DSpace Collection:https://hdl.handle.net/10316/277082024-03-29T09:04:41Z2024-03-29T09:04:41ZUsing social media and personality traits to assess software developers' emotional polaritySilva, LeoCastro, Marília Gurgel deSilva, Miriam BernardinoSantos, Milena do Carmo Cunha dosKulesza, UiráLima, MargaridaMadeira, Henriquehttps://hdl.handle.net/10316/1144852024-03-28T11:21:32Z2023-01-01T00:00:00ZTitle: Using social media and personality traits to assess software developers' emotional polarity
Authors: Silva, Leo; Castro, Marília Gurgel de; Silva, Miriam Bernardino; Santos, Milena do Carmo Cunha dos; Kulesza, Uirá; Lima, Margarida; Madeira, Henrique
Abstract: Although human factors (e.g., cognitive functions, behaviors and skills, human error models, etc.) are key elements to improve software development productivity and quality, the role of software developers' emotions and their personality traits in software engineering still needs to be studied. A major difficulty is in assessing developers' emotions, leading to the classic problem of having difficulties understanding what cannot be easily measured. Existing approaches to infer emotions, such as facial expressions, self-assessed surveys, and biometric sensors, imply considerable intrusiveness on developers and tend to be used only during normal working periods. This article proposes to assess the feasibility of using social media posts (e.g., developers' posts on Twitter) to accurately determine the polarity of emotions of software developers over extended periods in a non-intrusive manner, allowing the identification of potentially abnormal periods of negative or positive sentiments of developers that may affect software development productivity or software quality. Our results suggested that Twitter data can serve as a valid source for accurately inferring the polarity of emotions. We evaluated 31 combinations of unsupervised lexicon-based techniques using a dataset with 79,029 public posts from Twitter from sixteen software developers, achieving a macro F1-Score of 0.745 and 76.8% of accuracy with the ensemble comprised of SentiStrength, Sentilex-PT, and LIWC2015_PT lexicons. Among other results, we found a statistically significant difference in tweets' polarities posted during working and non-working periods for 31.25% of the participants, suggesting that emotional polarity monitoring outside working hours could also be relevant. We also assessed the Big Five personality traits of the developers and preliminarily used them to ponder the polarities inferences. In this context, Openness, Conscientiousness, and Extraversion were frequently related to neutral and positive posts, while Neuroticism is associated with negative posts. Our results show that the proposed approach is accurate enough to constitute a simple and non-intrusive alternative to existing methods. Tools using this approach can be applied in real software development environments to support software team workers in making decisions to improve the software development process.2023-01-01T00:00:00ZComputational methods applied to syphilis: where are we, and where are we going?Albuquerque, GabrielaFernandes, FelipeBarbalho, Ingridy M. P.Barros, Daniele M. S.Morais, Philippi S. G.Morais, Antônio H. F.Santos, Marquiony M.Galvão-Lima, Leonardo J.Sales-Moioli, Ana Isabela L.Santos, João Paulo Q.Gil, PauloHenriques, JorgeTeixeira, César A.Lima, Thaísa S.Coutinho, Karilany D.Pinto, Talita K. B.Valentim, Ricardo A. M.https://hdl.handle.net/10316/1144832024-03-28T10:52:56Z2023-01-01T00:00:00ZTitle: Computational methods applied to syphilis: where are we, and where are we going?
Authors: Albuquerque, Gabriela; Fernandes, Felipe; Barbalho, Ingridy M. P.; Barros, Daniele M. S.; Morais, Philippi S. G.; Morais, Antônio H. F.; Santos, Marquiony M.; Galvão-Lima, Leonardo J.; Sales-Moioli, Ana Isabela L.; Santos, João Paulo Q.; Gil, Paulo; Henriques, Jorge; Teixeira, César A.; Lima, Thaísa S.; Coutinho, Karilany D.; Pinto, Talita K. B.; Valentim, Ricardo A. M.
