AOI-shapes: An Efficient Footprint Algorithm to Support Visualization of User-defined Urban Areas of Interest

Understanding urban areas of interest (AOIs) is essential in many real-life scenarios, and such AOIs can be computed based on the geographic points that satisfy user queries. In this article, we study the problem of efficient and effective visualization of user-defined urban AOIs in an interactive manner. In particular, we first define the problem of user-defined AOI visualization based on a real estate data visualization scenario, and we illustrate why a novel footprint method is needed to support the visualization. After extensively reviewing existing “footprint” methods, we propose a parameter-free footprint method, named AOI-shapes, to capture the boundary information of a user-defined urban AOI. Next, to allow interactive query refinements by the user, we propose two efficient and scalable algorithms to incrementally generate urban AOIs by reusing existing visualization results. Finally, we conduct extensive experiments with both synthetic and real-world datasets to demonstrate the quality and efficiency of the proposed methods.

SUPPORT OF INFORMAL CARERS FOR PEOPLE AFTER A STROKE WITH CROWDSOURCING AND NATURAL LANGUAGE PROCESSING

For a long time, both professionals and the lay public showed little interest in informal carers. Yet these people deals with multiple and common issues in their everyday lives. As the population is aging we can observe a change of this attitude. And thanks to the advances in computer science, we can offer them some effective assistance and support by providing necessary information and connecting them with both professional and lay public community. In this work we describe a project called “Research and development of support networks and information systems for informal carers for persons after stroke” producing an information system visible to public as a web portal. It does not provide just simple a set of information but using means of artificial intelligence, text document classification and crowdsourcing further improving its accuracy, it also provides means of effective visualization and navigation over the content made by most by the community itself and personalized on a level of informal carer’s phase of the care-taking timeline. In can be beneficial for informal carers as it allows to find a content specific to their current situation. This work describes our approach to classification of text documents and its improvement through crowdsourcing. Its goal is to test text documents classifier based on documents similarity measured by N-grams method and to design evaluation and crowdsourcing-based classification improvement mechanism. Interface for crowdsourcing was created using CMS WordPress. In addition to data collection, the purpose of interface is to evaluate classification accuracy, which leads to extension of classifier test data set, thus the classification is more successful.

Explainable health prediction from facial features with transfer learning

In the recent years, Artificial Intelligence (AI) has been widely deployed in the healthcare industry. The new AI technology enables efficient and personalized healthcare systems for the public. In this paper, transfer learning with pre-trained VGGFace model is applied to identify sick symptoms based on the facial features of a person. As the deep learning model’s operation is unknown for making a decision, this paper investigates the use of Explainable AI (XAI) techniques for soliciting explanations for the predictions made by the model. Various XAI techniques including Integrated Gradient, Explainable region-based AI (XRAI) and Local Interpretable Model-Agnostic Explanations (LIME) are studied. XAI is crucial to increase the model’s transparency and reliability for practical deployment. Experimental results demonstrate that the attribution method can give proper explanations for the decisions made by highlighting important attributes in the images. The facial features that account for positive and negative classes predictions are highlighted appropriately for effective visualization. XAI can help to increase accountability and trustworthiness of the healthcare system as it provides insights for understanding how a conclusion is derived from the AI model.

A Visualization Method of the Economic Input–Output Table: Mapping Monetary Flows in the Form of Sankey Diagrams

The input–output table and input–output method have been widely used to understand complex economic structures and are often used in cross-disciplinary research between economics and other disciplines, such as analysis of embodied energy, carbon footprints, the water–food nexus, etc. However, when researchers present these results to audiences, especially policymakers, they often lack an effective visualization tool to present (1) the full picture of the input–output table; (2) the complicated upstream–downstream nexus, and (3) the input–output relationships between the economic sectors. Therefore, a better visualization method is developed to solve this problem. We propose mapping an input–output table into a Sankey diagram, a so-called monetary allocation Sankey diagram. We first designed the mapping structure of a monetary allocation Sankey diagram according to the general structure of an economic monetary input–output table to establish the correspondence nexus between the table and diagram. We used China as a case study to demonstrate the usage of the monetary allocation Sankey diagram. The purpose of the monetary allocation Sankey diagram is to help people understand the input–output table in a short time and quickly grasp the big picture of the economic system. To verify whether this goal is achieved, we presented and applied these Sankey diagrams on different occasions and obtained evaluations from scholars from different academic backgrounds. The evaluation shows that the monetary allocation Sankey diagram is not only a visualization result of the input–output table but also a miniature model of the economic system, which allows people to “truly observe” the complex input–output relationship and upstream–downstream nexus in the economic system. Researchers can quickly grasp the main features of the economic system by observing the miniature model, or they can use this miniature model as an auxiliary tool to introduce the economic system and its inherent complex relationships to the audience.

