2022 26th International Conference Information Visualisation (IV)最新文献_第7页

Visualizing Temporal Data using Time-dependent Non-decreasing Monotone Functions 利用时变非递减单调函数可视化时间数据

2022 26th International Conference Information Visualisation (IV) Pub Date : 2022-07-01 DOI: 10.1109/IV56949.2022.00015

Maria D'Amaral Ferreira, João Moura Pires, C. Damásio

{"title":"Visualizing Temporal Data using Time-dependent Non-decreasing Monotone Functions","authors":"Maria D'Amaral Ferreira, João Moura Pires, C. Damásio","doi":"10.1109/IV56949.2022.00015","DOIUrl":"https://doi.org/10.1109/IV56949.2022.00015","url":null,"abstract":"Ahstract- The occurrence of seasonal natural phenomena depends on the conditions leading to it and not directly on the progression of time, meaning its context varies across time and space. Examples of this include comparing plant growth, insect development or wildfire risk during the same time period at different locations or in different time periods at the same location. However, visualizing and comparing such phenomena usually implies plotting it across the time axis as it's perceived as temporal data. Since it's not directly dependent of time, identifying patters of recurrence using this technique is inefficient. Because of this, we proposed transforming (when needed) the dependent function to a non-decreasing monotone one, in order to preserve the monotonic property of time progression. Then we used the resulting function as a time axis replacement to achieve an equal ground of comparison between the different contexts in which the phenomenon occurs. We applied this technique to real data from seasonal natural phenomena, such as plant and insect growth, to compare its progression in different temporal and spatial contexts. Since the dependent function of the phenomenon was scientifically known, we were able to directly use the technique to infer its seasonality patterns. Furthermore, we applied the technique to real data from the coronavirus worldwide pandemic by hypothesizing its dependent function and analysing if it was able to reduce the existing temporal misalignment between different contexts, like years and countries. The results achieved were positive, although not as remarkable as when the dependent function was known.","PeriodicalId":153161,"journal":{"name":"2022 26th International Conference Information Visualisation (IV)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133339126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Traffic Flow Indicator: Predicting Jams in a City 交通流量指标:预测城市拥堵

2022 26th International Conference Information Visualisation (IV) Pub Date : 2022-07-01 DOI: 10.1109/IV56949.2022.00056

Joao Vaz, Nuno Datia, Matilde Pato, J. Pires

引用次数: 0

Evaluation of Deep Learning Context-Sensitive Visualization Models 深度学习上下文敏感可视化模型的评价

2022 26th International Conference Information Visualisation (IV) Pub Date : 2022-07-01 DOI: 10.1109/IV56949.2022.00066

A. Dunn, D. Inkpen, Razvan Andonie

{"title":"Evaluation of Deep Learning Context-Sensitive Visualization Models","authors":"A. Dunn, D. Inkpen, Razvan Andonie","doi":"10.1109/IV56949.2022.00066","DOIUrl":"https://doi.org/10.1109/IV56949.2022.00066","url":null,"abstract":"The introduction of Transformer neural networks has changed the landscape of Natural Language Processing (NLP) during the recent years. These models are very complex, and therefore hard to debug and explain. In this context, visual explanation became an attractive approach. The visualization of the path that leads to certain outputs of a model is at the core of visual explanation, as this illuminates the features or parts of the model that may need to be changed to achieve the desired results. In particular, one goal of a NLP visual explanation is to highlight the most significant parts of the text that have the greatest impact on the model output. Several visual explanation methods for NLP models were recently proposed. A major challenge is how to compare the performances of such methods since we cannot simply use the usual classification accuracy measures to evaluate the quality of visualizations. We need good metrics and rigorous criteria to measure how useful the extracted knowledge is for explaining the models. In addition, we want to visualize the differences between the knowledge extracted by different models, in order to be able to rank them. In this paper, we investigate how to evaluate explanations/visualizations resulted from machine learning models for text classification. The goal is not to improve the accuracy of a particular NLP classifier, but to assess the quality of the visualizations that explain its decisions. We describe several methods for evaluating the quality of NLP visualizations, including both automated techniques based on quantifiable measures and subjective techniques based on human judgements.","PeriodicalId":153161,"journal":{"name":"2022 26th International Conference Information Visualisation (IV)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128300081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comparative evaluation of the Scatter Plot Matrix and Parallel Coordinates Plot Matrix 散点图矩阵与平行坐标图矩阵的比较评价

