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Point-of-care (POC) detection devices for wound care and monitoring show promise but still have limitations and gaps in effectiveness. Validation through robust clinical trials is necessary to evaluate their accuracy, reliability, and impact on patient outcomes. Improved POC detection devices with enhanced sensitivity, specificity, accessibility, and integration with electronic health records can enhance accuracy and patient care. Challenges to widespread implementation include cost, training requirements, regulatory hurdles, expertise, data management, and interoperability. Technological advancements offer potential solutions, including cost-effective manufacturing, miniaturization, regulatory streamlining, and training solutions. Cloud computing, data storage, analytics, artificial intelligence, and machine learning can address data management challenges and assist analysis and decision support. Healthcare resources are heavily burdened by infections that impede the wound-healing process. A wide range of advanced technologies have been developed for detecting and quantifying infection biomarkers. Finding a timely, accurate, non-invasive diagnostic alternative that does not require a high level of training is a critical step toward arresting common clinical patterns of wound health decline. There is growing interest in the development of innovative diagnostics utilizing a variety of emerging technologies, and new biomarkers have been investigated as potential indicators of wound infection. In this review, we summarize diagnostics available for wound infection, including those used in clinics and still under development. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a general concept for the analysis of temporal and multivariate data and the system LFPeers that applies this concept to temporal similarity search and results exploration. The conceptual workflow divides the analysis in two phases: a search phase to find the most similar objects to a query object before a time point t 0 in the temporal data, and an exploration phase to analyze and contextualize this subset of objects after t 0. LFPeers enables users to search for peers through interactive similarity search and filtering, explore interesting behavior of this peer group, and learn from peers through the assessment of diverging behaviors. We present the conceptual workflow to learn from peers and the LFPeers system with novel interfaces for search and exploration in temporal and multivariate data. An earlier workshop publication for LFPeers included a usage scenario targeting epidemiologists and the public who want to learn from the Covid-19 pandemic and distinguish successful and ineffective measures. In this extended paper, we now show how our concept is generalized and applied by domain experts in two case studies, including a novel case on stocks data. Finally, we reflect on the new state of development and on the insights gained by the experts in the case studies on the search and exploration of temporal data to learn from peers. [Display omitted] • Conceptual framework for temporal and multimodal similarity search and exploration. • Visual Analytics system for cause–effect analysis on temporal data. • Interactive user-defined search and exploration of data objects by similarity. [ABSTRACT FROM AUTHOR]

We present ScrutinAI, a Visual Analytics tool to leverage semantic understanding for deep neural network (DNN) prediction analysis, focusing on models for object detection and semantic segmentation. Typical fields of application for such models, e.g., autonomous driving or healthcare, have a high demand for uncovering and mitigating data- and model-inherent shortcomings. Our approach helps analysts and/or auditors use their semantic understanding to identify and investigate potential weaknesses in DNN models. ScrutinAI includes interactive visualizations of the model's inputs and outputs, interactive plots with linked brushing, data filtering with textual queries on descriptive meta data and an interactive similarity based image retrieval feature. The different views and data filtering options enable global and local inspection of DNN predictions. Overall, the tool fosters hypothesis driven knowledge generation which aids in understanding the model's inner reasoning. Insights gained during the analysis process mitigate the "black-box character" of the DNN and thus support model improvement and the generation of a safety argumentation for AI applications. We present two case studies on the investigation of DNN models for pedestrian detection from the automotive domain. [Display omitted] • ScrutinAI assists in the investigation process of image based DNN predictions. • Identification and mitigation of data-/model-related shortcomings. • Hypothesis-driven knowledge generation workflow using human semantic understanding. • Two case studies within the automotive domain demonstrate the practical relevance. [ABSTRACT FROM AUTHOR]

Cryptocurrencies are a novel phenomenon in the finance world that is gaining more attention from the general public, banks, investors, and academic research lately. A characteristic of cryptocurrencies is to be the target of investments that, due to the volatility of most of the cryptocurrencies, tends to be at high risk and behave very differently from traditional currencies. A way of reducing this risk is to look at the history of existing cryptocurrencies and compare them to spot promising trends for increased gain. This paper introduces CryptoComparator, a Visual Analytics tool designed to analyze the correlations and trends of cryptocurrencies. The system exploits an initial proposal for a double elliptic graph layout, reconfigurable with three different ordering functions, to support a fast visual search of cryptocurrencies by correlation strength. Two use cases developed with a domain expert in cryptocurrency financial activities demonstrate qualitatively the support it provides for analyzing cryptocurrencies. [Display omitted] • A visual analytics solution for interactive analysis of cryptocurrencies. • Novel double ellipse graph layout. • Different graph orderings for correlation-based grouping of cryptocurrencies. • Correlation stability score to help the user in decision-making. • Two use cases on helping reducing volatility and building a crypto-wallet. [ABSTRACT FROM AUTHOR]

Item rankings are useful when a decision needs to be made, especially if there are multiple attributes to be considered. However, existing tools do not support both categorical and numerical attributes, require programming expertise for expressing preferences on attributes, do not offer instant feedback, lack flexibility in expressing various types of user preferences, or do not support all mandatory steps in the ranking-creation workflow. In this work, we present RankASco: a human-centered visual analytics approach that supports the interactive and visual creation of rankings. The iterative design process resulted in different visual interfaces that enable users to formalize their preferences based on a taxonomy of attribute scoring functions. RankASco enables broad user groups to (a) select attributes of interest, (b) express preferences on attributes through interactively tailored scoring functions, and (c) analyze and refine item ranking results. We validate RankASco in a user study with 24 participants in comparison to a general purpose tool. We report on commonalities and differences with respect to usefulness and usability and ultimately present three personas that characterize common user behavior in ranking-creation. On the human factors side, we have also identified a series of interesting behavioral variables that have an influence on the task performance and may shape the design of human-centered ranking solutions in the future. [Display omitted] • RankASco, a visual analytics approach for preference-based item ranking. • The validation of RankASco in a user study with 24 non-experts. • Presentation of three personas, characterizing user behaviors in ranking creation. [ABSTRACT FROM AUTHOR]

Multi-attribute decision making (MADM) has been extensively explored and applied in many real-world problems. However, comprehending the decision-making process and selecting a satisfactory choice from a large set of alternatives characterized by multiple conflicting attributes impose a significant cognitive burden on decision-makers. A visual analytics approach for MADM (MADM-VA) that integrates visual representations of alternatives with the decision-making analysis process is proposed to solve this problem. Firstly, to ensure the final decision meets the actual needs and expectations of decision-makers, an Intuitionistic Fuzzy Analytic Hierarchy Process based on Nonlinear Programming (IFAHP-NLP) is proposed. This approach directly determines the optimal crisp attribute weight vector according to the preferences of decision-makers and avoids information loss and distortion of attribute weights caused by defuzzification. Next, the Uniform Manifold Approximation and Projection (UMAP) is used to map the high-dimensional weighted normalized decision matrix to two-dimensional space, illustrating the distribution of alternatives. The concept of similarity to the ideal solution is introduced to enhance the interpretability of the generated space. Furthermore, the Voronoi diagram is innovatively adopted to assist decision-makers in gaining a better understanding of the decision-making process and help them visually identify the best choice, which is closest to the positive ideal solution and farthest from the negative ideal solution. Finally, experimental results from two case studies verify that MADM-VA is efficient and reliable. • An optimization method is proposed to determine the crisp attribute weights in IFAHP. • The improved IFAHP can avoid information loss caused by defuzzification. • A visual analytics approach is proposed to present the decision-making process. • UMAP and Voronoi diagram are combined to guide DM to select the best choice visually. [ABSTRACT FROM AUTHOR]

