same IFC standard, avoiding the complex procedure and information loss in semantics mapping between different standards. The final examinations show the robustness of our algorithm and the capability of handling large BIM building models.

关键词： Architectural CAD Building Management Systems Buildings Structures Solid Modelling Structural Engineering Computing Ubiquitous Computing BIM Building Exterior Building Information Modeling data Building Scenes Digital Devices Outdoor Scenes Interior Building data Automatic Annotation Voxel Index analysis Procedure BIM Building Models Semantic Annotation Industry Foundation Classes IFC Format Indexes Annotations Semantics data visualization Standards data Models

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Interactive visualization of Protein RINs using NetworKit in the Cloud

Interactive Visualization of Protein RINs using NetworKit in...

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ieee International symposium on Parallel and Distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Eugenio Angriman Fabian Brandt-Tumescheit Leon Franke Alexander van der Grinten Henning Meyerhenke Department of Computer Science Humboldt-Universitat zu Berlin Germany Department of Chemistry Konstanz Research School Chemical Biology University of Konstanz Germany

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

Network analysis has been applied in diverse application domains. We consider an application from protein dynamics, specifically residue interaction networks (RINs). While numerous RIN visualization tools exist, there are no solutions that are both easily programmable and as fast as optimized network analysis toolkits. In this work, we use NetworKit - an established package for network analysis - to build a cloud-based environment that enables domain scientists to run their visualization and analysis workflows on large compute servers, without requiring extensive programming and/or system administration knowledge. To demonstrate the versatility of this approach, we use it to build a custom Jupyter-based widget for RIN visualization. In contrast to existing RIN visualization approaches, our widget can easily be customized through simple modifications of Python code, while both supporting a comprehensive feature set and providing near real-time speed. Due to its integration into Jupyter notebooks, our widget can easily interact with other popular packages of the Python ecosystem to build custom analysis pipelines (e.g., pipelines that feed RIN data into downstream machine learning tasks).

关键词： Proteins Pipelines data visualization Network analyzers Machine learning Programming Real-time systems

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Intelligent analysis and decision making for monitoring data of ultra-high arch dams 3

Intelligent analysis and decision making for monitoring data...

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3rd International Conference on Information Science, Parallel and Distributed Systems, ISPDS 2022

作者： Wu, Jianming Hu, Yu Xu, Dan Tan, Yaosheng Liu, Chunfeng Pei, Lei Li, Zhuo Zhejiang Guangchuan Engineering Consulting Co. Ltd. Zhejiang Ocean University Zhejiang China School of Civil Engineering Tsinghua University Beijing China China Three Gorges Construction Engineering Corporation Sichuan China Nanjing Yangzi Petrochemical Engineering Company Nanjing China

ISBN: (数字)9781665487474

ISBN: (纸本)9781665487474

Intelligent construction in the process of application of extra-high arch dams, a large number of monitoring instruments buried inside the project during the construction process, reasonable use of good monitoring data to promote the construction of seamless dams, has important research significance. Monitoring data can sense the true state of the dam in all aspects and improve engineering guidance. In this paper, through the current stage of monitoring data of the extra-high arch dam, the objective, continuity, timeliness, accuracy and dynamics of the monitoring data, the visualization of the seepage, temperature and stress field monitoring data of the extra-high arch dam, the whole process analysis, and the application of monitoring data to provide comprehensive feedback on the construction process are conducive to promoting the progress of the design and construction of the extra-high arch dam, and promoting the construction of the extra-high arch dam with higher quality. © 2022 ieee.

关键词： Monitoring

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A Software Vulnerability dataset of large Open Source C/C++ Projects 27

A Software Vulnerability Dataset of Large Open Source C/C++ ...

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27th ieee Pacific Rim International symposium on Dependable Computing, PRDC 2022

作者： Pereira, José Dabruzzo Antunes, João Henggeler Vieira, Marco University of Coimbra CISUC DEI Coimbra Portugal

ISBN: (纸本)9781665485555

Automated tools, namely Static analysis Tools (SATs) and Penetration Testing Tools, are frequently used by developers to detect vulnerabilities. However, research and practice show that the effectiveness of those tools in large-scale projects is low, being prone to both false positives and false negatives. Thus, there is an urgent need for more effective techniques, which ultimately require representative field data for driving their design and testing. In this paper, we present a dataset of vulnerabilities from five large open-source C/C++ projects: Mozilla, Linux Kernel, Xen, httpd, and Glibc. For collecting the data, we designed an automated process grounded on vulnerabilities collected from the Common Vulnerability and Exposures (CVE) Details website. For each vulnerability, we retrieve the corresponding source code units from the project repository (including both vulnerable and fixed versions). We then compute a large set of Software Metrics (SMs) for those code units and run two SATs to collect security alerts (i.e., potential vulnerabilities and/or weaknesses). The dataset currently includes 5214 vulnerabilities. To demonstrate its usefulness, we explore the use of the dataset to train machine learning models to detect vulnerable C/C++ functions. Results clearly show that the dataset can be used in practice and is a key contribution for researchers working in software security. © 2022 ieee.

