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.

Abstract
Functional recovery following general nerve reconstruction is often associated with poor results. Comparing to rat and mice experimental studies, there are much fewer investigations on nerve regeneration and repair in the sheep, and there are no studies on this subject using gait analysis in the sheep model as an assessment tool. Additionally, this is the first study evaluating obstacle negotiation and the compensatory strategies that take place at each joint in response to the obstacle during locomotion in the sheep model. This study aims to get kinematic data to serve as a template for an objective assessment of the ankle joint motion in future studies of common peroneal nerve (CP) injury and repair in the ovine model. Our results show that a moderately high obstacle set to 10% of the sheep's hindlimb length was associated to several spatial and temporal strategies in order to increase hoof height during obstacle negotiating. Sheep efficiently cleared an obstacle by increasing knee, ankle and metatarsophalangeal flexion during swing, whereas the hip joint is not affected. This study establishes the bounds of normal motion in the neurologically intact hindlimb when approached and cleared an obstacle and provides baseline data for further studies of peripheral nerve research in the ovine model.

Abstract
Customer Relationship Management (CRM) is currently an important strategic tool used by organizations to gain competitive advantages. However, since the implementation of a CRM system is not risk-free, it is important to know about the factors that influence its success. This article presents the results of a literature review carried out aiming to identify and describe the main success factors of the implementation of CRM systems.

Abstract
Facial expressions are a spontaneous way of perceiving emotions, which can provide information related to the cognitive state of a person. Facial expression recognition of the elderly is an important aid to better care them, according to their state of mind, although it can be a difficult task because their expressions might not be as easily perceived as those from younger persons. We proposed a model to classify the facial expressions of the elderly, presenting the differences between facial expression recognition in the elder and in other age group, as well as methods to surpass these difficulties. Viola Jones with Haar Features was used to extract the faces and Gabor Filter to extract the facial characteristics. These characteristics are classified using a Multiclass Support Vector Machine. We got an accuracy of 90.32%, 84.61% and 66.6%, when detecting the neutral state, happiness and sadness respectively in the elderly. In the other age group, we got an accuracy of 95.24%, 88.57%, and 80%, while detecting the neutral, happiness, and sadness states and concluded that aging influences negatively the facial expressions recognition tasks.

Abstract
The continuous use of the muscles in any kind of physical exercises results in muscular fatigue, which can be defined as the incapability of the muscle to perform with the same effectiveness over the course of time. The analysis of physical exercise intensity has great importance in various fields, including sports and physiotherapy. In this paper, the rate of blinking eyes and the change in shape of mouth throughout the physical exercise are analyzed using computer vision techniques, and compared with the perceived exertion. The experiments were done using the facial video of three athletes, grabbed during a stationary cycle of physical exercise, until maximal muscle activity was achieved. The perceived exertion was reported at the end of each minute. The blinking of the eyes and opening of the mouth were detected by counting the number of bright pixels in the region of interest of an eye and of the mouth. These regions were detected using the Viola and Jones algorithm. We have proved the existence of a correlation between the opening and closing of the mouth and the eye-blinking rates with the physical exercise intensity (i.e., the higher the exercise intensity, the higher the rate of eye-blinking and mouth opening and closing). We obtained 95% accuracy in blinking eye detection.