94 research outputs found

    Visualizing Element Interactions in Dynamic Overlapping Sets

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    Elements-the members in sets-may change their memberships over time. Moreover, elements also directly interact with each other, indicating an explicit connection between them. Visualizing both together becomes challenging. Using an existing dynamic set visualization as a basis, we propose an approach to encode the interactions of elements together with changing memberships in sets. We showcase the value in visually analyzing both aspects of elements together through two application examples. The first example shows the evolution of business portfolio and interactions (e.g., acquisitions and partnerships) among companies. A second example analyzes the dynamic collaborative interactions among researchers in computer science

    Utilizing Big Data in Identification and Correction of OCR Errors

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    In this thesis, we report on our experiments for detection and correction of OCR errors with web data. More specifically, we utilize Google search to access the big data resources available to identify possible candidates for correction. We then use a combination of the Longest Common Subsequences (LCS) and Bayesian estimates to automatically pick the proper candidate. Our experimental results on a small set of historical newspaper data show a recall and precision of 51% and 100%, respectively. The work in this thesis further provides a detailed classification and analysis of all errors. In particular, we point out the shortcomings of our approach in its ability to suggest proper candidates to correct the remaining errors

    CohExplore : Visually Supporting Students in Exploring Text Cohesion

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    A cohesive text allows readers to follow the described ideas and events. Exploring cohesion in text might aid students enhancing their academic writing. We introduce CohExplore, which promotes exploring and reflecting on cohesion of a given text by visualizing computed cohesion-related metrics on an overview and detailed level. Detected topics are color-coded, semantic similarity is shown via lines, while connectives and co-references in a paragraph are encoded using text decoration. Demonstrating the system, we share insights about a student-authored text

    How Visualization PhD Students Cope with Paper Rejections

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    We conducted a questionnaire study aimed towards PhD students in the field of visualization research to understand how they cope with paper rejections. We collected responses from 24 participants and performed a qualitative analysis of the data in relation to the provided support by collaborators, resubmission strategies, handling multiple rejects, and personal impression of the reviews. The results indicate that the PhD students in the visualization community generally cope well with the negative reviews and, with experience, learn how to act accordingly to improve and resubmit their work. Our results reveal the main coping strategies that can be applied for constructively handling rejected visualization papers. The most prominent strategies include: discussing reviews with collaborators and making a resubmission plan, doing a major revision to improve the work, shortening the work, and seeing rejection as a positive learning experience

    Myocardial Infarction: A Comprehensive Review

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    Myocardial infarction (MI), commonly known as a heart attack, is a critical medical condition resulting from the blockage of one or more coronary arteries. MI has been classified differently over time, with the most recent classification proposed by the European Society of Cardiology and the American College of Cardiology. This new classification considers various types of MI based on clinical presentations and underlying mechanisms. MI is a significant public health issue globally, with a high prevalence and a substantial impact on healthcare resources and the economy. The pathophysiology of MI is multifactorial, with factors such as atherosclerosis, thrombosis, and inflammation playing crucial roles. Complications of MI can include heart failure, cardiogenic shock, and arrhythmias. Diagnosis of MI involves clinical evaluation, imaging studies, and biomarker testing. Treatment of MI includes reperfusion therapy, medical management, and cardiac rehabilitation. Reperfusion therapy, including thrombolytic therapy and primary percutaneous coronary intervention, is the cornerstone of treatment for ST-segment elevation MI. Medical management involves antiplatelet and anticoagulation therapy, as well as beta-blockers, while cardiac rehabilitation can help improve cardiovascular function and reduce the risk of further cardiac events. Prompt diagnosis and appropriate treatment are essential for improving outcomes and reducing morbidity and mortality associated with MI

