Building on the extensive production of provenance data recently, this article explains how we can expand the purview of computational analysis in humanistic and social sciences by exploring how digital methods can be applied to provenances. Provenances document chains of events of ownership and...

Most modern supervised statistical/machine learning (ML) methods are explicitly designed to solve prediction problems very well. Achieving this goal does not imply that these methods automatically deliver good estimators of causal parameters. Examples of such parameters include individual...

Long short-term memory (LSTM) networks are a state-of-the-art technique for sequence learning. They are less commonly applied to financial time series predictions, yet inherently suitable for this domain. We deploy LSTM networks for predicting out-of-sample directional movements for the...

Due to the advanced technology associated with Big Data, data availability and computing power, most banks or lending institutions are renewing their business models. Credit risk predictions, monitoring, model reliability and effective loan processing are key to decision-making and transparency....

Automatic modulations recognition is one of the most important aspects in cognitive radios (CRs). Unlicensed users or secondary users (SUs) tend to classify the incoming signals to recognize the type of users in the system. Once the available users are detected and classified accurately, the CR...

Roads are vital to support the transportation of people, goods, and services, among others. To yield their optimal socioeconomic impact, proper maintenance of existing roads is required; however, this is typically underfunded. Since detecting road quality is both labor and capital intensive,...

This paper addresses the steep learning curve in Machine Learning faced by noncomputer scientists, particularly social scientists, stemming from the absence of a primer on its fundamental principles. I adopt a pedagogical strategy inspired by the adage "once you understand OLS, you can work your...

In recent years, machine learning research has gained momentum: New developments in the field of deep learning allow for multiple levels of abstraction and are starting to supersede well-known and powerful tree-based techniques mainly operating on the original feature space. All these methods...

In the field of mortality, the Lee-Carter based approach can be considered the milestone to forecast mortality rates among stochastic models. We could define a 'Lee-Carter model family' that embraces all developments of this model, including its first formulation (1992) that remains the...

A regularization approach to model selection, within a generalized HJM framework, is introduced, which learns the closest arbitrage-free model to a prespecified factor model. This optimization problem is represented as the limit of a one-parameter family of computationally tractable penalized...