Abstract
Advances in Unmanned Aerial Systems (UAS) allowed them to become both flexible and cost-effective. When combined with computer vision data processing techniques they are a good way to obtain high-resolution imagery and 3D information. As such, UAS can be advantageous both for agriculture and forestry areas, where the need for data acquisition at specific times and within a specific time frame is crucial, enabling the extraction of several measurements from different crop types. In this study a low-cost UAS was used to survey an area mainly composed by chestnut trees (Castanea sativa Mill.). Flights were performed at different heights (ranging from 30 to 120 m), in single and double grid flight patterns, and photogrammetric processing was then applied. The obtained information consists of orthophoto mosaics and digital elevation models which enable the measurement of individual tree’s parameters such as tree crown diameter and tree height. Results demonstrate that despite its lower spatial resolution, data from single grid flights carried out at higher heights provided more reliable results than data acquired at lower flight heights. Higher number of images acquired in double grid flights also improved the results. Overall, the obtained results are encouraging, presenting a R2 higher than 0.9 and an overall root mean square error of 44 cm. © 2018 Association for Computing Machinery.

Abstract
With the deepening of the reform of the power system, the sale companies need to constantly explore new business models and marketing programs. At the same time, the power quality should meet the requirements of the user, in which the harmonic problem is a very important aspect. Based on the service level of the harmonic losses of the application of the evaluation value of governance, the harmonic characteristics of the user put forward the harmonic control scheme of passive and active service, and simulated under PSCAD verification. Based on the analyses of the effectiveness and cost of the above schemes, combined with the profit analysis of the selling companies, a value-added service package containing the harmonic treatment is designed for the users to choose. © 2018 IEEE

Abstract
Usually, during data stream classifier learning, we assume that labels of all incoming examples are available without any delay and they are used to update employing predictive model. Unfortunately, this assumption about access to all class labels is naive and it requires relatively high budget for labeling. It causes that methods which can train data stream classifiers on the basis of partially labeled data are highly desirable. Among them, active learning [1] seems to be a promising direction, which focuses on selecting only the most valuable learning examples to be labeled and used to produce an accurate predictive model. However, designing such a system we have to ensure that a cho-sen active learning strategy is able to handle changes in data distribution and quickly adapt to changing data distribution. In this work, we focus on novel active learning strategies that are designed for effective tackling of such changes. We propose a novel active data stream classifier learning method based on query by clustering approach. Experimental evaluation of the proposed methods prove the usefulness of the proposed approach for reducing labeling cost for classifier of drifting data streams.

Abstract

Abstract
Learning styles are strongly connected with learning and when it comes to acquiring new knowledge, attention is one the most important mechanisms. The learner's attention affects learning results and can define the success or failure of a student. When students are carrying out learning activities using new technologies, it is extremely important that the teacher has some feedback from the students' work in order to detect potential learning problems at an early stage and then to choose the appropriate teaching methods. In this paper we present a nonintrusive distributed system for monitoring the attention level in students. It is especially suited for classes working at the computer. The presented system is able to provide real-time information about each student as well as information about the class, and make predictions about the best learning style for a student using an ensemble of neural networks. It can be very useful for teachers to identify potentially distracting events and this system might be very useful to the teacher to implement more suited teaching strategies. (C) 2017 Published by Elsevier B.V.

Abstract
For the last decade, uebe.Q is being adopted by companies in different business areas and countries for managing compliance with solid referential information systems, such as ISO 9000 (for quality) and ISO 1400 (for environment). This is a long-term developed software, encompassing extensive, solid and valuable business logic. When it is deployed for/on a company, it usually demands an extensive and specific adaptation (i.e. software refinement) and configuration process involving DigitalWind's ISO 9000 and ISO 1400 experts as well as software development and operation teams. However, a recent business model change imposed that the evolution and configuration of the software, shifts from DigitalWind (and especially from the development team) to external consultants and to other business partners, along with the fact that different third-party's systems and respective data/information need to be integrated with minimal intervention of the development team. This paper presents and overview of the re-engineering process taken to handle this business model change by adopting (i) ontologies for the specification of business concepts, (ii) closed-world assumption (CWA) rules for the specification of the dynamics of the system and (iii) Domain Specific Language (DSL) for the configuration of the system by domain/business experts. The DSL approach is further described in detail. © 2018 IEEE.