Security and privacy (S&P) advice plays a crucial role in how people stay safe online. While prior work shows that the plethora of advice from varied sources makes it difficult for users to prioritize advice, the insights are primarily based on studies conducted in Western contexts. Other work shows that users outside the West have different S&P needs and thus, we cannot simply rely on advice curated in the West to generalize to the majority world - regions of Africa, Asia, Latin America, and the Middle East, where most of the world's population lives. We fill this gap by investigating S&P advice across nine majority world countries via 70 semi-structured interviews with local experts: cybercafe operators, tech repair specialists, and other community figures that people commonly rely on for tech support and S&P advice. We find that the advice provided by local experts in the majority world largely matches the advice they provide to their constituents and the advice from the West. However, we surface various significant barriers that hinder majority world users from implementing advice, including economic constraints, language barriers, and social friction from taking protective measures. Our findings further show how factors such as social norms and gender shape advice practices, e.g., by driving gendered advice-seeking. We discuss how S&P advice in the majority world can be improved and reflect on how the S&P community can better engage with local communities in conducting similar research.

The growing integration of sensors and embedded devices into distributed Internet of Things (IoT) systems has increased the demand for real-time data collection and processing solutions. However, the high volume and frequency of sensor data create challenges related to storage, transmission, and response latency, especially in resource-constrained environments. In this context, locally executed compression techniques, aligned with the Tiny Machine Learning (TinyML) paradigm, become differentiators for enabling embedded applications. Thus, this work proposes an autoencoder-based approach for efficiently compressing sensor data on edge devices. Three autoencoder variants (feed-forward, sparse, and contractive) are evaluated, combined with symmetric and asymmetric architectures, considering criteria such as compression ratio, information preservation, and embedded execution feasibility. For practical validation, a case study was conducted using vehicular data collected via the OBD-II interface, where the selected models were deployed on the OBDII Edge Freematics One+ device. The results show that the models could reduce data dimensionality with minimal information loss, maintain competitive performance on discriminative tasks, and exhibit inference times compatible with real-time applications. Autoencoders represent a viable neural compression solution for IoT environments, potentially applicable to various embedded sensing scenarios.

Kolmogorov–Arnold Networks (KANs) represent a promising machine learning architecture that leverages univariate functional decomposition to model complex phenomena using compact and interpretable structures. These characteristics make KANs especially attractive for deployment in TinyML environments, where memory, processing power, and energy consumption are strictly constrained. This paper evaluates the feasibility and trade-offs of using a KAN model to estimate the State of Charge (SoC) in electric vehicle batteries. We design a KAN tailored for embedded systems and compare its performance with a conventional Multilayer Perceptron (MLP) baseline under identical training and deployment conditions. Our evaluation includes predictive accuracy, training cost, model size, inference speed, and energy consumption on microcontrollers. Results show that the KAN model achieves a nearly ten times smaller memory footprint than the MLP (693 bytes vs. 6807 bytes) and maintains comparable energy consumption and inference speed across different embedded platforms. Although the MLP outperforms the KAN during training with faster convergence and lower energy requirements, the KAN demonstrates competitive predictive performance at inference time while significantly reducing deployment costs in terms of memory usage and energy efficiency at the edge. Furthermore, the KAN model produces symbolic mathematical expressions, offering direct interpretability and facilitating analytical validation — a critical advantage for embedded battery diagnostics and safety-critical applications.

The advancement of Industry 4.0 has intensified the demand for intelligent systems that can efficiently access and interpret technical information critical to industrial operations. However, recovering knowledge from extensive and complex technical documentation remains a significant challenge. This study examines the effectiveness of various Retrieval-Augmented Generation (RAG) strategies, combined with different Large Language Models (LLMs), in extracting and generating answers from industrial technical documents. A case study was conducted based on PROFIBUS, a widely adopted digital communication protocol in automation networks, with technical documents organized into categories for engineers and developers. Twenty questions of varying complexity were formulated, and responses were generated using three RAG strategies (Basic, Decomposition, and HyDE) combined with two LLMs (Gemma 3 and GPT-4o-mini). The outputs were compared against reference answers generated by the NotebookLM system and evaluated using automatic metrics, including ROUGE, METEOR, BERTScore, and MATTR. The results indicate that the Decomposition and HyDE strategies achieved superior semantic similarity scores when combined with more capable models. That model’s performance varied depending on the complexity of the document profile. These findings highlight the importance of tailored RAG strategies in enhancing intelligent information retrieval in industrial domains, which supports safer and more efficient operational environments.

