Acume: A new visualization tool for understanding facial expression and gesture data

Social media analytics has emerged as a critical tool for understanding consumer behavior in today’s digital landscape. This study explores the integration of social media analytics with Power BI to gain actionable insights into consumer preferences, behaviors, and trends at Cryptex Technologies, Nagpur. Leveraging the capabilities of Power BI, the research examines how data visualization and analytics tools can translate vast amounts of social media data into meaningful insights that drive decision-making. The study employs a mixed-methods approach, combining qualitative and quantitative data analysis, to evaluate the effectiveness of Power BI in analysing social media metrics such as engagement rates, sentiment analysis, and demographic segmentation. Results indicate that social media analytics, when effectively integrated with advanced visualization tools, significantly enhances the understanding of consumer behavior, enabling businesses to make data-driven marketing decisions. The findings further reveal that the use of interactive dashboards and real-time data processing capabilities of Power BI facilitates a deeper comprehension of market trends, helping organizations remain competitive in a rapidly evolving business environment. This research contributes to the growing body of knowledge on the strategic application of social media analytics in consumer behavior studies and offers practical insights for businesses aiming to optimize their social media strategies using data visualization tools.

VisOpt: a visual interactive optimization tool for P-median problems

The object-oriented theory has been widely used in software design and development and Java programming language is a popular object-oriented programming language for computer science related majors in higher education. With the huge demand for intelligent manufacturing employees in the work market, more and more universities have listed Java programming language in their curriculum for non-CS undergraduates. However, learning object-oriented programming languages, such as Java, is tough even for CS students. This paper presents how to improve non-CS undergraduate students’ learning outcomes of java language by adopting industry standard professional integrated development tool Eclipse and interactive visual educational tool BlueJ. Our Java programming language course is an alternative 8-week course including 16 h lecture and 16 h lab. Forty-seven junior students from mechanical engineering major were divided into two groups, 23 in the control group (CG) and 24 in the experimental group (EG). Students in both groups used Eclipse to write and run programs during the course while BlueJ was adopted to demonstrate the object-oriented concepts during the fifth and sixth weeks only for students in the EG. A pre-test, which was taken by students from both groups, shows no statistical differences in programming knowledge between the two groups. Results from the post-test at the end of the sixth week show students in EG achieved better performance than students in CG. Results of the survey for the EG reveal that students thought BlueJ was helpful for mastering the concepts of object-oriented programming.KeywordsJava programmingLearning outputHigher educationInteractiveVisualBlueJEclipse

BackgroundGlobal scale brain research collaborations such as the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) consortium are beginning to collect data in large quantity and to conduct meta-analyses using uniformed protocols. It becomes strategically important that the results can be communicated among brain scientists effectively. Traditional graphs and charts failed to convey the complex shapes of brain structures which are essential to the understanding of the result statistics from the analyses. These problems could be addressed using interactive visualization strategies that can link those statistics with brain structures in order to provide a better interface to understand brain research results.ResultsWe present ENIGMA-Viewer, an interactive web-based visualization tool for brain scientists to compare statistics such as effect sizes from meta-analysis results on standardized ROIs (regions-of-interest) across multiple studies. The tool incorporates visualization design principles such as focus+context and visual data fusion to enable users to better understand the statistics on brain structures. To demonstrate the usability of the tool, three examples using recent research data are discussed via case studies.ConclusionsENIGMA-Viewer supports presentations and communications of brain research results through effective visualization designs. By linking visualizations of both statistics and structures, users can gain more insights into the presented data that are otherwise difficult to obtain. ENIGMA-Viewer is an open-source tool, the source code and sample data are publicly accessible through the NITRC website (http://www.nitrc.org/projects/enigmaviewer_20). The tool can also be directly accessed online (http://enigma-viewer.org).

