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4th IEEE International conference on Quantum Computing and Engineering, QCE 2023

作者： Steed, Chad A. Chae, Junghoon Dasgupta, Samudra Humble, Travis S. Oak Ridge National Laboratory Oak Ridge United States

ISBN: (纸本)9798350343236

We present the preliminary design and results of QVis, a visual analytics tool for exploring quantum device performance data. QV is helps uncover temporal and multivariate variations in noise properties of quantum devices. We describe the implementations of these methods as well as applications to the analysis of a 127-qubit data set derived from the IBM washington processor over a 16-month period. Both human-interactive and semi-automated analytic methods are included to address requirements for visual exploration, thresholding, and clustering techniques. Our application of QVis to real-world scenarios demonstrates the ability to reveal noteworthy patterns in the behavior of the critical characterization metrics. © 2023 IEEE.

关键词： visualization

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A Survey of visualization and analysis in High-Resolution Connectomics

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COMPUTER GRAPHICS FORUM 2022年第3期41卷 573-607页

作者： Beyer, Johanna Troidl, Jakob Boorboor, Saeed Hadwiger, Markus Kaufman, Arie Pfister, Hanspeter Harvard Univ Visual Comp Grp Cambridge MA 02138 USA SUNY Stony Brook Dept Comp Sci Stony Brook NY 11794 USA King Abdullah Univ Sci & Technol Visual Comp Ctr Thuwal Saudi Arabia

The field of connectomics aims to reconstruct the wiring diagram of Neurons and synapses to enable new insights into the workings of the brain. Reconstructing and analyzing the Neuronal connectivity, however, relies on many individual steps, starting from high-resolution data acquisition to automated segmentation, proofreading, interactive data exploration, and circuit analysis. All of these steps have to handle large and complex datasets and rely on or benefit from integrated visualization methods. In this state-of-the-art report, we describe visualization methods that can be applied throughout the connectomics pipeline, from data acquisition to circuit analysis. We first define the different steps of the pipeline and focus on how visualization is currently integrated into these steps. We also survey open science initiatives in connectomics, including usable open-source tools and publicly available datasets. Finally, we discuss open challenges and possible future directions of this exciting research field.

关键词： CCS Concepts • Human-centered computing → visualization systems and tools Scientific visualization • Applied computing → Life and medical sciences Systems biology

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data Insights Nexus-Innovative Platform for Dynamic Modeling and visual Analytics 7

Data Insights Nexus-Innovative Platform for Dynamic Modeling...

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7th International conference on Inventive Computation Technologies, ICICT 2024

作者： Nagaraj, P. Muneeswaran, V. Gopal Reddy, B. Venu Naga Ramya Sri, P. Gayathri, N. Dastagiri Reddy, T. Venkata Kalasalingam Academy of Research And Education Department of Computer Science And Engineering Virudhunagar Krishnankoil India Kalasalingam Academy of Research And Education Department of Electronics And Communication Engineering Virudhunagar Krishnankoil India

ISBN: (纸本)9798350359299

The study explores the terrain of data analysis by delving into data Insights Nexus, a comprehensive system at the forefront of this field. The application integrates machine learning modeling, dynamic 2D/3D visualization, and robust anomaly detection within a unified framework. User-friendly interfaces empower effortless construction, fine-tuning, and deployment of intricate machine-learning models for deep insights from complex datasets. Advanced 2D/3D visualization transforms data relationships into visually intuitive representations. Furthermore, the system employs state-of-the-art anomaly detection, bolstering result accuracy by identifying elusive irregularities and outliers. data Insights Nexus, as a pioneering solution, holds the potential to redefine data exploration and analysis across diverse research domains. It not only enhances efficiency but also offers unprecedented effectiveness in dealing with complex data. This research meticulously explores its components, applications, and potential impacts, providing invaluable insights for data analysts and researchers. The application represents a groundbreaking tool that promises to revolutionize research methodologies, enabling researchers to uncover new facets of data-driven discoveries. © 2024 IEEE.

关键词： visualization

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Scientometric analysis of Building Energy analysis in the Construction During 2005–2022

Scientometric Analysis of Building Energy Analysis in the Co...

