Evaluation of the fuzzy ARTMAP neural network using off-line and on-line learning strategies

This paper describes an experimental study of the Fuzzy ARTMAP (FAM) neural network as an autonomous learning system for pattern classification tasks. A benchmark database of radar signals from ionosphere has been employed for the system to classify arbitrary sequences of pattern into distinct categories. A number of simulations have been conducted systematically to evaluate the applicability and usefulness of FAM in this context. First, we identify the 'optimum' parameter settings of FAM for the problem at hand, and investigate the effects of different training schemes and learning rules on classification results, using an off-line learning methodology. We then examine a voting strategy to improve classification accuracy by combining results from multiple FAM classifiers. In addition to off-line learning, we evaluate the prospect of using FAM as an autonomously learning pattern classification system for on-line, non-stationary environments. The performance of FAM is comparable with other reported results, but with the added advantage of on-line and incremental learning.

Can smartphone users turn off tracking service settings?

Tracking services play a fundamental role in the smartphone ecosystem. While their primary purpose is to provide a smartphone user with the ability to regulate the extent of sharing private information with external parties, these services can also be misused by advertisers in order to boost revenues. In this paper, we investigate tracking services on the Android and iOS smartphone platforms. We present a simple and effective way to monitor traffic generated by tracking services to and from the smartphone and external servers. To evaluate our work, we dynamically execute a set of Android and iOS applications, collected from their respective official markets. Our empirical results indicate that even if the user disables or limits tracking services on the smartphone, applications can by-pass those settings and, consequently, leak private information to external parties. On the other hand, when testing the location 'on' setting, we notice that generally location is not tracked.