Facial expression recognition benefits from deep learning models because of their ability to automatically extract features. However, these models face three important challenges: first, training tends to take longer times than with traditional machine learning models. Second, obtaining and labeling enough data samples can become a heavy burden due to the feature complexity usually involved in these problems. Third, it is also common to face class imbalance challenges. In this paper, we address these challenges by implementing transfer learning, oversampling and fine tuning to a facial expression recognition use case. Combining transfer learning with the use of a GPU helped us complete the training for our models in just about one hour. Furthermore, we achieved a 65.75% accuracy with one of the models. We provide measurements for metrics that are helpful when dealing with imbalanced data to assess that the models are not biased like precision, recall, F1 score and loss.

The amount of users interested in protecting their data and privacy on the Internet has increased lately. This has augmented the popularity of anonymization services such as Tor. However, the anonymization and the complication of being tracked provided by Tor has also been used for illintended purposes, such as evading security policies and controls. In this work, we implemented and evaluated an offline Tor traffic detector using white-box machine learning algorithms such as decision trees and random forests. On the one hand, our classifier achieves precision levels above 99 %. On the other hand, our approach is the first one to allow understanding and interpreting the classifier, thus understanding which variables play a significant role in the classification. We show that TCP window size, packet size and some time-related features can be used to identify Tor traffic.

This manuscript describes how we evaluated an implementation guide designed to help public organizations in Costa Rica that must implement an IT standard. In CLEI 2011 we described how we created the guide and how we used the guide in one financial entity. In this paper, we continue this effort by describing how we evaluated the quality of the designed implementation guide. To evaluate our product, we relied created surveys that were completed by a group of experts. The sample (nine experts in total) consisted of IT experts both from the entity that designed the IT standard as well as experts from the financial organization that participated of the implementation process of the standard. Therefore, our evaluation takes into consideration the opinion of both designers and implementers of the standard. Our results show that both groups of experts consider that our guide simplifies the implementation and appraisal processes. A majority of experts also believe that it is easier to understand the standard using our guide and that an implementation team would be more efficient if the guide is used.