Reviewerly/exHarmony huggingface dataset:
Potential of Visualization to Explain Quantum Algorithms

Quantum computers are the first computers based on a different paradigm since the establishment of classical computers. Quantum algorithms that run on quantum computers are created based on a fundamentally different way of thinking than algorithms that run on classical computers. Therefore, visualisation to support the understanding of quantum algorithms needs to be different from visualisation to support the understanding of conventional algorithms. In this paper, the differences between quantum algorithms and conventional algorithms are discussed from the perspective of visualisation.
Knowledge-Grounded Dialogue Generation for Medical Conversations: A Survey

Applying Artificial Intelligence (AI) techniques such as natural language generation in assisting medical treatment and diagnosis has made distinguished progress. One such technique is dialogue generation. The application of a medical dialogue system in assisting medical treatment has great potential to explore. This paper serves as a survey of digging application of AI techniques in knowledge-grounded dialogue generation for medical conversation systems. Meanwhile, we provide an academic visualization method to present such references.
Constructing a Cross-Disciplinary Idea Convergence System Using AIGC: A Case Study of Engineering and Design

In response to globalization and increased competition, individual professionals in a single field often cannot provide comprehensive solutions. Collaboration among professionals from different disciplines is required to find more effective solutions. This study aims to develop a system called AIGC (AI Generated Content) to facilitate idea convergence in interdisciplinary teams. The system aims to address the collaboration challenges between engineering and design fields and facilitate consensus through the introduction of images and visual elements. To achieve this goal, qualitative research methods were employed, including contextual interviews, the KJ method, brainstorming, personas, and user journey mapping, among others. These methods were chosen because they provide in-depth understanding of communication and collaboration patterns among participants and offer valuable insights. The selection of participants was based on their experience and expertise in interdisciplinary projects. Students from design and engineering fields were chosen as research participants to ensure diversity and representativeness. During the execution of the study, the research team collected data through contextual interviews and related qualitative research methods. The steps involved in contextual interviews included selecting appropriate scenarios, designing relevant guided questions, and conducting face-to-face interviews. Through these methods, we gained deep insights into participants' communication patterns, thought processes, and collaboration needs. The results of this study demonstrate that the AIGC system, which facilitates interdisciplinary idea convergence, effectively promotes consensus among participants. This is achieved through interactive processes such as selecting images, describing images, and engaging in interactive design. The application of the system is significant in addressing collaboration challenges between the engineering and design fields and holds potential value in enhancing teamwork and creative output.
Giving Shape to Words: Visual Knowledge Discovery for Textual Contents in Legal Scenarios

Visual Knowledge Discovery (VKD), the emerging research field exploring the integration between Artificial Intelligence and Visual Analytics is impacting a growing number of contexts. This paper dwells on the potential intersections between the VKD perspective and the legal world, a scenario for many reasons drawn by the idea of combining empirical insights offered by computation with intuitive, visually-enhanced forms of data mining. We will focus on criminal justice, taking the cue from an ongoing experimental research project that investigates novel applications of computational social science methods - complex network analysis - to analyze criminal organizations for both scientific and investigative purposes. The work will describe the VKD solutions devised to allow public prosecutors to visually browse the content of phone calls and environmental tapping gathered during preliminary inquiries. We will discuss how given visualizations - graph-based visualizations in the first place - can enhance text mining techniques helping to capture in-a-glance communication flows, discussions' topics and both structural and functional features of criminal networks at hand.
Adding Visual Data and Interactions for Dynamic Data Physicalization with Augmented Reality

In recent years, the physicalization area has expanded rapidly and has been utilized in numerous contexts, mostly in big open public areas or data-related places. Most data physicalizations have visual restrictions and no interaction. Hence, multiple technologies, including augmented reality (AR), have been applied to physical visualizations to address the above-mentioned issues. This research proposes dynamic data physicalization using AR virtual content (visual data items). The dynamic bar chart allows the configuration of numerical data to height, categorical data to color, and categorical data to the x-axis. The mobile augmented reality (MAR) application performs some Infovis tasks, such as settings, filters, details on demand, etc. A cloud server selects data, calculates visual elements or additional visualizations, calculates scales for physicalizing data, and enables the communication between the MAR application and dynamic physicalization. Lastly, dynamic and augmented data physicalization characteristics and scenarios are shown.
Visualizing Tennis Matches as Nested Stories

