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

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.

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

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.

Local area network (LAN) access is the top vector for insider threats and misuses according to the Verizon's Data Breach Investigations Report. Address Resolution Protocol (ARP) is often exploited by the attackers who have gained access to the LAN. It is critical for students to learn how attacks on ARP work and know the countermeasures. In an earlier work, authors developed a hands-on lab to help students learn how an ARP spoofing attack works by asking them to create and send attack packets. To enhance learning further, we present an interactive visualization tool that intuitively shows the effects of the ARP spoofing attack in real-time. The Hacker Graphical User Interface (HGUI) is an interactive visualization tool developed to assist students in learning how ARP Spoofing works. By modeling a controlled ARP Spoofing attack using virtual machines, we give students the ability to alter elements of the attack by interacting with the visualization. This tool was developed using Processing, an open source programming language used in many visual art communities. It runs on virtual machines installed with Kali Linux. This tool animates attack packets, normal packets, and the status of ARP cache in real-time. If students have successfully carried out the ARP spoofing attack, they can see the normal packets being routed to the attacker machine and the victim's ARP cache being poisoned. In this paper, we present the design, implementation, and evaluation of the lab. Tests were conducted to measure the performance of students before and after using this tool. We also gave students surveys after they completed the hands-on lab. The result shows that this tool can significantly enhance students' understanding of the concept of ARP spoofing attacks and motivate them in learning more about cyber security.

In Nigeria, the healthcare sector faces big challenges. Limited access to quality services and not enough resources are major issues. Using Artificial Intelligence (AI) could help improve healthcare. But understanding AI predictions is hard, especially in healthcare where transparency is crucial. This article looks at Explainable AI (XAI) to help with this problem in Nigeria. It talks about XAI techniques like feature importance examination, model-agnostic methods (e.g., LIME, SHAP), and interactive visualization tools. These tools can make AI models easier to understand and help with decision-making. A literature review was done to see how XAI can help healthcare in Nigeria. The review included scholarly articles, books, and reports on AI in Nigerian healthcare. We looked at methods from past XAI studies to find common approaches and best practices. XAI offers techniques that make AI models easier to understand in healthcare systems. These techniques include feature importance examination, model-agnostic methods, and interactive visualization tools. Case studies from Nigeria show how XAI is used in areas like disease diagnosis, treatment recommendations, and public health interventions. The findings show the importance of XAI in solving interpretability issues in healthcare AI, especially in places with limited resources like Nigeria. By explaining why AI makes certain predictions, XAI helps healthcare workers make better decisions for Nigerian patients. However, more research is needed to improve XAI techniques for Nigeria’s healthcare system. Policymakers and healthcare leaders should focus on using XAI-enabled systems to drive innovation and improve healthcare outcomes in Nigeria.

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.

The physiological age-related development of pediatric laboratory results interferes with pathological derangements, which can complicate the interpretation of test results. Recently proposed continuous reference intervals (RIs) promise to be beneficial, although their clinical use may depend on graphical presentations. To estimate the clinical utility of continuous RIs, we developed and evaluated an interactive visualization tool, and examined the differentiation of hemoglobinopathies that is attainable based on the underlying innovative RI model. The implemented web application allows users to easily enter laboratory test results, and displays various visualizations in conjunction with the corresponding RIs, such as charts and personalized Z-scores. To evaluate the usability of the visualization tool, we conducted concurrent think-aloud sessions with four physicians, who were prompted to solve a set of typical interpretation tasks, and acquired additional information through a questionnaire including the System Usability Scale (SUS). We used 85 de-identified clinical cases for an exemplified assessment of how well model-based interpretations of blood count parameters reproduced previously diagnosed hemoglobinopathies. Usability tests as well as questionnaire responses indicated that the developed tool was well received by the physicians. Results from the think-aloud evaluation revealed only minor problems and the tool reached an average SUS score of 86.9, suggesting good usability. Hemoglobinopathy discrimination depended on the considered subtype, although the overall performance of the novel method rivaled the one of the conventional approach. The interactive visualization of innovative continuous reference intervals demonstrated promising results, which justifies further testing on the path towards clinical routine.

Data Mining aims at extracting maximum of knowledge from huge databases. It is realized by an automatic process or by data visual exploration with interactive tools. Automatic data mining extracts all the patterns which match a set of metrics. The limit of such algorithms is the amount of extracted data which can be larger than the initial data volume. In this article, we focus on association rules extraction with Apriori algorithm. After the description of a characterization model of a set of association rules, we propose to explore the results of a Data Mining algorithm with an interactive visual tool. There are two advantages. First it will visualize the results of the algorithms from different points of view (metrics, rules attributes...). Then it allows us to select easily inside large set of rules the most relevant ones.

In this work, we present a demonstration of a visual interactive tool called ESSAVis that helps different engineers in collaborating together for understanding the failure mechanisms in complex embedded systems. ESSAVis provides a 2Dplus3D visual user interface that integrates intuitively between different data sets related with embedded systems failure mechanisms. The tool accepts a CFT model describing a specific hazard in the underlying system, and a CAD model describing the geometry of system components. In this paper, we present different interaction options of ESSAVis that are used for intuitively extracting safety aspects of the underlying embedded system.

