Success management – From theory to practice

Success Management focuses on defining, leveraging, and securing the success of endeavours at maximum levels by gaining a comprehensive awareness of what is valued by stakeholders to reach success and managing accordingly to that understanding. Success Management has proven to be valuable in the context of project management; however, previous research does not provide a theoretical sound basis or detailed guidance for its practical implementation. This article contributes to filling this gap in the literature by providing the theoretical foundation of Success Management and describing in detail the implementation and key findings of a Success Management process carried out in the context of an IT/IS project by a large multinational company. The results show that Success Management can both raise a holistic awareness of the success contributors and promote success-focused planning and action. In this article, researchers and practitioners can find a full perspective on Success Management, from the theoretical principles to a step-by-step guide for practical implementation.We would like to thank all the project team members who participated in the Success Management process, particularly: Pedro Ribeiro, Flávio Vilarinho, António Silva, Marcos Andrade, and João Ramos. We would also like to thank the Editors and Reviewers: first, for their vision, initiative and hard work in organizing a special issue under the important subject of project success; second, for their valuable comments for improving the original manuscript and for all the support provided. This work has been supported by FCT - Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020

Visualização de espaços arqueológicos usando High Dynamic Range

Comunicação apresentada no V Congresso CAA Portugal de Aplicações Informáticas à Arqueologia, Leiria, 2007.É hoje amplamente reconhecido que os sistemas informáticos desempenham um papel fundamental no estudo e interpretação de espaços arqueológicos. Uma das suas principais áreas de aplicação é, seguramente, a reconstrução virtual de ambientes históricos, em particular os que já não existem. Neste domínio, a forma como visualizamos tais ambientes é particularmente importante para uma correcta interpretação arqueológica do espaço em causa, seja ele qual for. No entanto, a busca pelo perfeccionismo na representação visual de um qualquer cenário, está estritamente relacionada com a tecnologia de visualização usada para o efeito. O Sistema Visual Humano tem, na realidade, uma capacidade extraordinária e consegue captar valores de intensidade e cromaticidade verdadeiramente astronómicos. No entanto, grande parte dessa amplitude dinâmica não tem representação possível no modelo RGB, usado praticamente na totalidade dos dispositivos de visualização actuais. High Dynamic Range (HDR) é uma área de investigação que se dedica ao estudo de formas e métodos que visam suprir essa lacuna. Para atingir tal intento, têm sido desenvolvidas novas técnicas para a geração, armazenamento e representação de imagens que consigam preservar a (elevada) amplitude dinâmica captada pelo Sistema Visual Humano. Neste artigo apresentamos uma metodologia de trabalho que utiliza este novo paradigma de visualização onde o seu potencial se apropria verdadeiramente, a arqueologia. Desse modo propomo-nos gerar imagens de um dos mais belos e imponentes espaços existente nas ruínas da antiga cidade Romana de Conimbriga, a Casa dos Repuxos. Para tal, iremos simular a visualização, com recurso a imagens HDR, dos frescos e mosaicos lá existentes usando luminárias da época

Metodologia para a geração de imagens High Dynamic Range em iluminação romana

Comunicação apresentada no International Association for the Scientific Knowledge - InterTIC'07, Porto, 2007.Num futuro muito próximo, o modo como veremos conteúdos num qualquer dispositivo de visualização irá sofrer profundas alterações. A luz captada pelo olho humano num simples passeio pela praia num radioso dia de sol, pode atingir valores de intensidade e cromaticidade verdadeiramente astronómicos. No entanto, grande parte dessa amplitude dinâmica não tem representação possível no modelo RGB, usado praticamente na totalidade dos dispositivos de visualização actuais. High Dynamic Range (HDR) é uma área de investigação que se dedica ao estudo de formas e métodos que visam suprir essa lacuna. Para atingir tal intento, têm sido desenvolvidas novas técnicas para a geração, armazenamento e representação de imagens que consigam preservar a elevada amplitude dinâmica captada pelo Sistema Visual Humano. Neste artigo apresentamos uma metodologia de trabalho que utiliza este novo paradigma de visualização onde o seu potencial é verdadeiramente apropriado, a arqueologia. A Casa dos Repuxos é o espaço mais belo e imponente existente nas ruínas de Conimbriga (Portugal) e que ainda hoje preserva alguns dos frescos e mosaicos originais. O nosso objectivo centra-se na geração de imagens HDR desses frescos e mosaicos iluminados por luminárias desse período, de modo a que a experiência visual seja a mais próxima possível de um habitante daquela mesma casa

