Abstract
The integration of meta-knowledge on the Web of data is essential to support trustworthiness. This is in fact an issue because of the enormous amount of data that exists on the Web of Data. Meta-knowledge describes how the data is generated, manipulated, and disseminated. In the last few years, several approaches have been proposed for tracing and representing meta-knowledge efficiently on a statement or on a set of statements in the Semantic Web. The approaches differ significantly; for instance, in terms of modelling patterns, the number of statements generation, redundancy of the resources, query length, or query response time. This article reports a systematic review of the various approaches of the four dimensions (namely time, trust, fuzzy, and provenance) to provide an overview of the meta-knowledge assertion techniques in the field of the Semantic Web. Some experiments are conducted to analyze the actual performance of the approaches of meta-knowledge assertion considering the provenance dimension. These experiments are based on specific parameters such as graph size, number of statements generation, redundancy, query length, and query response time. All the experiments are done with real-world datasets. The semantics of the different approaches are compared to analyze the methodology of the approaches. Our study and experiments highlight the advantages and limitations of the approaches in terms of the parameters mentioned above.

Abstract
Identifying influential spreaders in a complex network has practical and theoretical significance. In applications such as disease spreading, virus infection in computer networks, viral marketing, immunization, rumor containment, among others, the main strategy is to identify the influential nodes in the network. Hence many different centrality measures evolved to identify central nodes in a complex network. The degree centrality is the most simple and easy to compute whereas closeness and betweenness centrality are complex and more time-consuming. The k-shell centrality has the problem of placing too many nodes in a single shell. Over the time many improvements over k-shell have been proposed with pros and cons. The k-shell hybrid (ksh) method has been recently proposed with promising results but with a free parameter that is set empirically which may cause some constraints to the performance of the method. This paper presents an improvement of the ksh method by providing a mathematical model for the free parameter based on standard network parameters. Experiments on real and artificially generated networks show that the proposed method outperforms the ksh method and most of the state-of-the-art node indexing methods. It has a better performance in terms of ranking performance as measured by the Kendall's rank correlation, and in terms of ranking efficiency as measured by the monotonicity value. Due to the absence of any empirically set free parameter, no time-consuming preprocessing is required for optimal parameter value selection prior to actual ranking of nodes in a large network.

Abstract
Over 2.5 million scientific articles are published annually, totaling 6,849.32 per day in 2015; in 2018 this value was increased to over 3 million articles, totaling 8.219,18 per day [1]. Thus, finding the most relevant Research Outputs (ROs), such as articles, theses, patents, among others, is increasingly difficult due, in part, to the existing interfaces returning massive lists of results. The project aims to develop and test a platform that incorporates social data for capturing various usage metrics to define a new metric that we call Social Scholarly Experience Metrics (SSEM) and a new visualization technique that, jointly, will support the fast access to find relevant ROs.

Abstract
Teaching complex assembly and maintenance skills to human operators usually requires extensive reading and the help of tutors. In order to reduce the training period and avoid the need for human supervision, an immersive teaching system using spatial augmented reality was developed for guiding inexperienced operators. The system provides textual and video instructions for each task while also allowing the operator to navigate between the teaching steps and control the video playback using a bare hands natural interaction interface that is projected into the workspace. Moreover, for helping the operator during the final validation and inspection phase, the system projects the expected 3D outline of the final product. The proposed teaching system was tested with the assembly of a starter motor and proved to be more intuitive than reading the traditional user manuals. This proof of concept use case served to validate the fundamental technologies and approaches that were proposed to achieve an intuitive and accurate augmented reality teaching application. Among the main challenges were the proper modeling and calibration of the sensing and projection hardware along with the 6 DoF pose estimation of objects for achieving precise overlap between the 3D rendered content and the physical world. On the other hand, the conceptualization of the information flow and how it can be conveyed on-demand to the operator was also of critical importance for ensuring a smooth and intuitive experience for the operator.

Abstract
We face a technology growth period that promotes STEM (Science, Technology, Engineering and Mathematics) as basic set of competences in diversified professional backgrounds. However, the process of teaching this areas is complex and abstract. We propose using game-based learning (GBL) for increased engagement and efficacy. More specifically, we use pervasive games as the drive of the learning process, promoting challenges in the context of the learner. To develop pervasive games, we require mobile devices (for location-based games) and a set of functionalities regarding location, time, and context of the user. In this article, we present the design of a mobile app to implement pervasive games. The perks of the mobile devices will be used to get unique and engaging challenges, to stimulate learning. Therefore, the learning process will become more enjoyable, and it will accomplish the goal of learning "anytime anywhere". This work sets upon previous work developed in the H2020 BEACONING project and the mobile app presented will allow other developers to create pervasive games for distinct areas of learning in a more engaging and effective way.

Abstract
Augmented reality has seen an increased popularity among the last decades due to technological advances and, a consequent growth of the amount of augmented reality systems, became available. However, in order to diffuse this technology successfully, understand users' feelings when using augmented reality applications is considered a major issue. This study implemented this technology in a cultural heritage outdoor context and tested it in-situ to evaluate user's perspective regarding to personal satisfaction-including cultural enrichment acquired -, ease of use and their intention to use it. The results obtained, through questionnaires, presented the visitors acceptance regarding the usage of this type of solutions among a cultural heritage context, since it may become future visits more pleasant and desirable. The user's majority expressed the request to use this technology more often in cultural heritage spaces.