Abstract
Bayesian network structures are usually built using only the data and starting from an empty network or from a naive Bayes structure. Very often, in some domains, like medicine, a prior structure is already known based on expert knowledge. This structure can be automatically or manually refined in search for better performance models. In this work, we take Bayesian networks built by specialists and show that minor perturbations to this original network can yield better classifiers, while maintaining most of the interpretability of the original network.

Abstract
We expose an USB vulnerability in some vendors' customization of the android system, where the serial AT commands processed by the cellular modem are extended to allow other functionalities. We target that vulnerability for the specific vendor system and present a proof of concept of the attack in a realistic scenario environment. For this we use an apparently inoffensive smartphone charging station like the one that is now common at public places like airports. We unveil the implications of such vulnerability that culminate in flashing a compromised boot partition, root access, enable adb and install a surveillance application that is impossible to uninstall without re-flashing the android boot partition. All these attacks are done without user consent or knowledge on the attacked mobile phone.

Abstract
Several pilot tests show that patients who are able to access their Electronic Health Records (EHR), become more responsible and involved in the maintenance of their health. However, despite technologically feasible and legally possible, there is no validated or standardized toolset available yet, for patients to review and manage their EHR. Many privacy, security and usability issues must be solved first before this practice can be made mainstream. This paper proposes and discusses the design of an access control visual application that addresses most of these issues, and offers patients a secure, controlled and easy access to their EHR.

Abstract
Counting the occurrences of small subgraphs in large networks is a fundamental graph mining metric with several possible applications. Computing frequencies of those subgraphs is also known as the subgraph census problem, which is a computationally hard task. In this paper we provide a parallel multicore algorithm for this purpose. At its core we use FaSE, an efficient network-centric sequential subgraph census algorithm, which is able to substantially decrease the number of isomorphism tests needed when compared to past approaches. We use one thread per core and employ a dynamic load balancing scheme capable of dealing with the highly unbalanced search tree induced by FaSE and effectively redistributing work during execution. We assessed the scalability of our algorithm on a varied set of representative networks and achieved near linear speedup up to 32 cores while obtaining a high efficiency for the total 64 cores of our machine.

Abstract

Abstract
Courses in computer programming combine a number of different concepts, from general problem-solving to mathematical precepts such as algorithms and computational intelligence. Due to the complex nature of computer science education, teaching the novice programmer can be a challenge. Innovative Teaching Strategies and New Learning Paradigms in Computer Programming brings together pedagogical and technological methods to address the recent challenges that have developed in computer programming courses. Focusing on educational tools, computer science concepts, and educational design, this book is an essential reference source for teachers, practitioners, and scholars interested in improving the success rate of students.