Ten years of active learning techniques and object detection: a systematic review

Object detection (OD) coupled with active learning (AL) has emerged as a powerful synergy in the field of computer vision, harnessing the capabilities of machine learning (ML) to automatically identify and perform image-based objects localisation while actively engaging human expertise to iteratively enhance model performance and foster machine-based knowledge expansion. Their prior success, demonstrated in a wide range of fields (e.g., industry and medicine), motivated this work, in which a comprehensive and systematic review of OD and AL techniques was carried out, considering reputed technical/scientific publication databases—such as ScienceDirect, IEEE, PubMed, and arXiv—and a temporal range between 2010 and December 2022. The primary inclusion criterion for papers in this review was the application of AL techniques for OD tasks, regardless of the field of application. A total of 852 articles were analysed, and 60 articles were included after full screening. Among the remaining ones, relevant topics such as AL sampling strategies used for OD tasks and groups categorisation can be found, along with details regarding the deep neural network architectures employed, application domains, and approaches used to blend learning techniques with those sampling strategies. Furthermore, an analysis of the geographical distribution of OD researchers across the globe and their affiliated organisations was conducted, providing a comprehensive overview of the research landscape in this field. Finally, promising research opportunities to enhance the AL process were identified, including the development of novel sampling strategies and their integration with different learning techniques.This research was funded by the RRP- Recovery and Resilience Plan and the European NextGeneration EU Funds, within the scope of the Mobilizing Agendas for Business Innovation, under reference C644937233-00000047 and by the Vine&Wine Portugal Project, co-financed by the RRP- Recovery and Resilience Plan and the European NextGeneration EU Funds, within the scope of the Mobilizing Agendas for Reindustrialization, under reference C644866286-00000011

SHREWS: A game with augmented reality for training computational thinking

This paper proposes a game to help young students training Computational Thinking (CT) skills to aid in solving problems. CT is a problem-solving approach based on picking a complex problem, understand what the problem is, and develop solutions in a way that a computer or human could solve. To help in this task, Augmented Reality(AR) will provide a more engaging visual way of interaction to keep students motivated while they search for solving problems. This benefit is a consequence of the AR capability of providing a visual and dynamic representation of abstract concepts. This work investigates AR and CT concepts and the best way of combining them for training student's skills to understand software and its effects. Thus, these concepts will be explored for the construction of learning activities to explain and create analogies to understand complex concepts related to computer programs. So the focus of the paper is the introduction of Shrews, the game created in this context. The principle and the proposed architecture are detailed. At the end, there is a description of how the game works and the current state of the prototype. We believe that the immersive experience using AR and CT concepts is one of the important aspects of the game to maintain a motivational approach to students. An exploratory prototype is created to explore the topic of teaching CT skills via playing a video game.This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020

Defect detection in the textile industry using image-based machine learning methods: A brief review

Traditionally, computer vision solutions for detecting elements of interest (e.g., defects) are based on strict context-sensitive implementations to address contained problems with a set of well-defined conditions. On the other hand, several machine learning approaches have proven their generalization capacity, not only to improve classification continuously, but also to learn from new examples, based on a fundamental aspect: the separation of data from the algorithmic setup. The findings regarding backward-propagation and the progresses built upon graphical cards technologies boost the advances in machine learning towards a subfield known as deep learning that is becoming very popular among many industrial areas, due to its even greater robustness and flexibility to map and deal knowledge that is typically handled by humans, with, also, incredible scalability proneness. Fabric defect detection is one of the manual processes that has been progressively automatized resorting to the aforementioned approaches, as it is an essential process for quality control. The goal is manifold: reduce human error, fatigue, ergonomic issues and associated costs, while simultaneously improving the expeditiousness and preciseness of the involved tasks, with a direct impact on profit. Following such research line with a specific focus in the textile industry, this work aims to constitute a brief review of both defect types and Automated Optical Inspection (AOI) mostly based on machine learning techniques, which have been proving their effectiveness in identifying anomalies within the context of textile material analysis. The inclusion of Convolutional Neural Network (CNN) based on known architectures such as AlexNet or Visual Geometry Group (VGG16) on computerized defect analysis allowed to reach accuracies over 98%. A short discussion is also provided along with an analysis of the current state characterizing this field of intervention, as well as some future challenges.ERDF - European Regional Development Fund(undefined

