Abstract
Many cloud vision APIs are available on the internet to recognize emotion from facial images and video analysis. The capacity to recognize emotions under various poses is a fundamental requirement in the area of emotion recognition. In this paper, the performance of two famous emotion recognition APIs is evaluated under the facial images of various poses. The experiments were done with the public dataset containing 980 images of each type of five poses [full left, half-left, straight, half-right, and full-right] with the seven emotions (Anger, Afraid, Disgust, Happiness, Neutral, Sadness, Surprise). It has been discovered that overall recognition accuracy is best in Microsoft Azure for straight images, whereas the face detection capability is better in Google. The Microsoft did not detect almost any of the images with full left and full right profile, but Google detected almost all of them. The Microsoft API presents an average true positive value up to 60%, whereas Google presents the maximum true positive value 45.25%.

Abstract
This paper presents a Pattern Recognition System, which can be used in classification applications for hand gestures for control of robotic arms. The system based in three steps, uses feature matching for extracting objects from a scene, edge detector and deep learning. The use of extraction of the region of interest and edges segmentation reduces the amount of processing required to recognize signals, thus speeding up the recognition process. Experimental classification results were positive with good statistical results. The presented data were tested considering four different types of segmentation implementations.

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Higher education is a complex business including very different process. At its core are the teaching and research processes and along the edge of the business model are the administrative processes, targeted to the students and alumni. Some of these processes translate into services that must be available during the whole life of the users and the institutions. For example, a higher education institution is expected to issue diplomas during the whole lifetime of its former students. In this context, we've been working in order to use the current e-government infrastructure of electronic services as building blocks for some of the features of the higher education institution electronic services. This work proposes the adoption of a set of those services. We've concluded a successful testing stage and expect to deploy a full production system very soon. Copyright