Navigation/positioning systems have become critical to many applications, such as autonomous driving, Internet of Things (IoT), Unmanned Aerial Vehicle (UAV), and smart cities. However, it is difficult to provide a robust, accurate, and seamless solution with single navigation/positioning technology. For example, the Global Navigation Satellite System (GNSS) cannot perform satisfactorily indoors; consequently, multi-sensor integrated systems provide the solution, as they compensate for the limitations of single technology by using the complementary characteristics of different sensors. This article describes a thorough investigation into multi-sensor data fusion, which over the last ten years has been used for integrated positioning/navigation systems. In this article, different navigation/positioning systems are classified and elaborated upon from three aspects: (1) sources, (2) algorithms and architectures, and (3) scenarios, which we further divide into two categories: (i) analytics-based fusion and (ii) learning-based fusion. For analytics-based fusion, we discuss the Kalman filter and its variants, graph optimization methods, and integrated schemes. For learning-based fusion, several supervised, unsupervised, reinforcement learning, and deep learning techniques are illustrated in multi-sensor integrated positioning/navigation systems. Design consideration of these integrated systems is discussed in detail from several aspects and their application scenarios are categorized. Finally, future directions for their research and implementation are discussed. • Classifying integrated navigation systems with sources, algorithms, and scenarios. • Classifying multi-sensor fusion based on absolute and relative positioning sources. • Analytics-based and learning-based algorithms are discussed and classified. • Design considerations include state selection, observability, time synchronization. • Classifying application scenarios into 6 categories, including automated driving. [ABSTRACT FROM AUTHOR]

We investigate uncertainty guidance mechanisms to support proton therapy (PT) planning visualization. Uncertainties in the PT workflow pose significant challenges for navigating treatment plan data and selecting the most optimal plan among alternatives. Although guidance techniques have not yet been applied to PT planning scenarios, they have successfully supported sense- and decision-making processes in other contexts. We hypothesize that augmenting PT uncertainty visualization with guidance may influence the intended users' perceived confidence and provide new insights. To this end, we follow an iterative co-design process with domain experts to develop a visualization dashboard enhanced with distinct level-of-detail uncertainty guidance mechanisms. Our approach classifies uncertainty guidance into two dimensions: degree of intrusiveness and detail-orientation. Our dashboard supports the comparison of multiple treatment plans (i.e., nominal plans with their translational variations) while accounting for multiple uncertainty factors. We subsequently evaluate the designed and developed strategies by assessing perceived confidence and effectiveness during a sense- and decision-making process. Our findings indicate that uncertainty guidance in PT planning visualization does not necessarily impact the perceived confidence of the users in the process. Nonetheless, it provides new insights and raises uncertainty awareness during treatment plan selection. This observation was particularly evident for users with longer experience in PT planning. [Display omitted] • A dashboard for tumor PT plan comparison to tackle multiple sources of uncertainty. • Multi-level-of-detail guidance mechanisms to facilitate navigation of uncertainties. • An evaluation approach to validate uncertainty guidance in PT visualization. [ABSTRACT FROM AUTHOR]

The last two decades have exhibited a profound transformation of traditional urban mobility patterns partly due to the exponential growth in both number and popularity of public bicycle sharing systems (BSS). Analysis and visualization of the data generated by BSSs have become of special interest to municipalities to evaluate the effect of their mobility programs and offer integrated urban mobility solutions. In this paper, we present a visualization system that aims to assist city officials from small and medium cities in their decision-making process with an intuitive representation of BSS' data. It has been developed, tested, and evaluated together with officials and domain experts from the city of Logroño (Spain). Our tool presents usage information with different time granularities (yearly, monthly, weekly, or seasonally), shows traffic flows between stations, and provides an in-depth breakdown of users' data such as their registered address, traveled distance, or gender-based patterns. [Display omitted] • Web-based exploratory tool to analyze usage trends of bike sharing systems. • Analysis tool to support mobility policies planning. • A report-mode that enables generation of data-based detailed reports on demand. • Scalability to other bike sharing systems with similar needs. [ABSTRACT FROM AUTHOR]

Over the past decade, there has been a significant increase in the development of visual analytics systems dedicated to addressing urban issues. These systems distill intricate urban analysis workflows into intuitive, interactive visual representations and interfaces, enabling users to explore, understand, and derive insights from large and complex data, including street-level imagery, street networks, and building geometries. Developing urban visual analytics systems, however, is a challenging endeavor that requires considerable programming expertise and interaction between various multidisciplinary stakeholders. This situation often leads to monolithic and isolated prototypes that are hard to reproduce, combine, or extend. Concurrently, there has been an increase in the availability of general and urban-specific toolkits, frameworks, and authoring tools that are open source and abstract away the need to implement low-level visual analytics functionalities. This paper provides a hierarchical taxonomy of urban visual analytics systems to contextualize how they are usually designed, implemented, and evaluated. We develop this taxonomy across three distinct levels (i.e. , dimensions, categories, and tags), juxtaposing visualization with analytics, data, and system dimensions. We then assess the extent to which current open-source toolkits, frameworks, and authoring tools can effectively support the development of components tailored to urban visual analytics, identifying their strengths and limitations in addressing the unique challenges posed by urban data. In doing so, we offer a roadmap that can guide the effective employment of existing resources and chart a pathway for developing and refining future systems. [Display omitted] • Review of over 135 papers proposing urban visual analytics systems. • Hierarchical taxonomy of urban visual analytics systems considering over 160 tags. • Evaluation of toolkits, frameworks, and authoring tools for urban visual analytics. • We highlight the need for interoperability and sustainable cyberinfrastructures. [ABSTRACT FROM AUTHOR]

We present a data visualization approach that allows users to interactively analyse sequential movement patterns and automatically extract rules for marine animal behaviour. This enhances the ability of oceanic experts to understand marine ecosystems. Traditional time-series analysis, basic statistical methods, and single-view visualizations often fail to capture the intricate spatial and temporal dynamics of marine ecosystems. Our visual analytics approach, that leverages sequential pattern mining and rule mining techniques, integrates SPMF's Vertical Mining of Maximal Sequential Patterns (VMSP) algorithm and T-Rule Growth algorithm to efficiently extract and visualize movement patterns and behavioural rules of marine animals. The system also incorporates automated pre-processing to clean and prepare large datasets, reducing the time and effort required for manual handling. The visualization presents oceanic data from detection stations, allowing researchers to discover patterns in the movements of marine species. Our interface also provides geographical visualization of selected patterns or rules and helps users to navigate and find the relevant information. Key principles of our model include multi-view visualizations that provide comprehensive insights by displaying data from various perspectives simultaneously, and geo-spatial time-varying data visualization that captures and represents the dynamic movements of marine species. This approach allows domain experts to interactively explore and analyse sequential patterns and rules, enhancing their ability to make informed decisions. Additionally, feedback received, and insights gained from specialized professionals was used to determine if this approach would be a beneficial complement to existing practice. • A visualization for interactive analysis of sequential movement patterns • Automatic extraction of rules for marine animal behaviour • Enhances the ability of oceanic experts to make informed decisions • Domain experts performed an evaluation using exploratory methods [ABSTRACT FROM AUTHOR]

