This report presents the results from the 2021 friendly competition in the ARCH work- shop for the falsification of temporal logic specifications over Cyber-Physical Systems. We briefly describe the competition settings, which have been inherited from the previ- ous years, give background on the participating teams and tools and discuss the selected benchmarks. Apart from new requirements and participants, the major novelty in this instalment is that falsifying inputs have been validated independently. During this pro- cess, we uncovered several issues like configuration errors and computational discrepancies, stressing the importance of this kind of validation.

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Over the last few years, machine learning based methods have been applied to extract information from news flow in the financial domain. However, this information has mostly been in the form of the financial sentiments contained in the news headlines, primarily for the stock prices. In our current work, we propose that various other dimensions of information can be extracted from news headlines, which will be of interest to investors, policy-makers and other practitioners. We propose a framework that extracts information such as past movements and expected directionality in prices, asset comparison and other general information that the news is referring to. We apply this framework to the commodity "Gold" and train the machine learning models using a dataset of 11,412 human-annotated news headlines (released with this study), collected from the period 2000-2019. We experiment to validate the causal effect of news flow on gold prices and observe that the information produced from our framework significantly impacts the future gold price.

This report presents the results from the 2022 friendly competition in the ARCH work- shop for the falsification of temporal logic specifications over Cyber-Physical Systems. We briefly describe the competition settings, which have been inherited and adapted from the previous years, give background on the participating teams and tools, and discuss the selected benchmarks. In this year’s competition, in addition to the result validation introduced in the previous year, we change the experimental settings for a better account of the difficulty of benchmarks and for a better comparability between the tools.

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