The Internet of Things (IoT) generates massive multivariate time series data requiring real-time anomaly detection and correction for reliable monitoring. This challenges resource-constrained embedded devices due to conventional offline training and batch processing. To address this, we propose two algorithms derived from the TEDARLS framework: Multivariate Sequential TEDA (MST) and Multivariate Parallel TEDA (MPT). Derived from the TEDARLS framework, both enable on-device detection and correction of multivariate anomalies within TinyML constraints. A case study with real vehicular sensor data demonstrated low inference times and consistent embedded behavior. Supervised metrics were only assessed on synthetic data. MPT, though more sensitive, introduced greater signal distortions and required significantly longer processing times. Overall, MST demonstrated superior stability and suitability for real-time anomaly correction in resource-constrained IoT environments. This approach addresses an important gap in embedded analytics for IoT by enabling lightweight, accurate, and autonomous anomaly detection and correction at the edge.

The Internet of Things (IoT) paradigm has fostered several transformations in various industrial sectors, with important improvements in the automotive industry. Actually, the number of sensors and the computational power of modern vehicles have grown significantly, providing an opportunity for instrumentation, monitoring, and creation of increasingly efficient diagnostic algorithms. In fact, it is known that diagnosis is an essential requirement since the way of driving may have significant impacts in different contexts, such as traffic safety, fuel consumption, emissions, and maintenance, among others. Furthermore, solutions generally available in the literature for analyzing drivers’ behavior have focused on supervised offline learning models, fed with an entire dataset for training and testing. In this context, this paper proposes an approach for detecting drivers’ driving events, exploiting for that unsupervised online data flows and a specialized machine learning algorithm. The validation of the proposal was carried out with a case study in a real scenario with different conditions, which allowed the identification of daily driving operations. The results demonstrated the feasibility of the proposal as well as the identification of the different intended events.

In order to improve IT governance in APF, the TCU created, under the 2010 survey, the IT governance index (iGovTI). This type of research has as general objective, a situation of IT governance of each evaluated organization. Currently (2018), iGovTI evaluates organizations into four categories: initial, basic, intermediate, and enhanced. In this context, a research related to this problem seeks together with the ICT managers of Brazilian APF organizations of "initial" capacity, with the objective of not doing good practices in their own organizations. As a result of the chapter, they recognize that ITIL, COBIT and PMBOK are the best known and best practices used in participation organizations. And that, despite the majority of companies, has financial resources dedicated to the area, such as the difficulties in relation to knowledge about governance and good work and work practices.

The optional discipline "Governance of ICT directed to the Federal Public Administration (FPA)" in the curricular structure of the undergraduate courses in Information Systems and Computer Science of Institution X aims to analyze the dimensions (High Administration, Strategies and Plans, Information, People, Processes and Results of Management) in which the survey of the ICT Governance situation in the FPA is carried out. This article presents a practical approach that exemplifies the application of the knowledge acquired as a study of the programmatic content of the discipline. The programmatic content and its importance were evaluated through the application of a questionnaire to the students. The responses revealed that the teaching approach used and the content viewed added knowledge and are useful for training the ICT professional.

Governance of Information Technology and Communication (ICT) is still a challenging factor for organizations, both for the public and private sector. Public organizations are recognized by the control bodies (Audit Court of the Union - ACU, System of Administration of Information Technology Resources - SISP, Strategy of Digital Governance - EGD), which, through this recognition, cause the managers of these organizations to pay attention to this area. In this case, the adoption and direction of good practices that meet the requirements of the control bodies regarding ICT Governance are necessary. Thus, the objective of this article is to propose a framework called PeticGov, with the objective to support the organizations evaluated by the TCU and to direct the ICT managers in how to implement the good practices of ICT Governance considered relevant for TCU evaluation. In order to direct the organizations in which good practice (s) to adopt based on their deficit. As results, it is noticed that the PeticGov questionnaire returns the same result of the TCU.

In the last decade, the growth of the economic, automotive and technological sectors, has been notable. Alongside this growth, emerges the term ”Industry 4.0” which is used to represent the current Industrial Revolution. This revolution involves different areas: from manufacturing to healthcare. Industry 4.0 can create value during the entire product lifecycle, promoting customer feedback and having the entire product history throughout its life. The automatic communication between vehicle and factory was facilitated, allowing the accomplishment of different analysis regarding vehicles, such as the identification of a behavioral pattern through historical driver usage, fuel consumption, maintenance indicators and so on. This would allow the prevention of critical issues and undesired behaviors as it prevents the automakers from losing contactwiththe vehicle after the purchase. This paper aims to propose a customer feedback platform for vehicle manufacturing in Industry 4.0 context, capable of collecting and analyzing, through an OBD-II scanner the sensors available in vehicles, with the purpose of assisting in the management, prevention, and mitigation of different vehicular problems. An intercontinental experiment conducted in locations within Brazil and Italy show the feasibility of the platform and the potential to use preliminary results in order to improve the vehicle manufacturing.