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The 1st International Workshop on Federated Learning with Graph data (FedGraph) 22

The 1st International Workshop on Federated Learning with Gr...

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31st ACM International conference on Information and Knowledge Management (CIKM)

作者： Yang, Carl Li, Xiaoxiao Baracaldo, Nathalie Shah, Neil He, Chaoyang Lyu, Lingjuan Sun, Lichao Avestimehr, Salman Emory Univ Atlanta GA 30322 USA Univ British Columbia Vancouver BC Canada IBM Res San Jose CA USA Snap Res Seattle WA USA FedML Inc Los Angeles CA USA Sony AI Tokyo Japan Lehigh Univ Bethlehem PA 18015 USA Univ Southern Calif Los Angeles CA 90007 USA

ISBN: (纸本)9781450392365

The field of graph data mining, one of the most important AI research areas, has been revolutionized by graph neural networks (GNNs), which benefit from training on real-world graph data with millions to billions of nodes and links. Unfortunately, the training data and process of GNNs involving graphs beyond millions of nodes are extremely costly on a centralized server, if not impossible. Moreover, due to the increasing concerns about data privacy, emerging data from realistic applications are naturally fragmented, forming distributed private graphs of multiple "data silos", among which direct transferring of data is forbidden. The nascent field of federated learning (FL), which aims to enable individual clients to jointly train their models while keeping their local data decentralized and completely private, is a promising paradigm for large-scale distributed and private training of GNNs. FedGraph2022 aims to bring together researchers from different backgrounds with a common interest in how to extend current FL algorithms to operate with graph data models such as GNNs. FL is an extremely hot topic of large commercial interest and has been intensively explored for machine learning with visual and textual data. The exploration from graph mining researchers and industrial practitioners is timely catching up just recently. There are many unexplored challenges and opportunities, which urges the establishment of an organized and open community to collaboratively advance the science behind it. The prospective participants of this workshop will include researchers and practitioners from both graph mining and federated learning communities, whose interests include, but are not limited to: graph analysis and mining, heterogeneous network modeling, complex data mining, large-scale machine learning, distributed systems, optimization, meta-learning, reinforcement learning, privacy, robustness, explainability, fairness, ethics, and trustworthiness.

关键词： graph mining distributed learning data privacy federated learning graph neural networks

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visual analysis Tool for a BLE Technology based Tracking data 23

Visual Analysis Tool for a BLE Technology based Tracking Dat...

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23rd International conference on Enterprise Information Systems (ICEIS)

作者： Schneider, Flavia A. Branco, Adriano Rodrigues, Ariane M. B. Carvalho, Felipe Barbosa, Simone D. J. Endler, Markus Lopes, Helio Pontificia Univ Catolica Rio de Janeiro Dept Comp Rua Marques Sao Vicente 225 Rio de Janeiro Brazil

ISBN: (纸本)9789897585098

Several systems deal with human mobility. Most of them are for outdoor environments and use mobile phones to capture data. However, there is a growing interest of enterprises to consider indoor movement to take employees and client classes into account. Moreover, they usually want to assign semantics to the visited locations. We propose a visual exploration tool for analyzing the dynamics of individual movements in an indoor environment in this work. We present the use of suitable charts and animations to explore these complex data better. Finally, we argue that one could use our solution to monitor social distancing in indoor environments, which is a sensible thing during the current COVID-19 pandemic.

关键词： Internet of Mobile Things data visualization Indoor Positioning data Mining Information Systems

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Developing a Comprehensive Task Framework for Effective Workforce analysis 25

Developing a Comprehensive Task Framework for Effective Work...

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Companion Proceedings of the ACM on Web conference 2025

作者： Rebecca Dew Mingzhao Li Weidong Liu Sandya Baratha Raj Pearson Melbourne Australia Pearson Sydney Australia

ISBN: (纸本)9798400713316

Effective workforce analysis, planning, and management require a deep understanding of the tasks and skills associated with different roles. This paper introduces a novel methodology for developing a comprehensive task framework that leverages Large Language Models (LLMs) and large-scale job ads data. We first propose an innovative approach to task taxonomy design, which involves the decomposition and reconstruction of tasks into a hierarchical structure based on action-object pairings, systematically refined using LLMs. The methodology extends to integrating the taxonomy with occupation and skill linkages derived from job ads, ensuring alignment with real-world workforce dynamics. Finally, we demonstrate the practical value of this framework through a visual analytics system that enables interactive exploration and analysis of tasks, occupations, and associated skills, highlighting its potential to transform workforce analysis. Demo video: https://***/41txBZK

关键词： gpt

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Research on Interior Design Applications Incorporating Artificial Intelligence Technology

Research on Interior Design Applications Incorporating Artif...

