Reviewerly/exHarmony huggingface dataset:
Comparative evaluation of the Scatter Plot Matrix and Parallel Coordinates Plot Matrix

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.
The Emotional Burnout Syndrome Development Among Students of a Technical University

The article reveals the content of the “burnout syndrome” concept, presents a theoretical analysis of the emotional burnout phenomenon, its structure, stages of development, describes specific features of the emotional burnout syndrome, and analyzes the causes and factors contributing to its formation among students. The work revealed unfavorable factors that adversely affect mental health, the level of stress resistance of students, which, in particular, occur in emotional burnout syndrome. In our own empirical study using modern psycho-diagnostic tools, it was revealed that the emotional burnout syndrome among the students of Institute of Engine and Power Plant Engineering, Samara National Research University named after academician S. P. Korolev can be considered as a manifestation of distress induced by suboptimal learning conditions, and it comes through an individual symptoms combination of different level depending on the initial predisposing personal traits. It has been shown that even the early development stages of emotional burnout syndrome negatively affect the students' stress reactivity, making them more vulnerable to stress factors in the learning process. At the final stage, an analysis of possible ways and methods for burnout syndrome prevention among students was carried out, which is an effective way to prevent the development of emotional burnout syndrome among university students.
Kinematic Motion Analysis with Volumetric Motion Capture

Kinematic motion analysis is widely used in health-care, sports medicine, robotics, biomechanics, sports science, etc. Motion capture systems are essential for motion analysis. There are three types of motion capture systems: marker-based capture, vision-based capture, and volumetric capture. Marker-based motion capture systems can achieve fairly accurate results but attaching markers to a body is inconvenient and time-consuming. Vision-based, marker-less motion capture systems are more desirable because of their non-intrusiveness and flexibility. Volumetric capture is a newer and more advanced marker-less motion capture system that can reconstruct realistic, full-body, animated 3D character models. But volumetric capture has rarely been used for motion analysis because volumetric motion data presents new challenges. We propose a new method for conducting kinematic motion analysis using volumetric capture data. This method consists of a three-stage pipeline. First, the motion is captured by a volumetric capture system. Then the volumetric capture data is processed using the Iterative Closest Points (ICP) algorithm to generate virtual markers that track the motion. Third, the motion tracking data is imported into the biomechanical analysis tool OpenSim for kinematic motion analysis. Our motion analysis method enables users to apply numerical motion analysis to the skeleton model in OpenSim while also studying the full-body, animated 3D model from different angles. It has the potential to provide more detailed and in-depth motion analysis for areas such as healthcare, sports science, and biomechanics.
Managing Large Multiple-choice Test Items Repositories

Knowledge assessment in online platforms is widely based on multiple-choice questions (MCQs). In this paper we describe our proposal for a NLP-based system designed to support the management of large repositories of MCQs. Indeed, within large repositories of MCQs, it is common to have similar if not almost duplicated questions, and coping with them is a time consuming and error prone task. We propose an approach, based on Natural Language Processing (NLP), that i) computes the similarity between the items and ii) checks the similarity between the questions and, if available, the areas of the syllabus. The results of the analysis are also displayed in a graph (i.e. network) based view, providing a clear picture to the user.
Natural Language Interface for Data Visualization with Deep Learning Based Language Models

In this work we investigate the possibilities of integrating a Deep Learning language model for a Natural Language Interface (NLI) of an information visualisation software. For this purpose, we have developed a prototype web application that uses the deep learning model OpenAI Codex from the GPT3 family to create visualisations from text input. For comparison, we created a second prototype with a classical NLP approach based on NL4DV toolkit (with subtasks like part-of-speech (POS) tagging, entity recognition, and dependency parsing) and an almost identical interface. The two variants were subjected to a study with test persons, and the advantages and disadvantages of the two approaches and the suitability for the most common visualisation types were investigated. The Deep Learning approach offers greater expressiveness for describing the graphics, but also the danger of not always being entirely comprehensible. The participants were able to use it to create more complex visualisations, but also sometimes had problems finding the right text input to solve the tasks. In our preliminary usability study, the Deep Learning prototype performed slightly better than the comparison prototype and achieved a useful usability score.
Explainable Mixed Data Representation and Lossless Visualization Toolkit for Knowledge Discovery