We present a novel approach to understanding space use by moving entities based on repeated patterns of place visits and transitions. Our approach represents trajectories as text documents consisting of sequences of place visits or transitions and applies topic modelling to the corpus of these documents. The resulting topics represent combinations of places or transitions, respectively, that repeatedly co-occur in trips. Visualisation of the results in the spatial context reveals the regions of place connectivity through movements and the major channels used to traverse the space. This enables understanding of the use of space as a medium for movement. We compare the possibilities provided by topic modelling to alternative approaches exploiting a numeric measure of pairwise connectedness. We have extensively explored the potential of utilising topic modelling by applying our approach to multiple real-world movement data sets with different data collection procedures and varying spatial and temporal properties: GPS road traffic of cars, unconstrained movement on a football pitch, and episodic movement data reflecting social media posting events. The approach successfully demonstrated the ability to uncover meaningful patterns and interesting insights. We thoroughly discuss different aspects of the approach and share the knowledge and experience we have gained with people who might be potentially interested in analysing movement data by means of topic modelling methods. [Display omitted] • A novel approach to understanding space use by moving entities. • Represents trajectories as text documents for topic modeling. • Reveals repeated patterns of place visits and transitions. • Visualises patterns for interpretation and interactive investigation. • Establishing meanings of different pattern types. • Comparison of topic modeling to numeric measures of pairwise connectedness. • Discussion of the the possibilities provided by topic modeling. • Examination of the approach with diverse sets of movement data. [ABSTRACT FROM AUTHOR]

Visual analysis of unknown data requires the combined use of various functions that are often part of standalone visual analytics (VA) tools. Performing cross-tool visual analysis with standalone VA tools, however, is a challenging and cumbersome endeavor. Some dedicated frameworks address this issue, yet in order to utilize any of them, a visual analytics tool needs to support their required API or architecture. Contrary to most existing frameworks, we present an approach that does not rely on a single predefined interchange mechanism for the entire ensemble of VA tools. Instead, we propose using any available channel for data exchange between two consecutive VA tools. This allows mixing and matching of different data exchange strategies over the course of a cross-tool analysis. In this paper, we identify the challenges associated with establishing such tool chaining platform for data-driven coordination. We further describe the structure and capabilities of data exchange and explain various functionalities of our platform in detail. Based on a demonstrating example, we discuss the limitations of our approach and elaborate new insight for the coordination of the visual output of multiple VA tools. [Display omitted] • We provide an in-depth investigation on different aspects for the coordination of independent Visual Analytics (VA) tools that lead to the development of the the Analytical Process Constructor (AnyProc). • We address issues from related work regarding data exchange, UI integration and analytic process support and propose first solution ideas based on our conceptual preliminary work. • We discuss the idea of pairwise data exchange mechanisms for VA tool coordination in domain workflows and present design principles to deal with persisting challenges. • Our main contribution is the introduction of our platform for the coordination of independent VA tools. We describe its features in detail especially regarding different solutions to the implementation of data exchange mechanisms. • We demonstrate the use of our platform for an example scenario in the healthcare sector and link its video presentation from our original submission at the EuroVA 2021. • We discuss our recent advances regarding UI layouts and conclude by summarizing our findings and suggesting entry points for future research. [ABSTRACT FROM AUTHOR]

A major challenge for departments of public health (DPHs) in dealing with the ongoing COVID-19 pandemic is tracing contacts in exponentially growing SARS-CoV-2 infection clusters. Prevention of further disease spread requires a comprehensive registration of the connections between individuals and clusters. Due to the high number of infections with unknown origin, the healthcare analysts need to identify connected cases and clusters through accumulated epidemiological knowledge and the metadata of the infections in their database. Here we contribute a visual analytics dashboard to identify, assess and visualize clusters in COVID-19 contact tracing networks. Additionally, we demonstrate how graph-based machine learning methods can be used to find missing links between infection clusters and thus support the mission to get a comprehensive view on infection events. This work was developed through close collaboration with DPHs in Germany. We argue how our dashboard supports the identification of clusters by public health experts, discuss ongoing developments and possible extensions. [Display omitted] • Visual analysis of infections supports the work of public health experts in new ways. • We can predict undocumented links between infected persons from few features. • Geospatial, temporal and graph visualization provide insights into the infection data. • Public health experts give positive feedback on the visual analytics framework. [ABSTRACT FROM AUTHOR]

Background: Previous research has established that anxiety is associated with freezing of gait (FOG) in Parkinson's disease (PD). Although providing body-related visual feedback has been previously suggested to improve FOG, it remains unclear whether anxiety-induced FOG might be improved.Research Question: The current study aimed to evaluate whether body-related visual feedback (VF) improves FOG consistently across low and high threat conditions.Methods: Sixteen PD patients with FOG were instructed to walk across a plank in a virtual environment that was either located on the ground (low threat) or elevated above a deep pit (high threat). Additionally, visual feedback (VF) was either provided (+) or omitted (-) using an avatar that was synchronised in real-time with the participants movements.Results: revealed that in the low threat condition (i.e., ground), %FOG was significantly reduced when VF was provided (VF+) compared to when VF was absent (VF-). In contrast, during the elevated high threat condition, there were no differences in %FOG regardless of whether VF was provided or not.Significance: These findings confirm that although VF can aid in the reduction of FOG, anxiety may interfere with freezers' ability to use sensory feedback to improve FOG and hence, in high threat conditions, VF was unable to aid in the reduction of FOG. Future studies should direct efforts towards the treatment of anxiety to determine whether better management of anxiety may improve FOG. [ABSTRACT FROM AUTHOR]

In the last 20 years, extreme weather-related events like floods, landslides, droughts, and wildfires have caused the death of 1.23 million people and a loss of 2.97 trillion dollars. Studies show that low and lower-middle income countries are the most impacted ones given the lack of investment in disaster risk management. To reduce the impact of these events, weather researchers have been developing numerical weather models that inform public agencies about the impending extreme events in advance. Despite being powerful tools, these models can suffer from several sources of uncertainty, ranging from the approximation of micro-scale physical processes to the location-dependent calibration of parameters, which is especially critical in developing countries. To minimize uncertainty effects, researchers generate several different weather scenarios to compose an ensemble of simulations that typically are inspected using manual, laborious, and error-prone approaches. In this paper, we propose an interactive visual analytics system, called X-Weather , developed in close collaboration with weather researchers from Brazil. Our system contributes a set of statistics and probability-based visualizations that allows the assessment of extreme weather events by effortlessly navigating through and comparing ensemble members. We demonstrate the effectiveness of the system through two case studies analyzing tragic events that happened in the mountain region of Rio de Janeiro in Brazil. [Display omitted] • Our visualizations help identify extreme weather scenarios in large simulation ensembles. • Our web-based system enables the investigation of individual weather ensemble members. • Two case studies highlight our system's utility by analyzing extreme weather events. [ABSTRACT FROM AUTHOR]

Understanding user-level energy demand is pivotal for sustainable development and smart grid implementation, as it facilitates optimal resource allocation and energy conservation. Traditional machine learning models, however, often ignored the potential connections between users, limiting comprehension of user-level energy demands. Capturing behavioral correlations among users in high-dimensional temporal data remains challenging. In this paper, a novel scheme is proposed for revealing the implicit energy behavior correlation, which serves as prior knowledge to enhance predictive model performance. In addition, a spatio-temporal feature extraction framework is introduced to fuse spatial and temporal information, capturing the coherence of energy consumption data. This approach captures the correlation between the energy consumption patterns of different users, achieving highly accurate demand forecasting at the user level. In fine-grained demand forecasting experiments, the prediction accuracy of the proposed approach was improved by 14% compared with the baseline model. Based on fine-grained prediction results, an innovative visual analytical interface is also developed to characterize the migration of energy demand over time in both physical and topological space, offering valuable insights into demand-side energy management. In the empirical study, we found that there is an obvious mental inertia in the energy behavior of urban residents, which leads to energy waste. Our research provides critical insights for policymakers and planners in addressing the sustainability challenges of urban energy systems. • Mapped high-dimensional energy data in 2D space, identified 6 basic energy patterns. • Developed a user-level energy forecasting model with spatio-temporal correlations. • Created a visual tool to track demand migration, aided demand-side management. [ABSTRACT FROM AUTHOR]

