Visualization Prototype Research Articles

Body-Based Augmented Reality Feedback During Conversations

Engaging with our devices as we engage with each other is problematic as it distracts us and diminishes our social interactions. Subtle interactions have been presented as an approach to reconcile personal and computing interactions, through less disrupting technology. Along those lines, we investigate showing information right on and next to the people we are engaging with. Body-based data visualization allows us to maintain our attention with others, but to also receive information at the same time. We explore potential designs of such body-based and especially on-face visualizations and create a set of five prototype visualizations in a Snapchat lens. We use these prototypes in a video call study with 16 participants to evaluate how body-based visualizations affect actual conversations.

RSVP for VPSA : A Meta Design Study on Rapid Suggestive Visualization Prototyping for Visual Parameter Space Analysis.

Visual Parameter Space Analysis (VPSA) enables domain scientists to explore input-output relationships of computational models. Existing VPSA applications often feature multi-view visualizations designed by visualization experts for a specific scenario, making it hard for domain scientists to adapt them to their problems without professional help. We present RSVP, the Rapid Suggestive Visualization Prototyping system encoding VPSA knowledge to enable domain scientists to prototype custom visualization dashboards tailored to their specific needs. The system implements a task-oriented, multi-view visualization recommendation strategy over a visualization design space optimized for VPSA to guide users in meeting their analytical demands. We derived the VPSA knowledge implemented in the system by conducting an extensive meta design study over the body of work on VPSA. We show how this process can be used to perform a data and task abstraction, extract a common visualization design space, and derive a task-oriented VisRec strategy. User studies indicate that the system is user-friendly and can uncover novel insights.

Identifying Legal, BIM Data and Visualization Requirements to Form Legal Spaces and Developing a Web-Based 3D Cadastre Prototype: A Case Study of Condominium Building

Over the past decade, numerous countries and researchers have been investigating the potential of 3D cadastre based on the Building Information Model (BIM). In Türkiye, the General Directorate of Land Registry and Cadastre (GDLRC) has been engaged in the “Production of 3D City Models and Creation of 3D Cadastral Bases Project” since 2018. One of the objectives is to develop 3D (physical) digital building models (and databases) through the digitization of floor plans of architectural drawings. In order to ensure the long-term viability of this project, a new regulation was issued in 2021. This regulation mandates the submission of 3D digital building models to the land registry in order to register condominiums. The future goals of the GDLRC include utilizing the 3D digital building models produced to create legal spaces in condominium buildings for the 3D cadastre. However, no research has yet been carried out for this goal. The objective of this research is to identify the legal and BIM data requirements for deriving legal spaces in condominium buildings, in light of the legislative analysis, and to develop a web-based 3D cadastre visualization prototype (showing both legal spaces and physical objects) based on the requirements obtained from the scientific literature. The result of this research demonstrates that well-structured and annotated BIM data can be used to develop a 3D cadastre prototype that meets the legal requirements in the case of Türkiye. Moreover, it is evident that, although the GDLRC has initiated the construction of a robust foundation for a 3D cadastre based on BIM, further enhancements and resolutions must be implemented from a legal and technical standpoint. It is postulated that the identified requirements and the proposed methodology in this research may assist decision-makers in Türkiye and globally in formulating their strategic plans for a 3D cadastre.

A Hybrid Markov-SPC Approach to Assess Cost Differences in Urgent Care Utilization Using Patient-Reported Data in Inflammatory Bowel Disease.