This work is about data-driven storytelling in tennis. Tennis has a peculiar scoring system that makes it challenging for the general audience to understand tennis matches. But from a storytelling perspective, the tennis scoring system has unique benefits because it creates a nested story structure. Dividing a tennis match into sets and games helps manage the tempo and rhythm of a story while creating suspense on multiple levels. The ad-scoring rule helps prolong the struggle, delay the outcome, and create suspense. However, the traditional sports news media has failed to present tennis matches as nested stories, and the suspense is often lost in the news report and match highlight videos. In this paper, we describe a novel visualization method to present tennis matches as nested stories. This visualization can help readers better understand the narratives of tennis matches without the barriers of the scoring system and the written language. In essence, this is a universal visual language that presents the stories of tennis matches in an intuitive way. Such visualizations can supplement the traditional textual and video coverage of tennis matches. It can also be applied to other sports with similar nested story structures. Our preliminary user studies showed highly positive user first impressions.
ARWithDistance: Distance Awareness in Off-Screen Visualization Techniques for AR Applications

It is common in mobile augmented reality (AR) applications to use clues or visual markers to indicate the location of points of interest (POIs) that are outside the current field of view of the camera or screen. These clues can take many forms, such as arrows, symbols, or text labels, and are typically overlaid on the camera view or displayed in a separate area of the screen. Visual clues indicating relevance, orientation, and distance to off-screen objects help users better understand their surroundings and explore or navigate more effectively. This paper compares 4 proposed focus+context techniques with visual clues to off-screen POIs' position and distance: a modified 2D arrow and three arc-based representations. Moreover, we apply already known techniques to represent POIs' relevance and to solve the visual cluttering in environments with many POIs. The results of a user study revealed that the participants preferred the 2D arrow technique and could find the most relevant POI faster with it, even with a large number of objects. However, the EqualArc technique reduces error rates for POI selection accuracy.
No-Code Platform for Visual Knowledge Discovering in General Line Coordinates: DV 2.0

DV 2.0 is a no-code platform contributing to interpretable machine learning via Visual Knowledge Discovery in General Line Coordinates (GLC). GLC-Linear (GLC-L), a type of GLC, provides interactive tools to enhance the model creation process and create interpretable visualizations. DV 2.0 uses these techniques so end-users can directly involve themselves in the model creation pipeline. GLC-L visualizations are easily understood by end-users and allow for complex multidimensional relationships to be visualized. Additionally, using GLC-L allows both classification and regression models to be visualized with high degrees of accuracy. For classification, DV 2.0 also offers additional expansions of GLC-L such as GLC non-Linear (GLC-nL) and GLC Hyperblock Rules Linear (GLC-HBRL) to provide different interpretable non-linear classifiers for more complex data. DV 2.0 was tested on benchmark data from the UCI Machine Learning repository.
Workload Evaluation to Create Data Visualization Using ChatGPT

The value of good data visualization has already been shown in several scenarios. Still, it is not always easy to obtain it, as it depends on factors such as the dataset, the amount of data, task types, the user profile, the type of interaction, etc. To mitigate the challenges addressed, automated or semi-automated systems have been proposed, emphasizing rule-based/heuristic approaches and machine-learning models. However, many of these applications require specialized knowledge and present results (data visualizations) that are not flexible for customization. Papers have highlighted the ease of tools like ChatGPT in creating various tasks, including creating data charts. This facility, in addition to the intelligent computational model involved, is also due to the expressiveness used in the requests to execute the tasks by the users since these tools use Natural Language Interfaces. Despite adopting these tools overgrowing in different scenarios of society, studies on the best way to use them, integrate them into existing processes, or evaluative studies on their effectiveness or efficiency are still incipient. Thus, this paper will evaluate the workload for creating data visualization using ChatGPT 3.5. For assessment, the NASA Task Load Index (Nasa TLX) methodology was applied, and users with experience creating data visualization created two proposed scenarios. The preliminary results showed high temporal and mental demand, mainly due to the vocabulary used and the completeness of the user instructions. The average time to create and perform InfoVis tasks in two proposed evaluation scenarios was 33 and 44 minutes, and 14 queries were applied on average for both scenarios. The direct consequence was that the users have redone the requests and improved the instructions at each new iteration, and all users completed the proposed tasks.
Responsible Artificial Intelligence and Bias Mitigation in Deep Learning Systems

