Abstract
This paper proposes a method to avoid using a CORDIC or external memory to process the steering vectors to calculate the pseudospectrum of correlation-based beamforming algorithms. We show that if we decompose the steering vector equation, the size of the matrix to be saved in memory becomes independent of the antenna array size. Besides, the amount of data needed is small enough to be saved in the internal block RAMs of the FPGA SoC. Besides, this method greatly reduces the number of memory accesses, by offloading some processing to hardware, while keeping the frequency at 300MHz with a precision of 0.25 degrees. Finally, we show that this approach is scalable since the complexity grows logarithmically for bigger arrays, and the symmetry in the matrices obtained allows even more compact data.

Abstract
Post-hoc explanation methods have often been criticised for abstracting away the decision-making process of deep neural networks. In this work, we would like to provide natural language descriptions for what different layers of a vision backbone have learned. Our DeViL method generates textual descriptions of visual features at different layers of the network as well as highlights the attribution locations of learned concepts. We train a transformer network to translate individual image features of any vision layer into a prompt that a separate off-the-shelf language model decodes into natural language. By employing dropout both per-layer and per-spatial-location, our model can generalize training on image-text pairs to generate localized explanations. As it uses a pre-trained language model, our approach is fast to train and can be applied to any vision backbone. Moreover, DeViL can create open-vocabulary attribution maps corresponding to words or phrases even outside the training scope of the vision model. We demonstrate that DeViL generates textual descriptions relevant to the image content on CC3M, surpassing previous lightweight captioning models and attribution maps, uncovering the learned concepts of the vision backbone. Further, we analyze fine-grained descriptions of layers as well as specific spatial locations and show that DeViL outperforms the current state-of-the-art on the neuron-wise descriptions of the MILANNOTATIONS dataset.

Abstract
Education is vital in fostering economic growth and societal development, particularly in developing countries like Timor-Leste. As technology has revolutionised education in the digital transformation era, the concept of a smart university, driven by advanced technologies and data analytics, has gained prominence globally. Timor-Leste, amid its progress in institutional structures and public infrastructure, is also exploring integrating smart technologies in higher education. This underscores a commitment of The East Timor National Education Strategic Plan (NESP) 2011-2030 to meet national and international standards, positioning the country at the forefront of educational innovation. This study aims to assess the feasibility of implementing a Smart University in Timor-Leste to evaluate the readiness of the country to embrace digital technologies and integrate them into higher education practices. The research employs a Design Science Research methodology where qualitative and quantitative data are gathered through interviews, surveys, and document analysis. Design artefacts, including system architecture and an evaluation framework, are developed to comprehensively understand the technological and informatics aspects of implementing a Smart University in Timor-Leste. The findings will contribute to decision-making and inform the implementation plan, offering valuable insights into stakeholders' perspectives and perceptions, and will support the advancement of the educational landscape in Timor Leste by integrating smart technologies and innovative practices in higher education.

Abstract
HAL 9000 is an Intrusion Tolerant Systems (ITSs) Risk Manager, which assesses configuration risks against potential intrusions. It utilizes gathered threat knowledge and remains operational, even in the absence of updated information. Based on its advice, the ITSs can dynamically and proactively adapt to recent threats to minimize and mitigate future intrusions from malicious adversaries. Our goal is to reduce the risk linked to the exploitation of recently uncovered vulnerabilities that have not been classified and/or do not have a script to reproduce the exploit, considering the potential that they may have already been exploited as zero-day exploits. Our experiments demonstrate that the proposed solution can effectively learn and replicate National Vulnerability Database's evaluation process with 99% accuracy.

Abstract
O presente artigo concerne aspectos referentes aos conceitos de criatividade e invenção. A criatividade vista enquanto a capacidade de solucionar problemas, já a invenção é vista para além da resolução, mas a produção e criação de problemas. Desse modo, o texto visa contribuir na discussão de metodologias e práticas inventivas, de forma a contribuir com os processos de Inovação na Educação. A metodologia é de cunho exploratório, cartográfico e curatorial. Como resultados da pesquisa, sugerem-se avanços no desenvolvimento de práticas inventivas no campo educativo.

Abstract
Side-channel attacks exploit involuntary information leakages from devices, such as power consumption, electromagnetic radiation, visible light, or acoustic emanations. The objective of these attacks is to gain unauthorized access to data, such as passwords or other sensitive information. In particular, the keyboard acoustic side-channel attacks take advantage of the sound emanating from keystrokes to decipher which keys have been pressed by the user by comparing the produced sounds with a recorded dataset. This paper proposes a prototype that generates false and random keystroke sounds that are mixed with real keystroke sounds, to mitigate the impact of a keyboard acoustic side-channel attack. It includes three security modes low, medium, and high, each offering different levels of protection and user customization. This is accomplished by generating random key sounds and also randomizing the number of key sounds to play and the time between the generated key sounds, during the real keystroke sound. To test the proposed prototype, a dataset was generated and used to assess the security modes execution. The results show that the sound of the real keystrokes is scrambled with the fake keystrokes sounds, making it difficult to decipher which keys have been pressed by the user.