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Publications

2024

Cognitive Patterns for Developer Experience

Authors
Pinho, D; Aguiar, A; Amaral, V;

Publication
Proceedings of the 29th European Conference on Pattern Languages of Programs, People, and Practices, EuroPLoP 2024, Irsee, Germany, July 3-7, 2024

Abstract
The need for software developers is growing in a world where software systems play multiple roles in our daily lives. Due to the mental nature of their work, we are driven to address their needs. The concept of developer experience (DX) was defined as an extension of user experience, focusing on the dual role developers have as users and creators. This paper proposes a pattern language that can assist developers in improving their developer experience. In addition, we present a selection of patterns that focus on the cognitive dimension of the mind: Infrastructure Alignment, Adeqate Skills, It Takes A Village, Learning From A Master, Written Knowledge, and Practice Makes Perfect. © 2024 Copyright held by the owner/author(s).

2024

Sample Size Analysis for a Production Line Study of Time

Authors
da Silva, MI; Vaz, CB;

Publication
FLEXIBLE AUTOMATION AND INTELLIGENT MANUFACTURING: ESTABLISHING BRIDGES FOR MORE SUSTAINABLE MANUFACTURING SYSTEMS, FAIM 2023, VOL 2

Abstract
Setting labor standards is an important topic to operational and strategic planning which requires the time studies establishment. This paper applies the statistical method for the definition of a sample size in order to define a reliable cycle time for a real industrial process. For the case study it is considered a welding process performed by a single operator that does the load and unload of components in 4 different welding machines. In order to perform the time studies, it is necessary to collect continuously data in the production line by measuring the time taken for the operator to perform the task. In order to facilitate the measurements, the task is divided into small elements with visible start and end points, called Measurement Points, in which the measurement process is applied. Afterwards, the statistical method enables to determine the sample size of observations to calculate the reliable cycle time. For the welding process presented, it is stated that the sample size defined through the statistical method is 20. Thus, these time observations of the task are continuously collected in order to obtain a reliable cycle time for this welding process. This time study can be implemented in similar way in other industrial processes.

2024

Informative Classification of Capsule Endoscopy Videos Using Active Learning

Authors
Fonseca, F; Nunes, B; Salgado, M; Silva, A; Cunha, A;

Publication
Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST

Abstract
The wireless capsule endoscopy is a non-invasive imaging method that allows observation of the inner lumen of the small intestine, but with the cost of a longer duration to process its resulting videos. Therefore, the scientific community has developed several machine learning strategies to help reduce that duration. Such strategies are typically trained and evaluated on small sets of images, ultimately not proving to be efficient when applied to full videos. Labelling full Capsule Endoscopy videos requires significant effort, leading to a lack of data on this medical area. Active learning strategies allow intelligent selection of datasets from a vast set of unlabelled data, maximizing learning and reducing annotation costs. In this experiment, we have explored active learning methods to reduce capsule endoscopy videos’ annotation effort by compiling smaller datasets capable of representing their content. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

2024

Detection of Leak Areas in Vineyard Irrigation Systems Using UAV-Based Data

Authors
Pádua, L; Marques, P; Dinis, LT; Moutinho Pereira, J; Sousa, JJ; Morais, R; Peres, E;

Publication
DRONES

Abstract
Water is essential for maintaining plant health and optimal growth in agriculture. While some crops depend on irrigation, others can rely on rainfed water, depending on regional climatic conditions. This is exemplified by grapevines, which have specific water level requirements, and irrigation systems are needed. However, these systems can be susceptible to damage or leaks, which are not always easy to detect, requiring meticulous and time-consuming inspection. This study presents a methodology for identifying potential damage or leaks in vineyard irrigation systems using RGB and thermal infrared (TIR) imagery acquired by unmanned aerial vehicles (UAVs). The RGB imagery was used to distinguish between grapevine and non-grapevine pixels, enabling the division of TIR data into three raster products: temperature from grapevines, from non-grapevine areas, and from the entire evaluated vineyard plot. By analyzing the mean temperature values from equally spaced row sections, different threshold values were calculated to estimate and map potential leaks. These thresholds included the lower quintile value, the mean temperature minus the standard deviation (Tmean-sigma), and the mean temperature minus two times the standard deviation (Tmean-2 sigma). The lower quintile threshold showed the best performance in identifying known leak areas and highlighting the closest rows that need inspection in the field. This approach presents a promising solution for inspecting vineyard irrigation systems. By using UAVs, larger areas can be covered on-demand, improving the efficiency and scope of the inspection process. This not only reduces water wastage in viticulture and eases grapevine water stress but also optimizes viticulture practices.

2024

A comprehensive review of the literature on continuous improvement approaches in food services management

Authors
Monteiro, C; Rocha, A; Miguélis, V; Afonso, C;

Publication
INTERNATIONAL JOURNAL OF HOSPITALITY MANAGEMENT

Abstract
Continuous improvement (CI) have been recognised as one of the most effective ways to improve organisational performance. However, there is a lack of research on this topic from a food service perspective. Thus, the aim of this work is to explore the adoption of CI-focused methodologies in food services and to understand how they contribute to improving the performance of these services. Critical success factors and barriers to the implementation of CI are also analysed. This systematic review was conducted using the PRISMA methodology and a total of 43 studies were included in the analysis. This review shows that CI is effective in improving operations and performance, as well as increasing stakeholder satisfaction in the food service sector. Additionally, the review reveals that CI-focused tools are mainly used in problem identification, waste identification, planning, operations, and logistics. Human-related issues are the most frequently mentioned when it comes to the factors determining the success or failure of CI in food services.

2024

Validating Pattern Languages: A systematic literature review

Authors
Almeida, F; Pinho, D; Aguiar, A;

Publication
Proceedings of the 29th European Conference on Pattern Languages of Programs, People, and Practices, EuroPLoP 2024, Irsee, Germany, July 3-7, 2024

Abstract
The concept of patterns and pattern languages, although very common in software nowadays, was first approached by Christopher Alexander, in the area of architecture, in the book A pattern language: towns, buildings, construction. However, it was only in 1980 that the term was adapted for software development, gaining its popularity in 1994. Despite the fact that the concept of patterns has been used in the area of software development for more than 40 years, there is still no consensus on the best method to validate patterns and patterns languages, and the existing methods are scattered in several different papers and research across the scientific community. As such, in this paper, we conduct a systematic literature review about the existing methods in the scientific community to validate patterns and pattern languages. © 2024 Copyright held by the owner/author(s).

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