Abstract
In this paper, we introduce the streaming graph canonization problem. Its goal is finding a canonical representation of a sequence of graphs in a stream. Our model of a stream fixes the graph's vertices and allows for fully dynamic edge changes, meaning it permits both addition and removal of edges. Our focus is on small graphs, since small graph isomorphism is an important primitive of many subgraph-based metrics, like motif analysis or frequent subgraph mining. We present an efficient data structure to approach this problem, namely a graph isomorphism discrete finite automaton and showcase its efficiency when compared to a non-streaming-aware method that simply recomputes the isomorphism information from scratch in each iteration.

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.

Abstract

Abstract
Microgrid, as an emerging small-scale power system comprising a range of power sources, power electronic interfaces, loads, storage units, and being able to supply remote areas or local communities, either can be operated in islanded or grid-connected mode. Based on this concept, this paper proposes the scalability assessment and day-ahead optimization, with time-varying load and time-of-use tariff data in 48 time-periods, for multiple microgrids applied in the accommodation area in a UK university, based on an existing microgrid test system currently under investigation in its Smart Grid Laboratory. Four different scenarios, including weekdays and weekends over two seasons (summer and winter), are analyzed to achieve the optimal scheduling of the microgrid technologies. In addition, a long-term planning assessment, on optimization over 20 years, is presented to discuss the influence of microgrids' power component depreciation and life span on total energy costs and savings.

Abstract
Dizajn distributivne mreže predstavlja jednu od strateških odluka u konkurenciji za kompanije visokog uticaja. Optimalna lokacija objekata u odnosu na kapacitet ponude i potražnje omogucuje visokom nivou usluga prisustvo na tržištu. U ovom radu predstavljen je metodološki okvir za projektovanje distributivnih mreža kombinovanjem primene tehnika klastera i matematickog programiranja. Predložena metodologija je testirana sa realnim podacima dobijenim od kompanije bezalkoholnih pica u Kolumbiji. Pristup razmatra tri glavne faze. U prvoj fazi, proces klasteriranja kupaca vrši se pomocu K-sredstava kako bi se dobila lokacija za potencijalne distributivne centre (DC). U drugoj fazi, model za dizajn distributivne mreže se vrši pomocu mešovitog programiranja celih brojeva (MPCB) razmatrajuci razlicite opcije za dodeljivanje DC-ma. U finalnoj fazi vrši se procena predložene metodologije u realnom slucaju. Kao rezultat, definisana je distributivna šema koja omogucava ulaz u nova tržišna podrucja sa efikasnom strategijom za prodiranje proizvoda u velike gradove kao što je Bogota u Kolumbiji.