Abstract: Syphilis is an infectious disease that can be diagnosed and treated cheaply. Despite being a curable condition, the syphilis rate is increasing worldwide. In this sense, computational methods can analyze data and assist managers in formulating new public policies for preventing and controlling sexually transmitted infections (STIs). Computational techniques can integrate knowledge from experiences and, through an inference mechanism, apply conditions to a database that seeks to explain data behavior. This systematic review analyzed studies that use computational methods to establish or improve syphilis-related aspects. Our review shows the usefulness of computational tools to promote the overall understanding of syphilis, a global problem, to guide public policy and practice, to target better public health interventions such as surveillance and prevention, health service delivery, and the optimal use of diagnostic tools. The review was conducted according to PRISMA 2020 Statement and used several quality criteria to include studies. The publications chosen to compose this review were gathered from Science Direct, Web of Science, Springer, Scopus, ACM Digital Library, and PubMed databases. Then, studies published between 2015 and 2022 were selected. The review identified 1,991 studies. After applying inclusion, exclusion, and study quality assessment criteria, 26 primary studies were included in the final analysis. The results show different computational approaches, including countless Machine Learning algorithmic models, and three sub-areas of application in the context of syphilis: surveillance (61.54%), diagnosis (34.62%), and health policy evaluation (3.85%). These computational approaches are promising and capable of being tools to support syphilis control and surveillance actions.2023-01-01T00:00:00ZLocal Periodicity-Based Beat Tracking for Expressive Classical Piano MusicChiu, Ching-YuMüller, MeinardDavies, Matthew E. P.Su, Alvin Wen-YuYang, Yi-Hsuanhttps://hdl.handle.net/10316/1144822024-03-28T10:46:28Z2023-01-01T00:00:00ZTitle: Local Periodicity-Based Beat Tracking for Expressive Classical Piano Music
Authors: Chiu, Ching-Yu; Müller, Meinard; Davies, Matthew E. P.; Su, Alvin Wen-Yu; Yang, Yi-Hsuan
Abstract: Tomodel the periodicity of beats, state-of-the-art beat
tracking systems use “post-processing trackers” (PPTs) that rely
on several empirically determined global assumptions for tempo
transition, which work well for music with a steady tempo. For
expressive classical music, however, these assumptions can be too
rigid. With two large datasets of Western classical piano music,
namely the Aligned Scores and Performances (ASAP) dataset and
a dataset of Chopin’s Mazurkas (Maz-5), we report on experiments
showing the failure of existing PPTs to cope with local
tempo changes, thus calling for new methods. In this paper, we
propose a new local periodicity-based PPT, called predominant
local pulse-based dynamic programming (PLPDP) tracking, that
allows formore flexible tempo transitions. Specifically, the newPPT
incorporates a method called “predominant local pulses” (PLP)
in combination with a dynamic programming (DP) component to
jointly consider the locally detected periodicity and beat activation
strength at each time instant. Accordingly, PLPDP accounts for the
local periodicity, rather than relying on a global tempo assumption.
Compared to existing PPTs, PLPDP particularly enhances the
recall values at the cost of a lower precision, resulting in an overall
improvement of F1-score for beat tracking in ASAP (from 0.473 to
0.493) and Maz-5 (from 0.595 to 0.838).2023-01-01T00:00:00ZIntrusion and Anomaly Detection in Industrial Automation and Control SystemsRosa, LuisCruz, Tiago J.Simões, PauloMonteiro, Edmundohttps://hdl.handle.net/10316/1144772024-03-28T09:54:56Z2023-01-01T00:00:00ZTitle: Intrusion and Anomaly Detection in Industrial Automation and Control Systems
Authors: Rosa, Luis; Cruz, Tiago J.; Simões, Paulo; Monteiro, Edmundo
Abstract: In the domain of Industrial Automation and Control
Systems (IACS), security was traditionally downplayed to a
certain extent, as it was originally deemed an exclusive concern of
Information and Communications Technology (ICT) systems. The
myth of the air-gap, as well as other preconceived notions about
implicit IACS security, constituted dangerous fallacies that were
debunked once successful attacks become known. Ultimately,
the industry started shifting away from this dangerous mindset,
discussing how to properly secure those systems. In many ways,
IACS security should not be treated differently from modern
ICT security. For sure, IACS have distinct characteristics, assets,
protocols and even priorities that should be considered – but
security should never be an optional concern.
In this publication, we present the main results of a PhD
dissertation that proposes a holistic and data-driven framework
capable of leveraging distinct techniques to increase situational
awareness and provide continuous and near real-time monitoring
of IACS. For such purposes, it proposes an evolution of the
Security Information and Event Management (SIEM) concept,
geared towards providing a unified security data monitoring
solution by leveraging recent advances in the field of real-time
Big Data analytics. In the same way, the most recent machinelearning-
based anomaly-detection techniques (which are becoming
increasingly prominent in the cybersecurity field) are also
analyzed and studied to understand their benefits for developing
and advancing IACS cyber-intrusion detection processes.2023-01-01T00:00:00Z