Data visualisation is broadly utilised to aid in presenting complex information, facilitating decision-making, and enhancing operational efficiency in the mining sector. However, due to the extensive utilisation of CAD files and the complexity and difficulty of converting 2D drawings into 3D models, it is challenging to realise (near) real-time 3D data visualisation in mining applications. The difficulties are including site drawing complexity, lack of standardisation, time-consuming cleaning, and imperative manual intervention. This paper addresses these challenges by focusing on an automatic, data-driven approach for 3D visual model development in mining, with an emphasis on lifetime support and sustainability. Therefore, we introduce standardised visual model data diagram and proposed conversion workflows to leverage digitised 2D drawings in automatic 3D visual model development. Moreover, to improve visualisation efficiency and facilitate visual analytics construction in mining, this paper also proposes a 3D tile index methodology for efficient visual model reconstruction and generic production data integration. To ensure lifetime support and sustainable development in visualisation, we propose two model expansion schemes: one based on dxf file-driven model development, and the other one is interaction-oriented 3D model design and development, which the two schemes share the same data exchange and conversion workflows. Furthermore, to facilitate data fusion among various datasets, we also provide insights into revealing spatial–temporal patterns among multimodality data by real-time visualisation. These advancements aim to propel the field of underground mining visualisation, offering significant insights into enhancing the efficiency and safety of mining operations. • CAD drawing-based data-driven visual model development in underground mining. • Proposing 3D tile index for visual model reconstruction and interactive visualisation. • Developing interactive visualisation-oriented visual analytics framework for mining visualisation and data fusion. • Developing automation and real-time data visualisation system in underground mining. [ABSTRACT FROM AUTHOR]

Open data is a strategy used by governments to promote transparency and accountability in public procurement processes. To reap the benefits of open data, exploring and analyzing the data is necessary to gain meaningful insights into procurement practices. However, accessing, processing, and analyzing open data can be challenging for non-data-savvy users with domain expertise, creating a barrier to leveraging open procurement data. To address this issue, we present the design, development, and implementation of a visual analytics tool. This tool automates data extraction from multiple sources, performs data cleansing, standardization, and database processing, and generates meaningful visualizations to streamline public procurement analysis. In addition, the tool estimates and visualizes corruption risk indicators at different levels (e.g., regions or public entities), providing valuable insights into the integrity of the procurement process. Key contributions of this work include: (1) providing a comprehensive guide to the development of an open procurement data visualization tool; (2) proposing a data pipeline to support processing, corruption risk estimator and data visualization; (3) demonstrating through a case study how visual analytics can effectively use open data to generate insights that promote and enhance transparency. [ABSTRACT FROM AUTHOR]

Domain experts are driven by business needs, while data analysts develop and use various algorithms, methods, and tools, but often without domain knowledge. A major challenge for companies and organizations is to integrate data analytics in business processes and workflows. We deduce an interactive process and visualization framework to enable value creating collaboration in inter- and cross-disciplinary teams. Domain experts and data analysts are both empowered to analyze and discuss results and come to well-founded insights and implications. Inspired by a typical auditing problem, we develop and apply a visualization framework to single out unusual data in general subsets for potential further investigation. Our framework is applicable to both unusual data detected manually by domain experts or by algorithms applied by data analysts. Application examples show typical interaction, collaboration, visualization, and decision support. [ABSTRACT FROM AUTHOR]

• Hybrid models of machine learning and extreme value theory models for estimating pedestrian crashes. • Anomaly detection techniques identify traffic conflict extremes for modelling. • The minimum covariance determinant-based hybrid model shows better performance. • Generalised Pareto models performed better than Generalised extreme value models. Pedestrians represent a group of vulnerable road users who are at a higher risk of sustaining severe injuries than other road users. As such, proactively assessing pedestrian crash risks is of paramount importance. Recently, extreme value theory models have been employed for proactively assessing crash risks from traffic conflicts, whereby the underpinning of these models are two sampling approaches, namely block maxima and peak over threshold. Earlier studies reported poor accuracy and large uncertainty of these models, which has been largely attributed to limited sample size. Another fundamental reason for such poor performance could be the improper selection of traffic conflict extremes due to the lack of an efficient sampling mechanism. To test this hypothesis and demonstrate the effect of sampling technique on extreme value theory models, this study aims to develop hybrid models whereby unconventional sampling techniques were used to select the extreme vehicle–pedestrian conflicts that were then modelled using extreme value distributions to estimate the crash risk. Unconventional sampling techniques refer to unsupervised machine learning-based anomaly detection techniques. In particular, Isolation forest and minimum covariance determinant techniques were used to identify extreme vehicle–pedestrian conflicts characterised by post encroachment time as the traffic conflict measure. Video data was collected for four weekdays (6 am–6 pm) from three four-legged intersections in Brisbane, Australia and processed using artificial intelligence-based video analytics. Results indicate that mean crash estimates of hybrid models were much closer to observed crashes with narrower confidence intervals as compared with traditional extreme value models. The findings of this study demonstrate the suitability of machine learning-based anomaly detection techniques to augment the performance of existing extreme value models for estimating pedestrian crashes from traffic conflicts. These findings are envisaged to further explore the possibility of utilising more advanced machine learning models for traffic conflict techniques. [ABSTRACT FROM AUTHOR]

We introduce a novel visual-interactive approach for analyzing, understanding, and correcting neural machine translation. Our system supports users in automatically translating documents using neural machine translation and identifying and correcting possible erroneous translations. User corrections can then be used to fine-tune the neural machine translation model and automatically improve the whole document. While translation results of neural machine translation can be impressive, there are still many challenges such as over- and under-translation, domain-specific terminology, and handling long sentences, making it necessary for users to verify translation results. Our system aims at supporting users in this task. Our visual analytics approach combines several visualization techniques in an interactive system. A parallel coordinates plot with multiple metrics related to translation quality can be used to find, filter, and select translations that might contain errors. An interactive beam search visualization and graph- or matrix-based visualizations for attention weights can be used for post-editing and understanding machine-generated translations. The machine translation model is updated from user corrections to improve the translation quality of the whole document. We designed our approach for an LSTM-based translation model and extended it to also include the Transformer architecture. We show for representative examples possible mistranslations and how to use our system to deal with them. A user study revealed that many participants favor such a system over manual text-based translation, especially for translating large documents. Furthermore, we performed quantitative computer-based experiments that show that our system can be used to improve translation quality and reduce post-editing efforts for domain-specific documents. [Display omitted] • A visual-interactive approach for analyzing, understanding, and correcting NMT. • Visualizations show model-specific information and metrics for translation quality. • Our approach supports both LSTM-based NMT models and the Transformer architecture. • The quantitative computer-based evaluation reveals reduced post-editing efforts. [ABSTRACT FROM AUTHOR]