Cost is a key outcome in quality and value, but it is often difficult to estimate reliably and efficiently for use in real-time improvement efforts. We describe a method using patient-reported outcomes (PROs), Markov modeling, and statistical process control (SPC) analytics in a real-time cost-estimation prototype designed to assess cost differences between usual care and improvement conditions in a national multicenter improvement collaborative-the IBD Qorus Learning Health System (LHS). The IBD Qorus Learning Health System (LHS) collects PRO data, including emergency department utilization and hospitalizations from patients prior to their clinical visits. This data is aggregated monthly at center and collaborative levels, visualized using Statistical Process Control (SPC) analytics, and used to inform improvement efforts. A Markov model was developed by Almario et al to estimate annualized per patient cost differences between usual care (baseline) and improvement (intervention) time periods and then replicated at monthly intervals. We then applied moving average SPC analyses to visualize monthly iterative cost estimations and assess the variation and statistical reliability of these estimates over time. We have developed a real-time Markov-informed SPC visualization prototype which uses PRO data to analyze and monitor monthly annualized per patient cost savings estimations over time for the IBD Qorus LHS. Validation of this prototype using claims data is currently underway. This new approach using PRO data and hybrid Markov-SPC analysis can analyze and visualize near real-time estimates of cost differences over time. Pending successful validation against a claims data standard, this approach could more comprehensively inform improvement, advocacy, and strategic planning efforts.

Prototyping of portable medicine containers with embossed braille using an affordable desktop 3D printer

PurposePrototyping with affordable 3D printers empowers small businesses to create prototypes within a day and carry out multiple iterations of design, size, shape or assembly based on analytical results, bringing better products to market faster. This paper aims to turn the ideas into proofs of concept, advance these concepts to realistic prototypes and investigate the quality of printed prototypes prior to large-scale production.Design/methodology/approachThe experimental approach focuses on the prototyping of portable medicine containers by Fused Deposition Modeling (FDM), modifying the prototypes by adding auxiliary braille flags that indicate patient initials and dosing information, and performing the moisture permeation study as well as the stability study for model drug products (i.e. ibuprofen tablets, guaifenesin tablets, dextromethorphan HBr soft gel capsules).FindingsThe study shows that an affordable 3D printer helps to create functional and visual prototypes that give a realistic depiction of the design and offer physical objects that could be investigated for product quality and feasibility.Originality/valueTo the best of the authors’ knowledge, this study was the first attempt to use a desktop FDM-based 3D printer to prototype portable medicine containers in a blister packet appearance with auxiliary braille flags that help validate early concepts and facilitate the conversation on refining product features in a rapid and affordable manner.

Developing feedback visualizations to support older adults’ medication adherence

BackgroundBlood pressure control is critical for older adults because the prevalence of hypertension and resulting cardiovascular illness increases with age. Antihypertension medications are central to blood pressure treatment. However, nonadherence to antihypertension medications is high. Health technology such as smartphone apps provide an opportunity for users to manage their medication regimen and support processes related to medication-taking. ObjectiveWe implemented a user-centered evaluation approach to develop and refine adherence feedback visualizations for the MEDSReM© medication adherence app for older adults with hypertension. MethodsWe conducted a literature review and iterative usability testing to achieve this objective. We identified adherence goals, information needs, as well as design guidelines by reviewing theoretical frameworks and existing scientific evidence. We then used a two-phase iterative user-centered study and subject matter expert evaluation. Both quantitative and qualitative data were used to select and improve the current prototype and evolve to the next prototype. ResultsThe need for daily, weekly, and monthly adherence performance information as well as visualization formats for conveying this information was identified from the literature review. Overall, the information shown in visualization prototypes was successfully interpreted by participants. Comprehension issues of visualizations were identified and addressed from visual prototype revisions. Insights from both user and subject matter expert groups were used to select and refine the prototypes for the MEDSReM app. ConclusionEvidence-based and user-centered approaches were effective for developing visualizations about adherence performance feedback in the MEDSReM app and provided insight into how the app can be made easy to understand and use by older adults with hypertension, which will be evaluated in future effectiveness testing.

