Abstract
Tourism crowdsourcing platforms accumulate and use large volumes of feedback data on tourism-related services to provide personalized recommendations with high impact on future tourist behavior. Typically, these recommendation engines build individual tourist profiles and suggest hotels, restaurants, attractions or routes based on the shared ratings, reviews, photos, videos or likes. Due to the dynamic nature of this scenario, where the crowd produces a continuous stream of events, we have been exploring stream-based recommendation methods, using stochastic gradient descent (SGD), to incrementally update the prediction models and post-filters to reduce the search space and improve the recommendation accuracy. In this context, we offer an update and comment on our previous article (Veloso et al., 2019a) by providing a recent literature review and identifying the challenges laying ahead concerning the online recommendation of tourism resources supported by crowdsourced data.

Abstract
Wiki-based crowdsourced data sources generally lack reliability, as their provenance is not intrinsically marshalled. By using recommendation, one may arguably assess the reliability of wiki-based repositories in order to identify the most interesting articles for a given domain. In this commentary, we explore current trends in scalable modelling and recommendation methods based on side information such as the quality and popularity of wiki articles. The systematic parallelization of such profiling and recommendation algorithms allows the concurrent processing of distributed crowdsourced Wikidata repositories. These algorithms, which perform incremental updating, need further research to improve the performance and generate up-to-date high-quality recommendations. This article builds upon our previous work (Leal et al., 2019) by extending the literature review and identifying important trends and challenges pertaining to crowdsourcing platforms, particularly those of Wikidata provenance.

Abstract
Recommendation systems are usually evaluated through accuracy and classification metrics. However, when these systems are supported by crowdsourced data, such metrics are unable to estimate data authenticity, leading to potential unreliability. Consequently, it is essential to ensure data authenticity and processing transparency in large crowdsourced recommendation systems. In this work, processing transparency is achieved by explaining recommendations and data authenticity is ensured via blockchain smart contracts. The proposed method models the pairwise trust and system-wide reputation of crowd contributors; stores the contributor models as smart contracts in a private Ethereum network; and implements a recommendation and explanation engine based on the stored contributor trust and reputation smart contracts. In terms of contributions, this paper explores trust and reputation smart contracts for explainable recommendations. The experiments, which were performed with a crowdsourced data set from Expedia, showed that the proposed method provides cost-free processing transparency and data authenticity at the cost of latency. © 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
Nowadays, fraudsters are continually trying to explore technical gaps in telecom companies to get some profit. The high cost of international termination rates in Telecom Companies, and mainly because of their high asymmetry property, attracts the attention of fraudsters. In this paper, we explore the application of three deterministic algorithms and one probabilistic, that combined can help to identify possible abnormal behaviors. Interconnect Bypass Fraud (IBF) is on the top three (worldwide), most common frauds in the telecommunication domain. Typically, the Telecom Companies can detect IBF by the occurrence of bursts of calls, repetitions, and mirror behaviors from specific numbers. The goal of our work is to discover as soon as possible numbers with abnormal behaviors and based on this assumption we developed: ( i ) the lossy count algorithm with fast forgetting technique; and ( ii ) the single-pass hierarchical heavy hitter algorithm that also contains a forgetting technique; as well as the application of the HyperLogLog sketches, and the application of sticky sampling algorithm. We applied the four algorithms in two real datasets and did a parameter sensitivity analysis. The results show that our two proposals (Lossy Counting with fast forgetting and the Hierarchical Heavy Hitters) can capture the most recent abnormal behaviors, faster than the baseline algorithms. Nonetheless, these four algorithms combined can make the fraud task more difficult and can complement the techniques used by the Telecom Company.

Abstract
Recent studies have shown that the coverage of Scopus and Web of Science (WoS) databases differs substantially. Consequently, the citation counts of a paper are different depending on the database used, making it difficult to apply both together. To address this problem, this paper aims to examine whether the percentile- and stochastic-based approach is effective for converting citation counts between two databases while guaranteeing its time-normalization. For this analysis, we collected a dataset of 326,345 papers, published in 1987-2017 in the top 10% source titles of the following fields: Industrial and Manufacturing Engineering, Aquatic Science, Social Psychology and Archaeology. First, we applied the linear regression model to the citation percentiles of indexed papers in both databases. Secondly, we used the predicted results of this linear dependence, combined with the Monte Carlo simulations, to obtain the probability density function of a percentile from papers in the database in which they are missing. The results indicate that, with the method proposed in this paper, it is possible to convert the citation counts of articles between Scopus and WoS. In addition, it also predicts the citation impact of a missing paper on one of those databases, based on the citation impact on the other database. Tests on subsamples, using Lin's concordance coefficient, suggest substantial agreement between estimated and real citation values. This allows the combined use of the citation counts of two databases, improving the coverage and accuracy of both bibliometric studies and bibliometric indicators.

Abstract
The automotive industry faces major megatrends such as climate change and emissions control, digital transformation, and increased customer power, resulting in more intensive competition, and higher sophisticated vehicles. The application of QFD (Quality Function Deployment) can be particularly valuable to link customer expectations with the technical characteristics of the product. In the case of products, such as batteries for electric vehicles, where technology is not yet mature, and the technical requirements (e.g., autonomy) are continuously more demanding, this is particularly relevant. The QFD customer-oriented product development technique is applied to a cover of a battery pack, to improve the negotiation process with the car manufacturer, the automotive industry battery components supplier company and its suppliers, to ensure market success once the product is released. The application of the HoQ revealed that Product Design and Tolerancing are the main technical requirements with the most impact over the battery cover development, followed the Leakage ratio. This research confirms that the voice of the customer could be quite generic, and it is critical that these requirements are translated into engineering requirements, which, in turn, can be translated into items that can be measured quantitatively and actionable within the company. The application of the affinity diagram was found to be quite valuable to address the significant amount of subjective information, and it is also relevant that OEMs have a desire to standardize the electric vehicle platforms at least on fewer and general sizes, hinting the need for more collaborative team approaches. © 2020 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the FAIM 2021.