Abstract
Physical disability can, in certain cases, be a barrier for traditional human-computer interaction based on keyboard and mouse devices. Alternative ways of interaction based on computer vision may be successfully adapted in particular cases of disability. This paper purposes a vision-based assistive technology to help a child with a degenerative neuromuscular disease to interact with the computer through facial expression recognition. The proposed algorithm was evaluated in images extracted from videos of the child and the preliminary results indicate that computer-interaction via facial expression recognition can break down barriers for people with reduced mobility regarding their relation with computers. © 2016 ACM.

Abstract
In the later stages of the aging process, an elderly person might need the help of a family member or a caregiver. Technology can be used to help to take care of elderly persons. Autonomous systems, using special interfaces, can collect information from elderly people, which might be useful to predict and recognize health related problems or physical security problems in real time. The emerging technology of image processing, in particular, the emotion recognition, can be a good option to use in elderly care support systems. In this article, we implemented a Microsoft Azure – Emotion SDK to recognize emotion of elderly that able to detect faces and recognize emotions in real time and to be used for elderly care support. The analysis is done with an online video stream, which analyzes facial expression, so that in case of a critical emotion, e.g., if an elderly is very sad or crying, it will inform a caregiver or related entity. From the experiment, we concluded that emotion recognition is a reliable technology to be implemented in real time elderly care. © Springer International Publishing AG, part of Springer Nature 2018.

Abstract
The visually impaired must face several well-known difficulties on their daily life. The use of technology in assistive systems can greatly improve their lives by helping with navigation and orientation, for which several approaches and technologies have been proposed. Lately, it has been introduced powerful online image processing services, based on machine learning and deep learning, promising truly cognitive assessment capacities. Google and Microsoft are two of these main players. In this work we built a device to be used by the blind in order to test the usage of the Google and Microsoft services to assist the blind. The online services were tested by researchers in a laboratory environment and by blind users on a large meeting room, familiar to them. This work reports on our findings regarding the online services effectiveness, the user interface and system latency. © Springer International Publishing AG, part of Springer Nature 2018.

Abstract
The fourth author name was missed in the original publication. The correct list of authors should read as "Hugo Fernandes, Paulo Costa, Vitor Filipe, Hugo Paredes, Joao Barroso". It has been corrected in this erratum. The original article has been updated.

Abstract
Facial expression analysis has a wide area of applications including health, psychology, sports etc. In this study, we explored different methods of automatic classification of exercise intensities using facial image processing of a subject performing exercise on a cycloergometer during an incremental standardized protocol. The method can be implemented in real time using facial video analysis. The experiments were done with images extracted from a 12 min HD video collected in laboratorial normalized settings (TechSport from the University of Trás-os-Montes e Alto Douro) with a static camera (90° angle with face and camera). The time slot for video to extract images for a particular class of exercise intensity is correspondence to the incremental heart rate. The facial expression recognition has been performed mainly in two steps: facial landmark detection and classification using the facial landmarks. Luxand application was used to detect 70 landmarks were detect using the adaptation of code available in Luxand application and we applied machine learning classification algorithms including discriminant analysis, KNN and SVM to classify the exercise intensities from the facial images. KNN algorithms presents up to 100% accuracy in classification into 2 and 3 classes. The distances between a lowermost landmark of the faces, which is indicated in landmark number 11 in the Luxand application, and the 26 landmarks around mouth were calculated and considered as features vector to train and test the classifier. Separate experiments were done for classification into two, three, and four classes and the accuracy of each algorithm was analyzed. From the overall results, classification into two and three classes was easy and resulted in very good classification performance whereas the classification with four classes had poor classification performance in each algorithm. Preliminary results suggest that distinguishing more levels of exertion, might require additional feature variables. © 2018 IEEE.

Abstract
Customer relationship management is critical for organizations. The public institutions, in particular the municipalities, are not an exception to this fact. However, these institutions have a number of peculiarities, as such their systems must be appropriate to their specific reality. In this article are presented the fundamental concepts of CRM (Customer Relationship Management) and CzRM (Citizen Relationship Management), discussing the distinctive characteristics of municipalities regarding other organizations.