Abstract
We cannot neglect digital literacy because it is undeniable how much technology is part of our lives. Ignoring it and the tools and services it provides us, which greatly facilitate the human experience, is simply a mistake. Recognising the importance of digital literacy, primarily due to the digital transformation in Portugal, it will be necessary to have technological skills to overcome some limitations. Information and Communication Technologies are seen in this environment as a factor that can contribute, on a large scale, to the inclusion of individuals with a digital literacy deficit, both in the Portuguese Local Public Administration and in society in general. The growth of digital transformation causes almost all jobs to need digital skills and participation in society. It takes digitally intelligent employees who know not only to use but also innovate and lead to new technologies because digital transformation may not be successful without that capacity. Thus, it is pertinent to develop, propose and validate an explanatory model that improves the relationship between digital transformation in Portuguese Local Public Administration and the digital literacy of its employees. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Abstract
Social Engineering (SE) is a significant problem for enterprises. Cybercriminals continue developing new and sophisticated methods to trick individuals into disclosing confidential information or granting unauthorized access to infrastructure systems. These attacks remain a significant threat to enterprise systems despite significant investments in technical architecture and security measures. User awareness training and other behavioral interventions are critical for improving SE resilience. However, their effectiveness still needs to be determined, as personality traits may turn some individuals more susceptible to SE attacks. This paper aims to provide a comprehensive assessment of the state of knowledge in this field, identifying best practices for improving SE resilience in organizations and supporting the development of new research studies to address this issue. Its goal is to help enterprises of any size develop a framework to reduce the risk of successful SE attacks and create a culture of security awareness.

Abstract
Educational chatbots are digital tools designed to assist learners in various educational settings. These chatbots use natural language processing (NLP) and machine learning algorithms to simulate human conversation and respond to user queries in a way that facilitates learning. They can be integrated into various educational platforms such as learning management systems, educational apps, and websites to provide learners with a personalized and interactive learning experience. Our paper discusses different scenarios for educational purposes and suggests in total four scenarios for educational needs.

Abstract
Anomaly detection in the industrial context, identifying defective products and their categorization, is a prevalent task. It is aimed to acknowledge if training and testing multilabel classification models on textures to deploy on an MCU is possible. The focus is deploying lightweight models on MCUs, performing a multilabel classification on textures for industrial usage. For this purpose, a Systematic Literature Review was conducted, which allows knowing the commonly used machine learning models in industrial products anomaly detection and what methods are used to defect detection on textures. Through the Systematic Literature Review, was possible to understand the range of different and combined methods, the methods used in multilabel classification, the most common hyper-parametrizations and popular inferences engines to train machine-learning models to deploy on MCUs, and some techniques applied to overcome the restricted resources of memory and inference time associated with MCUs. © 2024 Elsevier B.V.. All rights reserved.

Abstract
The digital society’s portrait involves being daily connected to the Internet, at home, at work and in the social life. But seniors do not feel this need, despite this need is increasing as everything around them is online. So, seniors take a change on web browsing, without being aware of the it is dangers, from the theft of personal data, fake news, or online frauds. Therefore, the investigation promotes a Serious Game that exposes these insecure digital situations by challenges to a group of seniors from a network of senior universities. Web Segura is an online educational game developed on the WordPress platform and with challenges of the H5P plugin. © 2023, University of Algarve Research Centre for Arts and Communication. All rights reserved.

Abstract
Greenland ice core records display abrupt transitions, designated as Dansgaard-Oeschger (DO) events, characterised by episodes of rapid warming (typically decades) followed by a slower cooling. The identification of abrupt transitions is hindered by the typical low resolution and small size of paleoclimate records, and their significant temporal variability. Furthermore, the amplitude and duration of the DO events varies substantially along the last glacial period, which further hinders the objective identification of abrupt transitions from ice core records Automatic, purely data-driven methods, have the potential to foster the identification of abrupt transitions in palaeoclimate time series in an objective way, complementing the traditional identification of transitions by visual inspection of the time series.In this study we apply an algorithmic time series method, the Matrix Profile approach, to the analysis of the NGRIP Greenland ice core record, focusing on:- the ability of the method to retrieve in an automatic way abrupt transitions, by comparing the anomalies identified by the matrix profile method with the expert-based identification of DO events;- the characterisation of DO events, by classifying DO events in terms of shape and identifying events with similar warming/cooling temporal patternThe results for the NGRIP time series show that the matrix profile approach struggles to retrieve all the abrupt transitions that are identified by experts as DO events, the main limitation arising from the diversity in length of DO events and the method’s dependence on fixed-size sub-sequences within the time series. However, the matrix profile method is able to characterise the similarity of shape patterns between DO events in an objective and consistent way.