Digital libraries represent the most valuable resource for storing, querying, and retrieving scientific literature. Traditionally, the reader/analyst aims to compose a set of articles based on keywords, according to his/her preferences, and manually inspect the resulting list of documents. Except for the articles which share citations or common keywords, the results retrieved will be limited to those which fulfill a syntactic match. Besides, if instead of having an article as a reference, the user has an image, the process of finding and exploring articles with similar content becomes infeasible. This paper proposes a visual analytic methodology for exploring and analyzing scientific document collections that consider both textual and image content. The proposed technique relies on combining multiple Content-Based Image Retrieval (CBIR) components and multidimensional projection to map the documents to a visual space based on their similarity, thus enabling an interactive exploration. Moreover, we extend its analytical capabilities with visual resources to display complementary information on selected documents that uncover hidden patterns and semantic relations. We evidence the effectiveness of our methodology through three case studies and a user evaluation, which attest to its usefulness during the process of scientific collections exploration. [Display omitted] • A methodology for handling scientific articles based on textual and image content. • A visual analytic tool called DRIFT which implements the proposed methodology. • Three case studies and a user evaluation demonstrate the utility of our methodology. [ABSTRACT FROM AUTHOR]

Puzzle-based training is a common type of hands-on activity accompanying formal and informal cybersecurity education, much like programming or other IT skills. However, there is a lack of tools to help the educators with the post-training data analysis. Through a visualization design study, we designed the Training Analysis Tool that supports learning analysis of a single hands-on session. It allows an in-depth trainee comparison and enables the identification of flaws in puzzle assignments. We also performed a qualitative evaluation with cybersecurity experts and students. The participants apprised the positive influence of the tool on their workflows. Our insights and recommendations could aid the design of future tools supporting educators, even beyond cyber security. [Display omitted] • The logs from serious games are available, still, there is a lack of tools to help analyze their content. • Through a design study with domain experts, we created an analytical tool for cybersecurity training. • An evaluation confirmed the tool's usefulness in analyzing players' behavior and game design issues. • The core design requirements are transferable to other domains with puzzle-based serious games. [ABSTRACT FROM AUTHOR]

Deep learning (DL) models have achieved impressive performance in various domains such as medicine, finance, and autonomous vehicle systems with advances in computing power and technologies. However, due to the black-box structure of DL models, the decisions of these learning models often need to be explained to end-users. Explainable Artificial Intelligence (XAI) provides explanations of black-box models to reveal the behavior and underlying decision-making mechanisms of the models through tools, techniques, and algorithms. Visualization techniques help to present model and prediction explanations in a more understandable, explainable, and interpretable way. This survey paper aims to review current trends and challenges of visual analytics in interpreting DL models by adopting XAI methods and present future research directions in this area. We reviewed literature based on two different aspects, model usage and visual approaches. We addressed several research questions based on our findings and then discussed missing points, research gaps, and potential future research directions. This survey provides guidelines to develop a better interpretation of neural networks through XAI methods in the field of visual analytics. [Display omitted] • A comprehensive survey for visual analytics, particularly those that adopted explainable artificial intelligence (XAI) methods, in interpreting Neural Networks is conducted. • We reviewed the literature based on model usage and visual approaches. • We concluded some visual approaches commonly used to support the illustration of XAI methods for various types of data and machine learning models; however, a generic approach is needed for the field. • We listed several future work including data manipulations, scalability, and bias in data representation, and generalizable real-time visualizations integrating XAI. [ABSTRACT FROM AUTHOR]

• A multigranular model of co-adaptation with a focus on making co-adaptation testable. • Learning phases: define testable goals for user and system to structure design. • Adaptation processes: actors adapt knowledge representation models. • Interaction dynamics: applications enable adaptation in learning-teaching dynamics. • A discussion of actor expectations towards adaptation. • An overview of research opportunities in co-adaptive analytics. [Display omitted] Mixed-initiative visual data analysis processes are characterized by the co-adaptation of users and systems over time. As the analysis progresses, both actors – users and systems – gather information, update their analysis behavior, and work on different tasks towards their respective goals. In this paper, we contribute a multigranular model of co-adaptive visual analysis that is centered around incremental learning goals derived from a hierarchical taxonomy of learning goals from pedagogy. Our model captures how both actors adapt their data-, task-, and user/system-models over time. We characterize interaction patterns in terms of the dynamics of learning and teaching that drive adaptation. To demonstrate our model's applicability, we outline aspects of co-adaptation in related models of visual analytics and highlight co-adaptation in existing applications. We further postulate a set of expectations towards adaptation in mixed-initiative processes and identify open research questions and opportunities for future work in co-adaptation. [ABSTRACT FROM AUTHOR]

Crossmodal correspondences are spontaneous associations of non-redundant information across different modalities. Infants and some non-human animals (i.e., chimpanzees, rhesus monkeys, and dogs) showed crossmodal correspondences like adult humans, suggesting a shared origin (at least among mammals) of such a phenomenon. Here we investigate visual-spatial crossmodal correspondences in a precocial avian species, i.e., the domestic chicken. Three-day-old chicks (n = 40) were presented with two (one in the left and one in the right hemispace of an arena) identical panels, either dyed black (low luminance) or white (high luminance). Chicks could circumnavigate either panel to obtain a food reward. Akin to humans, they preferentially chose the left side when presented with black panels and the right side when presented with white panels. The control group (n = 39), tested with grey panels, showed no spatial preference. In light of our results, we discuss crossmodal correspondences in terms of an early available mechanism widespread across different species. [ABSTRACT FROM AUTHOR]

The use of clinical dashboards has expanded significantly in healthcare in recent years in a variety of settings. The ability to analyze data related to quality metrics in one screen is highly desirable for cardiac anesthesiologists, as they have considerable influence on important clinical outcomes. Building a robust quality program within cardiac anesthesia relies on consistent access and review of quality outcome measures, process measures, and operational measures through a clinical dashboard. Signals and trends in these measures may be compared to other cardiac surgical programs to analyze gaps and areas for quality improvement efforts. In this article, the authors describe how they designed a clinical cardiac anesthesia dashboard for quality efforts at their institution. [ABSTRACT FROM AUTHOR]

• Requirements for an uncertainty-aware visualization of proteins. • Uncertainty-aware description of a protein and its properties. • Uncertainty-aware visual analytics framework of protein structures and their properties. [Display omitted] Due to the limitations of existing experimental methods for capturing stereochemical molecular data, there usually is an inherent level of uncertainty present in models describing the conformation of macromolecules. This uncertainty can originate from various sources and can have a significant effect on algorithms and decisions based upon such models. Incorporating uncertainty in state-of-the-art visualization approaches for molecular data is an important issue to ensure that scientists analyzing the data are aware of the inherent uncertainty present in the representation of the molecular data. In this work, we introduce a framework that allows biochemists to explore molecular data in a familiar environment while including uncertainty information within the visualizations. Our framework is based on an anisotropic description of proteins that can be propagated along with required computations, providing multiple views that extend prominent visualization approaches to visually encode uncertainty of atom positions, allowing interactive exploration. We show the effectiveness of our approach by applying it to multiple real-world datasets and gathering user feedback. [ABSTRACT FROM AUTHOR]