    Uncertainty Analysis of Regional Rainfall Frequency Estimates in Northeast India

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    Estimation of rainfall quantile is an important step in regional frequency analysis for planning and design of any water resources project. Related evaluations of accuracy and uncertainty help to further assist in enhancing the reliability of design estimates. In this study, therefore, we investigate the accuracy and uncertainty of regional frequency analysis of extreme rainfall computed from genetic algorithm-based clustering. Uncertainty assessment is explored with prediction of quantiles with a new spatial Information Transfer Index (ITI) and Monte Carlo simulation framework. And, accuracy assessment is done with the comparison of regional growth curves to at-site analysis for each homogenous region. Further, uncertainty assessment with the ITI method is compared with Maximum Likelihood estimation (MLE) optimized by a genetic algorithm (GA) to check the suitability of the method. Results obtained suggest the ITI-based uncertainty assessment for regional estimates outperformed those of at-site estimates. The MLE-GA method based on at-site estimates was found to be better than at-site estimates based on L-moments, suggesting the former as a better alternative to compare with regional frequency estimates. Moreover, minimal bias and least deviation of the regional growth curve were obtained in the rainfall regions. The confidence intervals of regional estimates were seen to be well within the bounds of normality assumptions. Doi: 10.28991/cej-2021-03091762 Full Text: PD

    MemeMQA: Multimodal Question Answering for Memes via Rationale-Based Inferencing

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    Memes have evolved as a prevalent medium for diverse communication, ranging from humour to propaganda. With the rising popularity of image-focused content, there is a growing need to explore its potential harm from different aspects. Previous studies have analyzed memes in closed settings - detecting harm, applying semantic labels, and offering natural language explanations. To extend this research, we introduce MemeMQA, a multimodal question-answering framework aiming to solicit accurate responses to structured questions while providing coherent explanations. We curate MemeMQACorpus, a new dataset featuring 1,880 questions related to 1,122 memes with corresponding answer-explanation pairs. We further propose ARSENAL, a novel two-stage multimodal framework that leverages the reasoning capabilities of LLMs to address MemeMQA. We benchmark MemeMQA using competitive baselines and demonstrate its superiority - ~18% enhanced answer prediction accuracy and distinct text generation lead across various metrics measuring lexical and semantic alignment over the best baseline. We analyze ARSENAL's robustness through diversification of question-set, confounder-based evaluation regarding MemeMQA's generalizability, and modality-specific assessment, enhancing our understanding of meme interpretation in the multimodal communication landscape.Comment: The paper has been accepted in ACL'24 (Findings

    Spatio-temporal Analysis of Multi-agent Scheduling Behaviors on Fixed-track Networks

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    Multi-agent systems require coordination among the agents to solve a given task. For movement on fixed-track networks, traditional scheduling algorithms have dominated so far, but the interest in autonomous and intelligent agents is growing as they promise to react to unexpected and exceptional situations more robustly. In this paper, we study data from the Flatland 2020 NeurIPS Competition, where trains move through a virtual rail network. We developed a timeline-based visualization that provides an overview of all train movements in a simulated episode, clearly hinting at different phases, non-optimal routes, and issues such as deadlocks. This view is complemented with a map view and a graph view, interactively linked through highlighting and synchronous animation. Defining regions of interest in the map builds an analysis graph for detailed inspection. A comparison mode allows contrasting two different episodes regarding the same rail network across all views. We have conducted this application study in close collaboration with the Flatland community. Identified analysis goals stem from interviews with key persons of the community, while the approach itself was developed in two iterations based on feedback from experts with diverse backgrounds. This feedback, together with an analysis of the winning submissions from the competition, confirms that the initial analysis goals can be answered

    Visually Abstracting Event Sequences as Double Trees Enriched with Category‐Based Comparison

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    Event sequence visualization aids analysts in many domains to better understand and infer new insights from event data. Analysing behaviour before or after a certain event of interest is a common task in many scenarios. In this paper, we introduce, formally define, and position double trees as a domain-agnostic tree visualization approach for this task. The visualization shows the sequences that led to the event of interest as a tree on the left, and those that followed on the right. Moreover, our approach enables users to create selections based on event attributes to interactively compare the events and sequences along colour-coded categories. We integrate the double tree and category-based comparison into a user interface for event sequence analysis. In three application examples, we show a diverse set of scenarios, covering short and long time spans, non-spatial and spatial events, human and artificial actors, to demonstrate the general applicability of the approach
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