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Correction to: M. F. Silva et al. (Eds.): Robot 2019: Fourth Iberian Robotics Conference, AISC 1092, https://doi.org/10.1007/978-3-030-35990-4 The original version of the book was inadvertently published with incomplete information in the Organization page of the front matter, which has now been included. The book has been updated with the change. © Springer Nature Switzerland AG 2020.

Abstract
The high asymmetry of international termination rates is fertile ground for the appearance of fraud in telecom companies. International calls have higher values when compared with national ones, which raises the attention of fraudsters. In this paper, we present a solution for a real problem called interconnect bypass fraud, more specifically, a newly identified distributed pattern that crosses different countries and keeps fraudsters from being tracked by almost all fraud detection techniques. This problem is one of the most expressive in the telecommunication domain, and it has some abnormal behaviours like the occurrence of a burst of calls from specific numbers. Based on this assumption, we propose the adoption of a new fast forgetting technique that works together with the Lossy Counting algorithm. We apply frequent set mining to capture distributed patterns from different countries. Our goal is to detect as soon as possible items with abnormal behaviours, e.g., bursts of calls, repetitions, mirrors, distributed behaviours and a small number of calls spread by a vast set of destination numbers. The results show that the application of different techniques improves the detection ratio and not only complements the techniques used by the telecom company but also improves the performance of the Lossy Counting algorithm in terms of run-time, memory used and sensibility to detect the abnormal behaviours. Additionally, the application of frequent set mining allows us to capture distributed fraud patterns.

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The exponential growth of digital information is imposing increasing scale and efficiency demands on modern storage infrastructures. As infrastructure complexity increases, so does the difficulty in ensuring quality of service, maintainability, and resource fairness, raising unprecedented performance, scalability, and programmability challenges. Software-Defined Storage (SDS) addresses these challenges by cleanly disentangling control and data flows, easing management, and improving control functionality of conventional storage systems. Despite its momentum in the research community, many aspects of the paradigm are still unclear, undefined, and unexplored, leading to misunderstandings that hamper the research and development of novel SDS technologies. In this article, we present an in-depth study of SDS systems, providing a thorough description and categorization of each plane of functionality. Further, we propose a taxonomy and classification of existing SDS solutions according to different criteria. Finally, we provide key insights about the paradigm and discuss potential future research directions for the field.

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To infer an abstract model from source code is one of the main tasks of most software quality analysis methods. Such abstract model is called Abstract Syntax Tree and the inference task is called parsing. A parser is usually generated from a grammar specification of a (programming) language and it converts source code of that language into said abstract tree representation. Then, several techniques traverse this tree to assess the quality of the code (for example by computing source code metrics), or by building new data structures (e.g, flow graphs) to perform further analysis (such as, code cloning, dead code, etc). Parsing is a well established technique. In recent years, however, modern languages are inherently ambiguous which can only be fully handled by ambiguous grammars. In this setting disambiguation rules, which are usually included as part of the grammar specification of the ambiguous language, need to be defined. This approach has a severe limitation: disambiguation rules are not first class citizens. Parser generators offer a small set of rules that can not be extended or changed. Thus, grammar writers are not able to manipulate nor define a new specific rule that the language he is considering requires. In this paper we present a tool, name InDubio, that consists of an extensible combinator library of disambiguation filters together with a generalized parser generator for ambiguous grammars. InDubio defines a set of basic disambiguation rules as abstract syntax tree filters that can be combined into more powerful rules. Moreover, the filters are independent of the parser generator and parsing technology, and consequently, they can be easily extended and manipulated. This paper presents InDubio in detail and also presents our first experimental results.