NanoMethViz: An R/Bioconductor package for visualizing long-read methylation data

A key benefit of long-read nanopore sequencing technology is the ability to detect modified DNA bases, such as 5-methylcytosine. The lack of R/Bioconductor tools for the effective visualization of nanopore methylation profiles between samples from different experimental groups led us to develop the NanoMethViz R package. Our software can handle methylation output generated from a range of different methylation callers and manages large datasets using a compressed data format. To fully explore the methylation patterns in a dataset, NanoMethViz allows plotting of data at various resolutions. At the sample-level, we use dimensionality reduction to look at the relationships between methylation profiles in an unsupervised way. We visualize methylation profiles of classes of features such as genes or CpG islands by scaling them to relative positions and aggregating their profiles. At the finest resolution, we visualize methylation patterns across individual reads along the genome using the spaghetti plot and heatmaps, allowing users to explore particular genes or genomic regions of interest. In summary, our software makes the handling of methylation signal more convenient, expands upon the visualization options for nanopore data and works seamlessly with existing methylation analysis tools available in the Bioconductor project. Our software is available at https://bioconductor.org/packages/NanoMethViz.

Energy Atlas—mapping energy-related data for DoD lands in Alaska : Phase 1—assembling the data and designing the tool

The U.S. Army is the largest Department of Defense (DoD) land user in Alaska, including remote areas only accessible by air, water, or wintertime ice roads. Understanding where energy resources and related infrastructure exist on and adjacent to DoD installations and training lands can help in-form Army decision-makers, especially in remote locations like Alaska. The Energy Atlas–Alaska provides a value-added resource to support decision-making for investments in infrastructure and diligent energy management, helping Army installations become more resilient and sustainable. The Energy Atlas–Alaska utilizes spatial information and provides a consistent GIS (geographic information system) framework to access and examine energy and related resource data such as energy resource potential, energy corridors, and environmental information. The database can be made accessible to DoD and its partners through an ArcGIS-based user interface that provides effective visualization and functionality to support analysis and to inform DoD decision-makers. The Energy Atlas–Alaska helps DoD account for energy in contingency planning, acquisition, and life-cycle requirements and ensures facilities can maintain operations in the face of disruption.

High-Speed Visualization of Very Large High-Resolution Simulations for Air Hazard Transport and Dispersion

In the case of an atmospheric release of a noxious substance, modeling remains an essential tool to assess and forecast the impact of the release. The impact of such situations on populated, and hence built-up, areas is of the uttermost importance. However, modeling on such areas requires specific high-resolution approaches, which are complex to set up in emergency situations. Various approaches have been tried and evaluated: The EMERGENCIES and EMED project demonstrated an effective strategy using intensive parallel computing. Large amounts of data were produced that proved initially to be difficult to visualize, especially in a crisis management framework. A dedicated processing has been set up to allow for rapid and effective visualization of the modeling results. This processing relies on a multi-level tiled approach initiated in web cartography. The processing is using a parallel approach whose performances were evaluated using the large amounts of data produced in the EMERGENCIES and EMED projects. The processing proved to be very effective and compatible with the requirements of emergency situations.

BIM DESIGN LEAD FOR RESTORATION OF SHIPWRECK MUSEUM IN KYRENIA CASTLE IN CYPRUS

The paper deals with the application of Building Information Modelling (BIM) to the documentation and preservation of Archaeological Heritage. illustrating the implemantation process to a case study. The work process started from the historical analysis tighether with the geometric capturing of the built morphology. A 3D model was created by combining laser scans and a digital photogrammetric survey. To maka all 3D data sets interoperable, it was developed a BIM project execution plan focused on the restoration of Shipwreck Museum in the Kyrenia Castle in Cyprus. The HBIM approach not only allows ti represent the existing historic fabric with an effective visualization but also to lead a complex analysis of designed interventions in various scenarios. All the additional information besides the geometrical data were stored into the HBIM able ti document the manteinance and ti help the future works. It will be illustrated the procedure and the methodology by presenting the outcomes of the research.