In recent years, the tendency of the number of financial institutions to include cryptocurrencies in their portfolios has accelerated. Cryptocurrencies are the first pure digital assets to be included by asset managers. Although they have some commonalities with more traditional assets, they have their own separate nature and their behaviour as an asset is still in the process of being understood. It is therefore important to summarise existing research papers and results on cryptocurrency trading, including available trading platforms, trading signals, trading strategy research and risk management. This paper provides a comprehensive survey of cryptocurrency trading research, by covering 146 research papers on various aspects of cryptocurrency trading (e.g., cryptocurrency trading systems, bubble and extreme condition, prediction of volatility and return, crypto-assets portfolio construction and crypto-assets, technical trading and others). This paper also analyses datasets, research trends and distribution among research objects (contents/properties) and technologies, concluding with some promising opportunities that remain open in cryptocurrency trading.

We introduce a mutation-based approach to automatically discover and expose 'deep' (previously unavailable) parameters that affect a program's runtime costs. These discovered parameters, together with existing ('shallow') parameters, form a search space that we tune using search-based optimisation in a bi-objective formulation that optimises both time and memory consumption. We implemented our approach and evaluated it on four real-world programs. The results show that we can improve execution time by 12% or achieve a 21% memory consumption reduction in the best cases. In three subjects, our deep parameter tuning results in a significant improvement over the baseline of shallow parameter tuning, demonstrating the potential value of our deep parameter extraction approach.

Palladium(II)-catalyzed dual C-H functionalization of indoles with cyclic diaryliodoniums was successfully achieved, providing a concise method to synthesize dibenzocarbazoles. In a single operation, two C-C bonds and one ring were formed. The reaction was ligand free and tolerated air and moisture conditions.

David Bowes, Tracy Hall, Mark Harman, Yue Jia, Federica Sarro, and Fan Wu, 'Mutation-aware fault prediction', in Proceedings of the 25th International Symposium on Software Testing and Analysis, ISSTA 2016. Saarbrucken, Germany, 18-20 July September 2016. Andreas Zeller and Abhik Roychoudhury eds., e-ISBN 978-145034390-9, doi: 10.1145/2931037.2931039. The ACM Digital Library is published by the Association for Computing Machinery. Copyright ?? 2017 ACM, Inc.We introduce mutation-aware fault prediction, which leverages additional guidance from metrics constructed in terms of mutants and the test cases that cover and detect them. We report the results of 12 sets of experiments, applying 4 Different predictive modelling techniques to 3 large real-world systems (both open and closed source). The results show that our proposal can significantly (p ??? 0:05) improve fault prediction performance. Moreover, mutation-based metrics lie in the top 5% most frequently relied upon fault predictors in 10 of the 12 sets of experiments, and provide the majority of the top ten fault predictors in 9 of the 12 sets of experiments