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Intelligent Design (ICID), International conference on

作者： Ning Xian Yazhen Liu College of Design and Art Shenyang Jianzhu University Hunnan Shenyang China

ISBN: (数字)9798331541491

ISBN: (纸本)9798331541507

The purpose of this paper is to discuss the application of interior design incorporating artificial intelligence technology. The rapid development of artificial intelligence technology in the new era has produced innovative contributions to various industries, and interior design is no exception. This paper describes the concepts and development status of artificial intelligence and interior design, and analyzes the advantages and scope of its application in interior design according to the artificial intelligence technology that can be used in interior design. Indepth exploration of the potential of AI technology in the field of interior design, with the help of machine learning, big data analysis, virtual reality, augmented reality and intelligent customer feedback system and other tools, significantly improve the efficiency and quality of the design process, strengthen the interactivity and visual impact of the design.

关键词： Industries Computer science Technological innovation visualization Design methodology Catalysts Collaboration Machine learning Big data Augmented reality

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Space-Time Urban Explorer: A visual Tool for Exploring Spatiotemporal Crime and Patrolling data

Space-Time Urban Explorer: A Visual Tool for Exploring Spati...

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Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI)

作者： Tiago Paulino Santos João Matheus Siqueira Souza Thales Vieira Luis Gustavo Nonato Institute of Computing Federal University of Alagoas (UFAL) Maceio Brazil Institute of Mathematics and Computer Sciences University of Sao Paulo (ICMC-USP) Sao Carlos Brazil

ISBN: (数字)9798350376036

ISBN: (纸本)9798350376043

Spatiotemporal urban data gathered by public security departments holds immense potential for in-depth analysis, enhancing decision-making in areas such as crime prevention and patrolling strategies. However, extracting meaningful patterns from vast and complex spatiotemporal datasets presents a considerable challenge in the field of big data analytics. We introduce a novel visualization-assisted tool tailored for handling massive spatiotemporal urban datasets, with a specific focus on public security data. At the core of this tool are spatial graph vertex ordering algorithms that perform dimensionality reduction on the vertices' locations. To make this tool practical for handling massive spatiotemporal datasets, we present efficient preprocessing techniques. These techniques are carefully crafted to distill and represent urban spatiotemporal datasets, ensuring efficient data exploration. The effectiveness of the proposed solution is validated through case studies using real datasets from the Military Police of the State of Alagoas - Brazil. We demonstrate the effectiveness of the proposed solutions, showcasing the versatility of the visual tool in accomplishing various relevant analytical tasks.

关键词： Dimensionality reduction Law enforcement visual analytics Prevention and mitigation Decision making data visualization Public security Spatiotemporal phenomena data mining Socioeconomics

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Technology and Graphic Design: Building a STEM Education Ecosystem for the Future

Technology and Graphic Design: Building a STEM Education Eco...

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STEM Education conference (iSTEM-Ed), International

作者： Khanh Hoang Duong Luan Trong Nguyen FPT University Can Tho Campus Can Tho Vietnam

ISBN: (数字)9798350365979

ISBN: (纸本)9798350365986

This study investigates STEM (Science, Technology, Engineering, and Mathematics) with technology and graphic design in creating an advanced educational ecosystem. This research aims to develop a STEM education ecosystem for the future by leveraging technology and graphic design. The study sample includes students, teachers, and education experts in the STEM field, along with tools and resources related to technology and graphic design in education. Sampling methods will involve online surveys, individual and group interviews, as well as direct observations in educational settings. Research tools will encompass data analysis software, IT applications for data collection and processing, and graphic design tools. data will be analyzed using statistical methods and content analysis to understand the positive impact of integrating technology and graphic design into STEM education. The expected outcome is to provide specific recommendations to enhance the quality of education, foster creativity and exploration in STEM fields, and establish the groundwork for an advanced and future-focused educational environment. The article provides in-depth insights into the development of technological capabilities while emphasizing the crucial role of graphic design in creating a holistic learning environment. A comprehensive approach is proposed in the article to harmoniously combine these elements, providing a plan for developing and implementing an advanced educational ecosystem, preparing learners to face challenges and opportunities in the future. By emphasizing the importance of graphic design in enhancing visual communication skills and solving problems creatively, this article proposes an integrated approach to build a dynamic ecosystem. Education equips learners with new learning methods for the future. Based on current research and practical innovation, the article presents a framework for developing and implementing a sustainable and comprehensive STEM-based education model

关键词： Graphics Surveys Technological innovation visual communication Statistical analysis Education Ecosystems

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High-Level Grammars for visualization and visual Analytics

High-Level Grammars for Visualization and Visual Analytics

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Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI)

作者： Gustavo Moreira Marcos Lage Nivan Ferreira Fabio Miranda University of Illinois Chicago Universidade Federal Fluminense Universidade Federal de Pernambuco

The increase in data availability and the popularization of data science brought a continuously growing need to use visualizations to explore complex data. Creating new visualizations, however, is a difficult task that often requires extensive programming expertise. High-level grammars for authoring visualizations provide a systematic and flexible way to specify visualizations in a declarative manner, lowering the entry barrier for data analysis. Such grammars effectively assist in the exploration of the visualization design space and of complex datasets. The goal of this tutorial paper is to then provide a summary of recently proposed grammars, so that a broad audience (e.g., students, researchers, practitioners) can better understand how they are designed and used in practice for visualization and visual analytics tasks.

