Abstract
Data crowdsourcing is a data acquisition process where groups of voluntary contributors feed platforms with highly relevant data ranging from news, comments, and media to knowledge and classifications. It typically processes user-generated data streams to provide and refine popular services such as wikis, collaborative maps, e-commerce sites, and social networks. Nevertheless, this modus operandi raises severe concerns regarding ill-intentioned data manipulation in adver-sarial environments. This paper presents a simulation, modelling, and classification approach to automatically identify human and non-human (bots) as well as benign and malign contributors by using data fabrication to balance classes within experimental data sets, data stream modelling to build and update contributor profiles and, finally, autonomic data stream classification. By employing WikiVoyage - a free worldwide wiki travel guide open to contribution from the general public - as a testbed, our approach proves to significantly boost the confidence and quality of the classifier by using a class-balanced data stream, comprising both real and synthetic data. Our empirical results show that the proposed method distinguishes between benign and malign bots as well as human contributors with a classification accuracy of up to 92%.

Abstract

Abstract

Abstract
CloudAnchor is a multi-agent brokerage platform for the negotiation of Infrastructure as a Service cloud resources between Small and Medium Sized Enterprises, acting either as providers or consumers. This project entails the research, design, and implementation of a smart contract solution to permanently record and manage contractual and behavioural stakeholder data on a blockchain network. Smart contracts enable safe contract code execution, increasing trust between parties and ensuring the integrity and traceability of the chained contents. The defined smart contracts represent the inter-business trustworthiness and Service Level Agreements established within the platform. CloudAnchor interacts with the blockchain network through a dedicated Application Programming Interface, which coordinates and optimises the submission of transactions. The performed tests indicate the success of this integration: (i) the number and value of negotiated resources remain identical; and (ii) the run-time increases due to the inherent latency of the blockchain operation. Nonetheless, the introduced latency does not affect the brokerage performance, proving to be an appropriate solution for reliable partner selection and contractual enforcement between untrusted parties. This novel approach stores all brokerage strategic knowledge in a distributed, decentralised, and immutable database. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
Telecommunication operators compete not only for new clients, but, above all, to maintain current ones. The modelling and prediction of the top-up behaviour of prepaid mobile subscribers allows operators to anticipate customer intentions and implement measures to strengthen customer relationship. This research explores a data set from a Portuguese operator, comprising 30 months of top-up events, to predict the top-up monthly frequency and average value of prepaid subscribers using offline and online multi-target regression algorithms. The offline techniques adopt a monthly sliding window, whereas the online techniques use an event sliding window. Experiments were performed to determine the most promising set of features, analyse the accuracy of the offline and online regressors and the impact of sliding window dimension. The results show that online regression outperforms the offline counterparts. The best accuracy was achieved with adaptive model rules and a sliding window of 500,000 events (approximately 5 months). Finally, the predicted top-up monthly frequency and average value of each subscriber were converted to individual date and value intervals, which can be used by the operator to identify early signs of subscriber disengagement and immediately take pre-emptive measures.

Abstract
Extreme summer temperatures in the Iberia Peninsula are analyzed from ERA5-Land reanalysis data based on an extreme value mixture model combining a Normal distribution for the bulk distribution (i.e. for the non-extreme values) and a Generalized Pareto Distribution for the extremes in the upper tail. This approach allows to treat the threshold of temperature exceedances as being one of model parameters rather than fixed a priori, enabling to take into account its corresponding uncertainty. Extreme value mixture models are estimated individually for each location, and the analysis is performed separately for two distinct periods, namely from 1981 to 2000 and from 2000 to 2019, respectively. The results show significant differences in the extreme value mixture models for the two periods, and in their corresponding 20-year return levels. The mean of the bulk distribution of summer maximum temperature increases significantly, particularly in Eastern Iberia. The largest differences in the tails of the data distribution between the two periods occur in the eastern Mediterranean area, and are characterized by a significant increase in the threshold for temperature exceedances and in their corresponding return levels.