Abstract
The Power Normal (PN) family of distributions is obtained by inverting the Box-Cox (BC) transformation over a truncated normal (TN) (or for some cases normal) random variable. In this paper we explore the PN distribution. We give a formula for the ordinary moments and considering the bivariate PN (BPN) distribution we calculate the marginal and conditional probability density functions (pdf). We prove that they are not univariate PN distributed. We also calculate the correlation curve and we fit a power law model.

Abstract
Information and Communication Technologies (ICT) have revolutionised the tourism domain, providing a wide set of new services for tourists and tourism businesses. Both tourists and tourism businesses use dedicated tourism platforms to search and share information generating, constantly, new tourism crowdsourced data. This crowdsourced information has a huge influence in tourist decisions. In this context, the paper proposes a stream recommendation engine supported by crowdsourced information, adopting Stochastic Gradient Descent (SGD) matrix factorisation algorithm for rating prediction. Additionally, we explore different (i) profiling approaches (hotel-based and theme-based) using hotel multi-criteria ratings, location, value for money (VfM) and sentiment value (StV); and (ii) post-recommendation filters based on hotel location, VfM and StV. The main contribution focusses on the application of post-recommendation filters to the prediction of hotel guest ratings with both hotel and theme multi-criteria rating profiles, using crowdsourced data streams. The results show considerable accuracy and classification improvement with both hotel-based and theme-based multi-criteria profiling together with location and StV post-recommendation filtering. While the most promising results occur with the hotel-based version, the best theme-based version shows a remarkable memory conciseness when compared with its hotel-based counterpart. This makes this theme-based approach particularly appropriate for data streams. The abstract completely needs to be rewritten. It does not provide a clear view of the problem and its solutions the researchers proposed. In addition, it should cover five main elements, introduction, problem statement, methodology, contributions and results. Done.

Abstract
Wiki-based crowdsourced repositories have increasingly become an important source of information for users in multiple domains. However, as the amount of wiki-based data increases, so does the information overloading for users. Wikis, and in general crowdsourcing platforms, raise trustability questions since they do not generally store user background data, making the recommendation of pages particularly hard to rely on. In this context, this work explores scalable multi-criteria profiling using side information to model the publishers and pages of wiki-based crowdsourced platforms. Based on streams of publisher-page-review triads, we have modelled publishers and pages in terms of quality and popularity using different criteria and user-page-view events collected via a wiki platform. Our modelling approach classifies statistically, both page-review (quality) and pageview (popularity) events, attributing an appropriate rating. The quality-related information is then merged employing Multiple Linear Regression as well as a weighted average. Based on the quality and popularity, the resulting page profiles are then used to address the problem of recommending the most interesting wiki pages per destination to viewers. This paper also explores the parallelisation of profiling and recommendation algorithms using wiki-based crowdsourced distributed data repositories as data streams via incremental updating. The proposed method has been successfully evaluated using Wikivoyage, a tourism crowdsourced wiki-based repository.

Abstract
Nowadays, with the widely usage of on-line stream video platforms, the number of media resources available and the volume of crowd-sourced feedback volunteered by viewers is increasing exponentially. In this scenario, the adoption of recommendation systems allows platforms to match viewers with resources. However, due to the sheer size of the data and the pace of the arriving data, there is the need to adopt stream mining algorithms to build and maintain models of the viewer preferences as well as to make timely personalised recommendations. In this paper, we propose the adoption of optimal individual hyper-parameters to build more accurate dynamic viewer models. First, we use a grid search algorithm to identify the optimal individual hyper-parameters (IHP) and, then, use these hyper-parameters to update incrementally the user model. This technique is based on an incremental learning algorithm designed for stream data. The results show that our approach outperforms previous approaches, reducing substantially the prediction errors and, thus, increasing the accuracy of the recommendations. © 2019 for this paper by its authors.

Abstract
Crowdsourced repositories have become an increasingly important source of information for users and businesses in multiple domains. Everyday examples of tourism crowdsourcing platforms focusing on accommodation, food or travelling in general, influence consumer behaviour in modern societies. These repositories, due to their intrinsic openness, can strongly benefit from independent data quality modelling mechanisms. In this context, building trust & reputation models of contributors and storing crowdsourced data using distributed ledger technology allows not only to ascertain the quality of crowdsourced contributions, but also ensures the integrity of the built models. This paper presents a survey on distributed trust & reputation modelling using blockchain technology and, for the specific case of tourism crowdsourcing platforms, discusses the open research problems and identifies future lines of research. 2019 The Authors. Published by Elsevier B.V.

Abstract
In this paper, we study the concept of Evolutionarily Stable Strategies (ESSs) for symmetric games with n >= 3 players. The main properties of these games and strategies are analyzed and several examples are provided. We relate the concept of ESS with previous literature and provide a proof of finiteness of ESS in the context of symmetric games with n >= 3 players. We show that unlike the case of n = 2, when there are more than two populations an ESS does not have a uniform invasion barrier, or equivalently, it is not equivalent to the strategy performing better against all strategies in a neighborhood. We also construct the extended replicator dynamics for these games and we study an application to a model of strategic planning of investment.