关键词： Static analysis

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REMONI: An Autonomous System Integrating Wearables and Multimodal large Language Models for Enhanced Remote Health Monitoring

REMONI: An Autonomous System Integrating Wearables and Multi...

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ieee International Workshop on Medical Measurement and Applications (MEMEA)

作者： Thanh Cong Ho Farah Kharrat Abderrazek Abid Fakhri Karray Anis Koubaa Mohamed bin Zayed University of Artificial Intelligence Masdar City Abu Dhabi UAE Department of Electrical and Computer Engineering University of Waterloo Waterloo ON Canada College of Computer and Information Sciences Prince Sultan University

ISBN: (数字)9798350307993

ISBN: (纸本)9798350308006

With the widespread adoption of wearable devices in our daily lives, the demand and appeal for remote patient monitoring have significantly increased. Most research in this field has concentrated on collecting sensor data, visualizing it, and analyzing it to detect anomalies in specific diseases such as diabetes, heart disease and depression. However, this domain has a notable gap in the aspect of human-machine interaction. This paper proposes REMONI, an autonomous REmote health MONItoring system that integrates multimodal large language models (MLLMs), the Internet of Things (loT), and wearable devices. The system automatically and continuously collects vital signs, accelerometer data from a special wearable (such as a smartwatch), and visual data in patient video clips collected from cameras. This data is processed by an anomaly detection module, which includes a fall detection model and algorithms to identify and alert caregivers of the patient's emergency conditions. A distinctive feature of our proposed system is the natural lan-guage processing component, developed with MLLMs capable of detecting and recognizing a patient's activity and emotion while responding to healthcare worker's inquiries. Additionally, prompt engineering is employed to integrate all patient information seamlessly. As a result, doctors and nurses can access real-time vital signs and the patient's current state and mood by interacting with an intelligent agent through a user-friendly web application. Our experiments demonstrate that our system is implementable and scalable for real-life scenarios, potentially reducing the workload of medical professionals and healthcare costs. A full-fledged prototype illustrating the functionalities of the system has been developed and being tested to demonstrate the robustness of its various capabilities.

关键词： Human computer interaction visualization Costs Autonomous systems large language models data visualization Medical services

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Machine Learning-assisted Computational Steering of large-scale Scientific Simulations 19

Machine Learning-assisted Computational Steering of Large-sc...

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19th ieee International symposium on Parallel and Distributed Processing with Applications (ieee ISPA)

作者： Liu, Wuji Ye, Qianwen Wu, Chase Q. Liu, Yangang Zhou, Xin Shan, Yunpeng New Jersey Inst Technol Dept Comp Sci Newark NJ 07102 USA Brookhaven Natl Lab Environm & Climate Sci Dept Upton NY 11973 USA

ISBN: (纸本)9781665435741

Next-generation scientific applications in various fields are experiencing a rapid transition from traditional experiment-based methodologies to large-scale computation-intensive simulations featuring complex numerical modeling with a large number of tunable parameters. Such model-based simulations generate colossal amounts of data, which are then processed and analyzed against experimental or observation data for parameter calibration and model validation. The sheer volume and complexity of such data, the large model-parameter space, and the intensive computation make it practically infeasible for domain experts to manually configure and tune hyperparameters for accurate modeling in complex and distributed computing environments. This calls for an online computational steering service to enable real-time multi-user interaction and automatic parameter tuning. Towards this goal, we design and develop a generic steering framework based on Bayesian Optimization (BO) and conduct theoretical performance analysis of the steering service. We present a case study with the Weather Research and Forecast (WRF) model, which illustrates the performance superiority of the BO-based tuning over other heuristic methods and manual settings of domain experts using regret analysis.

关键词： Terms Computational steering parameter tuning bayesian optimization machine learning

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A Bio-Inspired Hierarchical Understanding Framework for Satellite Videos Leveraging Low-Rank Representation and large Language Model

A Bio-Inspired Hierarchical Understanding Framework for Sate...