Evaluation of a new artificial intelligence-based textile digitization using fabric drape

Three-dimensional (3D) textile-based garment prototyping, widely adopted in the apparel and textile industry, enhances cost efficiency, work productivity, and seamless communication via visual prototyping. Neural network-based 3D textile digitization has the potential to streamline manufacturing processes by negating the need for traditional physical property (PT) measurements. However, a research gap exists concerning the accuracy of the technology and its applicability to advanced functional apparel manufacturing. The primary research question is to investigate how variations in digitized physical properties obtained from PT measurements and artificial intelligence (AI)-based textile digitization impact the accuracy of a fabric’s mechanical representation. In this study, we aimed to evaluate AI-based textile digitization accuracy using a drape test method. The drape coefficient (DC) analysis revealed that the PT-based simulated DC exhibited a normalized mean absolute error (NMAE) ranging from 2% to 11%, while the AI-based simulated DC showed a range of 3–51%. Notably, for the samples, except those with very limp or very stiff fabric samples, the AI-based simulation exhibited a NMAE within 3–15%.

Multilevel data simplification methods for flood process visualizations based on visual perceptions

ABSTRACT Visualizations of flood processes require a balance between scientific authenticity and realistic expressions. As the flood area increases, the amounts of time-series data that are produced by flood models often exceed the capacity of computer rendering, leading to poor user experiences and low visualization effectiveness. Therefore, this study carried out visual perception experiments and proposed a multilevel simplification and scheduling method for flood process data based on visual perceptions. This method first simplifies flood data by flood feature point selection according to conclusions related to visual perception, then triangulated irregular networks are built, and finally, flood data scheduling and rendering are achieved through multiple levels of detail combined with visual ranges. An experimental analysis using a 3D visualization prototype system shows that the proposed method can improve the efficiency of visual expressions while preserving flood morphology features.

Decomposing Semantic Shifts for Composed Image Retrieval

Composed image retrieval is a type of image retrieval task where the user provides a reference image as a starting point and specifies a text on how to shift from the starting point to the desired target image. However, most existing methods focus on the composition learning of text and reference images and oversimplify the text as a description, neglecting the inherent structure and the user's shifting intention of the texts. As a result, these methods typically take shortcuts that disregard the visual cue of the reference images. To address this issue, we reconsider the text as instructions and propose a Semantic Shift Network (SSN) that explicitly decomposes the semantic shifts into two steps: from the reference image to the visual prototype and from the visual prototype to the target image. Specifically, SSN explicitly decomposes the instructions into two components: degradation and upgradation, where the degradation is used to picture the visual prototype from the reference image, while the upgradation is used to enrich the visual prototype into the final representations to retrieve the desired target image. The experimental results show that the proposed SSN demonstrates a significant improvement of 5.42% and 1.37% on the CIRR and FashionIQ datasets, respectively, and establishes a new state-of-the-art performance. The code is available at https://github.com/starxing-yuu/SSN.

Semantic-Guided Novel Category Discovery

The Novel Category Discovery problem aims to cluster an unlabeled set with the help of a labeled set consisting of disjoint but related classes. However, existing models treat class names as discrete one-hot labels and ignore the semantic understanding of these classes. In this paper, we propose a new setting named Semantic-guided Novel Category Discovery (SNCD), which requires the model to not only cluster the unlabeled images but also semantically recognize these images based on a set of their class names. The first challenge we confront pertains to effectively leveraging the class names of unlabeled images, given the inherent gap between the visual and linguistic domains. To address this issue, we incorporate a semantic-aware recognition mechanism. This is achieved by constructing dynamic class-wise visual prototypes as well as a semantic similarity matrix that enables the projection of visual features into the semantic space. The second challenge originates from the granularity disparity between the classification and clustering tasks. To deal with this, we develop a semantic-aware clustering process to facilitate the exchange of knowledge between the two tasks. Through extensive experiments, we demonstrate the mutual benefits of the recognition and clustering tasks, which can be jointly optimized. Experimental results on multiple datasets confirm the effectiveness of our proposed method. Our code is available at https://github.com/wang-weishuai/Semantic-guided-NCD.

