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Purpose One of the main tasks of a researcher is to properly communicate the results he obtained. The choice of the journal in which to publish the work is therefore very important. However, not all journals have suitable characteristics for a correct dissemination of scientific knowledge. Some publishers turn out to be unreliable and, against a payment, they publish whatever researchers propose. The authors call “predatory journals” these untrustworthy journals. The purpose of this paper is to analyse the incidence of predatory journals in computer science literature and present a tool that was developed for this purpose. Design/methodology/approach The authors focused their attention on editors, universities and publishers that are involved in this kind of publishing process. The starting point of their research is the list of scholarly open-access publishers and open-access stand-alone journals created by Jeffrey Beall. Specifically, they analysed the presence of predatory journals in the search results obtained from Google Scholar in the engineering and computer science fields. They also studied the change over time of such incidence in the articles published between 2011 and 2015. Findings The analysis shows that the phenomenon of predatory journals somehow decreased in 2015, probably due to a greater awareness of the risks related to the reputation of the authors. Originality/value We focused on computer science field, using a specific sample of queries. We developed a software to automatically make queries to the search engine, and to detect predatory journals, using Beall’s list.

Blockchain technology is an emerging technology that allows new forms of decentralized architectures, designed to generate trust among users, without the intervention of mediators or knowledge between the parties. Since 2015, thanks to the introduction of Smart Contracts by Ethereum, it is possible to run programs on the blockchain, greatly extending the potential of this technology. The programming of Smart Contract, through the Solidity language is different from the traditional one. First of all, any action that requires to modify the blockchain costs gas, which corresponds to a fraction of the currency used by that given blockchain, and therefore to real money. Gas optimization is a unique challenge in this context and has obvious implications. This document aims to provide a set of design patterns and tips to help gas saving in developing Smart Contracts on Ethereum. The provided patterns are presented divided into five main categories, based on their features.