Programming phenomenology: proof of concept on adaptivity

Phenomenology is the empirical study of mind and consciousness. Considering programming activity as a cognitive procedure, phenomenology may be applied to address some of its issues. A particular issue of interest is how one programming approach differs from others, i.e. the cognition performed when using an approach differs from the one performed using another? If they are similar the two approaches are like to be the same, otherwise essentially different. Since adaptive computing proposal is Turing-equivalent it may be discussed about the actual differences among those approaches. As an example, λ-calculus is also Turing-equivalent but since the cognition performed is different it justifies the several λ-based existing approaches. In order to accomplish such analysis, bergsonism will be used as phenomenological method to be applied in particular adaptive structure called adaptive-graph. As a result it will be argued cognition performed using adaptive-graph is different from the one performed when using non-adaptive one. Then it will generalized suggesting adaptive computing cognition shall be further explored.This paper has been supported by COMPETE: POCI-01-0145-FEDER-0070 43 and FCT Fundac ̧ ̃ao para a Ciˆencia e Tecnologia - Project UID/CEC/00319/2013

Weakness evaluation on in-vehicle violence detection: an assessment of X3D, C2D and I3D against FGSM and PGD

When constructing a deep learning model for recognizing violence inside a vehicle, it is crucial to consider several aspects. One aspect is the computational limitations, and the other is the deep learning model architecture chosen. Nevertheless, to choose the best deep learning model, it is necessary to test and evaluate the model against adversarial attacks. This paper presented three different architecture models for violence recognition inside a vehicle. These model architectures were evaluated based on adversarial attacks and interpretability methods. An analysis of the model’s convergence was conducted, followed by adversarial robustness for each model and a sanity-check based on interpretability analysis. It compared a standard evaluation for training and testing data samples with the adversarial attacks techniques. These two levels of analysis are essential to verify model weakness and sensibility regarding the complete video and in a frame-by-frame way.This work is funded by “FCT—Fundação para a Ciência e Tecnologia” within the R&D Units Project Scope: UIDB/00319/2020. The employment contract of Dalila Durães is supported by CCDR-N Project: NORTE-01-0145-FEDER-00008

Emotional and mental nuances and technological approaches: Optimising Fact-Check dissemination through cognitive reinforcement technique †

The issue of the dissemination of fake news has been widely addressed in the literature, but the issue of the dissemination of fact checks to debunk fake news has not received sufficient attention. Fake news is tailored to reach a wide audience, a concern that, as this paper shows, does not seem to be present in fact checking. As a result, fact checking, no matter how good it is, fails in its goal of debunking fake news for the general public. This paper addresses this problem with the aim of increasing the effectiveness of the fact checking of online social media posts through the use of cognitive tools, yet grounded in ethical principles. The paper consists of a profile of the prevalence of fact checking in online social media (both from the literature and from field data) and an assessment of the extent to which engagement can be increased by using simple cognitive enhancements in the text of the post. The focus is on Snopes and (Formula presented.) (formerly Twitter).FCT -Fundação para a Ciência e a Tecnologia(2022.06822