Editor's Note

Artificial Intelligence has become nowadays one of the main relevant technologies that is driven us to a new revolution, a change in society, just as well as other human inventions, such as navigation, steam machines, or electricity did in our past. There are several ways in which AI might be developed, and the European Union has chosen a path, a way to transit through this revolution, in which Artificial Intelligence will be a tool at the service of Humanity. That was precisely the motto of the 2020 European Conference on Artificial Intelligence (“Paving the way towards Human-Centric AI”), of which these special issue is a selection of the best papers selected by the organizers of some of the Workshops in ECAI 2020

The Semantics of History. Interdisciplinary Categories and Methods for Digital Historical Research

This paper aims at introducing and discussing the data modelling and labelling methods for interdisciplinary and digital research in History developed and used by the authors. Our approach suggests the development of a conceptual framework for interdisciplinary research in history as a much-needed strategy to ensure that historians use all vestiges from the past regardless of their origin or support for the construction of historical discourse. By labelling Units of Topography and Actors in a wide range of historical sources and exploiting the obtained data, we use the Monastery of Sant Genís de Rocafort (Martorell, Spain) as a lab example of our method. This should lead researchers to the development of an integrated historical discourse maximizing the potential of interdisciplinary and fair research and minimizing the risks of bias

Dynamical Systems

Complex systems are pervasive in many areas of science integrated in our daily lives. Examples include financial markets, highway transportation networks, telecommunication networks, world and country economies, social networks, immunological systems, living organisms, computational systems and electrical and mechanical structures. Complex systems are often composed of a large number of interconnected and interacting entities, exhibiting much richer global scale dynamics than the properties and behavior of individual entities. Complex systems are studied in many areas of natural sciences, social sciences, engineering and mathematical sciences. This special issue therefore intends to contribute towards the dissemination of the multifaceted concepts in accepted use by the scientific community. We hope readers enjoy this pertinent selection of papers which represents relevant examples of the state of the art in present day research. [...

A Deep Learning Approach for Automating Concrete Bridge Defect Assessment Using Computer Vision

Current bridge inspection practices are outdated compared to the advanced technologies available today, and there is significant room for improvement. For example, spalls are inspected by visual assessment and delaminations are inspected by sounding for hollow areas in the concrete. This yields coarse size estimation and subjective measuring, which is exacerbated by limited funding. These limitations severely restrict inspection information provided to an engineer, making adequate bridge management difficult and bridge repairs expensive. Current inspection researchers are aware of this problem, and therefore there is significant focus on applying advanced technologies to improve the accuracy and economic efficiency of routine bridge inspections for improved bridge management. The Structural Dynamics Identification and Control (SDIC) research lab at the University of Waterloo has been working to develop a process for automated end-to-end inspection of spalls and delaminations in reinforced concrete bridges that tightens size estimation, removes subjectivity, and improves accessibility. This process combines the accessibility benefits of robotics with the detailed 3D structural modelling of state-of-the-art simultaneous localization and mapping (SLAM), and the accurate and objective object labeling of state-of-the-art convolutional neural networks (CNN). Major steps required for this automated end-to-end inspection can be broadly divided into five components: 1) a mobile data collection platform complete with lidar and camera sensors, 2) a mapping component to fuse data from various sensors into a common reference frame, 3) a defect labeling component to automatically label defects in images, 4) a map labeling component to semantically enrich the 3D map with pixel information from images, and 5) a non-subjective and automated defect quantification component. The work in this thesis focuses specifically on components 3), 4), and 5). These three components assume that data is collected by lidar and camera sensors (Component 1) and a 3D map of the bridge structure has been generated by SLAM (Component 2). To achieve component 3, this thesis presents an implementation of MobileNetV2/Deeplab V3, which is a state-of-the-art pixel-wise CNN, for fully automated pixel-wise labeling of spalls and delaminations in visual and infrared images respectively. Spalls are labeled with 71.4% mean intersection over union (mIoU) and delaminations with 82.7% mIoU, which is reasonable compared to same CNN's score of 77.3% on benchmark datasets. For component 4, an algorithm is developed based on the pinhole camera model and ray-tracing to intelligently fuse the CNN and colour data stored in pixels with the generated 3D point cloud. This yields a spatially accurate 3D map of the scanned structure that is colourized and semantically enriched with defect information. This enables the last component, which implements an algorithm to automatically extract, organize, and quantify areas for both spall and delamination defects present in the semantically labeled 3D map. A comparison is performed to test the difference of using manual ground truth defect labels in the image versus automated CNN labels, with all else held constant. The comparison showed a defect size error of 25.9% for spalls and 13.6% for delaminations, which is proportional to the 28.6% and 17.3% mIoU errors reported for spalls and delamintions respectively at the image labeling step. This is evidence that this pipeline can be used for any defect area quantification, where the optimal CNN can be chosen for automated labeling based on a tradeoff of accuracy vs. computation requirements. Future work involves extending this pipeline to include more defect quantification, such as crack length and crack width

