Structural onomatology for username generation: A partial account

The username hints for most of the on-line social networks are mostly unpleasant for human beings since they are mostly a simple name variation followed by numbers. This paper shows that it is possible to generate human likable usernames through heuristics guided by structural onomastics. The objective then is to conceive heuristics as such and check its availability in Twitter in order to verify if is it possible to generate a sufficiently big and available username data-set that is able to justify the transitions from unpleasant to a pleasant username suggestion. This paper finds that it is possible to generate 8281 handles on average through the proposed heuristics and their permutations, therefore, the number of various possibilities is comfortable. This is a partial account since not all possibilities were explored and some improvements are required, but suits for a proof of concept and to indicate paths.FCT Fundação para a Ciência e Tecnologia within the RD Units Project Scope: UIDB/00319/202

Large language models: compilers for the 4th generation of programming languages?

This paper explores the possibility of large language models as a fourth generation programming language compiler. This is based on the idea that large language models are able to translate a natural language specification into a program written in a particular programming language. In other words, just as high-level languages provided an additional language abstraction to assembly code, large language models can provide an additional language abstraction to high-level languages. This interpretation allows large language models to be thought of through the lens of compiler theory, leading to insightful conclusions.FCT - Fundação para a Ciência e a Tecnologia(UIDB/00319/2020

SE4AI issues on social media agent design with use cases

This paper is the result of an endeavor of specifying a social media agent through Use Case 2.0 (the “agile Use Case”). That what was expected to be a straightforward specification task revealed issues that subverts a critical foundation of the Use Case conception, nonexistent use-case between the SuD and the actor, yielding to the extensions proposed in this paper.This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020

An exploratory design science research on troll factories

Private and military troll factories (facilities used to spread rumours in online social media) are currently proliferating around the world. By their very nature, they are obscure companies whose internal workings are largely unknown, apart from leaks to the press. They are even more concealed when it comes to their underlying technology. At least in a broad sense, it is believed that there are two main tasks performed by a troll factory: sowing and spreading. The first is to create and, more importantly, maintain a social network that can be used for the spreading task. It is then a wicked long-term activity, subject to all sorts of problems. As an attempt to make this perspective a little clearer, this paper uses exploratory design science research to produce artefacts that could be applied to online rumour spreading in social media. Then, as a hypothesis: it is possible to design a fully automated social media agent capable of sowing a social network on microblogging platforms. The expectation is that it will be possible to identify common opportunities and difficulties in the development of such tools, which in turn will allow an evaluation of the technology, but above all the level of automation of these facilities. The research is based on a general domain Twitter corpus with 4M+ tokens and on ChatGPT, and discusses both knowledge-based and deep learning approaches for smooth tweet generation. These explorations suggest that for the current, widespread and publicly available NLP technology, troll factories work like a call centre; i.e. humans assisted by more or less sophisticated computing tools (often called cyborgs).FCT - Fundação para a Ciência e a Tecnologia(2022.06822

Prediction of students’ grades based on non-academic data

This study examines the use of machine learning techniques to predict Math and Portuguese grades based on student demographics and survey data regarding their school experiences. Using a sample of 53 middle school students, an accuracy rate of 93% was achieved with a support vector machine model. This paper’s findings suggest that non-academic factors such as school climate and student engagement can have a significant impact on academic performance.This work is supported by: FCT - Fundação para a Ciên cia e Tecnologia within the RD Units Project Scope: UIDB/00319/2020 and the Northern Regional Operational Programme (NORTE 2020), under Portugal 2020 within the scope of the project “Hello: Plataforma inteligente para o combate ao insucesso escolar”, Ref. NORTE- 01-0247-FEDER-047004 and by FCT– Fundação para a Ciência e Tecnologia within the R&D Units Project Scope:UIDB/00319/2020

