Abstract
Despite the significant increase in the use of digital devices, and the access to e-books by younger ages, the printed book still remains very important. Nowadays, although many communication processes and information exchanges have a digital support, the importance of using printed paper is acknowledged in many contexts. Both the paper and the digital media have unique advantages: digital media integrate with audiovisual and interactive resources, and the paper book supports interactions such as tactile and kinesthetic feedback given to both hands. In recent years there have been several commercial products designated as "augmented books", using augmented reality technologies to provide the reader with more layers of information, thereby fostering the use of the book in new ways. So, in this concept paper we describe part of the research and outcomes of project CHIC – C3, aimed at designing and developing a platform for managing the production of digital content connected with printed books. Furthermore, we propose a model for the gamification of digital content based on the printed book, mainly aimed at educational purposes. A proof of concept for the model was built in the form of a companion platform, supported by the Moodle LMS, fully integrated with the main CHIC website. Readers are able to access the platform, engage in several content related games, and interact with other readers.

Abstract
Task scheduling in fog paradigm is highly complex and in the literature, there are still few studies. In the cloud architecture, it is widely studied and in many researches, it is approached from the perspective of service providers. Trying to bring innovative contributions in these areas, in this paper, we propose a model to the context-aware task-scheduling problem for fog paradigm. In our proposal, different context parameters are normalized through Min-Max normalization; requisition priorities are defined through the application of the Multiple Linear Regression (MLR) technique and scheduling is performed using Multi-Objective Non-Linear Programming Optimization (MONLIP) technique.

Abstract
Priority rules are applied in many commercial software tools for scheduling projects under limited resources because of their known advantages such as the ease of implementation, their intuitive working, and their fast speed. Moreover, while numerous research papers present comparison studies between different priority rules, managers often do not know which rules should be used for their specific project, and therefore have no other choice than selecting a priority rule at random and hope for the best. This paper introduces a decision tree approach to classify and detect the best performing priority rule for the resource-constrained project scheduling problem (RCPSP). The research relies on two classification models to map project indicators onto the performance of the priority rule. Using such models, the performance of each priority rule can be predicted, and these predictions are then used to automatically select the best performing priority rule for a specific project with known network and resource indicator values. A set of computational experiment is set up to evaluate the performance of the newly proposed classification models using the most well-known priority rules from the literature. The experiments compare the performance of multi-label classification models with multi-class classification models, and show that these models can outperform the average performance of using any single priority rule. It will be argued that this approach can be easily extended to any extension of the RCPSP without changing the methodology used in this study.

Abstract
In the past decades, the resource on the resource-constrained project scheduling problem (RCPSP) has grown rapidly, resulting in an overwhelming amount of solution procedures that provide (near)-optimal solutions in a reasonable time. Despite the rapid progress, little is still known what makes a project instance hard to solve. Inspired by a previous research study that has shown that even small instances with only up to 30 activities is sometimes hard to solve, the current study provides an analysis of the project data used in the academic literature. More precisely, it investigates the ability of four well-known resource indicators to predict the hardness of an RCPSP instance. The study introduces a new instance equivalence concept to show that instances might have very different values for their resource indicators without changing any possible solution for this instance. The concept is based on four theorems and a search algorithm that transforms existing instances into new equivalent instances with more compact resources. This algorithm illustrates that the use of resource indicators to predict the hardness of an instance is sometimes misleading. In a set of computational experiment on more than 10,000 instances, it is shown that the newly constructed equivalent instances have values for the resource indicators that are not only different than the values of the original instances, but also often are better in predicting the hardness the project instances. It is suggested that the new equivalent instances are used for further research to compare results on the new instances with results obtained from the original dataset.

Abstract
Telecoms are companies that provide communication services. In 2020 a drop in Telecom's revenues is expected due to COVID and this impact will remain for future years, with higher drops in coming years. The adoption of Self-Service Technologies allows to solve the problem of selling and serve the customers on-line, increasing customer loyalty, reducing cost, fostering innovation and thus increasing revenues. The major research question for this work is: Which factors influence the adoption of online self-service technologies by Business-to-Business customers of a Telecom?. In order to derive a conceptual framework to answer this question several theories where used: The Technology Acceptance Model (TAM), The Innovation Diffusion Theory (IDT) and the SST attitude/intention model. A conceptual framework that drives the adoption of Self-service technologies in Telecom B2B segments was proposed and tested.

Abstract
This paper aims to define a problem to be studied and summarize previous research work done. Remote work or telework enterprise model has been a growing trend. The Covid-19 pandemic forced businesses to adopt a temporary remote-working mode. Now managers are investigating whether this dnew working-mode is, or not, a better approach than the traditional working mode known as the office model. The proposed framework will support the transformation process from a physical into a remote work model supported by applying digital tools and methods. Furthermore, the framework proposed in this work aims to help business decision makers to decide which organizational model they should adopt after Covid-19: full-remote; hybrid or partially remote; or physical, also known as going back to the office. A simple and pragmatic framework is proposed to help organizations: understand if it's possible to transform a business from a physical (at the office) to a full or partial remote model; define the main goals of this transformation; create a simple template to map the new digital enterprise architecture in terms of tools, data management, business processes and human resources (job functions and digital skills).