This study explores different neural network architectures to evaluate their ability to extract spatial and temporal patterns from electrocardiographic (ECG) signals and classify them into three groups: healthy subjects, Left Bundle Branch Block (LBBB), and Strict Left Bundle Branch Block (sLBBB). Clinical Relevance, Innovative technologies enable the selection of candidates for Cardiac Resynchronization Therapy (CRT) by optimizing the classification of subjects with Left Bundle Branch Block (LBBB).

To this end, vast amounts of data generated by various sources, including sensors and other monitoring systems, need to be effectively structured and represented in a way that can be easily understood and processed by both Artificial Intelligence (AI) systems and humans. The digitalisation of the wind energy sector is one of the key drivers for reducing costs and risks over the whole wind energy project life cycle [2]. The digitalisation process encompasses solutions such as digital twins, decision support systems and AI systems, some of which need to still be developed, in order to contribute to reducing operation and maintenance costs, for increasing the amount of energy delivered, as well as for maximising the efficiency of wind energy systems. In this context, the term Knowledge-Based Systems (KBS) refers to AI systems that formalize knowledge as rules, logical expressions, and conceptualisations [3, 4]. Such systems can be realised as AI-enabled digital twins or decision support systems that rely on databases of knowledge (also referred to as knowledge bases or knowledge graphs), which contain machine-readable facts, rules, and logics about a domain of interest, to assist with problem-solving and decision-making [5].

Spatial and spatiotemporal prediction tasks are common in applications ranging from environmental sciences to archaeology and epidemiology. While sophisticated mathematical frameworks have long been developed in spatial statistics to characterize predictive uncertainties under well-defined mathematical assumptions such as intrinsic stationarity (e.g., Cressie 1993), computational estimation procedures have only been proposed more recently to assess predictive performances of spatial and spatiotemporal prediction models (Brenning 2005, 2012; Pohjankukka, Pahikkala, Nevalainen, and Heikkonen 2017; Roberts, Bahn, Ciuti, Boyce, Elith, Guillera-Arroita, Hauenstein, Lahoz-Monfort, Schröder, Thuiller, Warton, Wintle, Hartig, and Dormann 2017). Although alternatives such as the bootstrap exist since some decades (Efron and Gong 1983; Hand 1997), cross-validation (CV) is a particularly well-established, easy-to-implement algorithm for model assessment of supervised machine-learning models (Efron and Gong 1983, and next section) and model selection (Arlot and Celisse 2010). In its basic form, CV is based on resampling the data without paying attention to any possible dependence structure, which may arise from, e.g., grouped or structured data, or underlying environmental processes inducing some sort of spatial coherence at the landscape scale. In treating dependent observations as independent, or ignoring autocorrelation, CV test samples may in fact be heavily correlated with, or even pseudo-replicates of, the data used for training the model, which introduces a potentially severe bias in assessing the transferability of flexible machine-learning (ML) models.

A common need for mobile application development by end-users or in computing education is to transform a sketch of a user interface into wireframe code using App Inventor, a popular block-based programming environment. As this task is challenging and time-consuming, we present the Sketch2aia approach that automates this process. Sketch2aia employs deep learning to detect the most frequent user interface components and their position on a hand-drawn sketch creating an intermediate representation of the user interface and then automatically generates the App Inventor code of the wireframe. The approach achieves an average user interface component classification accuracy of 87,72% and results of a preliminary user evaluation indicate that it generates wireframes that closely mirror the sketches in terms of visual similarity. The approach has been implemented as a web tool and can be used to support the end-user development of mobile applications effectively and efficiently as well as the teaching of user interface design in K-12.

Machine-learning algorithms have gained popularity in recent years in the field of ecological modeling due to their promising results in predictive performance of classification problems. While the application of such algorithms has been highly simplified in the last years due to their well-documented integration in commonly used statistical programming languages such as R, there are several practical challenges in the field of ecological modeling related to unbiased performance estimation, optimization of algorithms using hyperparameter tuning and spatial autocorrelation. We address these issues in the comparison of several widely used machine-learning algorithms such as Boosted Regression Trees (BRT), k-Nearest Neighbor (WKNN), Random Forest (RF) and Support Vector Machine (SVM) to traditional parametric algorithms such as logistic regression (GLM) and semi-parametric ones like generalized additive models (GAM). Different nested cross-validation methods including hyperparameter tuning methods are used to evaluate model performances with the aim to receive bias-reduced performance estimates. As a case study the spatial distribution of forest disease Diplodia sapinea in the Basque Country in Spain is investigated using common environmental variables such as temperature, precipitation, soil or lithology as predictors. Results show that GAM and RF (mean AUROC estimates 0.708 and 0.699) outperform all other methods in predictive accuracy. The effect of hyperparameter tuning saturates at around 50 iterations for this data set. The AUROC differences between the bias-reduced (spatial cross-validation) and overoptimistic (non-spatial cross-validation) performance estimates of the GAM and RF are 0.167 (24%) and 0.213 (30%), respectively. It is recommended to also use spatial partitioning for cross-validation hyperparameter tuning of spatial data.