Abstract

Abstract

Abstract
O presente artigo propõe-se discutir práticas pedagógicas que promovam a inventividade, a cocriação e a colaboração e tem por objetivo apresentar a ação “Tô Ligado!”, que emergiu da vivência de cidadania digital MOVEOnCibricity no contexto do I Festival Internacional de Cidadania Digital, desenvolvida pela Rede Internacional ConectaKaT. A pesquisa se desenvolve a partir do Método Cartográfico de Pesquisa-Intervenção e está fundamentada na perspectiva da Educação OnLIFE. Tem como resultados o desenvolvimento de outras ações, constituindo a característica reticular e conectiva da ConectaKaT, sendo eles: a WebSérie Entrevistas, a Campanha/Jogo Segurança na Internet e nova WebSérie TomKaT nas Escolas. Esta ação apresenta contribuições significativas na construção de práticas pedagógicas inventivas e cocriadas na perspectiva de uma Educação OnLIFE, contribuindo também para um habitar engajado no ensinar e no aprender. Além disso, a ação “Tô Ligado!” se configurou como uma potente prática na construção de conhecimentos em torno de temáticas que se relacionam ao desenvolvimento sustentável. Assim, a ConectaKaT tem se estruturado enquanto uma plataforma viva de interação ecológica, propícia à invenção de novas metodologias e práticas pedagógicas, evidenciando uma nova política cognitiva em Educação.

Abstract
Since its appearance in 2008, blockchain technology has found multiple uses in fields such as banking, supply chain management, and healthcare. One of the most intriguing uses of blockchain is in voting systems, where the technology can overcome the security and transparency concerns that plague traditional voting systems. This paper provides a thorough examination of the implementation of a blockchain-based voting system. The proposed system employs cryptographic methods to protect voters' privacy and anonymity while ensuring the verifiability and integrity of election results. Digital signatures, homomorphic encryption (He), zero-knowledge proofs (ZKPs), and the Byzantine fault-tolerant consensus method underpin the system. A review of the literature on the use of blockchain technology for voting systems supports the analysis and the technical and logistical constraints connected with implementing the suggested system. The study suggests solutions to problems such as managing voter identification and authentication, ensuring accessibility for all voters, and dealing with network latency and scalability. The suggested blockchain-based voting system can provide a safe and transparent platform for casting and counting votes, ensuring election results' privacy, anonymity, and verifiability. The implementation of blockchain technology can overcome traditional voting systems' security and transparency shortcomings while also delivering a high level of integrity and traceability.

Abstract
Recommender systems position themselves as powerful tools in the support of relevance and personalization, presenting remarkable potential in the area of marketing. The cold-start customer problematic presents a challenge within this topic, leading to the need of distinguishing user features and preferences based on a restricted set of transactional information. This paper proposes a hybrid recommender system that aims to leverage transactional and portfolio information as indicating characteristics of customer behaviour. Four independent systems are combined through a parallelised weighted hybrid design. The first individual system utilises the price, target age, and brand of each product to develop a content-based recommender system, identifying item similarities. Secondly, a keyword-based content system uses product titles and descriptions to identify related groups of items. The third system utilises transactional data, defining similarity between products based on purchasing patterns, categorised as a collaborative model. The fourth system distinguishes itself from the previous approaches by leveraging association rules, using transactional information to establish antecedent and precedence relationships between items through a market basket analysis. Two datasets were analysed: product portfolio and transactional datasets. The product portfolio had 17,118 unique products and the included 4,408,825 instances from 2 June 2021 until 2 June 2022. Although the collaborative system demonstrated the best evaluation metrics when comparing all systems individually, the hybridisation of the four systems surpassed each of the individual systems in performance, with a 8.9% hit rate, 6.6% portfolio coverage, and with closer targeting of customer preferences and smaller bias.

Abstract
Identifying stress in people is not a trivial or straightforward task, as several factors are involved in detecting the presence or absence of stress. The problem of detect stress has attracted much attention in the last decade and is mainly addressed with physiological signals and in a controlled ambience with specific tasks. However, the widespread use of video cameras permitted the creation of a new non-invasive data collection techniques. The goal of this work is to provide an alternative way to detect stress in the workplace without the need of specific laboratory conditions. For that, a stress detection model based on images analysed with deep learning neural networks was developed. The trained model achieved a F1 = 79.9% on a binary dataset, of stress/non-stress, with an imbalanced ratio of 0.49. This model can be used in a non-invasive application to detect stress and provide recommendations to the collaborators in the workplace in order to help them to control their stress condition. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.