Methodological developments in constructing casual diagrams with counterfactual analysis of adolescent alcohol harm

Background and aims: Causal diagrams, or Directed Acyclic Graphs (DAGs), are mathematically formulated networks of nodes (variables) and arrows which rigorously identify adjustment sets for statistical models. They are thus promising tools for improving statistical analysis in health and social sciences. However, a lack of pragmatic yet robust guidance for building DAGs has been identified as problematic for their use in applied research. This thesis aims to contribute an example of such guidance in the form of a novel research method, and to demonstrate this method’s utility by applying it to observational data. Design: This thesis introduces ‘Evidence Synthesis for Constructing Directed Acyclic Graphs’ (ESC-DAGs) as a protocol for building DAGs from research evidence. It is demonstrated here in the context of parental influences on adolescent alcohol harm and the resulting DAGs are used to inform analysis of data from the Avon Longitudinal Study of Parents and Children (ALSPAC). Methods: ESC-DAGs integrates evidence synthesis principles with classic and modern perspectives on causal inference to produce complex DAGs in a systematic and transparent way. It was applied here to a subset of literature identified from a novel review of systematic reviews, which identified 12 parental influences on adolescent alcohol harm. ESC-DAGs was then further applied to the ALSPAC data to produce a ‘data integrated DAG’. The outcome measure was the Alcohol Use Disorders Identification Test (AUDIT) administered to adolescent participants at age 16.5 years. Nine parental influences were measured, alongside 22 intermediates (variables lying on the causal pathway between parental influences and AUDIT score). The DAGs were then used to direct two stages of analysis: 1) weighting and regression techniques were used to estimate Average Causal Effects (ACEs) for each parental influence and intermediate; and 2) causal mediation analysis was used to decompose the effect of maternal drinking on adolescent AUDIT score to estimate Natural Indirect Effects (NIEs) for the intermediates and the other parental influences. Findings: Evidence for an ACE was found for each parental influence. Parental drinking, low parental monitoring, and parental permissiveness towards adolescent alcohol use had larger effects that were more robust to sensitivity analysis. Several peer and intrapersonal intermediates had higher effects. There was little evidence of an NIE of maternal drinking through other parental influences. There were substantial NIEs for substance-related behaviours of the adolescent and their peers. Conclusions: ESC-DAGs is a promising tool for using DAGs to improve statistical practices. The DAGs produced were transparent and able to direct various forms of data analysis in an immediate sense while differentiating between a comparatively large volume of confounders and other covariates. Future development is possible and should focus on efficiency, replicability, and integration with other methods, such as risk of bias tools. ESC-DAGs may thus prove a valuable platform for discussion in the DAG and wider quantitative research communities. The statistical analyses were performed with methods that were novel to the literature and findings triangulated with the wider evidence base. Mediation analysis provided novel evidence on how parental drinking influences adolescent alcohol harm