For many years, students have been taught to visualise data by drawing graphs. Recently, there has been a growing trend to teach statistics, particularly statistical concepts, using interactive and dynamic visualisation tools. Free down-loadable teaching and simulation software designed specifically for schools, and more general data visualisation tools are increasingly being used in New Zealand classrooms. This paper discusses four examples: the use of GenStat for Teaching and Learning Schools and Undergraduate (GTL); Auckland University’s iNZight and VIT (Visual Inference Tools) for teaching bootstrapping and randomisation; the CAST e-books, and the use of data visualisation tools to assist learning concepts in official statistics. All these tools are publically available and several are already being used internationally. First published November 2014 at Statistics Education Research Journal Archives

This study forms a broader input on ICT and learner integration; it has specific scientific results that not only address access but also go beyond social and economic inequalities that communities face with limitations on social and economic activity that impact and scientific and academic growth of communities. This study aimed to investigate the effects of interactive visualization tools on the mathematical performance of intermediate pupils at Dangguinan Elementary School, Conner, Apayao, Philippines. The problem being studied centers on persistent difficulties in Mathematics performance, particularly learners' struggles in grasping abstract concepts and applying problem-solving skills effectively. Amongst the tools the study investigated included GeoGebra, simulations, and animated presentations could address persistent challenges in Mathematics performance. The research utilized a mixed-methods design, combining both quantitative and qualitative approaches to examine improvements in mathematics learning and explore implementation challenges. The study's mixed-methods approach offers a comprehensive understanding of both the quantitative impact on learning outcomes and the qualitative challenges encountered during implementation. Its findings are crucial for educational stakeholders in developing countries like the Philippines, as they highlight the potential of ICT integration in addressing persistent low math proficiency, while also underscoring the critical need to address infrastructure and digital literacy gaps for successful technology adoption in underserved areas. The study was conducted at Dangguinan Elementary School, Conner, Apayao, during the academic year 2024–2025. Forty-six pupils from Grades 4 to 6 participated in the study. The research applied the DepEd Likert Scale to group learners by their mathematics skills, instead of just using mean scores to assess them. Learners who had 15–17 points out of 20 (equivalent to 71%–85%) received the rating of "Proficient"—signifying they met the expected understanding and skills for their age. By using this method, educators could watch how each learner was improving and decide who might need more help. Rather than presenting average scores, the study used a thorough scale to better show how learning improved when interactive visualizations were used. A pre-test was administered to assess baseline mathematical proficiency. Following this, an instructional intervention was implemented using interactive visualization tools. After the intervention, a post-test was conducted to evaluate performance improvement. Additionally, interviews were carried out to understand the challenges encountered during the intervention. Quantitative results were analyzed using descriptive statistics and a paired sample t-test, while qualitative data were thematically analyzed. The pre-test mean score was 7.6, falling under the "Developing" performance level. Post-test scores improved significantly to a mean of 11.8, aligning with the "Approaching Proficiency" level. Statistical analysis revealed a significant difference between pre-test and post-test results (P = .000). Interviews revealed two major challenges: poor internet connectivity and insufficient orientation in using the tools, both of which affected the learners’ engagement with interactive visualization. To address this, alternative offline versions of instructional materials were provided, ensuring accessibility despite connectivity issues. Additionally, structured training sessions were introduced to familiarize learners with visualization tools before implementation, enhancing their confidence and ability to navigate digital learning environments effectively. The study concludes that interactive visualization tools significantly enhance intermediate pupils' mathematical understanding and performance. The research shows that using these interactive tools greatly improves how intermediate pupils understand and perform in mathematics. Still, overcoming problems with access and training is necessary to use them well. According to the study, upgrading the infrastructure and adding satellite technology for better connectivity, as well as apps that let learners work offline, would help make technology-aided math more effective. In addition, teachers should receive instruction, and learners should participate in orientation to help ICT become an important part of learning mathematics. Schools need support from educational stakeholders towards long-term digital change, helping them select and learn how to use the needed technology.

This paper proposes a dynamic analytical processing (DAP) visualization tool based on the Bubble-Wall Plot. It can be handily used to develop visual warning systems for visualizing the dynamic analytical processes of hazard data. Comparative analysis and case study methods are used in this research. Based on a literature review of Q1 publications since 2017, 23 types of data visualization approaches/tools are identified, including seven anomaly data visualization tools. This study presents three significant findings by comparing existing data visualization approaches. The primary finding is that no single visualization tool can fully satisfy industry requirements. This finding motivates academics to develop new DAP visualization tools. The second finding is that there are different views of Line Charts and various perspectives on Scatter Plots. The other one is that different researchers may perceive an existing data visualization tool differently, such as arguments between Scatter Plots and Line Charts and diverse opinions about Parallel Coordinate Plots and Scatter Plots. Users' awareness rises when they choose data visualization tools that satisfy their requirements. By conducting a comparative analysis based on five categories (Style, Value, Change, Correlation, and Others) with 26 subcategories of metric features, results show that this new tool can effectively solve the limitations of existing visualization tools as it appears to have three remarkable characteristics: the simplest cartographic tool, the most straightforward visual result, and the most intuitive tool. Furthermore, this paper illustrates how the Bubble-Wall Plot can be effectively applied to develop a warning system for presenting dynamic analytical processes of hazard data in the coal mine. Lastly, this paper provides two recommendations, one implication, six research limitations, and eleven further study topics.

