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 dev...
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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 o...
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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 dataexploration, 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.
The study explores the terrain of dataanalysis by delving into data Insights Nexus, a comprehensive system at the forefront of this field. The application integrates machine learning modeling, dynamic 2D/3D visualiza...
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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...
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As urbanization accelerates, data on the diverse aspects of urban life, including the environment, finance, and transportation, are increasing exponentially. Single-domain dataanalysis falls short for complex tasks, ...
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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 sol...
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"In a perfect world, free from budgets, every piece of data that is collectable would be collected, and every byte would be analyzed [...]". Big dataanalysis frameworks have already found their way into mai...
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"In a perfect world, free from budgets, every piece of data that is collectable would be collected, and every byte would be analyzed [...]". Big dataanalysis 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 dataexploration in a real-world big dataanalysis 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.
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 availab...
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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.
Medical dataanalysis 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 rese...
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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 datavisualizations pro...
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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 datavisualizations 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 analysisvisualization 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.
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