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Moral Foundations Theory is a socio-cognitive psychological theory that constitutes a general framework aimed at explaining the origin and evolution of human moral reasoning. Due to its dyadic structure of values and their violations, it can be ...

In the recent past, yearly CO${}_2$ emissions at the international level were studied from different points of view, due to their importance with respect to concerns about climate change. Nevertheless, related data (available at country-industry level ...${}_{\mathrm{}}$${}_{\mathrm{}}$

Industry 4.0 describes flexibly combinable production machines enabling efficient fulfillment of individual requirements. Timely and automated anomaly recognition by means of machine self-diagnosis might support efficiency. Various algorithms have ...

Matrix factorization (MF) has been widely used in drug discovery for link prediction, which aims to reveal new drug-target links by integrating drug-drug and target-target similarity information with a drug-target interaction matrix. The MF method ...

This work proposes codebook encodings for graph networks that operate on hyperbolic manifolds. Where graph networks commonly learn node representations in Euclidean space, recent work has provided a generalization to Riemannian manifolds, with a ...

Standard machine learning algorithms have limited knowledge extraction capability in discriminating cancer stages based on genomic characterizations, due to the strongly correlated nature of high-dimensional genomic data. Moreover, activation of ...

We compare accuracy metrics of the supervisor meta-learning artificial neural networks (ANN) that learn the trustworthiness of the Inception v.3 convolutional neural networks (CNN) ensemble prediction a priori of the “ground truth” verification on ...

Determining the crystal system and space group for a compound from its powder X-ray diffraction data represents the initial step of a crystal structure analysis. This task can constitute a bottleneck in the material science workflow and often ...

Neural architecture search (NAS) is a subdomain of AutoML that consists of automating the design of neural networks. NAS has become a hot topic in the last few years. As a result, many methods are being developed in this area. Local search (LS), ...

We study a relaxed version of the column-sampling problem for the Nyström approximation of kernel matrices, where approximations are defined from multisets of landmark points in the ambient space; such multisets are referred to as Nyström samples. ...

The COVID-19 pandemic poses new challenges on pharmaceutical supply chain including the delays and shortages of resources which lead to product backorders. Backorder is a common supply chain problem for pharmaceutical companies which affects ...

E-procurement platforms are marketplaces where manufacturing companies, termed buyers, frequently interact with tens of thousands of suppliers. Conversely, different suppliers compete against each other to be selected, by one or more buyers, as ...

We consider the approximation of unknown or intractable integrals using quadrature when the evaluation of the integrand is considered very costly. This is a central problem both within and without machine learning, including model averaging, (...

This paper is focused on sensitivity analysis of engineering structures using surrogate models. Two different techniques for surrogate modeling are utilized in order to obtain various sensitivity measures of quantity of interest. The artificial ...

The aim of this paper is to investigate the use of transformer-based neural network classifiers for the detection of misinformation on South African Twitter. Twitter COVID-19 misinformation data from four publicly available datasets are used for ...

Imputing missing data is still a challenge for mixed datasets containing variables of different nature such as continuous, count, ordinal, categorical, and binary variables. The recently introduced Mixed Deep Gaussian Mixture Models (MDGMM) ...

Nowadays, owners and developers of deep learning models must consider stringent privacy-preservation rules of their training data, usually crowd-sourced and retaining sensitive information. The most widely adopted method to enforce privacy ...

MicroRacer is a simple, open source environment inspired by car racing especially meant for the didactics of Deep Reinforcement Learning. The complexity of the environment has been explicitly calibrated to allow users to experiment with many ...

The last few decades have witnessed the increasing deployment of digital technologies in the urban environment with the goal of creating improved services to citizens especially related to their safety. This motivation, enabled by the widespread ...

We constructed a financial network based on the relationships of the customers in our database with our other customers or other bank customers using our large-scale data set of money transactions. There are two main aims in this study. Our first ...

Methane leak detection and remediation efforts are critical for combating climate change due to methane’s role as a potent greenhouse gas. In this work, we consider the problem of source attribution and leak quantification: given a set of methane ...

The issue of heavy vehicles rollover appears to be central in various sectors. This is due to the consequences entailed in terms of driver and passenger safety, other than considering aspects as environmental damaging and pollution. Therefore, ...

This paper considers the problem of tuning the hyperparameters of a random forest (RF) algorithm, which can be formulated as a discrete black-box optimization problem. Although default settings of RF hyperparameters in software packages work well ...

Knowledge bases (KBs) are ideal vehicles for tackling many challenges, such as Query Answering, Root Cause Analysis. Given that the world is changing over time, previously acquired knowledge can become outdated. Thus, we need methods to update the ...

In this paper we propose a novel, phenomenological approach to explainable Reinforcement Learning (RL). While the ever-increasing performance of RL agents surpasses human capabilities on many problems, it falls short concerning explainability, ...

Root-cause analysis (RCA) is a crucial task in software system maintenance, where system logs play an essential role in capturing system behaviours and describing failures. Automatic RCA approaches are desired, which face the challenge that the ...

Daily cash flow forecasting is important for maintaining company financial liquidity, improves resource allocation, and aids financial managers in decision making. On the one hand, it helps to avoid excessive amounts of cash outstanding on company’...

The unprecedented growth in machine learning has shed light on its unique set of challenges. One such challenge is apparent changes in the input data distribution over time known as Concept Drifts. In such cases, the model’s performance degrades ...