Abstract
Efficient detection and monitoring procedures of invasive plant species are required. It is of crucial importance to deal with such plants in aquatic ecosystems, since they can affect biodiversity and, ultimately, ecosystem function and services. In this study, it is intended to detect water hyacinth (Eichhornia crassipes) using multispectral data with different spatial resolutions. For this purpose, high-resolution data (<0.1 m) acquired from an unmanned aerial vehicle (UAV) and coarse-resolution data (10 m) from Sentinel-2 MSI were used. Three areas with a high incidence of water hyacinth located in the Lower Mondego region (Portugal) were surveyed. Different classifiers were used to perform a pixel-based detection of this invasive species in both datasets. From the different classifiers used, the results were achieved by the random forest classifiers stand-out (overall accuracy (OA): 0.94). On the other hand, support vector machine performed worst (OA: 0.87), followed by Gaussian naive Bayes (OA: 0.88), k-nearest neighbours (OA: 0.90), and artificial neural networks (OA: 0.91). The higher spatial resolution from UAV-based data enabled us to detect small amounts of water hyacinth, which could not be detected in Sentinel-2 data. However, and despite the coarser resolution, satellite data analysis enabled us to identify water hyacinth coverage, compared well with a UAV-based survey. Combining both datasets and even considering the different resolutions, it was possible to observe the temporal and spatial evolution of water hyacinth. This approach proved to be an effective way to assess the effects of the mitigation/control measures taken in the study areas. Thus, this approach can be applied to detect invasive species in aquatic environments and to monitor their changes over time.

Abstract
Since its first introduction in late 90s, the use of marketplaces has continued to grow, today virtually everything from physical assets to services can be purchased on digital marketplaces, real estate is not an exception. Some marketplaces allow acclaimed asset owners to advertise their products, to which the services gets commission/percentage from proceeds of sale/lease. Despite the success recorded in the use of the marketplaces, they are not without limitations which include identity and property fraud, impersonation and the use of centralised technology with trusted parties that are prone to single point of failures (SPOF). Being one of the most valuable assets, real estate has been a target for marketplace fraud as impersonators take pictures of properties they do not own, upload them on marketplace with promising prices that lures innocent or naive buyers. This paper addresses these issues by proposing a self sovereign identity (SSI) and smart contract based framework for identity verification and verified transaction management on secure digital marketplaces. First, the use of SSI technology enable methods for acquiring verified credential (VC) that are verifiable on a decentralised blockchain registry to identify both real estate owner(s) and real estate property. Second, the smart contracts are used to negotiate the secure transfer of real estate property deeds on the marketplace. To assess the viability of our proposal we define an application scenario and compare our work with other approaches.

Abstract
The multitude of applications and security configurations of mobile devices requires automated approaches for effective user privacy protection. Current permission managers, the core mechanism for privacy protection in smartphones, have shown to be ineffective by failing to account for privacy's contextual dependency and personal preferences within context. In this paper we focus on the relation between privacy decisions (e.g. grant or deny a permission request) and their surrounding context, through an analysis of a real world dataset obtained in campaigns with 93 users. We leverage such findings and the collected data to develop methods for automated, personalized and context-aware privacy protection, so as to predict users' preferences with respect to permission requests. Our analysis reveals that while contextual features have some relevance in privacy decisions, the increase in prediction performance of using such features is minimal, since two features alone are capable of capturing a relevant effect of context changes, namely the category of the requesting application and the requested permission. Our methods for prediction of privacy preferences achieved an F1 score of 0.88, while reducing the number of privacy violations by 28% when compared to the standard Android permission manager.

Abstract
In this paper we describe the EPISA Platform, a technical infrastructure designed and developed to support archival records management and access using linked data technologies. The EPISA Platform follows a client-server paradigm, with a central component, the EPISA Server, responsible for storage, reasoning, authorization, and search; and a frontend component, the EPISA ArchClient, responsible for user interaction. The EPISA Server uses Apache Jena Fuseki for storage and reasoning, and Apache Solr for search. The EPISA ArchClient is a web application implemented using PHP Laravel and standard web technologies. The platform follows a modular architecture, based on Docker containers. We describe the technical details of the platform and the main user interaction workflows, highlighting the abstractions developed to integrate linked data in the archival management process. The EPISA Platform has been successfully used to support research and development of linked data use in the archival domain in the context of the EPISA project. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)

Abstract
Considering the growing volume of information and services available on the web, it has become essential to provide websites and applications with tools, such as recommender systems, capable of helping users to obtain the information and services appropriate to their interests. Due to the complexity of web adaptation and the ability of multi-agent systems to deal with complex problems, the use of multi-agent approaches in recommender systems has been increasing. In the present work, we make a thorough review of the use of multi-agent-based recommender systems. The review shows the diversity of applications of multi-agent systems in recommender systems, namely on what concerns the diversity of domains, different types of approaches and contribution to the performance improvement of the recommender systems.

Abstract
Geolocation data identifies the geographic location of people or objects, which may unveil the performance of some activity or operation. A good example is, if a vehicle is in a gas station then one may assume that the vehicle is being refuelled. This work aims to obtain vehicle-based operations from geolocation data by analysing the stationary states of vehicles, which may identify some motionless event (e.g. bus line stops and traffic incidents). Ultimately, these operations may be analysed with Process Mining techniques in order to discover the most significant ones and extract process related information. In this work, we studied the application of diverse approaches for detecting vehicle-based operations and identified different operations related to the bus services. The operations were also characterized according the distribution of their events, allowing to identify specific operations characteristics. The public transport network of Rio de Janeiro is used as a case study, which is supported by a real-time data stream of buses geolocations.