2022 26th International Conference Information Visualisation (IV) Pub Date : 2022-07-01 DOI: 10.1109/IV56949.2022.00027

Hugh Garner, S. Fernstad

{"title":"Comparative evaluation of the Scatter Plot Matrix and Parallel Coordinates Plot Matrix","authors":"Hugh Garner, S. Fernstad","doi":"10.1109/IV56949.2022.00027","DOIUrl":"https://doi.org/10.1109/IV56949.2022.00027","url":null,"abstract":"The Scatter Plot Matrix (SPLOM) and the Parallel Coordinates Plot Matrix (PCPM) are frequently used in exploratory data analysis for multivariate data to explore pairwise relationships, clustering and outliers. The SPLOM and PCPM are complex visualization methods with many potential interactions between data, task and visual representation. While numerous studies exist evaluating the SPLOM and Parallel Coordinates Plot (PCP) there is, to the best of our knowledge, no existing study evaluating the PCPM. This pilot study presents an evaluation of the performance of the SPLOM and PCPM for a set of common explorative tasks and identifies key directions for future work. The overall results indicate a minimal performance difference between the visualization methods for most tasks, but with significant variance between users, interactions between data features and response by method, and strong user preferences depending on task. As such, we recommend careful consideration of the background of potential users when choosing a method, and/or the use of complementary or linked views. Further work is required to understand the particular mechanisms impacting users' highly variable performance with the PCPM.","PeriodicalId":153161,"journal":{"name":"2022 26th International Conference Information Visualisation (IV)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122846821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Eye of the Rider. Visualization and data-driven heuristics for the critical analysis of gig economy 骑士之眼。零工经济批判性分析的可视化和数据驱动启发式

2022 26th International Conference Information Visualisation (IV) Pub Date : 2022-07-01 DOI: 10.1109/IV56949.2022.00068

N. Lettieri, Delfina Malandrino, Alfonso Guarino, R. Zaccagnino

{"title":"The Eye of the Rider. Visualization and data-driven heuristics for the critical analysis of gig economy","authors":"N. Lettieri, Delfina Malandrino, Alfonso Guarino, R. Zaccagnino","doi":"10.1109/IV56949.2022.00068","DOIUrl":"https://doi.org/10.1109/IV56949.2022.00068","url":null,"abstract":"The digital evolution of economies and markets brings changes that go largely beyond growth and efficiency. In the gig economy, also fueled by algorithmic management solutions, digital labour platforms (DPLs) raise significant issues that include power asymmetries, new forms of workers' abuse and discrimination, algorithms' opacity and over-control. In such a scenario, while normative frameworks evolve novel safeguards, a crucial challenge is that of feeding public (social, institutional) oversight on the dynamics that, at various levels, affect the gig work world. In this paper, we show how the combination of visual analytics and data-driven heuristics can be used to offer new insights on and promote higher levels of transparency and awareness about the fairness of DPLs' activity seen, in the first place, from the workers' perspective. Solutions will be presented as part of GigAdvisor, an experimental cross-platform application developed within an ongoing research that draws on computational social science methods to enable new critical approaches to the digital economy.","PeriodicalId":153161,"journal":{"name":"2022 26th International Conference Information Visualisation (IV)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127499990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improving Cybersecurity Incident Analysis Workflow with Analytical Provenance 利用分析来源改进网络安全事件分析工作流程

2022 26th International Conference Information Visualisation (IV) Pub Date : 2022-07-01 DOI: 10.1109/IV56949.2022.00058

Vít Rusňák, L. Janečková, Filip Drgon, Anna-Marie Dombajova, Veronika Kudelkova

引用次数: 0

VennSOM: A SOM-Assisted Visualization of Binary Data 一个som辅助的二进制数据可视化

2022 26th International Conference Information Visualisation (IV) Pub Date : 2022-07-01 DOI: 10.1109/IV56949.2022.00072