«КОКТЕЙЛЬНЫЙ» ЧЕБУРАШКА: МЕДИЙНЫЕ ТЕХНОЛОГИИ СОЗДАНИЯ И ПРОДВИЖЕНИЯ ОБРАЗА В РОССИЙСКОМ МАССОВОМ КИНЕМАТОГРАФЕ

The Russian family comedy Cheburashka became not only an example of a successful commercial project, but also demonstrated an example of professional adaptation of Hollywood technologies in the modern domestic socio-cultural space. Against the background of a steady and continuously increasing nostalgia for the Soviet past, a creative product was created in which the audience’s illusions about the Soviet Cheburashka and new Western-oriented, ideologized cultural values were strangely combined, which turned out to be encoded in the characters, subject environment andplot twists of the film. The image of Cheburashka in Soviet culture evolved from an amorphous animal with an unspecified species to a common Soviet symbol of childhood with an appropriate set of existential meanings. According to the authors of the Russian Cheburashka, they created a new story of this character, at the same time, the retroatmosphere of the picture and the set of characters invariably refer viewers to the Soviet Cheburashka. But the new image can only formally be attributed to its visual prototype, since it was intentionally created as a visual and semantic structure appealing to the prevailing industrial thematism and consumer values in Russian society. That is why the new Cheburashka becomes an “Easter egg”, implicitly indicating the presence in one film of several dozen purposefully borrowed elements of images of popular characters from commercially successful Western, Soviet and Russian media projects of the past decades. Such technology makes it possible to fix the prevailing ideological trend associated with the comprehensive assimilation of Western values in the space of Russian culture.

Leveraging Digital Twins for Real-Time Environmental Monitoring in Battery Manufacturing

The demand for sustainable battery manufacturing necessitates solutions that allow for comprehensive and real-time environmental monitoring. Current approaches in the battery sector lack these capabilities, hindering timely detection of inefficiencies and resource optimization throughout the manufacturing process. Additionally, there is a lack of visualization solutions on the shop floor to enhance worker awareness for sustainability. This paper introduces a prototype for real-time environmental monitoring and visualization in battery cell manufacturing combining life cycle assessment (LCA) with a digital twin (DT) and a graphical user interface (GUI). Firstly, we calculated the product carbon footprint (PCF) of a battery cell and developed a data model based on this information to define sub-models for the digital twin. The digital twin can be linked to data sources from production and adjacent IT systems. For demonstration purposes it was connected to a data emulator. Using the data structure of the digital twin, a visualization concept was developed to display the current value and history of the product carbon footprint. This visualization is carried out via a GUI and can highlight hotspots in production, serving as a tool for process control and monitoring. Furthermore, the data can be used for energy-efficient process optimization, as demonstrated by the investigation of energy flexibility measures. The next step is the implementation of the demonstrator in a production environment, as well as the implementation of sensors for data acquisition.

PerSiVal: On-Body AR Visualization of Biomechanical Arm Simulations.

In this work, we explore different combinations of techniques for an interactive, on-body visualization in augmented reality (AR) of an upper arm muscle simulation model. In terms of data, we focus on a continuum-mechanical simulation model involving five different muscles of the human upper arm, with physiologically realistic geometry. In terms of use cases, we focus on the immersive illustration, education, and dissemination of such simulation models. We describe the process of developing six on-body visualization prototypes over the period of five years. For each prototype, we employed different types of motion capture, AR display technologies, and visual encoding approaches, and gathered feedback throughout outreach activities. We reflect on the development of the individual prototypes and summarize lessons learned of our exploration process into the design space of situated on-body visualization.

