Abstract
While Small and Medium-sized Enterprises (SMEs) in emerging markets have been adopting advanced digital technology to gain competitiveness and scale, they still face business cybersecurity risks due to limited resources, awareness, and expertise. This paper aims to systematically review the literature covering cybersecurity, conceptual frameworks, and challenges associated with Digital Transformation (DT) faced by Small and Medium-sized Enterprises (SMEs) in developing countries, with specific emphasis in Mozambique. The established inclusion criteria were papers published between 2015 to 2024, in English and Portuguese, focusing on cybersecurity, cybersecurity framework, Digital Transformation (DT), Small and Medium-sized Enterprises (SMEs) of any sector, and developing countries with emphasis of Mozambique or similar contexts. Studies related to large enterprises, and research published more than 10 years were excluded. A total of 22 out of 124 research articles, journals, and conferences, were analyzed in detail, according to bibliographic information, different research design, outcomes, and findings. The study used four digital libraries, namely: IEEE Xplore, American Computing Machinery (ACM), Science Direct, SCOPUS, and Google Scholar were used in addition. The quality assessment checklist was used in the included studies to evaluate the methodological rigor and relevance of primary studies. The findings reveal that even as the world becomes more aware of the need for Digital Transformation and cybersecurity, Small and Medium-sized Enterprises (SMEs) in developing countries continue to be underserved. This review allowed to identify a considerable disparity between the cybersecurity practices applied by SMEs in developing countries and the guidelines provided by international frameworks. The review emphasizes important areas that warrant longitudinal, sector-specific, impact-driven research, policy-oriented studies that can inform scalable, inclusive, and sustainable digital strategies, as well as a dedicated cybersecurity framework appropriate for the Mozambican context.

Abstract
The growing integration of Artificial Intelligence (AI) into interactive systems presents unique challenges and opportunities for Human-Computer Interaction (HCI) and User Experience (UX). While AI can enhance usability and provide novel interaction paradigms, it also raises concerns related to transparency, control, and user trust. This workshop seeks to bring together researchers and practitioners to discuss state-of-the-art engineering methods that support HCI and UX in AI-driven systems. By fostering interdisciplinary collaboration, we aim to identify key challenges, share best practices, and develop a roadmap for future research in this critical area.

Abstract

Abstract
Federated learning has emerged as a key paradigm for distributed optimization under privacy and communication constraints, yet classical aggregation schemes such as Federated Averaging (FedAvg) assume uniform client reliability and remain vulnerable to corrupted, noisy, or malfunctioning participants. This limitation is particularly critical in distributed sensing systems, where clients collect partial and heterogeneous observations of a shared physical signal. In this work, we propose a Trust-Aware Federated Signal Reconstruction (TAFSR) framework for robust federated linear inverse problems under client-level corruption. Unlike generic robust aggregators designed for update-space filtering, the proposed method introduces a residual-consistency trust mechanism tailored to inverse problems with partial observations, and admits both a block-wise IRLS interpretation and a spectral-norm error analysis. We formalize federated reconstruction as a distributed least-squares problem with unreliable clients and show that, under strong convexity and weighted-operator spectral bounds, the method converges linearly up to a corruption-dependent bias term. Under persistent residual separation between clean and corrupted clients, the idealized unclipped weighting rule further yields asymptotic attenuation of corrupted-client influence relative to uniform aggregation. The analysis also establishes spectral error bounds and clarifies the connection between trust-aware aggregation and robust M-estimation. Extensive reproducible experiments on industrial vibration data (CWRU) and clinical ECG datasets (MIT-BIH and PTB-XL) under heterogeneous noise and simulated corruption demonstrate that TAFSR consistently improves robustness over FedAvg. Under the default 20% corrupted-client setting, the mean final RMSE decreases from 0.3100 to 0.1999 on CWRU and from 0.2809 to 0.1815 on MIT-BIH, while the method also maintains an advantage on the more challenging low-redundancy PTB-XL regime. These results position TAFSR as a reproducible and task-aware alternative to uniform or generic update-space aggregation for distributed signal reconstruction in heterogeneous sensing environments.

Abstract
Safety-critical interactive systems pose design and evaluation challenges that go beyond usability. The safety of the system (i.e. the guarantee that it does not reach an undesirable or incorrect state) is also a relevant consideration. Traditional user-centred approaches (UCD) lack the rigour and thoroughness needed to address safety, and formal verification arises as a possible solution. Applying formal verification to a safety-critical interactive system design encompasses developing a model, expressing and verifying properties, and analysing the verification results. In the case of model checking, properties are typically expressed in temporal logic. This creates a gap between the languages used in UCD and the languages used for formal verification. Creating temporal logic properties manually requires expertise in formal methods and can be both time-consuming and error-prone. This paper explores how a patterns-based approach can be used to support the specification of properties in a natural language-based style. A prototype implementation of the approach is evaluated through a user study, and the results of this evaluation are discussed.

Abstract
With the rise of Transformers, Vision Transformers (ViTs) have become a new standard in visual recognition. This has led to the development of numerous architectures with diverse designs and applications. This survey identifies 22 key ViT and hybrid CNN-ViT models, along with 5 top Convolutional Neural Network (CNN) models. These were selected based on their new architecture, relevance to benchmarks, and overall impact. The models are organised using a defined taxonomy formed by CNN-based, pure Transformer-based, and hybrid architectures. We analyse their main components, training methods, and computational features, while assessing performance using reported results on standard benchmarks such as ImageNet and CIFAR, along with our training and fine-tuning evaluations on specific imaging datasets. In addition to accuracy, we look at real-world deployment issues by analysing the trade-offs between accuracy and efficiency in embedded, mobile, and clinical settings. The results indicate that modern CNNs are still very competitive in limited-resource environments, while advanced ViT variants perform well after large-scale pretraining, especially in areas with high variability. Hybrid CNN-ViT architectures, on the other hand, tend to offer the best balance between accuracy, data efficiency, and computational cost. This survey establishes a consolidated benchmark and reference framework for understanding the evolution, capabilities, and practical applicability of contemporary vision architectures.