Abstract
Ensuring privacy while sharing sensitive data is critical, particularly in fields such as healthcare, and everywhere compliance with data protection regulations is required. Anonymization and pseudonymization techniques are essential for preserving individual privacy but it is challenging to select the most appropriate methods given particular privacy and utility requirements. We conducted a focus group during the EuroPLoP 2024 conference that aimed to obtain feedback on patterns that we documented in this space and on a pattern map we outlined, and to identify patterns related to anonymization or pseudonymization of data that have not yet been documented. Some of the patterns we documented were not known by participants. On the other hand, we found some techniques that are potentially privacy-preserving patterns that have not yet been documented, and framed these techniques according to the category in our pattern map. Although the results suggest that our current patterns address some recurring privacy challenges, further exploration and documentation of the techniques are necessary to capture the full range of privacy-preserving solutions.

Abstract

Abstract
This study investigates the effectiveness of Transformer-based models for retail demand forecasting. We evaluated vanilla Transformer, Informer, Autoformer, PatchTST, and temporal fusion Transformer (TFT) against traditional baselines like AutoARIMA and AutoETS. Model performance was assessed using mean absolute scaled error (MASE) and weighted quantile loss (WQL). The M5 competition dataset, comprising 30,490 time series from 10 stores, served as the evaluation benchmark. The results demonstrate that Transformer-based models significantly outperform traditional baselines, with Transformer, Informer, and TFT leading the performance metrics. These models achieved MASE improvements of 26% to 29% and WQL reductions of up to 34% compared to the seasonal Na & iuml;ve method, particularly excelling in short-term forecasts. While Autoformer and PatchTST also surpassed traditional methods, their performance was slightly lower, indicating the potential for further tuning. Additionally, this study highlights a trade-off between model complexity and computational efficiency, with Transformer models, though computationally intensive, offering superior forecasting accuracy compared to the significantly slower traditional models like AutoARIMA. These findings underscore the potential of Transformer-based approaches for enhancing retail demand forecasting, provided the computational demands are managed effectively.

Abstract
While a wide range of resources is available on orchestration techniques and best practices for containerized software systems, many are not documented clearly or in detail. This complicates the process of selecting the most suitable methods for various usage scenarios. To address this gap, we documented a set of orchestration patterns. This paper reports the results of a focus group conducted during the EuroPLoP 2024 conference, where we aimed to obtain feedback on that group of patterns and on a wider pattern map we outlined. We also aimed to identify container orchestration patterns that have not yet been documented. We found that participants knew most of the patterns we included on the pattern map. Additionally, one of the practices mentioned by the participants (Node Balancing) was previously documented as a pattern by us with the name of Service Balancing. Finally, we found important insights into container orchestration patterns, expanding our pattern map to include eight new proto-patterns.

Abstract
The use of collaborative robots in industries is growing rapidly. To ensure the successful implementation of these devices, it is essential to consider the user experience (UX) during their design process. This study is aimed at testing the UX goals that emerge when users interact with a collaborative robot during the programming and collaborating phases. A framework on UX goals will be tested, in the geographical context of Portugal. For that, an experimental setup was introduced in the form of a laboratory case study in which the human-robot collaboration (HRC) was evaluated by the combination of both quantitative (applying the User Experience Questionnaire [UEQ]) and qualitative (semistructured interviews) metrics. The sample was constituted by 19 university students. The quantitative approach showed positive overall ratings for the programming phase UX, with attractiveness having the highest average value (M=2.21; SD=0.59) and dependability the lowest (M=1.64; SD=0.65). For the collaboration phase, all UX ratings were positive, with attractiveness having the highest average value (M=2.46; SD=0.78) and efficiency the lowest (M=1.93; SD=0.77). Only perspicuity showed significant differences between the two phases (t18=-4.335, p=0.002). The qualitative approach, at the light of the framework used, showed that efficiency, inspiration, and usability are the most mentioned UX goals emerging from the content analysis. These findings enhance manufacturing workers' well-being by improving cobot design in organizations.

Abstract
Quantification of erythrocytes and leukocytes is an essential aspect of hemogram diagno- 23 sis in Veterinary Medicine. Flow cytometry analysis, laser scattering, and impedance detection are 24 standard laboratory techniques, verified by manual microscopy counting. Although single-cell scat- 25 tering is already used as a standard technology for differentiating cell counts in flow cytometry, it 26 requires capillary cell separation. The current study investigates the scattering characteristics of 27 whole blood to identify correlations with erythrocytes and leukocytes counts. The scattering infor- 28 mation present in blood samples can be classified into three types: i) geometrical scattering, which 29 occurs when non-absorbed light is reflected and scattered, ii) Mie scattering, which happens when 30 light is scattered by particles of a similar size to the wavelength, and iii) Rayleigh scattering, which occurs when light is scattered by particles that are smaller than the incident light wavelength. In 32 this study, we investigate the scattering correction coefficients of dog blood absorption spectra in 33 the visible-near infrared range, to establish direct correlations with erythrocytes and leukocytes 34 counts, using multivariate linear regression. Our findings demonstrate the possibility of using the 35 scattering properties of dog blood, which is a step towards the existence of a portable and miniatur- 36 ized hemogram diagnosis in Veterinary Clinics worldwide.