Many tools empower analysts and data scientists to consume analysis results in a visual interface. When the underlying data changes, these results need to be updated, but this update can take a long time-all while the...
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Many tools empower analysts and data scientists to consume analysis results in a visual interface. When the underlying data changes, these results need to be updated, but this update can take a long time-all while the user continues to explore the results. Tools can either (i) hide away results that haven't been updated, hindering exploration;(ii) make the updated results immediately available to the user (on the same screen as old results), leading to confusion and incorrect insights;or (iii) present old-and therefore stale-results to the user during the update. To help users reason about these options and others, and make appropriate trade-offs, we introduce Transactional Panorama, a formal framework that adopts transactions to jointly model the system refreshing the analysis results and the user interacting with them. We introduce three key properties that are important for user perception in this context: visibility (allowing users to continuously explore results), consistency (ensuring that results presented are from the same version of the data), and monotonicity (making sure that results don't "go back in time"). Within transactional panorama, we characterize all feasible property combinations, design new mechanisms (that we call lenses) for presenting analysis results to the user while preserving a given property combination, formally prove their relative orderings for various performance criteria, and discuss their use cases. We propose novel algorithms to preserve each property combination and efficiently present fresh analysis results. We implement our framework into a popular, open-source BI tool, illustrate the relative performance implications of different lenses, and demonstrate the benefits of the novel lenses and our optimizations.
This paper presents an evaluation of the performance of visual-Selective visual-Inertial Odometry (vS-vIO), a hybrid learning-based multi-modal pose estimation framework, in the challenging underwater domain. The asse...
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Reconstruction of 3D scenes from 2D images is a technical challenge that impacts domains from Earth and planetary sciences and space exploration to augmented and virtual reality. Typically, reconstruction algorithms f...
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As the primary source of carbon dioxide (CO2) emissions, cities are the key to solving climate change. Prior works focus on CO2 emission drivers and CO2 emission city clusters. However, the methods of mining CO2 emiss...
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ISBN:
(纸本)9798400701405
As the primary source of carbon dioxide (CO2) emissions, cities are the key to solving climate change. Prior works focus on CO2 emission drivers and CO2 emission city clusters. However, the methods of mining CO2 emission drivers include narrow factors affecting CO2 emissions, and ignore the interactions among these factors. The intelligibility of the results of the Geographical Weighted Regression (GWR) that is used to analyze the spatial heterogeneity of drivers is poor. Moreover, the relations between CO2 emission city clusters and economic ties among cities are ignored. The lack of visualanalysis tools is also a problem that needs to be fixed. Hence we develop a novel analysis framework. Thereinto, a novel method based on the stepwise regression (SR) is used to mine drivers;the biclustering algorithm is introduced into the traditional GWR to improve the intelligibility of the GWR;the gravity-entropy model is adopted to analyze the relations between CO2 emission city clusters and economic correlation strength among cities. Furthermore, a visual analytics system is implemented to explore the situation of CO2 emissions. We demonstrate the effectiveness of our approach through case studies conducted with socioeconomic data from 169 Chinese cities and positive feedback from experts and volunteers.
The proceedings contain 8 papers. The topics discussed include: a visual representation of Wittgenstein’s Tractatus Logico-Philosophicus, including the Chinese, Korean and Japanese translation;analyzing the inquiry l...
ISBN:
(纸本)9789869531764
The proceedings contain 8 papers. The topics discussed include: a visual representation of Wittgenstein’s Tractatus Logico-Philosophicus, including the Chinese, Korean and Japanese translation;analyzing the inquiry learning abilities of elementary school students using a simulation system: a preliminary exploration;deriving temporal position of a period based on positional relationships between periods using linked data;ensuring fairness with transparent auditing of quantitative bias in AI systems;exploring cultural preservation and innovation: augmented reality packaging prototyping for the Mah Meri tribe in Malaysia;exploring Taiwan university students’ digital humanities literacy using visualization interactive dataanalysis dashboard;and when drone meets ai education: boosting high school students’ computational thinking and AI literacy.
The proceedings contain 46 papers. The topics discussed include: optimizing analytical query processing on disaggregated hardware;HARvEST: a complete solution for smart agriculture monitoring;exposing geospatial cultu...
The proceedings contain 46 papers. The topics discussed include: optimizing analytical query processing on disaggregated hardware;HARvEST: a complete solution for smart agriculture monitoring;exposing geospatial cultural heritage content in map-based applications;clustering, universalities, and evolutionary schema design;natural language data interfaces: from keyword search to ChatGPT, are we there yet?;a tool for visualexploration and analysis of solar photovoltaic module data;graph peeling semantics;towards a multi-model approach to support user-driven extensibility in data warehouses: agro-ecology case study;easy-to-use interfaces for supporting the semantic annotation of web tables;HEALER: a data lake architecture for healthcare;and Toulouse: learning join order optimization policies for rule-based data engines.
The main research content of machine vision technology is to identify objects through computers, extract the features of objects, and then divide the categories of objects according to the feature value and threshold....
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Previous research has demonstrated the advantages of integrating data from multiple sources over traditional unimodal data, leading to the emergence of numerous novel multimodal applications. We propose a multimodal c...
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ISBN:
(纸本)9798891760615
Previous research has demonstrated the advantages of integrating data from multiple sources over traditional unimodal data, leading to the emergence of numerous novel multimodal applications. We propose a multimodal classification benchmark MUG with eight datasets that allows researchers to evaluate and improve their models. These datasets are collected from four various genres of games that cover tabular, textual, and visual modalities. We conduct multi-aspect dataanalysis to provide insights into the benchmark, including label balance ratios, percentages of missing features, distributions of data within each modality, and the correlations between labels and input modalities. We further present experimental results obtained by several state-of-the-art unimodal classifiers and multimodal classifiers, which demonstrate the challenging and multimodal-dependent properties of the benchmark. MUG is released at https://***/lujiaying/MUG-Bench with the data, tutorials, and implemented baselines.
Memes are generally understood as a blend of visual and textual elements and have become an effective medium of communication on social media. However, this ease of communication has also led to challenges in content ...
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This project develops a versatile tool which is especially useful over the Tor Network, being characterized by anonymity, privacy and fast retrieval of the data from the onion domains. The method is efficient as it ca...
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