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International conference on Civil Engineering Innovative Development in Engineering Advances, ICC-IDEA 2023

作者： Nakkeeran, G. Krishnaraj, L. Department of Civil Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur 603203 India

ISBN: (纸本)9789819962327

The aim of this paper is to carry out a comprehensive review of 921 papers in the Scopus database on "building energy analysis using BIM" from 2005 to 2022. Using a systematic approach to the primary data in each document, we gather the various types of publications, citations, and predictions. visual analysis software Biblioshiny 2.0 is used to show the evolution of publications. There are guidelines for selecting documents for citation analysis. All of these factors are considered when assigning ranks based on citation count, the total number of documents and the average number of citations per document. Author-keywords and analysis of the data were used to identify software development hotspots and trends for building energy analysis. Bibliometric analysis is beneficial to building energy analysis researchers. Building energy analysis publications are studied extensively in a bibliometric study. To aim researchers in software for building energy analysis exploration, scholars should be aware of this field's hotspots’ current and future state. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Architectural design

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DM-Vis: A Graph-Based data Reconnaissance System for Multi-domain Urban data 41st

DM-Vis: A Graph-Based Data Reconnaissance System for Multi...

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41st Computer Graphics International conference, CGI 2024

作者： Wang, Hesong Wang, Song Wang, Yanru Long, Hao School of Computer Science and Technology Southwest University of Science and Technology Mianyang China

ISBN: (纸本)9783031818059

As urbanization accelerates, data on the diverse aspects of urban life, including the environment, finance, and transportation, are increasing exponentially. Single-domain data analysis falls short for complex tasks, necessitating multi-source data integration to offer decision-makers a holistic view of interconnections and trends. However, challenges arise in effectively representing data associations and swiftly identifying relevant datasets. This paper proposes techniques to integrate multi-domain urban data relationships and facilitate the acquisition of target datasets. We introduce a method to transform data entities into association graphs and a data extraction method to aid users in understanding data relationships and acquiring target datasets. Using these methods, we developed DM-Vis, a data reconnaissance visual analysis system, to facilitate data integration and exploration from multiple sources. Real-data case studies and positive feedback from users and experts validate the utility and reliability of our approach. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： data extract data integration data reconnaissance multi-domain data query embedding

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Genetic Algorithm with Efficient Selection Using Pareto Front Modeling and data Envelopment analysis 13

Genetic Algorithm with Efficient Selection Using Pareto Fron...

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Joint 13th International conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2024

作者： Doi, Mamoru Sugihara, Kenya Arakawa, Masao Mitsubishi Electric Corporation Kanagawa Japan Graduate School of Information Production and Systems Waseda University Fukuoka Japan

ISBN: (纸本)9798350373332

There exist various multi-objective optimization problems(MOPs) in the real world that require acquiring diverse solutions efficiently. Various multi-objective evolutionary algorithms (MOEAs) have been proposed to solve MOPs. Generally, in MOEAs, the same score criterion is conducted in Survival and Tournament Selection. In Survival, parent selection score should be conducted to ensure convergence and diversity, and in Tournament Selection, the selection score of individuals for crossover should be conducted to select individuals with high convergence compared to other individuals. Therefore, evolutionary computation algorithms are expected to perform efficient exploration while maintaining diversity. In this work, we propose an efficient selection algorithm using data envelopment analysis and pareto front modeling. In Survival, we perform an evaluation using existing methods to ensure high convergence and diversity, and in Tournament Selection, we use the data envelopment analysis method to select individuals with high convergence compared to other individuals. This allows us to balance both high convergence and diversity. Moreover, by implementing Pareto front modeling, we were also successful in addressing the issue of assuming convexity in the data envelopment analysis method. In comparison using the HV METRIC on the WFG and DTLZ benchmark functions, the proposed method showed superior results compared to the existing methods of NSGA-ii, AGE-MOEA-ii, and DEA-GA with the exception of 9-objective functions. © 2024 IEEE.

关键词： Genetic algorithms

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An AI-Based Approach to Identify Financial Risks in Transportation Infrastructure Construction Projects 1

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20th International conference on Artificial Intelligence Applications and Innovations (AIAI)

作者： Ivanova, Marina Grims, Michael Karas, Daniel Hoefinger, Gerhard Bornschlegl, Marco Xaver Hemmje, Matthias L. STRABAG Innovat & Digitalisat A-1220 Vienna Austria Univ Hagen Fac Math & Comp Sci D-58097 Hagen Germany