• We provide additional examples of how the five explanatory views in [Van Driel et al] and [Da Silva et al] can be combined in a visual analytics fashion to find relevant insights in high-dimensional datasets that cannot be found using a single view. • We illustrate the above process on five non-synthetic datasets, and correlate the obtained insights with ground-truth information independently extracted by other researchers from three of these datasets. Of these datsets, a single one has been used in the earlier work, the other being new. Also, the correlation of obtained insights with the ground-truth information is new. • We present a new explanatory method, variance ratio, for computing local dimensionality. • We discuss in detail the parameter settings and dependency on the used projection techniques of our proposed explanatory visualization. [Display omitted] The Graphical Abstract includes the first row of Figure 3 in the paper. From left to right, these images are the explanation of the wine quality dataset using the dimension contribution, variance, and dimension correlation. Multidimensional projections (MPs) are effective methods for visualizing high-dimensional datasets to find structures in the data like groups of similar points and outliers. The insights obtained from MPs can be amplified by complementing these techniques by several so-called explanatory mechanisms. We present and discuss a set of six such mechanisms that explain MPs in terms of similar dimensions, local dimensionality, and dimension correlations. We implement our explanatory tools using an image-based approach, which is efficient to compute, scales well visually for large and dense MP scatterplots, and can handle any projection technique. We demonstrate how the provided explanatory views can be combined to augment each other's value and thereby lead to refined insights in the data for several high-dimensional datasets, and how these insights correlate with known facts about the data under study. [ABSTRACT FROM AUTHOR]

The COVID-19 epidemic propagation computational models generate datasets that exhibit multi-level and time granulation. The Predictive Modelling of the Spatial Propagation of the COVID-19 Pandemic Project (ProME) has produced multi-scenario, multi-agent models for decision making support assessing the impact on the healthcare and the general population. In this paper we present the interactive software developed for models' calibration and visual analysis, addressing the needs of all aspects of data analytics and modeling that arise within ProME system. In order to deal with the Project's tasks we developed the application based on multi-modal, open-source VisNow platform. [ABSTRACT FROM AUTHOR]

[Display omitted] • A new blue noise sampling is proposed to preserve the spatial distribution of standard wells. • A stratigraphic correlation model is designed to estimate the similarity between wells. • A discrete choice model is used to select standard wells with stochastic utility maximization. • A visualizaiton framework is implemented enabling users to visually select and evaluate standard wells. It is a time-consuming and laborious task to conduct accurate geological interpretation based on large-scale logging data. A subset of drilling wells are always selected undergoing detailed stratigraphic correlation, aiming to enhance the efficiency of geological interpretation and retain the matching accuracy of stratigraphic structures. However, standard wells are selected by hand in the course of traditional standard well selection, which will easily bring many uncertainties for subsequent interpretation of geological structures. In this paper, we propose a visual analytics system to support supervised standard well selection via a discrete choice model. Firstly, an adaptive blue noise sampling model is applied to determine spatial distribution of standard wells, and a stratigraphic correlation model is designed to measure the similarity between drilling wells from different attribute perspectives. Then, we utilize a discrete choice model to select standard wells with the spatial distribution and multiple attributes taken into consideration. Furthermore, several meaningful visualizations are designed allowing users to get deeper insights into the selection of standard wells, and a rich set of interactions are also provided enabling users to further optimize the discrete choice model. At last, a visualization framework is implemented to integrate the models and visual designs, by means of which the geological interpreters are able to visually select, evaluate and optimize the standard wells. The effectiveness of our work and its application values for geological interpretation are further demonstrated through case studies based on real-world datasets and interviews with domain experts. [ABSTRACT FROM AUTHOR]

The framework for clustering of error messages, ClusterLogs, was developed as a flexible and modular tool for the needs of large-scale distributed computing infrastructures. Various types of failures are being constantly registered during the execution of millions of computing operations daily. Monitoring systems are faced with a challenging task of the analysis of considerable amount of multi-sourced error messages. It is critical to present the information about errors to human experts in a way that makes them able to analyze it. ClusterLogs is a pipeline of methods that allows the discovering of common patterns in error messages and group similar messages for further analysis. This pipeline is able to cluster heterogeneous messages without any prior knowledge about textual pattern and can be executed in a fully automated way. In this work we describe a visual analytics web application that provides the analyst with the visual means of setting up clustering pipeline parameters and facilitates the interpretation and validation of the clustering results. [ABSTRACT FROM AUTHOR]

Among the challenges of the big data era, the analysis of high-dimensional data is still an open research area. As a result, several multidimensional projection techniques have been developed to reduce data dimensionality, becoming important visualization and visual analytics tools. In order to ensure the quality of projections, it is necessary to assess the low-dimensional embeddings by using different dataset configurations as input and analyzing evaluation metrics. However, it is not clear to the user how factors such as the number of dimensions, instances, or clusters, can affect the projection mapping and its quality regarding different projection techniques and assessment metrics. The research reported in this paper aims to clarify how much these factors affect each response variable via performance evaluation planning. We present an evaluation approach, supported by factorial design, that carries out a complete analysis, in the sense of measuring all possible combinations of all the input factors. The results of the analyses of local and global structure preservation in the projections yield a better understanding of how distinct dataset properties can influence the choice of projections based on quality metrics results. [ABSTRACT FROM AUTHOR]

• An application for exploration and prediction of pelvic organ variability in radiotherapy. • Retrospective patient data are employed to predict anatomical changes in new incoming patients. • The prediction is linked to treatment plan evaluation, supporting the selection of the optimal treatment. • Usage scenarios and an evaluation; both conducted with eight domain experts. [Display omitted] Radiotherapy (RT) requires meticulous planning prior to treatment, where the RT plan is optimized with organ delineations on a pre-treatment Computed Tomography (CT) scan of the patient. The conventionally fractionated treatment usually lasts several weeks. Random changes (e.g., rectal and bladder filling in prostate cancer patients) and systematic changes (e.g., weight loss) occur while the patient is being treated. Therefore, the delivered dose distribution may deviate from the planned. Modern technology, in particular image guidance, allows to minimize these deviations, but risks for the patient remain. We present PREVIS : a visual analytics tool for (i) the exploration and prediction of changes in patient anatomy during the upcoming treatment, and (ii) the assessment of treatment strategies, with respect to the anticipated changes. Records of during-treatment changes from a retrospective imaging cohort with complete data are employed in PREVIS , to infer expected anatomical changes of new incoming patients with incomplete data, using a generative model. Abstracted representations of the retrospective cohort partitioning provide insight into an underlying automated clustering, showing main modes of variation for past patients. Interactive similarity representations support an informed selection of matching between new incoming patients and past patients. A Principal Component Analysis (PCA)-based generative model describes the predicted spatial probability distributions of the incoming patient's organs in the upcoming weeks of treatment, based on observations of past patients. The generative model is interactively linked to treatment plan evaluation, supporting the selection of the optimal treatment strategy. We present a usage scenario, demonstrating the applicability of PREVIS in a clinical research setting, and we evaluate our visual analytics tool with eight clinical researchers. [ABSTRACT FROM AUTHOR]

• Design and implementation of the interactive analysis tool ProSeCo. • Tackles the challenge of comparing separation measures qualitatively and quantitatively. • Comparison between up to 20 class separation measures. • Comparison between up to 4 dimensionality reduction techniques. • Analysis of 7 data characteristics in the context of class separation measures. [Display omitted] Class separation is an important concept in machine learning and visual analytics. We address the visual analysis of class separation measures for both high-dimensional data and its corresponding projections into 2D through dimensionality reduction (DR) methods. Although a plethora of separation measures have been proposed, it is difficult to compare class separation between multiple datasets with different characteristics, multiple separation measures, and multiple DR methods. We present ProSeCo, an interactive visualization approach to support comparison between up to 20 class separation measures and up to 4 DR methods, with respect to any of 7 dataset characteristics: dataset size, dataset dimensions, class counts, class size variability, class size skewness, outlieriness, and real-world vs. synthetically generated data. ProSeCo supports (1) comparing across measures, (2) comparing high-dimensional to dimensionally-reduced 2D data across measures, (3) comparing between different DR methods across measures, (4) partitioning with respect to a dataset characteristic, (5) comparing partitions for a selected characteristic across measures, and (6) inspecting individual datasets in detail. We demonstrate the utility of ProSeCo in two usage scenarios, using datasets [1] posted at https://osf.io/epcf9/. [ABSTRACT FROM AUTHOR]