M. Trutschl, P. Kilgore, Billy A. Tran, Hyun-Woong Nam, Eric Clifford, Adesewa Akande, U. Cvek

引用次数: 0

Design Thinking at a glance - An overview of models along with enablers and barriers of bringing it to the workplace and life 设计思维概览-概述模型以及将其带入工作场所和生活的促成因素和障碍

2022 26th International Conference Information Visualisation (IV) Pub Date : 2022-07-01 DOI: 10.1109/IV56949.2022.00046

S. Kernbach, Anja Svetina Nabergoj, Anastasia Liakhavets, Andrei Petukh

引用次数: 3

Explainable Mixed Data Representation and Lossless Visualization Toolkit for Knowledge Discovery 用于知识发现的可解释混合数据表示和无损可视化工具包

2022 26th International Conference Information Visualisation (IV) Pub Date : 2022-06-13 DOI: 10.1109/IV56949.2022.00060

B. Kovalerchuk, Elijah McCoy

{"title":"Explainable Mixed Data Representation and Lossless Visualization Toolkit for Knowledge Discovery","authors":"B. Kovalerchuk, Elijah McCoy","doi":"10.1109/IV56949.2022.00060","DOIUrl":"https://doi.org/10.1109/IV56949.2022.00060","url":null,"abstract":"Developing Machine Learning (ML) algorithms for heterogeneous/mixed data is a longstanding problem. Many ML algorithms are not applicable to mixed data, which include numeric and non-numeric data, text, graphs and so on to generate interpretable models. Another longstanding problem is developing algorithms for lossless visualization of multidimensional mixed data. The further progress in ML heavily depends on success interpretable ML algorithms for mixed data and lossless interpretable visualization of multidimensional data. The later allows developing interpretable ML models using visual knowledge discovery by end-users, who can bring valuable domain knowledge which is absent in the training data. The challenges for mixed data include: (1) generating numeric coding schemes for non-numeric attributes for numeric ML algorithms to provide accurate and interpretable ML models, (2) generating methods for lossless visualization of n-D non-numeric data and visual rule discovery in these visualizations. This paper presents a classification of mixed data types, analyzes their importance for ML and present the developed experimental toolkit to deal with mixed data. It combines the Data Types Editor, VisCanvas data visualization and rule discovery system which is available on GitHub.","PeriodicalId":153161,"journal":{"name":"2022 26th International Conference Information Visualisation (IV)","volume":"369 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122777909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Interpretable Machine Learning for Self-Service High-Risk Decision-Making 自助式高风险决策的可解释机器学习

2022 26th International Conference Information Visualisation (IV) Pub Date : 2022-05-09 DOI: 10.1109/IV56949.2022.00061

Charles Recaido, B. Kovalerchuk

{"title":"Interpretable Machine Learning for Self-Service High-Risk Decision-Making","authors":"Charles Recaido, B. Kovalerchuk","doi":"10.1109/IV56949.2022.00061","DOIUrl":"https://doi.org/10.1109/IV56949.2022.00061","url":null,"abstract":"This paper contributes to interpretable machine learning via visual knowledge discovery in general line coordinates (GLC). The concepts of hyperblocks as interpretable dataset units and general line coordinates are combined to create a visual self-service machine learning model. Dynamic Scaffolding Coordinates as lossless multidimensional coordinate systems are proposed, and their applications as visual models is shown. DSC1 and DSC2 can map multiple dataset attributes to a single two-dimensional (X, Y) Cartesian plane using a graph construction algorithm. The hyperblock analysis was used to determine visually appealing dataset attribute orders and to reduce line occlusion. It is shown that hyperblocks can generalize decision tree rules and a series of DSC1 or DSC2 plots can visualize a decision tree. The DSC1 and DSC2 plots were tested on benchmark datasets from the UCI ML repository. They allowed for visual classification of data. Additionally, areas of hyperblock impurity were discovered and used to establish dataset splits that highlight the upper estimate of worst-case model accuracy to guide model selection for high-risk decision-making. Major benefits of DSC1 and DSC2 is their highly interpretable nature. They allow domain experts to control or establish new machine learning models through visual pattern discovery.","PeriodicalId":153161,"journal":{"name":"2022 26th International Conference Information Visualisation (IV)","volume":"101 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120971591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2