The aim of this paper is to investigate possible advances obtained by the implementation of the framework of Fr\'echet mean and the generalized sense of mean that it offers, in the field of statistical process monitoring and control. In particular, the case of non-linear profiles which are described by data in functional form is considered and a framework combining the notion of Fr\'echet mean and deformation models is developed. The proposed monitoring approach is implemented to the intra-day air pollution monitoring task in the city of Athens where the capabilities and advantages of the method are illustrated.

Google has pledged to no longer build AIs for the fossil fuel industry as it further distances itself from controversial developments. A report from Greenpeace earlier this month exposed Google as being one of the top three developers of AI tools for the fossil fuel industry. Greenpeace found AI technologies boost production levels by as much as five percent. In an interview with CUBE's John Furrier, the leader of Google's CTO office, Will Grannis, said that Google will "no longer develop artificial intelligence (AI) software and tools for oil and gas drilling operations." The pledge from Google Cloud is welcome, but it must be taken in a wider context.

New breakthroughs in AI make the headlines everyday. Far from the buzz of customer-facing businesses, the wide adoption and powerful applications of Machine Learning in Finance are less well known. In fact, there are few domains with as much historical, clean and structured data as the financial industry -- making it one of those predestined use cases where'learning machines' made an early mark with tremendous success that still continues. About three years ago, I got involved in developing Machine Learning (ML) models for price predictions and algorithmic trading in Energy markets, specifically for the European market of Carbon emission certificates. In this article, I want to share some of the learnings, approaches and insights which I have found relevant in all my ML projects since.

The rise in popularity of social networking sites such as Twitter and Facebook has been paralleled by the rise of unwanted, disruptive entities on these networks- — including spammers, malware disseminators, and other content polluters. Inspired by sociologists working to ensure the success of commons and criminologists focused on deterring vandalism and preventing crime, we present the first long-term study of social honeypots for tempting, profiling, and filtering content polluters in social media. Concretely, we report on our experiences via a seven-month deployment of 60 honeypots on Twitter that resulted in the harvesting of 36,000 candidate content polluters. As part of our study, we (1) examine the harvested Twitter users, including an analysis of link payloads, user behavior over time, and followers/following network dynamics and (2) evaluate a wide range of features to investigate the effectiveness of automatic content polluter identification.