Responsible, ethical and trustworthy decision making powered by the new generation of artificial intelligence (AI) and deep learning (DL) recently emerged as one of the key societal challenges. The tutorial discusses key challenges, lists major applications, presents mitigation strategies and provides insights on the future developments in this emerging research domain.
The Design of Interactive Spatio-Temporal Information Visualization – A Conceptual Model

Interactive spatio-temporal (ST) visualization is a powerful tool for effectively communicating complex information by allowing users to locate and understand events in a specific time and location, as well as interact with their evolution. This research explores the design of interactive geospatial-temporal visualization and examines its design process. The study is divided into three phases. The first phase presents the results of interviews with experts in information visualization and proposes a definition of requirements, factors, and dimensions for designing such an interface. Moreover, the experts use the Think Aloud method to provide insight into their design process towards a hypothetical spatio-temporal visualization briefing. Subsequently, the second phase proposes a conceptual model based on the common patterns identified in the experts' processes, which are complemented with additional interface components. To conclude, the third phase evaluates the conceptual model's performance and relevance with one existing interactive spatio-temporal visualization.
Interactive Network Visualization of Educational Standards, Learning Resources and Learning Progressions

We present a novel, network- and browser-based visualization of the Next Generation Science Standards (NGSS). The NGSS are meant to guide (USA) K-12 science and engineering learning and are almost always presented using text and tables. Understanding, explaining and navigating the NGSS through tables and text, however, is difficult. In this paper we present a different approach, namely one that capitalizes on the explicit network structure of the NGSS. By rendering the NGSS and its parts as interactive, visual networks, the NGSS becomes much easier to comprehend and navigate. In addition, whereas representing the NGSS with tables and texts hides patterns and anomalies, network visualization makes them prominently stand out.
Modeling Human Recognition of Deformed Maps

In this paper, the authors presented the challenges in modeling human recognition of deformed maps and attempted to model human recognition using deformed maps of prefectures based on the values observed in previous studies as the values that our model should imitate. First, the similarity values between the original and deformed shapes were calculated using several distance functions, and the relationship between the similarity and observed values was visually analyzed. Consequently, the dynamic time warping (DTW) distance was adopted as the distance function. The sigmoid function was used to fit the similarity values to the observed values. However, through trials, the authors concluded that a single fitting function could not adequately represent the human cognitive abilities. Therefore, a model consisting of several submodels was constructed. Each submodel fit a cluster of prefectures depending on their characteristics. To determine the appropriate number of clusters, the authors examined the errors between the model output and the observed values for different numbers of clusters. Consequently, the human deformation map recognition ability was modeled with an average error of approximately 20–26% for nine clusters. For some prefectures, the error can be reduced to approximately 10-15%.
Hierarchical Data Visualization of Gender Difference: Application to Feeling of Temperature

New social problems caused by data bias have been an important issue in recent years. Data bias is sometimes difficult to determine using only quantitative methods, and it requires human decision-making to resolve. Visualization technology can help humans understand data bias and assist with this issue. This paper proposes a visualization method for detecting intersectional bias caused by multiple attributes. The method generates a hierarchical structure by classifying data items according to multiple attributes, and applies a hierarchical data visualization method equipped with band charts. It provides a visualization that gives an overview of the entire data and makes it easy to detect biases in a particular subset of the data. This paper introduces an application to the gender difference in the feeling of temperature with air conditioning systems, and presents the results of participant evaluations to verify the effectiveness of the proposed visualization over baseline implementation.
LifeTrack: Decades of EHR Data in a Single View

There are situations where we need to understand a person's health register data, get an overview of it and inspect the details of interest, preferably in a short period of time. Typical situations are a doctor seeing a patient for the very first time, or a researcher trying to understand what makes a person an outlier in a patient cohort. We have developed a visualization tool, called LifeTrack, that will show a person's whole health register history in a single view. The structure of the LifeTrack data view is based on concepts familiar to the target audience, and allows one to see both the overview and details of the data.
Boulez: A Chatbot-Based Federated Learning System for Distance Learning

