Abstract
The Internet of Things (IoT) emerged to describe a network of connected things on a large scale to offer services to a large number of applications in different environments and domains. Middleware is software that seeks to facilitate the management and communication of all these things, providing the necessary functionalities to manage things, to discover, to compose services, and perform communication. For this reason, several proposals for middleware solutions for IoT have been developed. In this article, we conducted a systematic review of the literature to bring together middleware solutions for IoT, identifying the requirements and communication protocols used. In addition, we present some gaps and directions for future research in the development of IoT middleware.

Abstract
The preservation and sustainability of the marine ecosystem could benefit from the surge of new technologies to design autonomous vehicles. These underwater robots operate in a complex environment where the loss of human lives is highly probable. Consequently, a considerable percentage of the ocean remains unexplored due to the complexities of the underwater environment. Robotics can be a solution to overcome these limitations. However, training these complex systems is challenging and resource expensive. Human-in-the-loop input is essential in accelerating the training process by teaching the robots how to perform in specific scenarios and validate the simulated environment. This work presents a case study that simulates the dynamics of a Remotely Operated Vehicle in an underwater environment and uses imitation learning to train the vehicle to navigate autonomously toward a target. It was possible to measure and observe the similarity between the expert and the autonomous trajectories generated by the ROV. However, the imitation learning performance cannot surpass the expert, considering the time and the number of successes in finding the target. © ESM 2022. All rights reserved.

Abstract
Despite being one of the most cost-effective methods, email marketing remains challenging due to the low rate of opened emails and the high percentage of unsubscribed campaigns. Since the sender and the subject line are the only information that the recipient sees at first when receiving an email, the decision to open an email critically depends on these two factors, which should stand out and catch the recipient's attention. Therefore, the motivation behind this study is to support email campaign editors in choosing a subject line based on its potential quality. We propose and compare several models to measure the quality of a subject line, considering its potential to promote the email opening. The subject lines' structure and content are explored together with different machine learning techniques (Random Forest, Decision Trees, Neural Networks, Naive Bayes, Support Vector Machines, and Gradient Boosting). To validate the proposed model, a data set of 140,000 emails' subject lines was used. The results revealed that the models proposed are very promising to support the definition of the email marketing subject lines and show that the combination of data regarding the structure, the content of the subject lines, and senders characteristics leads to more accurate classifications of the potential of the subject line.

Abstract

Abstract
By carefully recommending selected items to users, recommender systems ought to increase profit from product sales. To achieve this, recommendations need to be relevant, novel and diverse. Many approaches to this problem exist, each with its own advantages and shortcomings. This paper proposes a novel way to combine model, memory and content-based approaches in a cascade-hybrid system, where each approach refines the previous one, sequentially. It is also proposed a straight-forward way to easily incorporate time-awareness into rating matrices. This approach focuses on being intuitive, flexible, robust, auditable and avoid heavy performance costs, as opposed to black-box fashion approaches. Evaluation metrics such as Novelty Score are also for-malized and computed, in conjunction with Catalog Coverage and mean recommendation price to better capture the recommender's performance.

Abstract
Covid-19 posed several challenges to all organisations in general and to social solidarity cooperatives in particular. However, the challenges faced by these cooperatives have unique features arising from their special characteristics compared to other types of cooperatives. Therefore it is vital to study these challenges and the impacts of covid-19. This study has as main goal to understand those challenges and their impact. An exploratory study was undertaken by applying 11 interviews to 11 social solidarity cooperatives. The cooperatives were chosen to be heterogeneous among the existent cooperatives in Portugal. This study corresponds to the first phase of a project that is still underway. This article presents the main results of the content analysis of the data collected from the interviews. Data show cooperatives could promptly adapt and continue their mission under pressure from the pandemic despite the first difficulties encountered in a new and unknown situation, showing a capacity to adapt and serve their members. However, these members were also submitted to several increasing and new challenges. The adaptations were possible due to legal changes in the work organisation law, from layoff to telework, government support involving financial programs, VAT, and other tax relaxation, as well as due to human resources reorganisation and the cooperatives' staff positive attitude towards the difficulties (both leaders and general workers). Differences between the social solidarity cooperatives under study concerning digital technologies showed that those already having some infrastructure had minor adapting difficulties.