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- Life satisfaction of Paralympians: The role of needs satisfaction and passionPublication . Rodrigues, Filipe; Mageau, Geneviève A.; Lemelin, Emilie; Teixeira, Diogo; Vitorino, Anabela; Cid, Luis; Monteiro, DiogoThe current study examined the possible role of basic psychological needs and passion in Paralympians’ life satisfaction. A mediational model was tested where autonomy, competence and relatedness were hypothesized to be linked to athlete life satisfaction via harmonious and obsessive passion. The sample comprised 91 Portuguese Paralympians aged between 18 and 59 years (M = 31.01; SD = 3.78). Athletes completed self-reports of needs satisfaction in their sport, passion towards their sport, and general life satisfaction. Perceptions of competence and relatedness were associated with harmonious passion (β = .37, p > .01; β = .21, p > .05, respectively), while perceptions of autonomy were associated with obsessive passion (β = .39, p > .05). Additionally, harmonious passion, but not obsessive passion, was associated with life satisfaction (β = .40, p > .01), and only the indirect effect from competence to life satisfaction, via harmonious passion, was significant. These results suggest that feeling autonomous may not necessarily translate into more harmonious passionate engagement but is associated with higher levels of obsessive passion. In contrast, competence and relatedness appear to play an important role in the life of athletes who experience a more harmonious passion towards their sport practice. Perceptions of mastery and competence, as well as sport-related social connections could be important to consider improving the lives of athletes with Paralympic experience.
- Comparative Analysis of the Importance of Determining Factors in the Choice and Sale of ApartmentsPublication . Santos, E.; Tavares, F.; Tavares, V.; Ratten, V.The motivation to compare the importance that buyers and sellers give to the diverse characteristics of apartments is its pertinency to grasping the housing market. The objective of this article is to compare the determining factors in the choice and sale of apartments among the potential buyers and sellers. During a sale, the realtors exhibit the dwellings’ positive characteristics, the so-called amenities. The homebuyers must analyse the deal in a rational and well-weighed way, striving to know its characteristics to reduce the information asymmetry. The study focuses on two distinct samples, with the common goal of transacting housing. One of the samples is composed of individuals who are looking for apartments, and the other one of individuals who are selling apartments, both being collected in mainland Portugal. It was verified that there are statistically significant differences between buyers and sellers. Buyers give more importance to certain rooms and the inexistence of negative externalities near their future residence. Sellers emphasise positive externalities and parking spots. This study is expected to contribute to the increase in scientific knowledge on the housing market and to the decrease of the information asymmetry between sellers and buyers. Knowing the importance that buyers and sellers give to the main different factors in the Portuguese real estate market constitutes an advancement of knowledge in this area.
- A Dataset of Photos and Videos for Digital Forensics Analysis Using Machine Learning ProcessingPublication . Ferreira, Sara; Antunes, Mário; Correia, Manuel E.Deepfake and manipulated digital photos and videos are being increasingly used in a myriad of cybercrimes. Ransomware, the dissemination of fake news, and digital kidnapping-related crimes are the most recurrent, in which tampered multimedia content has been the primordial disseminating vehicle. Digital forensic analysis tools are being widely used by criminal investigations to automate the identification of digital evidence in seized electronic equipment. The number of files to be processed and the complexity of the crimes under analysis have highlighted the need to employ efficient digital forensics techniques grounded on state-of-the-art technologies. Machine Learning (ML) researchers have been challenged to apply techniques and methods to improve the automatic detection of manipulated multimedia content. However, the implementation of such methods have not yet been massively incorporated into digital forensic tools, mostly due to the lack of realistic and well-structured datasets of photos and videos. The diversity and richness of the datasets are crucial to benchmark the ML models and to evaluate their appropriateness to be applied in real-world digital forensics applications. An example is the development of third-party modules for the widely used Autopsy digital forensic application. This paper presents a dataset obtained by extracting a set of simple features from genuine and manipulated photos and videos, which are part of state-of-the-art existing datasets. The resulting dataset is balanced, and each entry comprises a label and a vector of numeric values corresponding to the features extracted through a Discrete Fourier Transform (DFT). The dataset is available in a GitHub repository, and the total amount of photos and video frames is 40, 588 and 12, 400, respectively. The dataset was validated and benchmarked with deep learning Convolutional Neural Networks (CNN) and Support Vector Machines (SVM) methods; however, a plethora of other existing ones can be applied. Generically, the results show a better F1-score for CNN when comparing with SVM, both for photos and videos processing. CNN achieved an F1-score of 0.9968 and 0.8415 for photos and videos, respectively. Regarding SVM, the results obtained with 5-fold cross-validation are 0.9953 and 0.7955, respectively, for photos and videos processing. A set of methods written in Python is available for the researchers, namely to preprocess and extract the features from the original photos and videos files and to build the training and testing sets. Additional methods are also available to convert the original PKL files into CSV and TXT, which gives more flexibility for the ML researchers to use the dataset on existing ML frameworks and tools.