Abstract
As the amount of generated information grows, reading and summarizing texts of large collections turns into a challenging task. Many documents do not come with descriptive terms, thus requiring humans to generate keywords on-the-fly. The need to automate this kind of task demands the development of keyword extraction systems with the ability to automatically identify keywords within the text. One approach is to resort to machine-learning algorithms. These, however, depend on large annotated text corpora, which are not always available. An alternative solution is to consider an unsupervised approach. In this article, we describe YAKE!, a light-weight unsupervised automatic keyword extraction method which rests on statistical text features extracted from single documents to select the most relevant keywords of a text. Our system does not need to be trained on a particular set of documents, nor does it depend on dictionaries, external corpora, text size, language, or domain. To demonstrate the merits and significance of YAKE!, we compare it against ten state-of-the-art unsupervised approaches and one supervised method. Experimental results carried out on top of twenty datasets show that YAKE! significantly outperforms other unsupervised methods on texts of different sizes, languages, and domains.

Abstract
The practice is the crux of learning to program. Automated assessment plays a key role in enabling timely feedback without access to teachers but alone is insufficient to engage students and maximize the outcome of their practice. Graphical feedback and game-thinking promote positive effects on students' motivation as shown by some serious programming games, but those games are complex to create and adapt. This paper presents Asura, an environment for assessment of game-based coding challenges, built on a specialized framework, in which students are invited to develop a software agent (SA) to play it. During the coding phase, students can take advantage of the graphical feedback to complete the proposed task. Some challenges also encourage students to think of a SA that plays in a setting with interaction among SAs. In such a case, the environment supports the creation and visualization of tournaments among submitted agents. Furthermore, the validation of this environment from the learners' perspective is also described. 2012 ACM Subject Classification Applied computing ! Interactive learning environments; Applied computing ! E-learning.

Abstract
With the advent of cloud platforms and the IoT paradigm, the concept of micro-services has gained even more strength, making crucial the process of selection, manipulation, and deployment. However, this whole process is time-consuming and error pruning. In this paper, we present the design of a framework that allows the chaining of several microservices as a composite service in order to solve a single problem. The framework includes a client that will allow the orchestration f the composite service based on a straightforward API. The framework also includes a gamification engine to engage users not only to use the framework, by contributing with new microservices. We expect to have briefly a functional prototype of the framework so we can prove this concept. © 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
Successful software systems tend to grow considerably, ending up suffering from essential complexity, and very hard to understand as a whole. Software visualization techniques have been explored as one approach to ease software understanding. This work presents a novel approach and environment for software development that explores the use of liveness and virtual reality (VR) as a way to shorten the feedback loop between developers and their software systems in an interactive and immersive way. As a proof-of-concept, the authors developed a prototype that uses a visual city metaphor and allows developers to visit and dive into the system, in a live way. To assess the usability and viability of the approach, the authors carried on experiments to evaluate the effectiveness of the approach, and how to best support a live approach for software development.

Abstract
Medico-scientific concepts are not easily understood by laypeople that frequently use lay synonyms. For this reason, strategies that help users formulate health queries are essential. Health Suggestions is an existing extension for Google Chrome that provides suggestions in lay and medico-scientific terminologies, both in English and Portuguese. This work proposes, evaluates, and compares further strategies for generating suggestions based on the initial consumer query, using multi-concept recognition and the Unified Medical Language System (UMLS). The evaluation was done with an English and a Portuguese test collection, considering as baseline the suggestions initially provided by Health Suggestions. Given the importance of understandability, we used measures that combine relevance and understandability, namely, uRBP and uRBPgr. Our best method merges the Consumer Health Vocabulary (CHV)-preferred expression for each concept identified in the initial query for lay suggestions and the UMLS-preferred expressions for medico-scientific suggestions. Multi-concept recognition was critical for this improvement. © Springer Nature Switzerland AG 2020.

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. © Springer Nature Switzerland AG 2020.