Big data analytics in healthcare would be almost useless, without suitable tools allowing users see" them, and gain insight for their situated decisions. The VVH (Valuable Visualization in Healthcare) workshop focuses on the role of interactive data visualization tools by which people can make sense of healthcare data; these data include sensor data, the messages exchanged in social media, the emails between patients and their doctors, the content of patient records as well as the discussions among different specialists that led to such record content. All these data are used by different types of users, like doctors, nurses, policy makers and common citizens. The VVH workshop aims at contributing on: The assessment of the usability of advanced interactive tools of health-related data visualization; the assessment of the quality of the information and value for insight that these tools make available to their users; the collection of reports of either success stories or failures in the appropriation and use of complex and multidimensional healthcare datasets; the collection of methodological and design-oriented contributions that could share methods, techniques, and heuristics for the design of interactive tools and applications supporting data work, data telling and data interpretation in healthcare.

Facial and head actions contain significant affective information. To date, these actions have mostly been studied in isolation because the space of naturalistic combinations is vast. Interactive visualization tools could enable new explorations of dynamically changing combinations of actions as people interact with natural stimuli. This paper describes a new open-source tool that enables navigation of and interaction with dynamic face and gesture data across large groups of people, making it easy to see when multiple facial actions co-occur, and how these patterns compare and cluster across groups of participants. We share two case studies that demonstrate how the tool allows researchers to quickly view an entire corpus of data for single or multiple participants, stimuli and actions. Acume yielded patterns of actions across participants and across stimuli, and helped give insight into how our automated facial analysis methods could be better designed. The results of these case studies are used to demonstrate the efficacy of the tool. The open-source code is designed to directly address the needs of the face and gesture research community, while also being extensible and flexible for accommodating other kinds of behavioral data. Source code, application and documentation are available at http://affect.media.mit.edu/acume.

A photogenic location is a good place to take photos. It generally has beautiful scenery, historical structures, etc. When amateur photographers plan a photo trip, they often decide beforehand on photogenic locations to visit. They collect information about the locations and consider various aspects. The aim of the authors is to assist amateur photographers in planning a photo trip. Information and procedures have been organized to help select photogenic locations from the perspective of information design. In addition, an interactive visual tool has been developed to help select photogenic locations. The tool displays information on photogenic locations extracted from a large collection of geotagged photos, provided by a photo-sharing site. The tool allows us to select locations while referring to their geographic relationships. Consequently, the process of selecting photogenic locations becomes easy.

A number of approaches and tools exist that extract and visualize the changes between two versions of a file and thereby help developers to understand them. DiffViz is an interactive visualization tool that visualizes the changes independent from the differencing algorithm. It supports, but is not limited to, a granularity on the level of abstract syntax trees. Furthermore, it provides several new features, such as node matching and the mini-map, to navigate and analyze the changes. A demo of the installation and example usage of the tool is available here: https://youtu.be/RF93ey9GYoc.

The detection of anomalous events in large multivariate data is sought in many domains. Analysis of data is an important fraud detection procedure in detecting suspicious events and prevent attempts to defraud. While now the data is becoming more complicated and difficult as data scales and complexities increase than ever before, the rich insights within the data may be difficult to identify by traditional means and often remain hidden. People require powerful tools to extract valid conclusions from the data while maintaining trustworthy and interpretable results. Hence, various fraud detection approaches have started to exploit Visual Analytics (VA) techniques to reveal the hidden knowledge in such fraudulent activities. Interactive data visualization tools have substantial potential for making the detection of fraudulent transactions more efficient and effective by allowing the investigator to change the representation of data from text and numeric into graphics and filter out subsets of transactions for further fraud investigation. However, little research to date has directly examined the efficacy of data visualization techniques for fraud detection especially telecommunication fraud. In this paper, we present an overview of several fraud detection solutions that use data visualization techniques to detect fraudulent transactions in the telecommunication domain. The paper concludes by discussing how academic research might proceed in investigating the efficacy of interactive data visualization tools for fraud detection.

The development of custom interactive visualization tools for specific domains and applications has been made much simpler recently by a surge of visualization tools, libraries and frameworks. Most of these tools are developed for classical data science applications, where a user is supported in analyzing measured or simulated data. But recently, there has also been an increasing interest in visual support for understanding machine learning algorithms and frameworks, especially for deep learning. Many, if not most, of the visualization support for (deep) learning addresses the developer of the learning system and not the end user (data scientist). Here we show on a specific example, namely the development of a matrix calculus algorithm, that supporting visualizations can also greatly benefit the development of algorithms in classical domains like in our case computer algebra. The idea is similar to visually supporting the understanding of learning algorithms, namely provide the developer with an interactive, visual tool that provides insights into the workings and, importantly, also into the failures of the algorithm under development. Developing visualization support for matrix calculus development went similar as the development of more traditional visual support systems for data analysts. First, we had to acquaint ourselves with the problem, its language and challenges by talking to the core developer of the matrix calculus algorithm. Once we understood the challenge, it was fairly easy to develop visual support that streamlined the development of the matrix calculus algorithm significantly.