Online payment fraud:from anomaly detection to risk management

Online banking fraud occurs whenever a criminal can seize accounts and transfer funds from an individual’s online bank account.Successfully preventing this requires the detection of as many fraudsters as possible,without producing too many false alarms.This is a challenge for machine learning owing to the extremely imbalanced data and complexity of fraud.In addition,classical machine learning methods must be extended,minimizing expected financial losses.Finally,fraud can only be combated systematically and economically if the risks and costs in payment channels are known.We define three models that overcome these challenges:machine learning-based fraud detection,economic optimization of machine learning results,and a risk model to predict the risk of fraud while considering countermeasures.The models were tested utilizing real data.Our machine learning model alone reduces the expected and unexpected losses in the three aggregated payment channels by 15%compared to a benchmark consisting of static if-then rules.Optimizing the machine-learning model further reduces the expected losses by 52%.These results hold with a low false positive rate of 0.4%.Thus,the risk framework of the three models is viable from a business and risk perspective.

Learning-based adaptive optimal output regulation of linear and nonlinear systems:an overview

This paper reviews recent developments in learning-based adaptive optimal output regulation that aims to solve the problem of adaptive and optimal asymptotic tracking with disturbance rejection.The proposed framework aims to bring together two separate topics—output regulation and adaptive dynamic programming—that have been under extensive investigation due to their broad applications in modern control engineering.Under this framework,one can solve optimal output regulation problems of linear,partially linear,nonlinear,and multi-agent systems in a data-driven manner.We will also review some practical applications based on this framework,such as semi-autonomous vehicles,connected and autonomous vehicles,and nonlinear oscillators.

ON DIFFERENT TYPES OF OUTPUT AND THE ELICITATION OF OPTIMAL OUTPUT

By differentiating four different types of output, the paperargues that their roles are also different. And learners of differ-ent language proficiency levels should be guided to produce dif-ferent types of output. Thus the paper introduces a new concept,namely "optimal output". Blindly making the learner produceoutput which is far beyond his current English level will only doharm to his interlanguage development, making it fossilize at anearly stage.

An investigation on stress distribution effect on multi- piezoelectric energy harvesters

With the fast development of piezoelectric materials and due to its green and renewable characteristics, the piezoelectric energy harvesting technology has been paid more and more attention by pavement engineers. The stress distribution will significantly affect the piezoelectric material performance. In this paper, the effects of multiple piezoelectric elements on the generation of electrical energy and output power are studied. In the case of constant external load, the number of the piezoelectric units does not necessarily produce more energy. When the same multi piezoelectric units work together, if the stress state of the piezoelectric units is different, the total output energy affected by the connection mode. For uneven stress distribution, the optimal output mode is that each of the piezoelectric units rectified before connected in parallel.