Abstract: As biometric systems become widely deployed, the security of identification systems can be easily attacked by various spoof materials. This paper contributes to finding a reliable and practical anti-spoofing method using Convolutional Neural Networks (CNNs) based on the types of loss functions and optimizers. The types of CNNs used in this paper include AlexNet, VGGNet, and ResNet. By using various loss functions including Cross-Entropy, Center Loss, Cosine Proximity, and Hinge Loss, and various loss optimizers which include Adam, SGD, RMSProp, Adadelta, Adagrad, and Nadam, we obtained significant performance changes. We realize that choosing the correct loss function for each model is crucial since different loss functions lead to different errors on the same evaluation. By using a subset of the Livdet 2017 database, we validate our approach to compare the generalization power. It is important to note that we use a subset of LiveDet and the database is the same across all training and testing for each model. This way, we can compare the performance, in terms of generalization, for the unseen data across all different models. The best CNN (AlexNet) with the appropriate loss function and optimizers result in more than 3% of performance gain over the other CNN models with the default loss function and optimizer. In addition to the highest generalization performance, this paper also contains the models with high accuracy associated with parameters and mean average error rates to find the model that consumes the least memory and computation time for training and testing. Although AlexNet has less complexity over other CNN models, it is proven to be very efficient. For practical anti-spoofing systems, the deployed version should use a small amount of memory and should run very fast with high anti-spoofing performance. For our deployed version on smartphones, additional processing steps, such as quantization and pruning algorithms, have been applied in our final model.
Abstract: Past earthquakes have shown that seismic events may incur large economic losses in buildings. FEMA P-58 provides engineers a practical tool for the performance seismic assessment of buildings. In this study, FEMA P-58 is applied to two typical Italian pre-1970 reinforced concrete frame buildings, characterized by plain rebars as steel reinforcement and masonry infills and partitions. Given that suitable tools for these buildings are missing in FEMA P- 58, specific fragility curves and loss functions are first developed. Next, building performance is evaluated following a time-based assessment approach. Finally, expected annual losses for the selected buildings are derived and compared with past applications to old RC frame buildings representative of the US building stock.
Abstract: In this paper, we consider the application of Extreme
Value Theory as a risk measurement tool. The Value at Risk, for a set
of indices, from six Stock Exchanges of Frontier markets is
calculated using the Peaks over Threshold method and the
performance of the model index-wise is evaluated using coverage
tests and loss functions. Our results show that “fattailedness” alone of
the data is not enough to justify the use of EVT as a VaR approach.
The structure of the returns dynamics is also a determining factor.
This approach works fine in markets which have had extremes
occurring in the past thus making the model capable of coping with
extremes coming up (Colombo, Tunisia and Zagreb Stock
Exchanges). On the other hand, we find that indices with lower past
than present volatility fail to adequately deal with future extremes
(Mauritius and Kazakhstan). We also conclude that using EVT alone
produces quite static VaR figures not reflecting the actual dynamics
of the data.
Abstract: A comprehensive Bayesian analysis has been carried out in the context of informative and non-informative priors for the shape parameter of the Burr type X distribution under different symmetric and asymmetric loss functions. Elicitation of hyperparameter through prior predictive approach is also discussed. Also we derive the expression for posterior predictive distributions, predictive intervals and the credible Intervals. As an illustration, comparisons of these estimators are made through simulation study.
Abstract: The article is concerned with analysis of failure rate (shape parameter) under the Topp Leone distribution using a Bayesian framework. Different loss functions and a couple of noninformative priors have been assumed for posterior estimation. The posterior predictive distributions have also been derived. A simulation study has been carried to compare the performance of different estimators. A real life example has been used to illustrate the applicability of the results obtained. The findings of the study suggest that the precautionary loss function based on Jeffreys prior and singly type II censored samples can effectively be employed to
obtain the Bayes estimate of the failure rate under Topp Leone distribution.
Abstract: In this paper a nonlinear model is presented to
demonstrate the relation between production and marketing
departments. By introducing some functions such as pricing cost and
market share loss functions it will be tried to show some aspects of
market modelling which has not been regarded before. The proposed
model will be a constrained signomial geometric programming
model. For model solving, after variables- modifications an iterative
technique based on the concept of geometric mean will be introduced
to solve the resulting non-standard posynomial model which can be
applied to a wide variety of models in non-standard posynomial
geometric programming form. At the end a numerical analysis will
be presented to accredit the validity of the mentioned model.