A decade of Portuguese research in e-government: Evolution, current standing, and ways forward

In this paper, we present an investigation of the Portuguese research on e-government. Bibliometric techniques are used to explore all the documents published by researchers affiliated to Portuguese institutions from 2005 to 2014 and listed in the Scopus® database. Research production, impact, source types, language used, subject areas, topics, scopes, methods, authors, institutions, networks, and international cooperation are analysed and discussed. We conclude that so that Portuguese research on e-government can evolve, more researchers should be involved, international cooperation should be developed, and more attention should be given to the study of the reasons behind the very good results of the country in the provision of e-government services, as measured by the international rankings. By establishing the evolution and current standing of e-government research in Portugal and exploring the ways forward, our conclusions may prove useful to e-government researchers, research managers, and research policy makers. © Copyright 2016 Inderscience Enterprises Ltd

Design and Implementation of an Android Sleep Monitoring Framework

Smartphones were originally mainly used for making phone calls and playing games, but as they become more powerful and are equipped with a wide variety of sensors new use cases become interesting. One of these use cases is sleep monitoring, which is interesting for many different research areas. The goal of this bachelor thesis is to develop a sleep monitoring framework for the Android platform which can be used easily by third party applications. The framework takes care of detecting sleep related events like snoring and movement as well as monitoring the ambient light during the night. Additionally, a demo application is developed to demonstrate the functionality of the framework and to highlight some best practices regarding Android background services as they are essential for monitoring sleep

Investigation of the deployment of Android as a user interface for ovens

This theses is conducted in cooperation with BSH Hausgeräte GmbH. The target is the investigation of the applicability of Android as an operating system for home appliances, specifically for ovens. In order to draw conclusions in regard of applicability, three major topics will be investigated. For a start, the performance of Android running on moderate target hardware will be analysed. The focus lies on graphics, since a high quality graphical user interface is most likely to be the crucial point in terms of performance. Providing a smooth and responsive graphical user interface is decisive for a satisfying user experience. Furthermore, originating from the mobile domain, Android requires an array of modifications prior to being embedded into an oven. The goal is to identify these potential aspects that need modification and give appropriate solutions, thus also providing an estimate of the required effort for the embedding process. Finally, potential inter-process communication mechanisms will be investigated with the objective to identify the most eligible method(s) for the communication between an Android application and the underlying oven hardware

Supervised and Unsupervised Categorization of an Imbalanced Italian Crime News Dataset

The automatic categorization of crime news is useful to create statistics on the type of crimes occurring in a certain area. This assignment can be treated as a text categorization problem. Several studies have shown that the use of word embeddings improves outcomes in many Natural Language Processing (NLP), including text categorization. The scope of this paper is to explore the use of word embeddings for Italian crime news text categorization. The approach followed is to compare different document pre-processing, Word2Vec models and methods to obtain word embeddings, including the extraction of bigrams and keyphrases. Then, supervised and unsupervised Machine Learning categorization algorithms have been applied and compared. In addition, the imbalance issue of the input dataset has been addressed by using Synthetic Minority Oversampling Technique (SMOTE) to oversample the elements in the minority classes. Experiments conducted on an Italian dataset of 17,500 crime news articles collected from 2011 till 2021 show very promising results. The supervised categorization has proven to be better than the unsupervised categorization, overcoming 80% both in precision and recall, reaching an accuracy of 0.86. Furthermore, lemmatization, bigrams and keyphrase extraction are not so decisive. In the end, the availability of our model on GitHub together with the code we used to extract word embeddings allows replicating our approach to other corpus either in Italian or other languages