Interactive information visualisation (IViz) and visual analytics (VA) that adopt a complex system perspective would increase the understanding of the maritime traffic network (MTN) and improve maritime situational awareness (MSA).In this work, we concentrate on the relationships between the MTN components at different scales of the system, illustrating examples of IViz/VA tools that integrate visual elements to help focus the end user’s attention on these complex system features. The approach facilitates the discovery of "unknown unknowns" and the detection of emerging behaviours, facilitating the understanding of the elaborated relationships among system elements which often produce outcomes difficult to explain if individual components are examined in isolation. IViz/VA tools in support of use cases where the user is required to assess a maritime security or safety situation are illustrated.

COVID-19 is an infectious disease caused by the SARS-CoV-2 virus. It was first detected in China in December 2019. It spreads between people in close contact. On the 11thMarch 2020 the WHO declared the outbreak of the virus as a pandemic, which signaled a significant acceleration in the global response to the COVID-19 outbreak, and recognized the widespread transmission of the virus across multiple countries and continents. Data regarding COVID-19 was gathered and made available for open access. These data sources offer invaluable information for tracking, raising awareness and understanding of COVID-19, recognizing its impact, as well as informing the general public, health authorities, policy makers, situation managers and decision makers. However, COVID-19 data in its raw form is complex and difficult to understand and analyze. The application of visualization together with human factor design principles in a complex systems framework provides an effective means for exploiting these big and complex datasets. These techniques can transform such inherently non-visual data into intuitive visual forms that enable users to gain insight into, and understanding of, information contained within the data. This paper discusses the application of visualization and development of interactive dashboards, set in a complex systems framework, to provide an effective means for the users to explore, analyze and gain awareness of the situation, thus enabling informed decision making.

Problem solving in complex situations or systems requires an appropriate and reliable understanding of the problem. We propose a framework that combines the Iceberg and Situational Awareness models. The goal is to raise decision makers' awareness so that, depending on the system layers, problems can be perceived as events and trends within the system structure. Depending on what has been perceived, decision makers are then able to understand the impact of problems or their underlying causes and conditions. The appropriateness of the perceived and integrated information depends on whether it helps decision makers answer their questions and whether it corresponds to reality. Reliability depends on the completeness and certainty of the information presented. The perception of system properties is influenced by the (in)transparency of the real system and the prevailing paradigm and mindset of the observer. So, we propose a system taxonomy within the framework. This summarizes system properties of simple, complicated and complex systems and the resulting behavioral characteristics identified in the literature. Decision makers can use the taxonomy to explore the properties of real systems to develop an appropriate system model and assess its reliability, or to evaluate existing system models whether, or to what extent, they are suitable for decision support. This is because, based on these models, decision makers develop a corresponding understanding of the problem, upon which their decisions for possible solutions are based.

The following topics are dealt with: data visualisation; data analysis; security of data; data mining; pattern classification; Web sites; computer network security; Internet; invasive software; and sentiment analysis.

This report describes work carried out under the Automatic Information Retrieval project at DERA, in building a 4D landscape (a TextScape) which acts as a graphical user interface (GUI) to the DERA-OKAPI search engine. Traditionally search engines present a 2D visual interface (usually a windows-icons-menus-pointer (WIMP) interface) to the user. Key words are entered and the 'hits' are displayed usually in a list form. Unfortunately, the number of documents retrieved is often overwhelming and a significant amount of time is still required to read abstracts and sections of documents in order to determine if they are relevant, even after the search engine has performed relevance ranking. The challenge is how to improve on this. We propose to render the information most immediately required onto the visual attributes of a 3D landscape. Shape, colour and size can then project the information that is sought, and mining the object hierarchy visually returns information more rapidly.

An application is described of dynamic-programming-based matching to car model classification. An edge map is first extracted, and a sequence of pre-processing stages is carried out, culminating in the use of a 2-dimensional circle Hough transform to generate a cued region containing a putative car. Within the cued area a dynamic programming search (edge-based classification) is carried out to classify the model or type of car. Experimental results are given.>