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Obtaining, analysis and visualization of Mobility and Traffic data in Quito - Ecuador 7

Obtaining, Analysis and Visualization of Mobility and Traffi...

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7th ieee Ecuador Technical Chapters Meeting, ECTM 2023

作者： Herrera-Herrera, Nelson Ivan Morillo, Andrés Mauricio Montenegro Carvajal, Eddy Reynaldo Vargas Toasa, Renato M. Universidad Tecnológica Israel Departamente de Ciencias de la Ingeniería Quito Ecuador Universidad UTE Quito Ecuador Instituto Superior Tecnológico Japón Quito Ecuador

ISBN: (纸本)9798350338232

The increase of traffic in large cities cause serious problems, such as traffic accidents, delays at workplaces, stress and other problems, it makes it necessary to use new technologies to identify these zones and make a better decision on vehicular circulation. This work proposes to collect data using a Drone, analyze this data and predict traffic in a sector from patterns, using traffic and weather photographs. Using Python code, through an ETL the data is extracted from a non-relational database, then loaded into MongoDB, the downloaded images will be passed to an object detection tool that will be used to locate vehicles in each image, a type of traffic (heavy, medium, low, empty) is assigned based on the number of vehicles found in the image, with this data, predictive models can be tested. The model will finally perform traffic forecasts using data visualization tools to better reflect the days and times of traffic flow in the sector. The initial results show great effectiveness in identifying areas with high vehicular flow, which will allow an adequate management when circulating in these areas. © 2023 ieee.

关键词： Object detection

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Parallel Assemblies Plot, a visualization tool to explore categorical and quantitative data: application to digital mobility outcomes

Parallel Assemblies Plot, a visualization tool to explore ca...

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Pacific (formerly Asia-Pacific APVIS) visualization symposium

作者： Alma Cantu Maria Encarna Micó-Amigo Silvia Del Din Sara Johansson Fernstad School of Computing Newcastle University Translational and´ Clinical Research Institute Newcastle University

This paper presents a tool enabling the visual analysis of multivariate heterogeneous data. large amounts of measured and contextual data are being gathered for a large number of applications, increasing connectivity across different data types. While measured data are often quantitative, contextual data tend to be categorical. This results in datasets containing multivariate data with heterogeneous properties. Difference in the natures of these properties raises challenges when combining them for analysis. This paper presents the design of a tool that enables the exploration of multivariate heterogeneous data by combining the strengths of Parallel Coordinates and Parallel Sets. The design relied on the application domain of real-life mobility monitoring that is particularly affected by the challenge mentioned above. To validate the suggested approach this paper presents the result of a usability evaluation, which confirms that the presented design is as efficient as other exiting tools while providing more features for correlation analysis.

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PRODUCING A SCIENCE-READY COMMERCIAL data ARCHIVE: A WORKFLOW FOR ESTIMATING SURFACE REFLECTANCE FOR HIGH RESOLUTION MULTISPECTRAL IMAGERY

PRODUCING A SCIENCE-READY COMMERCIAL DATA ARCHIVE: A WORKFLO...

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ieee International Geoscience and Remote Sensing symposium (IGARSS)

作者： Montesano, Paul Carroll, Mark Neigh, Christopher Macander, Matthew Caraballo-Vega, Jordan Frost, Gerald Tamkin, Glenn NASA Goddard Space Flight Ctr Greenbelt MD USA ADNET Syst Inc Bethesda MD 20817 USA Alaska Biol Res Fairbanks AK 80410 USA InuTeq LLC Beltsville MD 20705 USA

ISBN: (纸本)9798350320107

Scientific analysis of changes of the Earth's land surface benefit from well characterized, science quality remotely sensed data. This data quality is the result of models that estimate and remove atmospheric constituents and account for sun-sensor geometry [1]-[3]. Surface reflectance (SR) in commercial very high resolution (< 5 m;VHR) spaceborne imagery routinely varies for unchanged surface features because of signal variation from the combined effects of atmospheric haze and a range of sun-sensor geometric scenarios of acquisitions [4]. Consistency from this imagery must be sufficient to identify and track the change or stability of fine-scale features that, though small, may be widely distributed across remote domains, and serve as key indicators of critical broad-scale environmental change [5], [6]. Currently commercial SR products are available, but typically the model employed is proprietary and the costs for using these products over a large domain can be significant ( e.g., Planet Surface Reflectance v.2). Here we describe an open source workflow for the scientific community to improve detection of fine-scale change with commercial VHR imagery.

