Abstract
Accurate 3D reconstruction and realistic visualization of cultural heritage allow experts to fine-tune their theories on the lost links in the history of civilization. Although the 3D reconstruction is a significant challenge, precisely because of the state of degradation over the years, it constitutes a crucial task for experts to study and interact with long disappeared settlements and structures. Furthermore, the public, in general, will be provided with the conditions to explore them in virtual environments, thus fostering cultural, social, and scientific participation. Highly accurate reconstruction is, nevertheless, a very complex task, where all stages of image synthesis must be carefully executed from highly detailed 3D models to obtain a faithful depiction of the object of interest. Meanwhile, the textual descriptions and geospatial data collected by archaeologists on-site may be used to overcome the absence of visual information. Still, this data will not suffice, in which case procedural modeling turns out to be essential to avoid a great deal of time and labor-consuming modeling processes. Procedural modeling tools automatically generate three-dimensional models through computational processes that extend the base information according to a specific algorithm. In order to avoid reprograming the procedural modeling systems, we use mathematical methods that operate on parametrical symbolic descriptions that, flexibly, can model different types of objects. The most used mathematical methods are fractal geometry and formal grammars, particularly L-systems and shape grammars. In this chapter, we will approach the current advances in the area of procedural modeling and how these tools can be used to generate 3D models of cultural heritage. We also explore the relevant dimension of time, extending the modeling tasks to 4D. These applications do not focus on very specific landmarks, like cathedrals or palaces, which require manual effort or image-based techniques to capture the model with a high level of visual fidelity. Instead, we focus on modeling cities and their evolutions or the surroundings of these landmarks, that allow for an increased automation of the modeling process. © 2020, Springer Nature Switzerland AG.

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Abstract
Researchers are being prompted by funders and institutions to expose the variety of results of their projects and to submit a Data Management Plan as part of their funding requests. In this context, institutions are looking for solutions to provide support to research data management activities in general, including DMP creation. We propose a collaborative approach where a researcher and a data steward create a DMP, involving other parties as required. We describe this collaborative method and its implementation, by means of a set of case studies that show the importance of the data steward in the institution. Feedback from researchers shows that the DMP are simple enough to lead people to engage in data management, but present enough challenges to constitute an entry point to the next level, the machine-actionable DMP.

Abstract
The significant evolution of smartphones has given ordinary people the power to create good-quality content which can then be spread, by the press, over multiple platforms. Citizens are almost always the first ones to arrive at a breaking news location and can provide the initial images of the scene. However, existing crowdsourced tools and platforms are predominantly centralized and are usually fed with unreliable and untrustworthy information. This work introduces a Crowd Journalism ecosystem whose core is a video marketplace web tool based on an organization-level decentralized system that can store, visualize, rate, and execute transactions of live-made videos. Smart contracts ensure that all the transactions are transparent and secure. This approach to Crowd Journalism exploits the inherent features of a blockchain such as offering trustful, anonymized, and immutable transactions, which has the potential to revolutionize the way news content is shared and commercially exploited.

Abstract
Internet-of-Things systems are comprised of highly heterogeneous architectures, where different protocols, application stacks, integration services, and orchestration engines co-exist. As they permeate our everyday lives, more of them become safety-critical, increasing the need for making them testable and fault-tolerant, with minimal human intervention. In this paper, we present a set of self-healing extensions for Node-RED, a popular visual programming solution for IoT systems. These extensions add runtime verification mechanisms and self-healing capabilities via new reusable nodes, some of them leveraging meta-programming techniques. With them, we were able to implement self-modification of flows, empowering the system with self-monitoring and self-testing capabilities, that search for malfunctions, and take subsequent actions towards the maintenance of health and recovery. We tested these mechanisms on a set of scenarios using a live physical setup that we called SmartLab. Our results indicate that this approach can improve a system’s reliability and dependability, both by being able to detect failing conditions, as well as reacting to them by self-modifying flows, or triggering countermeasures.