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ieee International symposium on Geoscience and Remote Sensing (IGARSS)

作者： Xuhui Zhao Zhenghao Liao Yue Huang Siqi Liu Zhi Gao School of Remote Sensing and Information Engineering Wuhan University Wuhan China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

After years of research, moving target tracking in satellite videos has attracted wide attention with various algorithms. However, a big gap still exists between current research and practical satellite video applications with great value. The biggest problem may be the mere trajectories from current methods cannot essentially meet the needs for tasks, and much latent information in video has not been fully exploited. Focusing on this dilemma, we propose a bio-inspired hierarchical satellite video understanding framework (SV-Chat) leveraging LRR (Low-Rank Representation) and LLM (large Language Model). The LRR is first utilized to separate static backgrounds and moving objects for detailed understanding, imitating the visual pathways and attention mechanism in HVS (Human Vision System). Then, the LLM is enhanced by in-context learning with tailored hierarchical understanding paradigms for satellite videos and conducts corresponding analysis. Finally, the spatial and temporal information from both backgrounds and movements are jointly analyzed for the final report with insights. Experiments demonstrate the feasibility and effectiveness of our framework with a high accuracy of 87.36% compared with ground truth.

关键词： visualization Satellites Target tracking Simultaneous localization and mapping large language models Machine vision Focusing

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FEM Simulation data-driven Fault Diagnosis Method for Bearing based on Improved Dynamic Adversarial Adaptive Network with Attention

FEM Simulation Data-driven Fault Diagnosis Method for Bearin...

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2023 CAA symposium on Fault Detection, Supervision and Safety for Technical Processes, SAFEPROCESS 2023

作者： Deng, Congying Ding, Huiling Miao, Meiling Chongqing University of Posts and Telecommunications School of Advanced Manufacturing and Engineering Chongqing400065 China Shanxi Institute of Technology Department of Electrical and Control Engineering Yangquan045000 China

ISBN: (纸本)9798350337754

In practical engineering, data has the characteristic of no label. Existing researches on bearing fault diagnosis without label mostly use test bench data to construct source domain, which is expensive. Therefore, FEM simulation data-driven fault diagnosis method for bearing based on improved dynamic adversarial adaptive network with attention is proposed in this paper. Firstly, finite element simulation methods are used to approximate operating conditions of target domain to obtain different health state simulation data, which are used as source domain data to reduce hardware resource dependence on fault simulation of the experimental platform. Then, dynamic adversarial adaptive networks based on efficient channel attention are constructed, and the attention mechanism and dynamic adversarial adaptive networks are used to achieve enhanced feature extraction ability of the model and sub-domain alignment, global alignment and fault identification of cross-domain samples, respectively. Finally, a large number of labeled finite element simulation samples and unlabeled data from the target domain are used to train the model for cross-domain diagnosis. The method validation was conducted on the bearing experimental dataset, and the results showed that the finite element simulation data can effectively replace the test bench data to achieve high-precision fault diagnosis in the target domain without labels. The cross-domain diagnosis ability of the proposed attention dynamic adversarial adaptive network is significantly better than that of the comparative method. © 2023 ieee.

关键词： Failure analysis

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Bibliometric visualization analysis of Sustainable Design from 2001 to 2021 14

Bibliometric Visualization Analysis of Sustainable Design fr...

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14th International symposium on Computational Intelligence and Design, ISCID 2021

作者： Cai, Ruiyi Xiang, Yudi Modern Industrial Design Institute Zhejiang University Hangzhou China School of Software Technology Zhejiang University Hangzhou China

ISBN: (纸本)9781665438568

Based on the data of 1,430 sustainable design literature included in the Web of Science database from 2001 to 2021, this paper integrates the analytical advantages of CiteSpace and VOSviewer, and conducts field-specific bibliometric analysis and visualization presentation of results. The article analyzes the distribution and sources of the literature at the micro level, combs the research hotspots, branches and development frontiers at the macro level, providing a comprehensive and clear context combing of the sustainable design field. © 2021 ieee.

关键词： visualization

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analysis of Etcher Configuration on Part Marking Characteristics for Counterfeit Identification

Analysis of Etcher Configuration on Part Marking Characteris...

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International symposium on Physical & Failure analysis of Integrated Circuits

作者： Jacob Harrison Nathan Jessurun Raphael R. Dos Santos Shajib Ghosh Navid Asadi Mark Tehranipoor University of Florida Gainesville Florida Terraverum LLC ustin Texas

ISBN: (数字)9798350360608

ISBN: (纸本)9798350360615

Laser mark analysis is common when analyzing suspect counterfeit electronics, but prior mark analysis has been limited to subjective quality evaluation or visual comparison against golden samples. In an effort to identify objective metrics for part mark analysis, we explore how laser etcher characteristics and configurations impact a mark's appearance. We first analyze the theoretical impact of etcher configuration changes on mark characteristics, then conduct a controlled study of fifty integrated circuits etched with different settings to assess which settings can be discerned in low-magnification images. Our study suggests that observable part marking characteristics can indicate part provenance and inform objective, data-driven decisions about integrated circuit authenticity.

关键词： Integrated circuits Measurement visualization Laser theory Lasers Failure analysis Object recognition

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