关键词： surface reflectance imagery commercial high-resolution

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Comparative visualization for Noise Simulation data

Comparative Visualization for Noise Simulation Data

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IS and T International symposium on Electronic Imaging: visualization and data analysis, VDA 2023

作者： Vugdelija, Nikola Splechtna, Rainer Todorović, Goran Sužnjević, Mirko Matković, Krešimir Gamepires D.o.o. Zagreb Croatia VRVis Research Center Vienna Austria AVL-AST Doo Zagreb Croatia Faculty of Electrical Engineering and Computing University of Zagreb Croatia

Noise, vibration, and harshness (NVH) simulation represents an important step in modern automotive design. This type of simulation produces large and complex data that is hard to analyze. The data resides in two domains, the spatial and the frequency domain. In this paper, we extend the current state of the art in the visual exploration of such data by supporting comparison tasks. We support the comparison of velocity values of a subset of surface elements for multiple frequency bands. We combine data aggregation on the 3D model with multiple bar charts in a coordinated multiple views system. This new approach allows for an intuitive comparison of multiple velocity values in the context of both domains. We demonstrate the deployment of this approach for an example from the automotive industry, but it can be used with any simulation that simultaneously relates to two domains. © 2023, Society for Imaging Science and Technology.

关键词： Automotive industry

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Thicket: Seeing the Performance Experiment Forest for the Individual Run Trees 23

Thicket: Seeing the Performance Experiment Forest for the In...

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32nd International symposium on High-Performance Parallel and Distributed Computing (HPDC) part of the ACM Federated Computing Research Conference (FCRC)

作者： Brink, Stephanie McKinsey, Michael Boehme, David Scully-Allison, Connor Lumsden, Ian Hawkins, Daryl Burgess, Treece Lama, Vanessa Luettgau, Jakob Isaacs, Katherine E. Taufer, Michela Pearce, Olga Lawrence Livermore Natl Lab Lawrence CA 94550 USA Texas A&M Univ College Stn TX USA Univ Utah Salt Lake City UT USA Univ Tennessee Knoxville TN USA

ISBN: (纸本)9798400701559

Thicket is an open-source Python toolkit for Exploratory data analysis (EDA) of multi-run performance experiments. It enables an understanding of optimal performance configuration for large-scale application codes. Most performance tools focus on a single execution (e.g., single platform, single measurement tool, single scale). Thicket bridges the gap to convenient analysis in multi-dimensional, multi-scale, multi-architecture, and multi-tool performance datasets by providing an interface for interacting with the performance data. Thicket has a modular structure composed of three components. The first component is a data structure for multi-dimensional performance data, which is composed automatically on the portable basis of call trees, and accommodates any subset of dimensions present in the dataset. The second is the metadata, enabling distinction and sub-selection of dimensions in performance data. The third is a dimensionality reduction mechanism, enabling analysis such as computing aggregated statistics on a given data dimension. Extensible mechanisms are available for applying analyses (e.g., top-down on Intel CPUs), data science techniques (e.g., K-means clustering from scikit-learn), modeling performance (e.g., Extra-P), and interactive visualization. We demonstrate the power and flexibility of Thicket through two case studies, first with the open-source RAJA Performance Suite on CPU and GPU clusters and another with a large physics simulation run on both a traditional HPC cluster and an AWS Parallel Cluster instance.

关键词： HPC exploratory data analysis performance analysis parallel profile multi-dimensional

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Proceedings - 2024 ieee 24th International symposium on Cluster, Cloud and Internet Computing, CCGrid 2024

Proceedings - 2024 IEEE 24th International Symposium on Clus...

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24th ieee/ACM International symposium on Cluster, Cloud and Internet Computing, CCGrid 2024

ISBN: (纸本)9798350395662

The proceedings contain 75 papers. The topics discussed include: accelerating large language model training with hybrid GPU-based compression;demystifying swarm learning: an emerging decentralized federated learning system;IDIOMS: index-powered distributed object-centric metadata search for scientific data management;Brug: an adaptive memory (re-)allocator;MDSTGCN : multi-scale dynamic spatial-temporal graph convolution network with edge feature embedding for traffic forecasting;fair, efficient multi-resource scheduling for stateless serverless functions with Anubis;STRonG: system topology risk analysis on graphs;federated semi-supervised learning with local and global updating frequency optimization;and towards better QoS and lower costs of P4 EIP gateway at the edge.

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KeywordMap: Attention-based Visual Exploration for Keyword analysis 14

KeywordMap: Attention-based Visual Exploration for Keyword A...