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.
Regression estimation model for emotion and intensity of speech using perception rating

An emotional intensity regression estimation model was created using calculated perceived intensity values and deep learning. In our previous study, we considered emotional intensity using 10 categories and estimated emotional intensity by categorization, but the flexibility of the method was insufficient. In order to solve this problem, an emotional intensity estimation model which takes into account differences in the perceptual intensity value of each category of emotional intensity was used in this study. For this purpose, two types of perceived intensity values were calculated for a Japanese speech corpus of sounds uttered in an emotion perception rating experiment. In the results, the average correlation coefficient between the estimated intensity value and the set intensity value of the sounds was 0.73 for the emotional intensity estimation model when perceived intensity values were used. These results suggest the possibility of successfully estimating emotional intensity using regression.
Biomechanical Modeling and Pre-Operative Projection of A Human Organ using an Augmented Reality Technique During Open Hepatic Surgery

Augmented Reality (AR) technology offers innovative ways in order to visualize and manipulate a 3D model of an object by superimposing computer-generated images onto another object interactively. The ability to interact with digital and spatial information in real-time offers new opportunities to manipulate and process medical data easily and efficiently. During surgical interventions, surgeons face various challenges dealing with digital patient data. Several methods are used to visualize the operative areas, such as fluoroscopy and ultrasound techniques. These techniques have several limitations. Thus, the augmented reality technique could serve as a better alternative to project a three-dimensional model of the target organ into the surgeon's perspective and field of view to improve the accuracy and efficiency of the medical intervention intraoperatively. In this paper, a new AR method is proposed in order to visualize and simulate the biomechanical model of the liver organ during open hepatic surgery. In this regard, the 3D model based on the patient's preoperative CT scans is first reconstructed. Then, the reconstructed model is projected using the AR headset. After that, the biomechanical model is generated and prepared for the simulation. The proposed approach is validated using acquired CT scans of the human organ.
Affective Color Palette Recommendations with Non-negative Tensor Factorization

Color is an essential factor that influences human perception, and thus, the proper selection of color sets is crucial in creating informative and appealing visual content. Furthermore, the choice of such color palettes often reflects the underlying emotional intention of creators, especially when they want to introduce specific affective styles. This paper presents a color palette recommendation system that facilitates preferred colors and affective expressions in visual content. This is accomplished by introducing non-negative tensor factorization (NTF), which extends the conventional matrix-based collaborative filtering for recommending items through ratings of multiple users. In our approach, we composed a rating tensor that constitutes the scores for colors in terms of affective factors provided by participants in the user study. With this rating tensor, we explored the meaningful relation between affective expression and color preference. Our experiments exposed that we can successfully apply a tensor-based approach to recommending convincing sets of colors in several possible cases by predicting the underlying emotional intentions in the visual content design.
Mapping the Colocalization Network: A Wayfinding Approach to Interacting with Complex Network Diagrams

Although network visualizations are becoming increasingly common, designing such visualizations can be challenging due to the number of visual elements and non-linear relations that they need to display. The main design challenge faced is finding the right trade-off between providing a sufficient level of information detail while keeping the visual complexity of the visualization as low as possible. One way of overcoming this challenge is to rely on the use of mental models that are familiar to the users of network visualizations. In this paper, we propose the use of a mental interaction model similar to that of map visualizations - generally based on geographical maps - as the basis for visual design of network diagrams. We argue that such a mental model would foster a set of network interaction tasks that can be defined broadly as wayfinding. We present the process of wayfinding from a semiotic standpoint, and match its main key points to those of interaction tasks with network diagrams. As a case study for this analysis, we also present a prototype network diagram visualization tool, called Colocalization Network Explorer, which we have developed to support the exploration of the relationships between various diseases and the portion of the human genome that is potentially involved in their onset. Additionally, we describe how the design process has benefited from the adoption of the wayfinding mental model.
VennSOM: A SOM-Assisted Visualization of Binary Data