The detection of known and unknown attacks usually requires the interpretation and presentation of very large amounts of time-oriented security data. Using regular means for displaying the data, such as text or tables, is often ineffective. Furthermore, displaying only raw data is not sufficient, because the security expert is still required to derive meaningful conclusions from large amounts of data. In addition, in many cases (e.g., for detecting a virus spreading in the network), an aggregated view of multiple network devices is more effective than a view of each individual device. In this paper we propose an intelligent interface used by a distributed architecture that was described in our previous work, specific to the tasks of knowledge-based interpretation, summarization, query, visualization and interactive exploration of large numbers of time-oriented data. In order to support the interpretation and computation process, we provide automated mechanisms that perform derivation of context-specific, interval-based abstract interpretations (also known as Temporal Abstractions) from raw time-stamped security data, by using a domain-specific knowledge-base (e.g., a period of 5 hours, during the night, of a high number of FTP connections within the context of No User Activity, which might indicate the existence of a Trojan in the computer). The proposed visualization tool includes several functionalities for querying, visualization and exploration of both raw and abstracted time-oriented security data regarding single and multiple network devices.

Rationale: Existing data processing applications for SP-ICP-ToF-MS, while valuable, have some limitations including restricted multi-sample analysis capabilities, vendor-specific constraints and the lack of efficient visualization approaches. Researchers need flexible, interactive visualization tools that can reveal compositional patterns and enable side-by-side sample comparisons for rapid trend identification and comprehensive data exploration across large datasets. Methodology: we developed IsotopeTrack, an open-source Python-based platform designed specifically for SP-ICP-ToF-MS data processing with cross-platform compatibility (Windows and MacOS). The application implements complete calibration methodologies including transport rate and sensitivity calibrations, three distinct peak detection algorithms (Currie method, Formula C, and compound Poisson log-normal) and element specific parameter optimization. Performance was validated using diverse engineered nanoparticles including TiO₂, CeO₂, metallic alloys (Ni-Fe-Co, Ni-Fe-Cr-Mn, Ni-Fe-Mo), and Au/Ag core-shell particles. The platform features an interactive results canvas with drag and drop capabilities for constructing customized analysis pipelines and it supports multiple file formats. Results: IsotopeTrack successfully analyzed multi-element alloy compositions with a high accuracy. For Ni-Fe-Co alloys, measured mass compositions of ⁶⁰Ni (29.3%), ⁵⁷Fe (55.1%), and ⁵⁹Co (17.7%) closely matched known values of 28% Ni, 55% Fe, and 17% Co. Ultra-uniform gold nanoparticles yielded mean diameters of 52.0±5.0 nm, 29.5±4.4 nm, and 21.1±4.3 nm for nominal 50, 30, and 20 nm particles. The platform generated comprehensive visualizations including elemental correlations, isotopic ratio distributions, ternary diagrams, and composition heatmaps. Processing time was reduced from hours to minutes through parallel processing. The comparison of environmental samples consisting of hundreds of thousands of particles was greatly facilitated. Discussion: IsotopeTrack addressed critical limitations in SP-ICP-ToF-MS data analysis by providing batch processing and interactive visualization tools. Element specific optimization ensured analytical rigor, while dramatically reducing processing time. This open-source framework represents a significant advance in single particle analysis, enabling efficient processing of large datasets essential for nanoparticle characterization in complex systems.