Deep Learning (DL)-based visual analytic systems have demonstrated substantial potential in enhancing construction management. Yet, these systems should go beyond merely facilitating recognition-based tasks and strive to capture high-level semantics. While ontologies have emerged as foundational tools for semantic-enriched visual analytic systems, many existing application-oriented ontologies in the construction domain lack a comprehensive conceptualization and formalization of visual information, restricting their robustness and applicability. Furthermore, there is a notable gap in integrating low-level visual information and high-level semantics. To address these challenges, we introduce the Construction Semantic Enrichment ontology (ConSE). ConSE offers a formal representation of visual information relevant to construction site imagery. It is designed to support the development of semantic-enriched visual analytic systems in the construction domain. To evaluate the ontology, we implement an automated consistency-checking process internally. In addition, we execute three task-based use cases to validate the proposed ontology. The evaluation results demonstrate the efficacy and practicality of the ConSE, confirming its ability to bridge the identified research gaps and contribute to the advancement of semantic-enriched visual analytic systems in the construction domain. [ABSTRACT FROM AUTHOR]

The Knowledge Discovery from Data (KDD) process is widely used across various domains to get valuable insights from data. Many platforms, like KNIME or RapidMiner, offer effective tools for KDD analysts, allowing them to perform data analytics tasks in a visual fashion, without writing code. In recent years, the increasing availability of mobility data has led to a surge in KDD-based initiatives from both industry and academia in the Intelligent Transportation Systems (ITS) domain. Still, KDD platforms lack comprehensive support for some typical mobility data manipulation tasks. As a result, mobility data analysis still requires a significant coding phase, with reduced productivity and hindered replicability of results. To address this gap, this paper presents a novel solution aimed at supporting ITS data analysts in defining KDD processes more efficiently. More in detail, we extended the KNIME platform by introducing a collection of new components explicitly tailored to facilitate some peculiar KDD tasks from mobility data. These components encompass critical functionalities such as map coverage analysis, trajectory partitioning and map-matching. To showcase the effectiveness of the proposed solution, we used it to replicate a study published in the ITS data analytics domain. Thanks to our proposal, such replication can be accomplished in a few minutes and with just a few clicks, without any manual coding, resulting in a pipeline that is easier to understand, distribute and re-execute, also for domain experts with no programming experience. Our solution is open-source and freely downloadable from the Knime Hub. In this way, we aim to foster data-driven research and practice in the ITS field, by providing researchers and practitioners with more effective analytics tools to handle mobility data. • Visual analytics platforms lack support for handling mobility data in ITS scenarios. • We present a visual-based toolkit to support mobility data analytics for ITS. • The solution allows practitioners to perform complex analyses without writing code. • The solution is open-source and integrated within the KNIME Analytics Platform. • The effectiveness of the solution is assessed in a real-world ITS analytics scenario. [ABSTRACT FROM AUTHOR]

• A framework to support the analysis of iterative LtR models and the interpretation of the produced results. • A visualization to compare two different rankings, allowing the ranking evolution analysis based on user feedback. • A visual representation that explains a ranking by showing how (individual) features contribute to the data instances ranking positions. • A visualization that supports the interpretation of ranking results considering the complex interplay among data features. Although Machine Learning (ML) tools and techniques are widespread and quite popular, in some cases, the patterns found by a model are either not accessible or not easily understandable from a human perspective. Therefore, not only model fitness given a quality metric is important, but also understanding how it makes decisions has become critical. One example of an ML approach growing in relevance that still lacks support to interpretation is the Learning to Rank (LtR). LtR models are typically used to rank elements, and, as in most ML areas, much effort has been put into creating more accurate models, but little or no effort has been devoted to understanding how elements are ranked. In this paper, we propose RankViz , a novel visualization framework that aims to fill this gap by supporting LtR model analysis and interpretation through a set of coordinated visualizations. RankViz provides information about the most important data features to a specific ranking result, supports a detailed comparative analysis of elements' positions, and enables the investigation of iterative models evolution. Our results and study cases show the usefulness of the proposed framework to investigate a model and to aid users in creating and understanding a ranking, supporting tasks that are difficult, if not impossible, to be executed without proper visual representations. [ABSTRACT FROM AUTHOR]

Background: Despite advances in business intelligence software and evidence that feedback to doctors can improve outcomes, objective feedback regarding patient outcomes for individual anaesthetists is hampered by lack of useful benchmarks. We aimed to address this issue by producing case-mix and risk-adjusted postanaesthesia care unit (PACU) length of stay (LOS) benchmarks for integration into modern reporting tools.Methods: We extended existing hospital information systems to calculate predicted PACU LOS using a neural network trained on patient age, surgery duration, sex, operating specialty, urgency, weekday, and insurance status (n=100 511). We then calculated the difference between observed mean and predicted PACU LOS for individual doctors, and compared the results with and without case-mix adjustment. We report practical implications of using visual analytics dashboards displaying the difference between observed and predicted PACU LOS to provide feedback to anaesthetic doctors.Results: The neural network accounted for over half of observed variation in individual doctors' mean PACU LOS (mean predicted and mean actual LOS Spearman's r2=0.57). Account for case-mix reduced apparent spread, with 80% of individual doctors falling in a band of 4.3 min after case-mix adjusting, compared with a range of 24 min without adjustment. Case-mix adjusting also identified different individual doctors as outliers (Weighted Cohen's kappa [κ]=0.27). Finally, we demonstrated that we were able to integrate the adjusted metrics into routine reporting tools.Conclusion: With caution, case-mix adjustment of anaesthetic outcome measures such as PACU LOS potentially provides a useful continuous quality improvement tool. Unadjusted outcome measures are imprecise at best and misleading at worst. [ABSTRACT FROM AUTHOR]

The purpose of camouflage is to be inconspicuous against a given background. Colour is an important component of camouflage, and the task of designing a single camouflage pattern for use against multiple different backgrounds is particularly challenging. As it is impossible to match the colour gamut of each background exactly, the question arises which colours from the different backgrounds should be incorporated in a camouflage pattern to achieve optimal concealment. Here, we used a visual search paradigm to address this question. Observers searched multi-coloured continuous textures for target regions defined by either the presence or absence of additional hues. Targets could be either a combination of five hues against a four-hued background ("patches"), or a combination of four hues against a five-hued background ("holes"). In Experiment 1, a search asymmetry was observed for the different targets, as observers were less accurate and slower at detecting holes than patches. Additionally, we observed a linear separability effect: search for a target was more difficult when the hue that defined the target was within the gamut of distractor colours (e.g. orange amongst reds and yellows). In Experiment 2, we further investigated "hole" targets designed for two different backgrounds and found that optimal concealment against both backgrounds was achieved by including intermediate colours that represented a compromise between the common colours and the unique colours of each background. The findings provide insights into how search asymmetries can be extended to complex texture properties and help inform the design process of camouflage for multiple backgrounds. [ABSTRACT FROM AUTHOR]

