The need for large, high-quality annotated datasets continues to represent a primary limitation in training Object Detection (OD) models. To mitigate this challenge, we present VILOD, a Visual Interactive Labeling tool that integrates Active Learning (AL) with a suite of interactive visualizations to create an effective Human-in-the-Loop (HITL) workflow for OD annotation and training. VILOD is designed to make the AL process more transparent and steerable, empowering expert users to implement diverse, strategically guided labeling strategies that extend beyond algorithmic query strategies. Through comparative case studies, we evaluate three visually guided labeling strategies against a conventional automated AL baseline. The results show that a balanced, human-guided strategy—leveraging VILOD's visual cues to synthesize information about data structure and model uncertainty—not only outperforms the automated baseline but also achieves the highest overall model performance. These findings emphasize the potential of visually guided, interactive annotation to enhance both the efficiency and effectiveness of dataset creation for OD.

Mapping labeled high-dimensional data to colors based on class labels in low-dimensional projections is effective for enhancing pattern recognition and reducing misinterpretation of clusters. However, automatic coloring of unlabeled high-dimensional data remains challenging for revealing unknown patterns or class structures in the data. To address this, we propose ColorPCA, a scalable method that improves existing dimensionality reduction-based automatic coloring by integrating Principal Component Analysis with alpha compositing. Rather than mapping reduced dimensions to color coordinates, ColorPCA directly encodes data into color space to enhance pattern discovery in unlabeled datasets. We implemented ColorPCA in a web-based visual analytics system for interactive exploration and evaluated it through three case studies using benchmark, simulated, and real-world climate datasets. Additionally, we conducted three user studies, two with generic users and one with climate domain experts. Comparisons with two state-of-the-art coloring methods based on PCA and t-SNE demonstrate that ColorPCA improves visual separability and facilitates deeper insight extraction in high-dimensional data visualization.

This study presents an interactive visualization tool that facilitates fairness-aware urban planning. The system introduces afairness scale to assess the accessibility of potential new developments, using color-coded scatter plots to visualize disparities.An intuitive interaction design minimizes complexity while enhancing usability, enabling users to analyze urban infrastructureand services. Developed with web technologies, the tool leverages OpenStreetMap data to ensure adaptability across differentcities. Future optimizations include advanced analytical capabilities and broader dataset integrations to improve decision-making in urban development.

Dimensionality reduction (DR) techniques help analysts to understand patterns in high-dimensional spaces. These techniques, often represented by scatter plots, are employed in diverse science domains and facilitate similarity analysis among clusters and data samples. For datasets containing many granularities or when analysis follows the information visualization mantra, hierarchical DR techniques are the most suitable approach since they present major structures beforehand and details on demand. This work presents HUMAP, a novel hierarchical dimensionality reduction technique designed to be flexible on preserving local and global structures and preserve the mental map throughout hierarchical exploration. We provide empirical evidence of our technique's superiority compared with current hierarchical approaches and show a case study applying HUMAP for dataset labelling.

A dynamic (or temporal) network is a widely used structure that enables understanding dynamic systems by modeling interactions among system components over time. In many real-world cases, however, components (called nodes) and/or interactions (called edges) contain numerous meaningful attributes, leading to the need for a more suitable instrument for representing and analyzing these dynamic and complex systems with multiple attributes: the Dynamic Multivariate Network (DMVN). In this work, we extended LargeNetVis, a visualization system specifically designed for large dynamic networks that focus on network community structure and dynamics, to enable the visual exploration of DMVNs and their communities. The newly introduced visual encodings and interactions allow the visualization of nodes' and edges' attributes at different granularity levels and produce a node tracking capability from both top-down and bottom-up perspectives. With these functionalities, one can track individual nodes across dynamic communities over time. The proposed approach is validated by comparing it with the original LargeNetVis system and conducting a user evaluation involving 37 participants.

As simulation applications become vital for understanding and predicting complex systems, analyzing data from repeated simulation runs is essential to gauge model uncertainty and identify optimal parameter settings. This paper presents a visualization tool for analyzing time series ensemble data generated by discrete-event simulations, focusing on feature importance within clustering results. The tool combines dimensionality reduction, clustering, and SHapley Additive exPlanations (SHAP) to highlight influential features and identify trends within clustered simulation data, advancing previous approaches focusing solely on visualization or clustering without analyzing specific feature contributions. By analyzing a manufacturing use case, we show how the visualization supports decision-makers by depicting the main features driving cluster formation and displaying time intervals critical to characterizing distinct system behaviors.

