Further thoughts on precision

Background: There has been much discussion amongst automated software defect prediction researchers regarding use of the precision and false positive rate classifier performance metrics. Aim: To demonstrate and explain why failing to report precision when using data with highly imbalanced class distributions may provide an overly optimistic view of classifier performance. Method: Well documented examples of how dependent class distribution affects the suitability of performance measures. Conclusions: When using data where the minority class represents less than around 5 to 10 percent of data points in total, failing to report precision may be a critical mistake. Furthermore, deriving the precision values omitted from studies can reveal valuable insight into true classifier performancePeer reviewedFinal Accepted Versio

More security or less insecurity

We depart from the conventional quest for ‘Completely Secure Systems’ and ask ‘How can we be more Secure’. We draw heavily from the evolution of the Theory of Justice and the arguments against the institutional approach to Justice. Central to our argument is the identification of redressable insecurity, or weak links. Our contention is that secure systems engineering is not really about building perfectly secure systems but about redressing manifest insecurities.Final Accepted Versio

Geometric approach to Fletcher's ideal penalty function

Original article can be found at: www.springerlink.com Copyright Springer. [Originally produced as UH Technical Report 280, 1993]In this note, we derive a geometric formulation of an ideal penalty function for equality constrained problems. This differentiable penalty function requires no parameter estimation or adjustment, has numerical conditioning similar to that of the target function from which it is constructed, and also has the desirable property that the strict second-order constrained minima of the target function are precisely those strict second-order unconstrained minima of the penalty function which satisfy the constraints. Such a penalty function can be used to establish termination properties for algorithms which avoid ill-conditioned steps. Numerical values for the penalty function and its derivatives can be calculated efficiently using automatic differentiation techniques.Peer reviewe

Inelastic neutron scattering study on the resonance mode in an optimally doped superconductor LaFeAsO0.92_{0.92}F0.08_{0.08}

An optimally doped iron-based superconductor LaFeAsO$_{0.92}$F$_{0.08}$ with $T_c = 29$ K has been studied by inelastic powder neutron scattering. The magnetic excitation at $Q=1.15$ \AA$^{-1}$ is enhanced below $T_c$, leading to a peak at $E_{res}\sim13$ meV as the resonance mode, in addition to the formation of a gap at low energy below the crossover energy $\Delta_{c}\sim10 meV$. The peak energy at $Q=1.15$ \AA$^{-1}$ corresponds to $5.2 k_B T_c$ in good agreement with the other values of resonance mode observed in the various iron-based superconductors, even in the high-$T_c$ cuprates. Although the phonon density of states has a peak at the same energy as the resonance mode in the present superconductor, the $Q$-dependence is consistent with the resonance being of predominately magnetic origin.Comment: 4 pages, 5 Postscript figure

Drive-Thru User Testing

User testing doesn’t have to be expensive or tedious. Join us to find out more about drive-thru user testing: cheap, quick, and easy ways to find out what your users expect from your services. Find out when to use different types of user testing to figure out the specific answers to your questions -- and which methods may seem easy when they aren’t