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analysis of Faces in a Decade of US Cable TV News 21

Analysis of Faces in a Decade of US Cable TV News

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27th ACM SIGKDD International conference on Knowledge Discovery and data Mining (KDD)

作者： Hong, James Crichton, Will Zhang, Haotian Fu, Daniel Y. Ritchie, Jacob Barenholtz, Jeremy Hannel, Ben Yao, Xinwei Murray, Michaela Moriba, Geraldine Agrawala, Maneesh Fatahalia, Kayvon Stanford Univ Stanford CA 94305 USA Brown Inst Media Innovat Providence RI USA

ISBN: (纸本)9781450383325

Cable (TV) news reaches millions of US households each day. News stakeholders such as communications researchers, journalists, and media monitoring organizations are interested in the visual content of cable news, especially who is on-screen. Manual analysis, however, is labor intensive and limits the size of prior studies. We conduct a large-scale, quantitative analysis of the faces in a decade of cable news video from the top three US cable news networks (CNN, FOX, and MSNBC), totaling 244,038 hours between January 2010 and July 2019. Our work uses technologies such as automatic face and gender recognition to measure the "screen time" of faces and to enable visual analysis and exploration at scale. Our analysis method gives insight into a broad set of socially relevant topics. For instance, male-presenting faces receive much more screen time than female-presenting faces (2.4x in 2010, 1.9x in 2019). To make our dataset and annotations accessible, we release a public interface at https://*** that allows the general public to write queries and to perform their own analyses.

关键词： Cable news screen time visual analysis at scale

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Lunar terrain coverage analysis data delivery workflow

Lunar terrain coverage analysis data delivery workflow

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Accelerating Space Commerce, exploration, and New Discovery conference, ASCEND 2021

作者： Lee, Carlyn Lee, Charles H. Shaikh, Mohsin A. Michels, Dominik L. NASA Jet Propulsion Laboratory California Institute of Technology PasadenaCA United States KAUST Core Labs Thuwal Saudi Arabia KAUST Visual Computing Center Thuwal Saudi Arabia

ISBN: (数字)9781624106125

ISBN: (纸本)9781624106125

In this work, we are developing a lunar terrain database to enable fast rendering of sun illumination and earth visibility for a proposed coverage analysis tool. This development will advance lunar mission design and formulation for current and future communications architectures, and will aid in lunar surface mission planning and communications/navigation operations. Our effort can be described in three steps: (1) we parallelize a brute force algorithm, which computes elevation masks from laser altimetry data acquired by the Lunar Reconnaissance Orbiter’s (LRO) Lunar Orbiter Laser Altimeter (LOLA);(2) we investigate parallel I/O methods to store terrain mask information from step (1) into a parallel file system;and (3) we finally deliver data to the terrain coverage analysis tool. © 2021, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Orbits

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New Version of HEAT: Calibration and visualization Tool for Thermal Imagers on Hayabusa2 and Hera

New Version of HEAT: Calibration and Visualization Tool for ...

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IEEE conference on Aerospace

作者： Ramon Vilardell Belles Hirohide Demura Ryuji Konno Toko Mori Kengo Kakazu Takehiko Arai Hiroki Senshu Naoya Sakatani Tatsuaki Okada University of Aizu Aizu-Wakamatsu Japan Maebashi Institute of Technology Maebashi Japan Chiba Instiute of Technology Chiba Japan Institute of Space and Astronautical Science Japan Aerospace Exploration Agency Sagamihara Japan

The Hayabusa2/Hera exploration Assistant for Thermal Imagers (HEAT) is a powerful tool for understanding asteroid thermal characteristics. HEAT serves as a versatile solution for the calibration and conversion of data from thermal infrared imagers, TIR and TIRI, on the Hayabusa2 and Hera spacecraft, targeting asteroids 162173 Ryugu and 65803 Didymos, along with its moon Dimorphos. HEAT enables the production of brightness temperature images and the thermal inertia of the asteroid’s surface. In response to evolving mission needs, HEAT has undergone significant enhancement. Python-based wrapping is introduced to HEAT to improve usability and accommodate the expansion of the capabilities of TIRI, which has seven bands, a mode closed shutter, and 3D visualization capabilities. The development tool prioritizes user-friendliness, cross-platform compatibility, and efficient data handling, making it an indispensable resource for asteroid research. By implementing thread parallelization utilizing OpenMP, we substantially reduced the calculation time for the thermal data. The architecture-independent nature of our parallelization approach ensures compatibility with various computational resources, contributing to the scalability and adaptability of HEAT for future missions. Moreover, the 3D and 2D visualization address the need for improved data interpretation and analysis. Users now have access to an easy-to-use 3D visualization and crater detection tool, empowering them to explore and manipulate 3D models. This tool also incorporates advanced features, such as Laplacian smoothing and dynamic color mapping, ensuring that the visual representation of celestial bodies’ thermal behavior is accurate and insightful. The future goals of HEAT are to develop customized algorithms for detecting depressions and improve graphical user interfaces for data analysis. This promises to further improve the usability of HEAT.

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