We present HeapCraft: an open-source suite of interactive data exploration and visualization tools that allows researchers, server administrators and game designers to analyze and potentially influence player behavior in Minecraft. Our framework includes a telemetry system, several tools for visualizing and representing the collected data, and tools for modifying the game experience in controlled ways. Measures that we use to quantify and visualize player behavior and collaboration have been derived from a large data set containing 3451 player-hours from 908 players and 43 different servers. HeapCraft has been demonstrated on a variety of tasks including player behavior classification, as well as quantifying and improving collaboration of players on Minecraft servers. HeapCraft is freely available and serves to democratize game analytics for the Minecraft community at large.

The rich data produced by the second phase of the Human Microbiome Project (iHMP) offers a unique opportunity to test hypotheses that interactions between microbial communities and a human host might impact an individual's health or disease status. In this work we describe infrastructure that integrates Metaviz, an interactive microbiome data analysis and visualization tool, with the iHMP Data Coordination Center web portal and the HMP2Data R/Bioconductor package. We describe integrative statistical and visual analyses of two datasets from iHMP using Metaviz along with the metagenomeSeq R/Bioconductor package for statistical analysis of differential abundance analysis. These use cases demonstrate the utility of a combined approach to access and analyze data from this resource.

Nearly one third of the world's population live in coastal areas, and ten of the fifteen most populous cities in the world lie on a coast. Inhabitants of the Low Elevation Coastal Zone (LECZ) — defined as the contiguous area along the coast that is less than 10 meters above sea level — make up 10% of the world's population and 13% of the world's urban population. Sea level rise, coastal inundation and associated shoreline retreat have emerged as one of the primary threats to these populations and the resources located near the coastal fringe. To meet the needs of governments, planners and managers, researchers are continually faced with the challenge of integrating large volumes of complex environmental and spatial-temporal data. Typically the spatial and temporal components of data sets are underutilized because methods for effectively handling these data have not been available. To address these issues, Eonfusion, a 4-Dimensional software solution, is easily incorporated into the geospatial workflow to significantly enhance the ease with which we can now integrate and explore complex spatially and temporally variant data sets. This paper explores case studies along prominent coastal regions in which, as example, models are developed to predict the impact of rising sea levels on low-lying coastal areas focusing on: ✓ Coastal Inundation ✓ Coastal Vulnerability ✓ Property Devaluation The inundation is spatially modeled as a function of time and enables the visualization of sea level rise scenarios to assess the extent and impact. The coastal vulnerability mapping highlights the analytical power of Eonfusion, through the efficient integration of inundation and vulnerability models to demonstrate the universal application of the software to the field of climate change research. The third model fuses the cadastral layer and a simple property valuation model to complete the scenario, thus demonstrating a powerful pathway for the estimation and visualization of the impact of these climate change events. The data for these case studies include: • Sea level rise scenarios from IPCC stage 4 • LID AR Elevation Data • Cadastral Parcels and Value Indicators • Storm Surge Information • Vulnerability Mapping The processing steps required to integrate, analyze and visualize these models are: • Generation of 3D terrain model from LID AR data • Adjustments for any Height Data (AHD) discrepancies • Integrate IPCC sea level rises and storm surges into Sea level rise timeseries • Identification of sinks using Eonfusion API application • Calculate inundation levels and tipping points at which sinks get filled • Fuse Cadastre and value model with 3D surface — value decreases as % of title flooded • Airphoto drape • Set up visualization scene with integrated graph for all scenarios The outcomes from this work include the identification of powerful pathways through the employment of new visualization and spatial-temporal analysis tools for: • Dynamic scenario based modeling for assessing cost and environmental impacts from climate change; • The provision of a mechanism enabling the visualization of the complex spatial and temporal patterns from a wide range of data derived empirically and from models. This enables key stakeholders to rapidly assess scenarios and their likely impacts • Modeling of this type could be used in a number of areas including fire, flood, tsunami, hurricanes, etc