Using deep learning to detect the presence/absence of defects on leather: On the way to build an industry-driven approach

In textile/leather manufacturing environments, as in many other industrial contexts, quality inspection is an essential activity that is commonly performed by human operators. Error, fatigue, ergonomic issues, and related costs associated to this fashion of carrying out fabric validation are aspects concerning companies' strategists, whose mission includes to watch over the physical integrity of their employees, while aiming at enhanced quality control methods implementation towards profit maximization. Considering these challenges from a technical/scientific perspective, machine/deep learning approaches have been showing great skills in adapting a wide range of contexts and, in particular, industrial environments, complementing traditional computer vision methods with characteristics such as increased accuracy while dealing with image classification and segmentation problems, capacity for continuous learning from experts input and feedback, flexibility to easily scale training for new contextualization classes – unknown types of occurrences relevant to characterize a given problem –, among other advantages. The goal of crossing deep learning strategies with fabric inspection processes is pursued in this paper. After providing a brief but representative characterization of the targeted industrial context, in which, typically, fabric rolls of rawmaterial mats must be processed at a relatively low latency, an Automatic Optical Inspection (AOI) system architecture designed for such environments is revisited [1], for contextualization purposes. Afterwards, a set of deep learning-oriented training methods/processes is proposed in combination with neural networks built based on Xception architecture, towards the implementation of one of the components that integrate the aforementioned system, from which is expected the identification of presence/absence of defective textile/leather raw material at a low-latency. Several models powered by Xception were trained with different tunning parameters, resorting to datasets variations that were set up from raw images of leather, following different annotation strategies (meticulous and rough). The model that performed better reached 96% of accuracy.ERDF - European Regional Development Fund(undefined

Automatic verification of design rules in PCB manufacturing

Nowadays, electronics can be found in almost every available device. At the core of electronic devices there are Printed Circuit Boards (PCB). To create a suitable PCB there is the need of complying with several constraints, both concerning electrical and layout design. Thus, the design rules related to the PCB manufacturing and assembly are very important since these restrictions are fundamental to ensure the creation of a viable physical PCB. Electrical Computer Aided Design (ECAD) tools are able to automatically verify such rules, but they only consider a subset of the total required rules. The remaining rules are currently manually checked, which may increase the occurrence of errors and, consequently, increase the overall costs in designing and in the manufacturing process of a PCB. Being the design a crucial phase in the manufacturing procedure, a software system that automatically verifies all design rules and produce the corresponding assessment report is fundamental. Such software system is addressed in this paper.This work was funded by the project “iFACTORY: Novas Capacidades de Industrialização”, with reference 002814 supported by FEDER trough “Portugal2020 Programa Operacional Competitividade e Internacionalização” (COMPETE2020). This work has been supported by COMPETE: POCI-01-0145-FEDER-007043and FCT - Fundação para a Ciência e a Tecnologia within the Project Scope: UID/CEC/00319/201

Polymerase chain reaction for soybean detection in heat processed meat products.

Since vegetable proteins are considerably cheaper than muscle proteins, they are frequently used as meat extenders in order to reduce the cost of the final product. Due to several interesting characteristics, soybean is reported to be the most widely used vegetable protein in the meat industry. Nevertheless, soybean is included in the group of 12 ingredients potentially allergenic, which should therefore be labelled according to the Codex Alimentarius FAO/WHO and the European Commission (Directive 2003/89/EC). In fact, it has been described that amounts of soy bellow 0.1% and 1% (w/w) can lead to allergic reactions in sensitive consumers (1)

A ubiquitous service-oriented automatic optical inspection platform for textile industry

Within a highly competitive market context, quality standards are vital for the textile industry, in which related procedures to assess respective manufacture still mainly rely on human-based visual inspection. Thereby, factors such as ergonomics, analytical subjectivity, tiredness and error susceptibility affect the employee's performance and comfort in particular and impact the economic healthiness of each company operating in this industry, generally. In this paper, a defect detection-oriented platform for quality control in the textile industry is proposed to tackle these issues and respective impacts, combining computer vision, deep learning, geolocation and communication technologies. The system under development can integrate and improve the production ecosystem of a textile company through a properly adapted information technology setup and associated functionalities such as automatic defect detection and classification, real-time monitoring of operators, among others.This work was financed by the project “Smart Production Process” (No. POCI-01-0247-FEDER-045366), supported under the Incentive System for Research and Technological Development - Business R&DT (Individual Projects)