Optical fibres are the backbone of modern Information Technology infrastructure connecting billions of users through high-speed networks. With a drastic increase in the number of internet users, vulnerability and security issues in the optical fibre network become increasingly important. In this paper, we propose a detection method for early warning of anomalies in optical networks. The method is based on monitoring and analyzing changes in the state of polarization of the optical signal targeting differentiating between different physical impacts and movements of the fibre caused by e.g. eavesdropping, cut by digging and thunderstorms.

There are large skill differences among software developers, and clients and managers will benefit from being able to identify those with better skill. This study examines the relations between low effort estimates, and other commonly used skill indicators, and measured programming skill. One hundred and four professional software developers were recruited. After skill-related information was collected, they were asked to estimate the effort for four larger and five smaller programming tasks. Finally, they completed a programming skill test. The lowest and most over-optimistic effort estimates for the larger tasks were given by those with the lowest programming skill, which is in accordance with the well-known Dunning-Kruger effect. For the smaller tasks, however, those with the lowest programming skill had the highest and most over-pessimistic estimates. The other programming skill indicators, such as length of experience, company assessed skill and self-assessed skill, were only moderately correlated with measured skill and not particularly useful in guiding developer skill identification. A practical implication is that for larger and more complex tasks, the use of low effort estimates and commonly used skill indicators as selection criteria leads to a substantial risk of selecting among the least skilled developers.