Abstract
There is a growing demand for persistent data in IoT, edge and similar resource-constrained devices. However, standard FLASH memory-based solutions present performance, energy, and reliability limitations in these applications. We propose MRAM persistent memory as an alternative to FLASH based storage. Preliminary experimental results show that its performance, power consumption, and reliability in typical database workloads is competitive for resource-constrained devices. This opens up new opportunities, as well as challenges, for small-scale database systems. MRAM is tested for its raw performance and applicability to key-value and relational database systems on resource-constrained devices. Improvements of as much as three orders of magnitude in write performance for key-value systems were observed in comparison to an alternative NAND FLASH based device. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

Abstract

Abstract
The pursuit of happiness is a fundamental human goal that has been studied by philosophers, theologians, and scientists for centuries. Despite its universal importance, the definition and means of achieving happiness vary greatly across cultures and individual experiences (Uchida, Norasakkunkit and Kitayama, 2004). Cultures have different beliefs, values, and customs that shape their understanding of happiness. For example, some cultures may place a higher value on material wealth and success, while others may prioritize spiritual fulfilment or strong relationships (Joshanloo and Weijers, 2014). In this autoethnographic paper, I reflect on my own personal journey towards happiness during a one-year travel across 22 countries within southern Africa, southeast Asia, and south America, focusing on the first part of the trip – southern Africa. Autoethnography is a qualitative research method that involves the researcher reflecting on their personal experiences and cultural positionality in order to understand and analyse cultural phenomena (Bunyan, 2021). It combines elements of autobiography and ethnography, as the researcher uses their own experiences as a way to explore and understand the cultural context in which they participate (Hamilton, Smith and Worthington, 2008). Through the use of personal narrative and cultural analysis, I delve into the ways in which my own cultural background and societal expectations shaped my understanding of happiness. I also explore the ways in which immersing myself in a new culture and community impacted my pursuit of happiness and well-being. By reflecting on my own experiences and observations, I aim to shed light on the complexities of the pursuit of happiness and the potential for personal and cultural growth that can result from stepping outside of one's comfort zone. Through this autoethnographic lens, we hope to offer a unique and personal perspective on the pursuit of happiness, and to encourage readers to consider the cultural and individual factors that influence their own pursuit of this universal goal. We also reflect on how innovation and technology, essential to business, may not be as important to achieve happiness in certain contexts. This essay is a call for reflection on what truly matters in life.

Abstract
Information security has become a necessity for all organizations. ITIL, designed for large organizations, has also been gradually adopted by smaller companies and has incorporated practices related to information security management (ISM). This study aims to understand the main risks associated with ISM, considering the context of micro companies. For this purpose, a qualitative model was built based on four case studies of micro companies in the information technology industry. The results show that companies are concerned about information security, given the growth of external threats. However, these companies have a lack of commitment, of resources, and of knowledge that hinder the implementation of an ISM policy. Therefore, it is evident that the challenge of ISM is demanding and should be addressed, considering that the security of an organization should be analyzed in a holistic context, where all perspectives should be considered to reflect the multidisciplinary nature of security.

Abstract
A perception module is a vital component of a modern robotic system. Vision, radar, thermal, and LiDAR are the most common choices of sensors for environmental awareness. Relying on singular sources of information is prone to be affected by specific environmental conditions (e.g., visual cameras are affected by glary or dark environments). Thus, relying on different sensors is an essential step to introduce robustness against various environmental conditions. Hence, a perception system with sensor fusion capabilities produces the desired redundant and reliable awareness critical for real-world systems. This paper proposes a novel early fusion module that is reliable against individual cases of sensor failure when detecting an offshore maritime platform for UAV landing. The model explores the early fusion of a still unexplored combination of visual, infrared, and LiDAR modalities. The contribution is described by suggesting a simple methodology that intends to facilitate the training and inference of a lightweight state-of-the-art object detector. The early fusion based detector achieves solid detection recalls up to 99% for all cases of sensor failure and extreme weather conditions such as glary, dark, and foggy scenarios in fair real-time inference duration below 6 ms.

Abstract
This paper describes the participation of the Artificial Intelligence and Information Retrieval (AIIR) Lab from the University of Southern Maine and the Laboratory of Artificial Intelligence and Decision Support (LIAAD) lab from INESC TEC in the CLEF 2023 SimpleText lab. There are three tasks defined for SimpleText: (T1) What is in (or out)?, (T2) What is unclear?, and (T3) Rewrite this!. Five runs were submitted for Task 1 using traditional Information Retrieval, and Sentence-BERT models. For Task 2, three runs were submitted, using YAKE! and KBIR keyword extraction models. Finally, for Task 3, two models were deployed, one using OpenAI Davinci embeddings and the other combining two unsupervised simplification models.