Data Visualization Analysis in the Management Information System for Submitting Credit Scores for Education Personnel

Education staff have an important role in facilitating the implementation of the tridharma of higher education and are an indicator of educational administration success. This study looks at how data visualization was implemented in the Management Information System for Submitting Credit Scores for Education Personnel. The research is primarily concerned with the Functional Position of Intermediate Expert Computer Staff. The study's goal is to create web-based data visualization tools to enable submitting and assessing credit scores for Intermediate Computer Experts easier, thereby meeting the requirement for efficiency and accuracy in the promotion and position process. This study employs a research and development (R&D) methodology with an iterative prototype approach. The initial step involves a needs analysis, including an in-depth understanding of the Associate Computer Expert promotion process and the associated administrative requirements. Next, a web-based data visualization prototype was designed, which included an intuitive user interface, database integration, and a credit number calculation algorithm. The prototype was then implemented and tested on a limited basis in the IPDN environment. The proposed system is designed to provide easy access, facilitate data verification, and enable computerized sorting and calculation of credit numbers. The main contribution of this research lies in developing information technology solutions that can increase the transparency and effectiveness of administrative processes in the IPDN environment. It is hoped that the output of this research can provide conceptual and practical contributions to managing the credit figures for educational staff and provide a basis for developing similar systems in other educational institutions.

Proposal of a Visualization System for a Hierarchical Clustering Algorithm: The Visualize Proximity Matrix

Visual data mining is a new and effective strategy for dealing with the growing phenomenon of information overload. There is an urgent need for effective visual data mining because the growth of data sets from different domains and sources has made exploring, managing and analyzing vast volumes of data increasingly difficult. Identifying location-based trends and anomalies in data from a numeric output is challenging. The outputs of data mining procedures are often quite difficult to interpret. There is no human input to data exploration or direct involvement in the data mining process. Thus, it is difficult to analyze, explore, understand and view the data. In data mining, massive data sets are clustered using an iterative process that includes human input but data mining algorithms can miss some knowledge and observations. In addition, many users cannot take the appropriate action to address problems or opportunities because the huge amounts of data prevent them from staying aware of what is happening in and around their environments. Visual data mining can help in dealing with information overload and it is effective in analyzing large complex data sets. Visual data mining assists users in gaining a deep visual understanding of data. In the information age, users need to study and observe vast amounts of data to acquire important knowledge, and thus the need for visual and interactive analytical tools is particularly pressing. Visual cluster analysis has long utilized shaded similarity matrices, and this study investigated how they can be used in clustering visualization. We focused on proposing an agglomerative hierarchical clustering method. Ensemble cluster visualization is also presented for handling large data sets. This study proposes the adoption of a shaded similarity matrix to visually cluster knowledge discovered using data mining. Using the technology acceptance model as the measuring tool, we questioned respondents to evaluate the visualization prototype. The findings demonstrated that the visualization was effective and easy to use, and satisfied users.

Leveraging data-driven and procedural methods for generating high-fidelity visualizations of real forests

Forests are indispensable in mitigating climate change due to their ability to sequester and store large amounts of carbon. Forest restoration and maintenance activities are crucial for retaining and enhancing this capacity. To promote sustainable practices, stakeholders need to assess the potential impacts of forestry operations. But in-depth comprehension of the forest dynamics is challenging due to the complex interactions between various ecosystem components. High-fidelity, immersive visualizations can support sense- and decision-making processes by facilitating the exploration of scientific and non-scientific data in the respective spatial, temporal, and semantic contexts. However, producing accurate representations of diverse ecosystems is complex and time-consuming. In this paper, we introduce a forest visualization prototype providing realistic representations of forest stands and different simulated scenarios. The utilized data-driven and procedural approach ensures efficiency, reproducibility, and accuracy. Furthermore, we present an array of requirements for realistic forest visualizations gathered through questionnaires and interviews with forestry experts.

