Abstract
USB dongles have been used by a wide range of software manufacturers to store a copy-protected of their application's license. The licenses validation procedure through USB dongles faces several concerns, as the risks of theft or losing dongle. Also, in scenarios where the number of dongles is reduced, users may have to wait for dongle access, which may lead to loss of productivity. In this paper we propose a client/server distributed architecture for remote software licenses validation, through USB/IP protocol. The proposed approach aims to take advantage of USB/IP for distributed access to a set of USB dongles physically connected to a remote USB server, over a TCP/IP network. We describe the deployment and enhancements made to an existing open source USB/ IP implementation and also present the results obtained with this architecture in a real world scenario, for validation of computer forensics applications licenses that uses USB dongles.

Abstract
Learning in non-stationary environments is not an easy task and requires a distinctive approach. The learning model must not only have the ability to continuously learn, but also the ability to acquired new concepts and forget the old ones. Additionally, given the significant importance that social networks gained as information networks, there is an ever-growing interest in the extraction of complex information used for trend detection, promoting services or market sensing. This dynamic nature tends to limit the performance of traditional static learning models and dynamic learning strategies must be put forward. In this paper we present a learning strategy to learn with drift in the occurrence of concepts in Twitter. We propose three different models: a time-window model, an ensemble-based model and an incremental model. Since little is known about the types of drift that can occur in Twitter, we simulate different types of drift by artificially timestamping real Twitter messages in order to evaluate and validate our strategy. Results are so far encouraging regarding learning in the presence of drift, along with classifying messages in Twitter streams.

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.