• An application for cohort exploration of pelvic organ variability in radiotherapy. • Global exploration of anatomical variability in an abstracted tabular view. • Local exploration of anatomical variability with 2D/3D comparative visualizations. • Correlation of anatomical variability with radiation doses and toxicity. • Four application scenarios conducted with domain experts to showcase VAPOR. In radiation therapy (RT) for prostate cancer, changes in patient anatomy during treatment might lead to inadequate tumor coverage and higher irradiation of healthy tissues in the nearby pelvic organs. Exploring and analyzing anatomical variability throughout the course of RT can support the design of more robust treatment strategies, while identifying patients that are prone to radiation-induced toxicity. We present VAPOR , a novel application for the exploration of pelvic organ variability in a cohort of patients, across the entire treatment process. Our application addresses: (i) the global exploration and analysis of anatomical variability in an abstracted tabular view, (ii) the local exploration and analysis thereof in anatomical 2D/3D views, where comparative and ensemble visualizations are integrated, and (iii) the correlation of anatomical variability with radiation doses and potential toxicity. The workflow is based on available retrospective cohort data, which include segmentations of the bladder, the prostate, and the rectum through the entire treatment period. VAPOR is applied to four usage scenarios, which were conducted with two medical physicists. Our application provides clinical researchers with promising support in demonstrating the significance of treatment adaptation to anatomical changes. [ABSTRACT FROM AUTHOR]

The importance of quality and efficiency has increased in recent years. Moreover, the rise of computational power and the development of advanced analytics has enabled the industry to enhance the performance of manufacturing systems. Therefore, further transparency of intermediate product states is necessary to derive appropriate actions. The goal of this paper is to develop a framework to enable the data-driven analysis of product state propagation within manufacturing systems to improve the transparency of product quality related cause-effect relationships. Based on their intermediate product features, machine learning algorithms assign products to classes of similar characteristics. This approach is practically applied to a case study from the electronic production industry. By using visual analytics tools, the propagation of product states along the manufacturing process chain is exemplarily analyzed. [ABSTRACT FROM AUTHOR]

This paper presents the design and a prototypical implementation of a tool for short-term energy demand forecasting in prosumer scenarios in local energy systems. The prototype combines explainable machine learning and visual analytics to facilitate the forecasting and analysis of energy demand and supply in a way usable for small utilities not used to complex analytical approaches. It applies a kNN (k-Nearest Neighbour) algorithm to forecast energy demand and supply and presents the results in an interactive visual dashboard that allows the user to analyze different forecast alternatives, to understand how they relate to the input parameters and identify consumption and production patterns. Initial assessment of the forecasting accuracy (MAPE 5.77%) on a limited dataset and the feedback from target users support the feasibility of the proposed approach. [ABSTRACT FROM AUTHOR]

Persona is a common human-computer interaction technique for increasing stakeholders' understanding of audiences, customers, or users. Applied in many domains, such as e-commerce, health, marketing, software development, and system design, personas have remained relatively unchanged for several decades. However, with the increasing popularity of digital user data and data science algorithms, there are new opportunities to progressively shift personas from general representations of user segments to precise interactive tools for decision-making. In this vision, the persona profile functions as an interface to a fully functional analytics system. With this research, we conceptually investigate how data-driven personas can be leveraged as analytics tools for understanding users. We present a conceptual framework consisting of (a) persona benefits, (b) analytics benefits, and (c) decision-making outcomes. We apply this framework for an analysis of digital marketing use cases to demonstrate how data-driven personas can be leveraged in practical situations. We then present a functional overview of an actual data-driven persona system that relies on the concept of data aggregation in which the fundamental question defines the unit of analysis for decision-making. The system provides several functionalities for stakeholders within organizations to address this question. [ABSTRACT FROM AUTHOR]

Nowadays stream data, flowing over the modern networks between disparate data sources, become the norm. The broadband Internet, the Internet of Things (IoT) and cloud computing require to analyze the data from streams to make data-driven decisions in real time. In today's world of more complex and increasing in the number of network attacks, one of their most important data is the data from network security (NS) tools, ensuring their secure and resilient operations and uninterrupted provision of services to its users. At Gartner Data & Analytics Summit-2019 augmented analytics and data management, as well as continuous intelligence and explainable artificial intelligence were indicated among the top trends in data and analytics technology that have significant disruptive potential over the next 3-5 years. In practice, complexities of the modern attack scenarios often make it difficult for NS administrators to understand the current NS-related status and to recognize emerging patterns of attacks in a vast amount of raw data before they make a substantial impact. To benefit from the NS-related stream data, businesses require powerful analytics tools for ingesting and processing it. There are four consequent levels of analytics maturity - namely descriptive, diagnostic, predictive, prescriptive. In this paper, a simplified NS-related stream data architecture, suitable for predicting attacks against network assets and services provided is proposed. In turn, MITRE ATT&CK Matrix is proposed as a source for attacks' Indicators of Compromise (IoCs) development. [ABSTRACT FROM AUTHOR]

• A rapid sampling model is employed to retain the topic distribution of social media datasets. • A blue noise sampling model is coupled with rapid sampling model to retain the spatial distribution. • A set of enhanced visualizations are designed to evaluate the results from different perspectives. • A visual analytics framework is implemented enabling users to explore social behaviors. A great deal of text and geographical information is provided in the geo-tagged social media data, which offers unprecedented opportunities to get insights into the social behaviors across different local areas. With the increasing size of geo-tagged social media data, a large number of visual mapping elements overlap with each other, which makes it difficult to visually capture topics of interest as well as their spatial distribution. In this paper, we propose a visual abstraction framework for the exploration of large scale geo-tagged social media data. Probabilistic topic modeling is firstly utilized to summarize the semantics of texts and extract a set of topic features of interest. Then, a multi-objective sampling model is designed to generate a subset of original dataset, which will not only reduce the visual clutter of large-scale social media data visualization, but also retain the ordering of topic features of interest as well as the geographical distribution of original social media datasets. A rich set of visual designs such as word cloud, text stream and heat map are integrated into the visual abstraction framework, enabling users to evaluate the sampled results from different perspectives including semantic topics, temporal changes and spatial distribution. Case studies based on real-world datasets and interviews with domain experts have demonstrated the effectiveness of our system in simplifying the geographical visualization of large scale geo-tagged social media data and exploring the social behaviors across different local areas. [ABSTRACT FROM AUTHOR]

• We propose a model-agnostic visual analytics tools to assist in understanding an ensemble of classifiers and its underlying models. • We provide an interactive visual analytics tools that allow both decision boundary and instance-based inspections. • We tested our approach using the MNIST benchmark data set. Inspecting the outputs of classification algorithms is becoming progressively difficult due to the increase in both scale and complexity of both the data and the algorithms. This has led to research efforts to develop new techniques to interpret the behavior of these algorithms and to facilitate the understanding of their results. A common classification approach is the "ensemble of classifiers", where a set of classifiers c ∈ C is trained on the input data set and the final classification is computed by "voting", i.e., ranking their results. One of the issues with this approach, however, is that instead of having only one classifier to analyze, now there are | C |, each with its characteristics. Thus, there is a demand for methods that provide insights into the results of an ensemble of classifiers and at the same time allow a detailed analysis of each classifier in the ensemble. Our work proposes to draw on dimensionality reduction techniques to provide visual tools to interpret the results of an ensemble of classifiers, while also giving insights into how each classifier contributes to the final results. Our approach also presents a measure of classification uncertainty by highlighting regions where there is a divergence among the classifiers in the ensemble, allowing one to focus their analysis on these regions. We tested our approach using the Digits MNIST and Fashion MNIST data sets. Through the use of maps that provide an overview of a classifier behavior to instance-based visualizations, we show how our approach can assist in the interpretation of why a specific decision (classification) was made. [ABSTRACT FROM AUTHOR]