Venn diagrams are a useful method of visualizing Boolean data; however, their data aggregation causes fine detail about the data to be lost. In this paper, we present a method of augmenting Venn diagrams, so that they may depict similarity relationships among individual records in the data using the Self-Organizing Map. We applied this method to a synthetic data set and an empirical proteomics data set. We found that we were able to separate data within each region of the Venn diagram based on dimensional values, and that we can highlight the clustering of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$p$</tex> -values in the empirical set.
Preoperative Image Segmentation for Organ Visualization Using Augmented Reality Technology During Open Liver Surgery

With the emergence of Computed Tomography (CT) and Magnetic Resonance Imaging (MRI), three-dimensional images facilitate the generation of 3D models of a patient, providing a new practical and accurate assistance, particularly for surgical planning. These images can be manipulated to produce an accurate 3D representation of an organ. The reconstructed mesh can be used to generate and visualize a deformable model during surgical intervention using Augmented Reality (AR) technology. To obtain an efficient reconstruction, a segmentation of these medical images using deep learning architecture can be used to extract the target organ's properties. Many methods were proposed based on the captured pre-operative patient's CT scans. Generally, the segmentation process is done manually using image processing software. In this context several approaches were proposed, these methods are not efficient and need human interaction to select the segmentation area correctly. This work aims to develop a deep learning method using a Convolutional Neural Network (CNN) that captures the liver organ from a set of CT scans. Given preoperative patient-specific data (CT scans), the U-net architecture is implemented to detect the liver organ. As a result, the segmented 2D images are used to generate a 3D patient-specific liver model.
A Flexible Pipeline to Create Different Types of Data Physicalizations

The process of creating physical data visualizations is not a trivial task. In general, it may require skills from the user in information visualization, tangible interaction, 3D modeling, fabrication of physical objects, etc. In addition, few works have presented computational support to the entire digital and physical rendering pipeline of data visualization, characterizing many steps of this process as manual. From this context, this work presents a process that facilitates the generation of physical data visualization. Besides that, It allows one to define which type of physical visualization to create, among passive, rearrangeable, and dynamic physicalization types. Finally, the pipelines for each physicalization type are presented, with scenarios including physical bar charts, stacked bar charts, and grouped bar charts.
Visualization Tool for Comparative Analysis of Seabird Movement Data

Seabirds have varied movement patterns depending on their group, nesting site, or season. Therefore, seabird experts need to compare movement data under various conditions for conservation and elucidation of ecology. Visualization of movement data helps intuitive analysis. However, it is not easy for seabird experts to design suitable visual representations for comparison. The purpose of our study is to develop a tool to support the visualization process for comparison of seabird movement. There are three components to visualization techniques for comparison: Juxtaposition, Superposition, and Explicit Encodings. In our study, we aim to support comparative analysis under complex conditions using flexible Small Multiples based on juxtaposition and superposition. We designed visual representations for the comparative analysis of seabird movement data. We implemented such visual representations into a tool and confirmed its effectiveness for comparative analysis of seabird movement data.
App2Net: Moving Application Functions to Network &amp; a Case Study on Low-latency Feedback

Recent advances in programmable networks enable custom processing of data at hundreds of gigabits per second. These advances can boost the performance of many distributed applications. Yet the high-level languages used by application developers are different from the data plane programming languages (such as P4 and NPL) used by network equipment. This language barrier slows innovation. Our hourglass-shaped architectural solution aims to lower this language barrier. This enables the application developer community to leverage programmable networks for achieving better performance. In this paper we propose a JSON-based intermediate representation to bridge the gap between applications and in-network computing. We demonstrate an instance of the solution in the context of a low-latency feedback application that enables SQL-based data filtering in a P4-based programmable environment. We also present a prototype compiler to convert an intermediate representation in JSON to P4 source.
Code-Space Quality Evaluation for Information Visualization