<p>Strong winds associated with extratropical cyclones are one of the most dangerous natural hazards in Europe. These high winds are mostly connected with five mesoscale dynamical features, namely the warm (conveyor belt) jet (WJ), the cold (conveyor belt) jet (CJ), (post) cold-frontal convective gusts (CFC), strong cold sector winds (CS) and – at least in some storms – the sting jet (SJ). While all these have strong winds in common, the timing, location and some further characteristics tend to differ and hence likely also the forecast errors occurring in association with them.</p><p>Here we present a novel objective identification approach for the features listed above, based on a probabilistic random forest using each feature’s most important characteristics in wind, rainfall, pressure and temperature evolution. However, as CJ and SJ turn out to be difficult to distinguish in surface observations alone, we decided to consider the two features together. This identification can then be used to generate a climatology for Central Europe, to analyse forecast errors specific to individual features, and to ultimately improve forecasts of high wind events through feature-dependent statistical post-processing. To achieve this, we strive to identify the features in irregularly spaced surface observations and in gridded analyses and forecasts in a consistent way, thus making it independent of spatial dependencies and gradients.</p><p>To train the probabilistic random forest, we subjectively identify the four storm features in twelve winter storm cases between 2015 and 2020 in both hourly surface observations and high-resolution reanalyses of the German COSMO model over Europe, using an interactive data analysis and visualisation tool. Results show that mean sea-level pressure (tendency), potential temperature, precipitation amount and wind direction are most important for the distinction between the features. From the random forest we get occurrence probabilities for each feature at every station, which can be converted into areal information using Kriging.</p><p>The results show a satisfactory identification for all features, especially for WJ and CFC. We encounter, however, some difficulties to clearly distinguish the CJ and CS, which are dynamically similar. A climatology is currently being compiled for both surface observations and the reanalyses over a period of around 20 years using the trained probabilistic random forests and further for high-resolution COSMO ensemble forecasts, for which we want to analyse forecast errors and develop feature-dependent postprocessing procedures.</p>

BackgroundA major challenge of high throughput transcriptome studies is presenting the data to researchers in an interpretable format. In many cases, the outputs of such studies are gene lists which are then examined for enriched biological concepts. One approach to help the researcher interpret large gene datasets is to associate genes and informative terms (iTerm) that are obtained from the biomedical literature using the eGIFT text-mining system. However, examining large lists of iTerm and gene pairs is a daunting task.ResultsWe have developed WebGIVI, an interactive web-based visualization tool (http://raven.anr.udel.edu/webgivi/) to explore gene:iTerm pairs. WebGIVI was built via Cytoscape and Data Driven Document JavaScript libraries and can be used to relate genes to iTerms and then visualize gene and iTerm pairs. WebGIVI can accept a gene list that is used to retrieve the gene symbols and corresponding iTerm list. This list can be submitted to visualize the gene iTerm pairs using two distinct methods: a Concept Map or a Cytoscape Network Map. In addition, WebGIVI also supports uploading and visualization of any two-column tab separated data.ConclusionsWebGIVI provides an interactive and integrated network graph of gene and iTerms that allows filtering, sorting, and grouping, which can aid biologists in developing hypothesis based on the input gene lists. In addition, WebGIVI can visualize hundreds of nodes and generate a high-resolution image that is important for most of research publications. The source code can be freely downloaded at https://github.com/sunliang3361/WebGIVI. The WebGIVI tutorial is available at http://raven.anr.udel.edu/webgivi/tutorial.php.

Electronic health records (EHRs) can be used to make critical decisions, to study the effects of treatments, and to detect hidden patterns in patient histories. In this paper, we present a framework to identify and analyze EHR-data-driven tasks and activities in the context of interactive visualization tools (IVTs)—that is, all the activities, sub-activities, tasks, and sub-tasks that are and can be supported by EHR-based IVTs. A systematic literature survey was conducted to collect the research papers that describe the design, implementation, and/or evaluation of EHR-based IVTs that support clinical decision-making. Databases included PubMed, the ACM Digital Library, the IEEE Library, and Google Scholar. These sources were supplemented by gray literature searching and reference list reviews. Of the 946 initially identified articles, the survey analyzes 19 IVTs described in 24 articles that met the final selection criteria. The survey includes an overview of the goal of each IVT, a brief description of its visualization, and an analysis of how sub-activities, tasks, and sub-tasks blend and combine to accomplish the tool’s main higher-level activities of interpreting, predicting, and monitoring. Our proposed framework shows the gaps in support of higher-level activities supported by existing IVTs. It appears that almost all existing IVTs focus on the activity of interpreting, while only a few of them support predicting and monitoring—this despite the importance of these activities in assisting users in finding patients that are at high risk and tracking patients’ status after treatment.