Abstract
Quality control inspection systems are crucial and a key factor in maintaining and ensuring the integrity of any product. The quality inspection task is a repetitive task, when performed by operators only, it can be slow and susceptible to failures due to the lack of attention and fatigue. This work focuses on the inspection of parts made of high-pressure diecast aluminum for components of the automotive industry. In the present case study, last year, 18240 parts needed to be reinspected, requiring approximately 96 hours, a time that could be spent on other tasks. This article performs a comparison of four deep learning models: Faster R-CNN, RetinaNet, YOLOv7, and YOLOv7-tiny, to find out which one is more suited to perform the quality inspection task of detecting metal filings on casting aluminum parts. As for this use-case the prototype must be highly intolerant to False Negatives, that is, the part being defective and passing undetected, Faster R-CNN was considered the bestperforming model based on a Recall value of 96.00%.

Abstract
Class imbalance is a common issue while developing classification models. In order to tackle this problem, synthetic data have recently been developed to enhance the minority class. These artificially generated samples aim to bolster the representation of the minority class. However, evaluating the suitability of such generated data is crucial to ensure their alignment with the original data distribution. Utility measures come into play here to quantify how similar the distribution of the generated data is to the original one. For tabular data, there are various evaluation methods that assess different characteristics of the generated data. In this study, we collected utility measures and categorized them based on the type of analysis they performed. We then applied these measures to synthetic data generated from two well-known datasets, Adults Income, and Liar+. We also used five well-known generative models, Borderline SMOTE, DataSynthesizer, CTGAN, CopulaGAN, and REaLTabFormer, to generate the synthetic data and evaluated its quality using the utility measures. The measurements have proven to be informative, indicating that if one synthetic dataset is superior to another in terms of utility measures, it will be more effective as an augmentation for the minority class when performing classification tasks.

Abstract
Modern I/O applications that run on HPC infrastructures are increasingly becoming read and metadata intensive. However, having multiple applications submitting large amounts of metadata operations can easily saturate the shared parallel file system's metadata resources, leading to overall performance degradation and I/O unfairness. We present PADLL, an application and file system agnostic storage middleware that enables QoS control of data and metadata workflows in HPC storage systems. It adopts ideas from Software-Defined Storage, building data plane stages that mediate and rate limit POSIX requests submitted to the shared file system, and a control plane that holistically coordinates how all I/O workflows are handled. We demonstrate its performance and feasibility under multiple QoS policies using synthetic benchmarks, real-world applications, and traces collected from a production file system. Results show that PADLL can enforce complex storage QoS policies over concurrent metadata-aggressive jobs, ensuring fairness and prioritization.

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

Abstract
The management of health systems has been one of the main challenges in several European countries, especially where the aging population is increasing. This led to the adoption of smarter technologies as a means to automate the processes within hospitals. One of the technologies adopted is active location solutions, which allows the staff within the hospital to quickly find any sort of entity, from key persons to equipment. In this work, we focus on developing a reliable method for active location based on RSSI antennas, passive tags, and ML models. Since the tags are passive, the usage of RSSI is discouraged, since it does not vary sufficiently based on our experiments. We explored the usage of alternative features, such as the number of activations per tag within a time slot. Throughout our evaluation, we were able to reach an average error of 0.275 m which is similar to existing RSSI IPS.