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14th ieee Pacific visualization symposium (ieee PacificVis)

作者： Tu, Yamei Xu, Jiayi Shen, Han-Wei Ohio State Univ Columbus OH 43210 USA

ISBN: (纸本)9781665439312

With the high growth rate of text data, extracting meaningful information from a large corpus becomes increasingly difficult. Keyword extraction and analysis is a common approach to tackle the problem, but it is non-trivial to identify important words in the text and represent the multifaceted properties of those words effectively. Traditional topic modeling based keyword analysis algorithms require hyper-parameters which are often difficult to tune without enough prior knowledge. In addition, the relationships among the keywords are often difficult to obtain. In this paper, we utilize the attention scores extracted from Transformer-based language models to capture word relationships. We propose a domain-driven attention tuning method, guiding the attention to learn domain-specific word relationships. From the attention, we build a keyword network and propose a novel algorithm, Attention-based Word Influence (AWI), to compute how influential each word is in the network. An interactive visual analytics system, KeywordMap, is developed to support multi-level analysis of keywords and keyword relationships through coordinated views. We measure the quality of keywords captured by our AWI algorithm quantitatively. We also evaluate the usefulness and effectiveness of KeywordMap through case studies.

关键词： Text/Document data Machine Learning Task and Requirements analysis

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Fault diagnosis method of launch vehicle based on sequential neural network 2

Fault diagnosis method of launch vehicle based on sequential...

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2nd International symposium on Aerospace Engineering and Systems, ISAES 2023

作者： Yan, Yushen Wei, Xuanbo Li, Xinguo Li, Shiyao Northwestern Polytechnical University School of Aerospace Xi'an China

ISBN: (纸本)9798350303728

Traditional fault diagnosis technology or a single defect diagnostic approach cannot match the actual needs of specific and sophisticated large-scale systems such as launch rockets. The wide application of artificial intelligence technology provides a new means for fault diagnosis of complex large-scale systems. Combining the test data and fault diagnosis experience accumulated in the aerospace range with artificial intelligence technology has important significance and practical value for the optimization of the efficiency and accuracy of carrier rocket fault diagnosis. According to the requirements of fault diagnosis in the launch vehicle test and launch process, based on the analysis of the existing fault diagnosis methods in the launch vehicle test and launch process, a fault diagnosis method based on sequential neural network is proposed in this paper. The diagnosis approach is validated by using real-world measured data, demonstrating the validity of the fault diagnosis system's architecture. © 2023 ieee.

关键词： Failure analysis

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TimeTables: Embodied Exploration of Immersive Spatio-Temporal data 29

TimeTables: Embodied Exploration of Immersive Spatio-Tempora...

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ieee Conference on Virtual Reality and 3D User Interfaces (ieee VR)

作者： Zhang, Yidan Ens, Barrett Satriadi, Kadek Ananta Prouzeau, Arnaud Goodwin, Sarah Monash Univ Melbourne Vic Australia Univ South Australia Adelaide SA Australia Inria Bordeaux France

ISBN: (纸本)9781665496179

We propose TimeTables, a novel prototype system that aims to support data exploration, using embodiment with space-time cubes in virtual reality. TimeTables uses multiple space-time cubes on virtual tabletops, which users can manipulate by extracting time layers or individual buildings to create new tabletop views. The surrounding environment includes a large space for multiple linked tabletops and a storage wall. TimeTables presents information at different time scales by stretching layers to drill down in time. Users can also jump into tabletops to inspect data from an egocentric perspective. We present a use case scenario of energy consumption displayed on a university campus to demonstrate how our system could support data exploration and analysis over space and time. From our experience and analysis we believe the system has a high potential in assisting spatio-temporal data exploration and analysis.

关键词： Human-centered computing visualization visualization application domains Information visualization Human-centered computing Human computer interaction (HCI) Interaction paradigms Virtual reality Human-centered computing Human computer interaction (HCI) Interaction techniques

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Research on Program Automatic Repair Method Combining Context Optimization Strategy and large Language Models 4

Research on Program Automatic Repair Method Combining Contex...

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4th International symposium on Computer Technology and Information Science, ISCTIS 2024

作者： Li, Yalong Guo, Junxia Beijing University of Chemical Technology Beijing China

ISBN: (纸本)9798350354560

Automated program repair techniques address software errors, vulnerabilities, and defects through automation. With the rapid development of deep learning, deep learning-based automated repair techniques have improved repair performance but still face challenges of high data demands and low accuracy. The emergence of large language models offers new solutions. This paper proposes a program automated repair method called CodeFixer, which combines contextual optimization strategies and large language models. This method utilizes a Tree-LSTM model to learn the relationship between erroneous statements and their contexts, providing relevant contextual information to the large language model. By employing prompt engineering, the program repair process is divided into error analysis and patch generation stages. During patch generation, the quality and reliability of patches are enhanced by integrating tree-of-thoughts and model fine-tuning, and candidate patches are optimized through an automated evaluation consistency strategy. To evaluate the performance of CodeFixer, experiments were conducted on 395 errors in the Defects4J V1.2 dataset, successfully repairing 92 errors. Validation results on the QuixBugs dataset in both Java and Python programming languages also outperformed existing automated repair techniques. © 2024 ieee.

关键词： Deep learning

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