Towards a Structural Framework for Explicit Domain Knowledge in Visual Analytics

Clinicians and other analysts working with healthcare data are in need for better support to cope with large and complex data. While an increasing number of visual analytics environments integrates explicit domain knowledge as a means to deliver a precise representation of the available data, theoretical work so far has focused on the role of knowledge in the visual analytics process. There has been little discussion about how such explicit domain knowledge can be structured in a generalized framework. This paper collects desiderata for such a structural framework, proposes how to address these desiderata based on the model of linked data, and demonstrates the applicability in a visual analytics environment for physiotherapy.Comment: 8 pages, 5 figure

Trustworthy Visual Analytics in Clinical Gait Analysis: A Case Study for Patients with Cerebral Palsy

Three-dimensional clinical gait analysis is essential for selecting optimal treatment interventions for patients with cerebral palsy (CP), but generates a large amount of time series data. For the automated analysis of these data, machine learning approaches yield promising results. However, due to their black-box nature, such approaches are often mistrusted by clinicians. We propose gaitXplorer, a visual analytics approach for the classification of CP-related gait patterns that integrates Grad-CAM, a well-established explainable artificial intelligence algorithm, for explanations of machine learning classifications. Regions of high relevance for classification are highlighted in the interactive visual interface. The approach is evaluated in a case study with two clinical gait experts. They inspected the explanations for a sample of eight patients using the visual interface and expressed which relevance scores they found trustworthy and which they found suspicious. Overall, the clinicians gave positive feedback on the approach as it allowed them a better understanding of which regions in the data were relevant for the classification.Comment: 7 pages, 4 figures; supplemental material 9 pages, 8 figures; to be published in the proceedings of the 2022 IEEE Workshop on TRust and EXpertise in Visual Analytics (TREX

Knowledge-Assisted Rule Building for Malware Analysis 103 -Recent Advances in Multimedia Processing, Organization and Visualization beyond Domains and Disciplines

Abstract Due to the increasing threat from malicious software (malware), the monitoring of vulnerable systems is becoming increasingly important, which includes the need to log and analyze activity ranging from networks, individual computers, to mobile devices. Currently available tools in behavior-based malware analysis do not meet all experts' needs, such as selecting different rules, categorizing them by their task and storing them in the database as well as manually adapting and/or tuning the rules identified. To close this gap, we designed CallNet, a knowledge-assisted visual analytics and rulebuilding tool for behavior-based malware analysis. The paper at hand is a design study which describes the design, a usage scenario, and the paper prototype evaluation. We report on the validation of CallNet by expert reviews, reflect on the insights gained from the reviews and, finally discuss the advantages and disadvantages of the prototype design including the visualization techniques applied

Twenty-first-century climate impacts from a declining Arctic sea ice cover

A steady decline in Arctic sea ice has been observed over recent decades. General circulation models predict further decreases under increasing greenhouse gas scenarios. Sea ice plays an important role in the climate system in that it influences ocean-to-atmosphere fluxes, surface albedo, and ocean buoyancy. The aim of this study is to isolate the climate impacts of a declining Arctic sea ice cover during the current century. The Hadley Centre Atmospheric Model (HadAM3) is forced with observed sea ice from 1980 to 2000 (obtained from satellite passive microwave radiometer data derived with the Bootstrap algorithm) and predicted sea ice reductions until 2100 under one moderate scenario and one severe scenario of ice decline, with a climatological SST field and increasing SSTs. Significant warming of the Arctic occurs during the twenty-first century (mean increase of between 1.6° and 3.9°C), with positive anomalies of up to 22°C locally. The majority of this is over ocean and limited to high latitudes, in contrast to recent observations of Northern Hemisphere warming. When a climatological SST field is used, statistically significant impacts on climate are only seen in winter, despite prescribing sea ice reductions in all months. When correspondingly increasing SSTs are incorporated, changes in climate are seen in both winter and summer, although the impacts in summer are much smaller. Alterations in atmospheric circulation and precipitation patterns are more widespread than temperature, extending down to midlatitude storm tracks. Results suggest that areas of Arctic land ice may even undergo net accumulation due to increased precipitation that results from loss of sea ice. Intensification of storm tracks implies that parts of Europe may experience higher precipitation rates

Effect of changes in climate and emissions on future sulfate-nitrate-ammonium aerosol levels in the United States

Global simulations of sulfate, nitrate, and ammonium aerosols are performed for the present day and 2050 using the chemical transport model GEOS-Chem. Changes in climate and emissions projected by the IPCC A1B scenario are imposed separately and together, with the primary focus of the work on future inorganic aerosol levels over the United States. Climate change alone is predicted to lead to decreases in levels of sulfate and ammonium in the southeast U.S. but increases in the Midwest and northeast U.S. Nitrate concentrations are projected to decrease across the U.S. as a result of climate change alone. In the U.S., climate change alone can cause changes in annually averaged sulfate-nitrate-ammonium of up to 0.61 μg/m^3, with seasonal changes often being much larger in magnitude. When changes in anthropogenic emissions are considered (with or without changes in climate), domestic sulfate concentrations are projected to decrease because of sulfur dioxide emission reductions, and nitrate concentrations are predicted to generally increase because of higher ammonia emissions combined with decreases in sulfate despite reductions in emissions of nitrogen oxides. The ammonium burden is projected to increase from 0.24 to 0.36 Tg, and the sulfate burden to increase from 0.28 to 0.40 Tg S as a result of globally higher ammonia and sulfate emissions in the future. The global nitrate burden is predicted to remain essentially constant at 0.35 Tg, with changes in both emissions and climate as a result of the competing effects of higher precursor emissions and increased temperature

Toward an improved representation of middle atmospheric dynamics thanks to the ARISE project

This paper reviews recent progress toward understanding the dynamics of the middle atmosphere in the framework of the Atmospheric Dynamics Research InfraStructure in Europe (ARISE) initiative. The middle atmosphere, integrating the stratosphere and mesosphere, is a crucial region which influences tropospheric weather and climate. Enhancing the understanding of middle atmosphere dynamics requires improved measurement of the propagation and breaking of planetary and gravity waves originating in the lowest levels of the atmosphere. Inter-comparison studies have shown large discrepancies between observations and models, especially during unresolved disturbances such as sudden stratospheric warmings for which model accuracy is poorer due to a lack of observational constraints. Correctly predicting the variability of the middle atmosphere can lead to improvements in tropospheric weather forecasts on timescales of weeks to season. The ARISE project integrates different station networks providing observations from ground to the lower thermosphere, including the infrasound system developed for the Comprehensive Nuclear-Test-Ban Treaty verification, the Lidar Network for the Detection of Atmospheric Composition Change, complementary meteor radars, wind radiometers, ionospheric sounders and satellites. This paper presents several examples which show how multi-instrument observations can provide a better description of the vertical dynamics structure of the middle atmosphere, especially during large disturbances such as gravity waves activity and stratospheric warming events. The paper then demonstrates the interest of ARISE data in data assimilation for weather forecasting and re-analyzes the determination of dynamics evolution with climate change and the monitoring of atmospheric extreme events which have an atmospheric signature, such as thunderstorms or volcanic eruptions