Data fusion for prediction of variations in students grades

Considering the undeniable relevance of education in today’s society, it is of great interest to be able to predict the academic performance of students in order to change teaching methods and create new strategies taking into account the situation of the students and their needs. This study aims to apply data fusion to merge information about several students and predict variations in their Portuguese Language or Math grades from one trimester to another, that is, whether the students improve, worsen or maintain their grade. The possibility to predict changes in a student’s grades brings great opportunities for teachers, because they can get an idea, from the predictions, of possible drops in grades, and can adapt their teaching and try to prevent such drops from happening. After the creation of the models, it is possible to suggest that they are not overfitting, and the metrics indicate that the models are performing well and appear to have high level of performance. For the Portuguese Language prediction, we were able to reach an accuracy of 97.3%, and for the Mathematics prediction we reached 95.8% of accuracy.H2020 - Universidad de Alicante(PID2020-115454GB-C22/AEI/10.13039/501100011033)This work is supported by: FCT - Fundação para a Ciência e Tecnologia within the RD Units Project Scope: UIDB/00319/2020 and the Northern Regional Operational Programme (NORTE 2020), under Portugal 2020 within the scope of the project “Hello: Plataforma inteligente para o combate ao insucesso escolar”, Ref. NORTE-01-0247-FEDER-04700

A conversational agent for smart schooling: a case study on K-12 dropout risk assessment

The goal of smart education is to utilize advanced technology in order to improve the teaching experience by establishing a stimulating and interactive atmosphere for learning. Conversational agents emerge as an aid for a smarter education. One of the possibilities to be explored is the building of tools that help predict and prevent student failure or dropout. This case study presents a research project that consists on the creation of a school platform for student interaction, in which a conversational agent, developed using Rasa, communicates with both the students and the class director and is able to assign a risk of academic failure, based on their answers to questionnaires scripted by a team of psychologists. XGBoost outperfomed AdaBoost, Decision Tree and Random Forest algorithms with an accuracy of 97%.This work is supported by: FCT - Fundação para a Ciência e Tecnologia within the RD Units Project Scope: UIDB/00319/2020 and the Northern Regional Operational Programme (NORTE 2020), under Portugal 2020 within the scope of the project “Hello: Plataforma inteligente para o combate ao insucesso escolar”, Ref. NORTE-01-0247-FEDER-047004

Microblogging environment simulator: an ethical approach

Social media were originally created as a means of communication, but have eventually become an important way of producing and sharing information and news. Beyond that, the scientific community’s interest in conducting studies in numerous fields using Twitter is growing daily. This paper presents a platform that simulates a microblogging, inspired by the Twitter layout, with the aim of creating an environment where social media-focused studies can be conducted without compromising ethical values. The end result is a in lab environment with the ability to present the content (in the format of post) to validate or investigate and record user interactions.This work has been supported by FCT - Fundação para a Ciência e Tecnologia within the R &D Units Project Scope: UIDB/00319/2020

Applying multisensor in-car situations to detect violence

Violence recognition is challenging because it can be presented in very different forms. For example, it can be present in an image by a person hitting another person or present in audio by a person being rude to another. Thus, audio and video are essential features to be analysed. In the audio approach, speech processing, music, and ambient sound are some of the main points of this problem since finding similarities and differences between these domains is necessary. Human activity can be classified into four different categories in the video approach, depending on the complexity and the number of body parts involved in the action. Examples of Human activity categories are considered: gestures, actions, interactions and activities. Recognizing human actions in the video becomes a challenge with this varied set of human activities. Furthermore, in the last years, the growth of deep learning techniques applied to this area has been enormous, and the reason is that their results surpass traditional signal processing on a large scale. This article is based on audio and video signals inside a vehicle to detect violence. Furthermore, the architecture used was ResNet model with Mel-spectrogram methodology for audio signals. The proposed method for video signal representation was RGB, which applied four different models: C2D, I3D, X3D, and Flow-Gated. Finally, multimodal fusion was applied at the end of the process.FCT - Fundação para a Ciência e a Tecnologia(039334

Neural network explainable AI based on paraconsistent analysis: an extension

This paper explores the use of paraconsistent analysis for assessing neural networks from an explainable AI perspective. This is an early exploration paper aiming to understand whether paraconsistent analysis can be applied for understanding neural networks and whether it is worth further develop the subject in future research. The answers to these two questions are affirmative. Paraconsistent analysis provides insightful prediction visualisation through a mature formal framework that provides proper support for reasoning. The significant potential envisioned is the that paraconsistent analysis will be used for guiding neural network development projects, despite the performance issues. This paper provides two explorations. The first was a baseline experiment based on MNIST for establishing the link between paraconsistency and neural networks. The second experiment aimed to detect violence in audio files to verify whether the paraconsistent framework scales to industry level problems. The conclusion shown by this early assessment is that further research on this subject is worthful, and may eventually result in a significant contribution to the field.This work is financed by National Funds through the Portuguese funding agency, FCT— Fundação para a Ciência e a Tecnologia within project DSAIPA/AI/0099/2019