Prototype of the request in the service queue

The development of a visual prototype of an application in a single queue for service is given. The pre-image connects the accounting for the growth of losses due to the duration of waiting with such sides of the importance of the application as urgency and priority. Varieties of change of urgency in time are discussed. The possibility of interpreting service as a lasting good is considered. On this basis, it is proposed to use relative losses as the main measure for determining the place of an application in the queue - the ratio of losses due to waiting to the duration of service. The example shows that the order in which service requests arrive depends on the time when the service facility starts accessing the queue and on the ratio of the service durations of individual requests. For the case of returning the service facility to the request after a forced interruption, the possibility of rivalry with it by other requests in the queue that have not yet arrived for servicing is considered. The possibility of working with the queue when servicing in small parts with a change in serviced requests is shown. For this type of work, expressions for step-by-step calculations are given. Calculations are made for two complicated cases of servicing a queue of five requests with a contradictory combination of the initial data of requests. A diagram of the switching of service facilities between requests is given. Taking into account the four sides of the description of the request in the queue at once provides the prototype with greater flexibility than most widely used queuing disciplines. The proposed prototype can be taken as a basis for building multi-parameter models of applications and algorithms for automatic control of the queue of applications for social, economic and computational applications.

The Challenge of Interdisciplinarity at the Intersection of Groundwater Management and Visualization Research.

This design study presents an analysis and abstraction of temporal and spatial data, and workflows in the domain of hydrogeology and the design and development of an interactive visualization prototype. Developed in close collaboration with a group of hydrogeological researchers, the interface supports them in data exploration, selection of data for their numerical model calibration, and communication of findings to their industry partners. We highlight both pitfalls and learnings of the iterative design and validation process and explore the role of rapid prototyping. Some of the main lessons were that the ability to see their own data changed the engagement of skeptical users dramatically and that interactive rapid prototyping tools are thus powerful to unlock the advantage of visual analysis for novice users. Further, we observed that the process itself helped the domain scientists understand the potential and challenges of their data more than the final interface prototype.

Immersive Unit Visualization with Augmented Reality

Immersive Unit Visualization is an emergent form of visualization that arose from Immersive Analytics where, unlike traditional visualizations, each data point is represented by an individual visual mark in an immersive virtual environment. This practice has focused almost exclusively on virtual reality, excluding augmented reality (AR). This article develops and tests a prototype of an Immersive Unit Visualization (Floating Companies II) with two AR devices: head-mounted display (HMD) and hand-held display (HHD). Results from the testing sessions with 20 users were analyzed through qualitative research analysis and thematic coding indicating that, while the HHD enabled a first contact with AR visualization on a familiar device, HMD improved the perception of hybrid space by supporting greater stability of virtual content, wider field of view, improved spatial perception, increased sense of immersion, and more realistic simulation, which had an impact on information reading and sense-making. The materialization of abstract quantitative values into concrete reality through its simulation in the real environment and the ludic dimension stand out as important opportunities for this type of visualization. This paper investigates the aspects distinguishing two experiences regarding data visualization in hybrid space, and characterizes ways of seeing information with AR, identifying opportunities to advance information design research.

How deep learning influences workflows and roles in virtual surgical planning

BackgroundDeep learning (DL) has the potential to transform surgical practice, altering workflows and changing the roles of practitioners involved. However, studies have shown that introducing such change requires user acceptance. Following the development and presentation of a visual prototype for planning facial surgery interventions, the project aimed to understand the utility of DL, the implied workflow and role changes it would entail, and the potential barriers to its adoption in practice.MethodThis paper presents a multi-year case study providing insights from developing and introducing a visual prototype. The prototype was co-developed by facial surgeons, DL experts, and business process engineers. The study uses project data involving semi-structured interviews, workgroup results, and feedback from an external practitioner audience exposed to the prototype regarding their views on adopting DL tools in practice.FindingsThe surgeons attested a high utility to the application. However, the data also highlights a perceived need to remain in control, be able to intervene, and override surgical workflows in short intervals. Longer intervals without opportunities to intervene were seen with skepticism, suggesting that the practitioners’ acceptance of DL requires a carefully designed workflow in which humans can still take control of events.ConclusionDeep learning can improve and accelerate facial surgery intervention planning. Models from the business and management literature partially explain the acceptance of new technologies. Perceived ease of use seems less relevant than the perceived usefulness of new technology. Involving algorithms in clinical decision-making will change workflows and professional identities.