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Publications

Publications by CRACS

2023

A WebApp for Reliability Detection in Social Media

Authors
David, F; Guimaraes, N; Figueira, A;

Publication
Procedia Computer Science

Abstract

2023

Bibliometric Analysis of Automated Assessment in Programming Education: A Deeper Insight into Feedback

Authors
Paiva, JC; Figueira, A; Leal, JP;

Publication
ELECTRONICS

Abstract
Learning to program requires diligent practice and creates room for discovery, trial and error, debugging, and concept mapping. Learners must walk this long road themselves, supported by appropriate and timely feedback. Providing such feedback in programming exercises is not a humanly feasible task. Therefore, the early and steadily growing interest of computer science educators in the automated assessment of programming exercises is not surprising. The automated assessment of programming assignments has been an active area of research for over a century, and interest in it continues to grow as it adapts to new developments in computer science and the resulting changes in educational requirements. It is therefore of paramount importance to understand the work that has been performed, who has performed it, its evolution over time, the relationships between publications, its hot topics, and open problems, among others. This paper presents a bibliometric study of the field, with a particular focus on the issue of automatic feedback generation, using literature data from the Web of Science Core Collection. It includes a descriptive analysis using various bibliometric measures and data visualizations on authors, affiliations, citations, and topics. In addition, we performed a complementary analysis focusing only on the subset of publications on the specific topic of automatic feedback generation. The results are highlighted and discussed.

2023

On the Quality of Synthetic Generated Tabular Data

Authors
Espinosa, E; Figueira, A;

Publication
MATHEMATICS

Abstract
Class imbalance is a common issue while developing classification models. In order to tackle this problem, synthetic data have recently been developed to enhance the minority class. These artificially generated samples aim to bolster the representation of the minority class. However, evaluating the suitability of such generated data is crucial to ensure their alignment with the original data distribution. Utility measures come into play here to quantify how similar the distribution of the generated data is to the original one. For tabular data, there are various evaluation methods that assess different characteristics of the generated data. In this study, we collected utility measures and categorized them based on the type of analysis they performed. We then applied these measures to synthetic data generated from two well-known datasets, Adults Income, and Liar+. We also used five well-known generative models, Borderline SMOTE, DataSynthesizer, CTGAN, CopulaGAN, and REaLTabFormer, to generate the synthetic data and evaluated its quality using the utility measures. The measurements have proven to be informative, indicating that if one synthetic dataset is superior to another in terms of utility measures, it will be more effective as an augmentation for the minority class when performing classification tasks.

2023

Jay: A software framework for prototyping and evaluating offloading applications in hybrid edge clouds

Authors
Silva, J; Marques, ERB; Lopes, LMB; Silva, FMA;

Publication
SOFTWARE-PRACTICE & EXPERIENCE

Abstract
We present Jay, a software framework for offloading applications in hybrid edge clouds. Jay provides an API, services, and tools that enable mobile application developers to implement, instrument, and evaluate offloading applications using configurable cloud topologies, offloading strategies, and job types. We start by presenting Jay's job model and the concrete architecture of the framework. We then present the programming API with several examples of customization. Then, we turn to the description of the internal implementation of Jay instances and their components. Finally, we describe the Jay Workbench, a tool that allows the setup, execution, and reproduction of experiments with networks of hosts with different resource capabilities organized with specific topologies. The complete source code for the framework and workbench is provided in a GitHub repository.

2023

MHVG2MTS: Multilayer Horizontal Visibility Graphs for Multivariate Time Series Analysis

Authors
Silva, VF; Silva, ME; Ribeiro, P; Silva, FMA;

Publication
CoRR

Abstract

2023

Multilayer Quantile Graph for Multivariate Time Series Analysis and Dimensionality Reduction

Authors
Silva, VF; Silva, ME; Ribeiro, P; Silva, FMA;

Publication
CoRR

Abstract

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