Dimensionality Reduction (DR) scatterplot layouts have become a ubiquitous visualization tool for analyzing multidimensional datasets. Despite their popularity, such scatterplots suffer from occlusion, especially when informative glyphs are used to represent data instances, potentially obfuscating critical information for the analysis under execution. Different strategies have been devised to address this issue, either producing overlap-free layouts that lack the powerful capabilities of contemporary DR techniques in uncovering interesting data patterns or eliminating overlaps as a post-processing strategy. Despite the good results of post-processing techniques, most of the best methods typically expand or distort the scatterplot area, thus reducing glyphs’ size (sometimes) to unreadable dimensions, defeating the purpose of removing overlaps. This article presents Distance Grid (DGrid) , a novel post-processing strategy to remove overlaps from DR layouts that faithfully preserves the original layout's characteristics and bounds the minimum glyph sizes. We show that DGrid surpasses the state-of-the-art in overlap removal (through an extensive comparative evaluation considering multiple different metrics) while also being one of the fastest techniques, especially for large datasets. A user study with 51 participants also shows that DGrid is consistently ranked among the top techniques for preserving the original scatterplots’ visual characteristics and the aesthetics of the final results.

There is a significant amount of data available about students and their learning activities in many educational systems today. However, these datasets are frequently spread across several different digital services, making it challenging to use them strategically. In addition, there are no established standards for collecting, processing, analyzing, and presenting such data. As a result, school leaders, teachers, and students do not capitalize on the possibility of making decisions based on data. This is a serious barrier to the improvement of work in schools, teacher and student progress, and the development of effective Educational Technology (EdTech) products and services. Data standards can be used as a protocol on how different IT systems communicate with each other. When working with data from different public and private institutions simultaneously (e.g., different municipalities and EdTech companies), having a trustworthy data pipeline for retrieving the data and storing it in a secure warehouse is critical. In this study, we propose a technical solution containing a data pipeline by employing a secure warehouse—the Swedish University Computer Network (SUNET), which is an interface for information exchange between operational processes in schools. We conducted a user study in collaboration with four municipalities and four EdTech companies based in Sweden. Our proposal involves introducing a data standard to facilitate the integration of educational data from diverse resources in our SUNET drive. To accomplish this, we created customized scripts for each stakeholder, tailored to their specific data formats, with the aim of merging the students’ data. The results of the first four steps show that our solution works. Once the results of the next three steps are in, we will contemplate scaling up our technical solution nationwide. With the implementation of the suggested data standard and the utilization of the proposed technical solution, diverse stakeholders can benefit from improved management, transportation, analysis, and visualization of educational data.

As the complexity of Machine Learning (ML) models increases and their application in different (and critical) domains grows, there is a strong demand for more interpretable and trustworthy ML. A direct, model-agnostic, way to interpret such models is to train surrogate models—such as rule sets and decision trees—that sufficiently approximate the original ones while being simpler and easier-to-explain. Yet, rule sets can become very lengthy, with many if-else statements, and decision tree depth grows rapidly when accurately emulating complex ML models. In such cases, both approaches can fail to meet their core goal—providing users with model interpretability. To tackle this, we propose DeforestVis, a visual analytics tool that offers summarization of the behavior of complex ML models by providing surrogate decision stumps (one-level decision trees) generated with the Adaptive Boosting (AdaBoost) technique. DeforestVis helps users to explore the complexity vs fidelity trade-off by incrementally generating more stumps, creating attribute-based explanations with weighted stumps to justify decision making, and analyzing the impact of rule overriding on training instance allocation between one or more stumps. An independent test set allows users to monitor the effectiveness of manual rule changes and form hypotheses based on case-by-case analyses. We show the applicability and usefulness of DeforestVis with two use cases and expert interviews with data analysts and model developers.

Visual Learning Analytics (VLA) uses analytics to monitor and assess educational data by combining visual and automated analysis to provide educational explanations. Such tools could aid teachers in primary and secondary schools in making pedagogical decisions, however, the evidence of their effectiveness and benefits is still limited. With this scoping review, we provide a comprehensive overview of related research on proposed VLA methods, as well as identifying any gaps in the literature that could assist in describing new and helpful directions to the field. This review searched all relevant articles in five accessible databases — Scopus, Web of Science, ERIC, ACM, and IEEE Xplore — using 40 keywords. These studies were mapped, categorized, and summarized based on their objectives, the collected data, the intervention approaches employed, and the results obtained. The results determined what affordances the VLA tools allowed, what kind of visualizations were used to inform teachers and students, and, more importantly, positive evidence of educational interventions. We conclude that there are moderate-to-clear learning improvements within the limit of the studies’ interventions to support the use of VLA tools. More systematic research is needed to determine whether any learning gains are translated into long-term improvements.