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Publications

Publications by CESE

2022

Continuously Learning from User Feedback

Authors
Carneiro, D; Sousa, M; Palumbo, G; Guimaraes, M; Carvalho, M; Novais, P;

Publication
INFORMATION SYSTEMS AND TECHNOLOGIES, WORLDCIST 2022, VOL 1

Abstract
Machine Learning has been evolving rapidly over the past years, with new algorithms and approaches being devised to solve the challenges that the new properties of data pose. Specifically, algorithms must now learn continuously and in real time, from very large and possibly distributed sets of data. In this paper we describe a learning system that tackles some of these novel challenges. It learns and adapts in realtime by continuously incorporating user feedback, in a fully autonomous way. Moreover, it allows for users to manage features (e.g. add, edit, remove), reflecting these changes on-the-fly in the Machine Learning pipeline. The paper describes some of the main functionalities of the system, which despite being of general-purpose, is being developed in the context of a project in the domain of financial fraud detection.

2022

Explainable Decision Tree on Smart Human Mobility

Authors
Rosa, L; Guimarães, M; Carneiro, D; Silva, F; Analide, C;

Publication
Workshops at 18th International Conference on Intelligent Environments (IE2022), Biarritz, France, 20-23 June 2022.

Abstract
Artificial Intelligence is a hot topic and Machine Learning is one of the most fluent approaches and practices. The problem with many AI models is that they can be useful for predicting but they are bad at explaining why they behave a certain way. In some contexts, the explanation may even be more important than the prediction itself, mainly in systems in which decisions are made based on their predictions. Therefore, it is increasingly necessary to provide a forecast accompanied by an explanation, when decisions are made automatically. This paper aims to contribute to the solution of problem based on human mobility research, or at least, to be a starting point for its solution.

2022

Using Evolving Ensembles to Deal with Concept Drift in Streaming Scenarios

Authors
Ramos, D; Carneiro, D; Novais, P;

Publication
INTELLIGENT DISTRIBUTED COMPUTING XIV

Abstract
In a time in which streaming data becomes the new normal in Machine Learning problems, to the detriment of batch data, new challenges arise. In the past, a data source would be static in the sense that all data were known at the moment of the training of the model. A model would be trained and it would be in use for relatively long periods of time. Nowadays, data arrive in real-time and their statistical properties may also change over time, rendering trained models outdated. In this paper we propose an approach to deal with the concept drift problem with minimal computational effort. Specifically, we continuously update an ensemble with new weak learners and adjust their weights according to their performance. This approach is suitable to be used in real-time in the form of an ever-evolving model that adapts to change in the data.

2022

A predictive and user-centric approach to Machine Learning in data streaming scenarios

Authors
Carneiro, D; Guimaraes, M; Silva, F; Novais, P;

Publication
NEUROCOMPUTING

Abstract
Machine Learning has emerged in the last years as the main solution to many of nowadays' data-based decision problems. However, while new and more powerful algorithms and the increasing availability of computational resources contributed to a widespread use of Machine Learning, significant challenges still remain. Two of the most significant nowadays are the need to explain a model's predictions, and the significant costs of training and re-training models, especially with large datasets or in streaming scenarios. In this paper we address both issues by proposing an approach we deem predictive and user-centric. It is predictive in the sense that it estimates the benefit of re-training a model with new data, and it is user centric in the sense that it implements an explainable interface that produces interpretable explanations that accompany predictions. The former allows to reduce necessary resources (e.g. time, costs) spent on re-training models when no improvements are expected, while the latter allows for human users to have additional information to support decision-making. We validate the proposed approach with a group of public datasets and present a real application scenario.

2022

Gamification of the Learning Process

Authors
Carneiro, D; Caceres, P; Carvalho, MR;

Publication
INTERACTION DESIGN AND ARCHITECTURES

Abstract

2022

A Framework for Online Education in Computer Science Degrees with a Focus on Motivation

Authors
Carneiro, D; Barbosa, R;

Publication
METHODOLOGIES AND INTELLIGENT SYSTEMS FOR TECHNOLOGY ENHANCED LEARNING

Abstract
The way students learn changed significantly over the past two years, due to the current pandemic. However, this change was neither desired not planed beforehand. As a result, in many cases, it may have been undertaken without the appropriate care. In this paper we propose a framework for online education tailored for Computer Science degrees. Its goals are twofold: to avoid disruptive changes by providing a familiar and supportive structure for teaching/learning activities, and to motivate Students to learn autonomously, despite their reduced contact with their peers or the Teacher.

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