In recent years, also due to the covid-19 pandemic, the possibilities for distance learning have increased considerably, through web-based learning platforms, available on the Internet without space and time limits. As a result, the offer of courses and the number of enrolled students has grown exponentially. In order to be able to guarantee students a better learning support service, one of the proposals regards the intelligent Chatbots. These well known interactive applications are based mainly on machine or deep learning and in this paper we present Boulez, a system allowing the orchestration of a community of individual chatbots, each one with its algorithm and its private training dataset. We apply a technique called Federated Learning, where several individual chatbots, collaborate. In particular, here the approach is “centralized”, meaning that a main system orchestrates the collaboration of the federated systems. By addressing the communication inefficiencies and privacy issues of conventional federated learning, Boulez offers a more efficient and effective approach to chatbot interaction, ultimately leading to improved user experience. The paper presents the Boulez system, its operation principle, methods used, and potential benefits, along with a use case of its application.
Visual Analytics for Forecasting Technological Trends from Text

Knowledge of emerging and declining trends and their potential future course is highly relevant in many application domains, particularly in corporate strategy and foresight. The early awareness of trends allows reacting to market, political, and societal changes and challenges at an appropriate time. In our previous works, we presented approaches for the early identification and analysis of emerging trends. Although our previous approaches are detecting emerging trends appropriately, they lack the ability to predict the potential future course of a trend or technology. We present in this work a novel Visual Analytics approach for forecasting emerging trends that combines interactive visualizations with machine learning techniques and statistical approaches to detect, analyze, and predict trends from textual data. We extend our previous work on analyzing technological trends from text and propose an advanced approach that includes forecasting through hybrid techniques consisting of neural networks and established statistical methods. Our approach offers insights from enormous data sets and the potential future course of trends based on their occurrence in textual data. We contribute with a novel approach for identifying and forecasting trends, a hybrid forecasting method to predict trends from text, and interactive visualization techniques on macro level, micro level, and monitoring topics of interest.
General Line Coordinates in 3D

Interpretable interactive visual pattern discovery in lossless 3D visualization is a promising way to advance machine learning. It enables end users who are not data scientists to take control of the model development process as a self-service. It is conducted in 3D General Line Coordinates (GLC) visualization space, which preserves all n-D information in 3D. This paper presents a system which combines three types of GLC: Shifted Paired Coordinates (SPC), Shifted Tripled Coordinates (STC), and General Line Coordinates-Linear (GLC-L) for interactive visual pattern discovery. A transition from 2-D visualization to 3-D visualization allows for a more distinct visual pattern than in 2-D and it also allows for finding the best data viewing positions, which are not available in 2-D. It enables in-depth visual analysis of various class-specific data subsets comprehensible for end users in the original interpretable attributes. Controlling model overgeneralization by end users is an additional benefit of this approach.
3DSPOPP - 3D Scatter Plots of Octants with Projection Planes

This paper proposes a web-based visualization tool called 3DSPOPP for visualizing the relationship between sets of attributes as two data points put on separate planes and connected by a line in a 3D scatter plot. The motivation for creating this tool is to visualize darknet traffic data to find communication anomalies. Although 3D scatter plots can visualize network packet data by assigning time, source IP address and destination IP address to each axis, users can not easily understand the relationship between one set of source IP addresses and source ports and another set of destination IP addresses and destination ports. For anomaly detection, these four attributes are all significant. Therefore, the authors elongate each axis to create multiple plot zones of up to eight separate areas in 3D space, usually called octants. This tool is called 3D Scatter Plots of Octants, abbreviated as 3DSPO. Furthermore, projection planes of two octants are combined with 3DSPO to visualize the relationship between two attribute pairs of two octants such as source IP/port pair and destination IP/port pair. This combined tool is called 3DSPOPP.
A Data Discovery and Visualization Tool for Visual Analytics of Time Series in Digital Agriculture

In the current era of big data, huge volumes of data can be easily generated and collected at a high velocity from a wide variety of rich data sources. Embedded in these big data-which may also contain many labels or tags-are implicit, previously unknown and potential useful information that can be discovered. Discovered knowledge helps user get a better understanding of the data. However, amounts of discovered knowledge from these huge volumes of big data can also be large. To help users comprehend the discovered knowledge, visualization approaches are in demand. In this paper, we present a data discovery and visualization tool. The tool enables users to visually monitor and explore multi-sourced, multi-tagged time-series data. It also enables users to conduct visual analytics to discover interesting data/knowledge and to visualize this information. Although we demonstrate the practicality of our tool for multi-sourced, multi-tagged time-series data from the agricultural sector, our tool can be applicable to a wide variety of other domains.