This work presents a method based on information‐theoretic analysis of iris biometric that aims to extract homogeneous regions of high entropy. Successful extraction of these regions facilitates the development of effective systems for generation of cryptographic keys of lengths up to 400 bits per iris. At the same time, this approach allows for the application of simpler error correction codes with equal false accept rate levels, which reduces the overall complexity of this class of systems.

It is every parent’s wish to protect their children from online pornography, cyber bullying and cyber predators. Several existing approaches analyze a limited amount of information stemming from the interactions of the child with the corresponding online party. Some restrict access to websites based on a blacklist of known forbidden URLs, others attempt to parse and analyze the exchanged multimedia content between the two parties. However, new URLs can be used to circumvent a blacklist, and images, video, and text can individually appear to be safe, but need to be judged jointly. We propose a highly modular framework of analyzing content in its final form at the user interface, or Human Computer Interaction (HCI) layer, as it appears before the child: on the screen and through the speakers. Our approach is to produce Children’s Agents for Secure and Privacy Enhanced Reaction (CASPER), which analyzes screen captures and audio signals in real time in order to make a decision based on all of the information at its disposal, with limited hardware capabilities. We employ a collection of deep learning techniques for image, audio and text processing in order to categorize visual content as pornographic or neutral, and textual content as cyberbullying or neutral. We additionally contribute a custom dataset that offers a wide spectrum of objectionable content for evaluation and training purposes. CASPER demonstrates an average accuracy of 88% and an F1 score of 0.85 when classifying text, and an accuracy of 95% when classifying pornography.

The case study presented in this paper describes the pedagogical aspects and experience gathered while using an e-learning tool named IPA-PBL. Its main purpose is to provide additional motivation for adopting theoretical principles and procedures in a computer networks course. In the proposed model, the sequencing of activities of the learning process is grouped into three phases based on educational goals. In this way, the same tool is used on several courses with different curricula. In IPA-PBL, problem-based learning (PBL) is applied as a pedagogical strategy, as well as a set of concrete methods implemented in the software. Together with the pedagogical model, specific domain ontology is designed. In this way, the learner's knowledge can be analyzed in order to collect data necessary for the dynamic adaptation of system behavior. The results collected while using IPA-PBL are compared to those obtained without using the system. Statistical analysis, together with pertaining considerations and conclusions, are also presented in the paper.