Current mobile devices (e.g., smartphones) are equipped with several sensors that allow different forms of userinteraction. They also offer several connectivity options and a growing computing power which supports itsuse in new Human Computer Interaction (HCI) scenarios. This paper presents the mobileWheel, a system thatexploits the capabilities of current mobile devices as a means of interaction with a real-time graphical drivingsimulation running on a desktop computer. The application on the mobile device performs data acquisitionfrom various sensors (focusing on the 3D accelerometer) and also provides different types of feedback tothe user. This system represents a ubiquitous, simple and affordable alternative approach to the traditionalcontrol of virtual vehicles in driving simulators and could also be applied in other similar architectures. Toevaluate and validate this approach several tests were conducted with volunteer users. The control mode wherethe virtual vehicle is fully controlled by the accelerometer had the highest acceptance and produced the bestresults.

Web Archiving (WA) deals with the preservation of portions of the World Wide Web (WWW) allowing their availability for future access. Arquivo.pt is a WA initiativeholding a huge amount of content, including image files.However, some of these images contain nudity and pornography, that can be offensive for the users, and thus being Not SuitableFor Work (NSFW). This work proposes a methodology to classify NSFW images available at Arquivo.pt, using deep neural network approaches. A large dataset of images is built using Arquivo.pt data and two pre-trained neural network models, namely ResNet and SqueezeNet, are evaluated and improved for the NSFW classification task, using the dataset.The evaluation of these models reported an accuracy of 93% and 72%, respectively. After a fine tuning stage, the accuracy of these models improved to 94% and 89%, respectively.The proposed solution is integrated into the Arquivo.pt Image Search System, enabling the filtering of the problematic NSFW images. At the time of this writing, the proposed solution is in production at https://arquivo.pt/images.jsp

The interest in monitoring a drivers conditions and performance has increased in the past years, to make the roads safer both for drivers and pedestrians. This raised the idea of developing a system to monitor the drivers conditions to prevent road disasters. In this paper, we propose a system to monitor the drivers fatigue and drowsiness, based on the Car- dioWheel system, developed by CardioID. The proposed system records both the persons ECG signal and the motion of the steering wheel during the driving session. The amount of data acquired demands a compression stage for transmission with the goal to reduce the required bandwidth. The transmission of the compressed data is done via Bluetooth Low Energy, with an exclusive profile developed for this system. To detect fatigue and drowsiness patterns, a machine learning approach was taken. Among the evaluated classifiers, the Support Vector Machines technique proved to be the best classification method with the highest accuracy. Thus, the developed prototype has the ability to warn the driver about his physiological and physical states, increasing the safety in the roads.