ISBN: (数字)9783031632150

ISBN: (纸本)9783031632174;9783031632150;9783031632143

"In a perfect world, free from budgets, every piece of data that is collectable would be collected, and every byte would be analyzed [...]". Big data analysis frameworks have already found their way into mainstream application and have seen wide-spread deployment in scientific communities as well as in organizations across different industry fields. Moreover, also AI-support is a necessary requirement for modern (big data) analysis applications nowadays. An exemplar industrial application domain highlighting the necessity of AI-supported data exploration in a real-world big data analysis application scenario is the risk analysis (economic risks) of building and construction projects. Currently, economical risk analysis is largely based on so-called expert knowledge, an experience- and intuition-based analysis of the risk. Even this experience-based (manual) process can be formalized in different ways, "construction projects are characterized by carrying a high level of uncertainty and complexity". With a strong focus on the project execution phase, this paper outlines how ML algorithms can be applied to automatically predict the financial outcome based on financial controlling data and thus to potentially assist in mitigation of financial losses.

关键词： data Science Artificial Intelligence Machine Learning AI2VIS4Bigdata DARIA

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FedLID: Self-Supervised Federated Learning for Leveraging Limited Image data

FedLID: Self-Supervised Federated Learning for Leveraging Li...

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IEEE/CVF International conference on Computer Vision (ICCV)

作者： Psaltis, Athanasios Kastellos, Anestis Patrikakis, Charalampos Z. Daras, Petros Ctr Res & Technol Hellas Thessaloniki Greece Univ West Attica Dept Elect & Elect Engn Athens Greece

ISBN: (纸本)9798350307443

This study investigates the challenging task of training visual models with very few available data, further complicated by the distribution being imbalanced and scattered across nodes. To address this diverse availability of training data in different federated settings, a customized self-supervised learning approach tailored specifically for each scenario is being proposed. In particular, a hybrid approach combining self-supervised and supervised learning techniques under a federated umbrella has been utilized at both the global and local level, harnessing the potential of unlabeled data. Extensive experiments provide a detailed analysis of the problem at hand and demonstrate the particular characteristics of the proposed learning schemes in distributed scenarios. The overall proposed approach achieves superior recognition performance in the currently broadest public dataset, surpassing all baselines by a substantial margin. The proposed solution can operate efficiently at a local level without prior knowledge of the characteristics or distribution of data across nodes.

关键词： Contrastive Learning Federated Learning Limited data

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The use of image graph in the medical data analyzing 1

The use of image graph in the medical data analyzing

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1st IEEE International conference on Advances in data-Driven Analytics and Intelligent Systems, ADACIS 2023

作者： Laghridat, Charifa Essalih, Mohamed Mounir, Ilham Cadi Ayyad University Lapssii The Safi's Graduate School of Technology Marrakesh Morocco Mohammed v University in Rabat Lrit Cnrst Urac 29 Rabat It Center Faculty of Sciences Morocco

ISBN: (纸本)9798350344158

Medical data analysis is a critical process aimed at extracting valuable insights and knowledge from complex healthcare information. It plays a vital role in enhancing diagnostics, treatment planning, and medical research. In this context, medical images serve as a fundamental source of information, providing visual representations of anatomical structures and pathological conditions. Image processing techniques, utilizing mesh-based representations, offer unique opportunities for advancing the analysis of medical *** article presents a new method for analyzing medical data based on the concept of meshing. By representing medical images as undirected graphs, the proposed approach enables efficient exploration and analysis of spatial relationships. © 2023 IEEE.

关键词： Medical imaging

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Tracking Students' Progress in Educational Escape Rooms Through a Sequence analysis Inspired Dashboard 19th

Tracking Students' Progress in Educational Escape Rooms Thro...

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42nd conference and Exposition on Structural Dynamics (IMAC)

作者： Lopez-Pernas, Sonsoles Gordillo, Aldo Barra, Enrique Saqr, Mohammed Univ Eastern Finland Joensuu 80100 Finland Univ Politecn Madrid Madrid 28040 Spain

ISBN: (纸本)9783031723117;9783031723124

Learning analytics dashboards are the main vehicle for providing educators with a visual representation of data and insights related to teaching and learning. Recent research has found that the data visualizations provided by dashboards are often very basic and do not take advantage of the latest research advances to analyze and depict the learning process. In this article, we present a success story of how we adapted a visualization used for research purposes for its integration in a dashboard for its use by teachers in daily practice. Specifically, we described the process of transforming and integrating a static sequence analysis visualization into an interactive web visualization in a learning analytics dashboard for monitoring students' temporal trajectories in educational escape rooms in real time. We interviewed teachers to find out how they made use of the dashboard and present a qualitative content analysis of their responses.

关键词： learning analytics sequence analysis game-based learning educational escape rooms dashboards

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