The quality evaluation is essential to creating effective data visualization designs. The data visualization research community has produced many quality metrics for evaluating data visualization. However, these quality metrics are rarely integrated into popular data visualization tools. As a result, most data visualization creators are either not aware of these quality metrics or do not know how to apply these metrics to the visualization creation process. In this paper, we propose a novel quality evaluation method that integrates quality metrics into popular data visualization programming tools. Our main contribution is a code-space quality evaluation method, different from the traditional image-space or data-space quality evaluation method. Using our method, a visualization programmer passes a coded data visualization design to a quality evaluation function that generates warnings, comments, and design recommendations. This allows users to integrate quality checks into the design process.
Task-based Quantitative Evaluation of the Concordance Mosaic Visualization

Researchers working in areas such as lexicography, translation studies, and computational linguistics, use a combination of automated and semi-automated tools to analyze the content of text corpora. Concordancing - or the arranging of passages of a textual corpus in alphabetical order according to user-defined keywords - is one of the oldest and still most widely used forms of text analysis. Concordance Mosaic is an interactive concordance visualization which emphasises quantitative information such as word frequency. While Concordance Mosaic is in active use by humanities scholars, no quantitative evaluation of the technique exists. In this paper, the Concordance Mosaic is quantitatively evaluated in comparison to a typical concordance browser. The comparison is evaluated using speed and accuracy on identified corpus analysis actions.
A Systematic Literature Review of Solution-Space Visualization Approaches in the Context of Optimization Problems

The solution space of an optimization problem consists of all its feasible solutions. In this work, we present a systematic literature review on the application of Information Visualization (IV) techniques for understanding and exploring such solution spaces. The review was conducted on several search databases, and we identified 264 papers that satisfied our inclusion criteria. A performance filter was applied to these papers, and we further analyzed and extracted data from 65 of them. Our analysis shows that there are a variety of solution space visualization approaches and provides useful references to support further studies on the subject.
Gaze Analysis in Spot the Difference

The ability to see and find things is very important in our daily lives. For example, when looking for mistakes in debugging a program, or when looking for misspellings in documents, etc., the visual sense is mainly used. The search may or may not be successful. Is there any difference in the way of searching when the search is successful or unsuccessful? The aim of this study is to analyse the gaze while searching and to clarify the differences between successful and unsuccessful searches, using ‘spot the difference’ as a subject. We have developed an experimental application to measure people's gaze while they are looking ‘spot the difference'. In the experiment conducted in this study, 29 subjects have asked to perform ‘spot the difference’ of multiple problems and their gaze have been measured. Analysis of the data obtained from this experiment shows that in many cases, subjects who could not find a difference were not looking at the location of the difference. On the other hand, the existence of ‘cases of looking but not finding’, in which the difference is not detected even though the difference is fully looked at, is also identified. In the present experiment, ‘looking but not finding’ cases account for 15% of all non-correct responses in all questions.
More Sustainable Text Classification via Uncertainty Sampling and a Human-in-the-Loop

Text classification by large deep learning networks achieves high accuracy, but uses a lot of computing power and requires high resource investment. In the age of climate change, sustainable solutions are sought that can attain acceptable accuracy with less resource investment. In this paper, we investigate lightweight text classifiers and combine them with a human-in-the-loop approach. Our solution identifies instances that are uncertain to classify and assigns them preferentially to a human domain expert. Experiments performed with nine classifiers on six datasets show that with manually labelling less than 30%, an F1-score between $$\sim $$ 95% and 99% is achievable for five of six datasets. The inference on energy-efficient GPU-less infrastructure with only 4 GB can be done in less than 1 s.