Open University of Catalonia

About: Open University of Catalonia is a education organization based out in Barcelona, Spain. It is known for research contribution in the topics: Collaborative learning & Educational technology. The organization has 1943 authors who have published 4646 publications receiving 64200 citations. The organization is also known as: Universitat Oberta de Catalunya & UOC.

Using safety stocks and simulation to solve the vehicle routing problem with stochastic demands

Abstract: After introducing the Vehicle Routing Problem with Stochastic Demands (VRPSD) and some related work, this paper proposes a flexible solution methodology. The logic behind this methodology is to transform the issue of solving a given VRPSD instance into an issue of solving a small set of Capacitated Vehicle Routing Problem (CVRP) instances. Thus, our approach takes advantage of the fact that extremely efficient metaheuristics for the CVRP already exists. The CVRP instances are obtained from the original VRPSD instance by assigning different values to the level of safety stocks that routed vehicles must employ to deal with unexpected demands. The methodology also makes use of Monte Carlo simulation (MCS) to obtain estimates of the reliability of each aprioristic solution – that is, the probability that no vehicle runs out of load before completing its delivering route – as well as for the expected costs associated with corrective routing actions (recourse actions) after a vehicle runs out of load before completing its route. This way, estimates for expected total costs of different routing alternatives are obtained. Finally, an extensive numerical experiment is included in the paper with the purpose of analyzing the efficiency of the described methodology under different uncertainty scenarios.

Educational Data Mining and Learning Analytics: differences, similarities, and time evolution

Abstract: Technological progress in recent decades has enabled people to learn in different ways. Universities now have more educational models to choose from, i.e., b-learning and e-learning. Despite the increasing opportunities for students and instructors, online learning also brings challenges due to the absence of direct human contact. Online environments allow the generation of large amounts of data related to learning/teaching processes, which offers the possibility of extracting valuable information that may be employed to improve students’ performance. In this paper, we aim to review the similarities and differences between Educational Data Mining and Learning Analytics, two relatively new and increasingly popular fields of research concerned with the collection, analysis, and interpretation of educational data. Their origins, goals, differences, similarities, time evolution, and challenges are addressed, as are their relationship with Big Data and MOOCs.

A Systematic Mapping Study of Software Development With GitHub

Abstract: Context: GitHub, nowadays the most popular social coding platform, has become the reference for mining Open Source repositories, a growing research trend aiming at learning from previous software projects to improve the development of new ones. In the last years, a considerable amount of research papers have been published reporting findings based on data mined from GitHub. As the community continues to deepen in its understanding of software engineering thanks to the analysis performed on this platform, we believe that it is worthwhile to reflect on how research papers have addressed the task of mining GitHub and what findings they have reported. Objective: The main objective of this paper is to identify the quantity, topic, and empirical methods of research works, targeting the analysis of how software development practices are influenced by the use of a distributed social coding platform like GitHub. Method: A systematic mapping study was conducted with four research questions and assessed 80 publications from 2009 to 2016. Results: Most works focused on the interaction around coding-related tasks and project communities. We also identified some concerns about how reliable were these results based on the fact that, overall, papers used small data sets and poor sampling techniques, employed a scarce variety of methodologies and/or were hard to replicate. Conclusions: This paper attested the high activity of research work around the field of Open Source collaboration, especially in the software domain, revealed a set of shortcomings and proposed some actions to mitigate them. We hope that this paper can also create the basis for additional studies on other collaborative activities (like book writing for instance) that are also moving to GitHub.

Individual Differential Privacy: A Utility-Preserving Formulation of Differential Privacy Guarantees

Abstract: Differential privacy is a popular privacy model within the research community because of the strong privacy guarantee it offers, namely that the presence or absence of any individual in a data set does not significantly influence the results of analyses on the data set. However, enforcing this strict guarantee in practice significantly distorts data and/or limits data uses, thus diminishing the analytical utility of the differentially private results. In an attempt to address this shortcoming, several relaxations of differential privacy have been proposed that trade off privacy guarantees for improved data utility. In this paper, we argue that the standard formalization of differential privacy is stricter than required by the intuitive privacy guarantee it seeks. In particular, the standard formalization requires indistinguishability of results between any pair of neighbor data sets, while indistinguishability between the actual data set and its neighbor data sets should be enough. This limits the data controller’s ability to adjust the level of protection to the actual data, hence resulting in significant accuracy loss. In this respect, we propose individual differential privacy , an alternative differential privacy notion that offers the same privacy guarantees as standard differential privacy to individuals (even though not to groups of individuals). This new notion allows the data controller to adjust the distortion to the actual data set, which results in less distortion and more analytical accuracy. We propose several mechanisms to attain individual differential privacy and we compare the new notion against standard differential privacy in terms of the accuracy of the analytical results.

Learnheuristics: hybridizing metaheuristics with machine learning for optimization with dynamic inputs

Abstract: Abstract This paper reviews the existing literature on the combination of metaheuristics with machine learning methods and then introduces the concept of learnheuristics, a novel type of hybrid algorithms. Learnheuristics can be used to solve combinatorial optimization problems with dynamic inputs (COPDIs). In these COPDIs, the problem inputs (elements either located in the objective function or in the constraints set) are not fixed in advance as usual. On the contrary, they might vary in a predictable (non-random) way as the solution is partially built according to some heuristic-based iterative process. For instance, a consumer’s willingness to spend on a specific product might change as the availability of this product decreases and its price rises. Thus, these inputs might take different values depending on the current solution configuration. These variations in the inputs might require from a coordination between the learning mechanism and the metaheuristic algorithm: at each iteration, the learning method updates the inputs model used by the metaheuristic.