Abstract
In this paper, we consider a sequential allocation problem with n individuals. The first individual can consume any amount of a resource, leaving the remainder for the second individual, and so on. Motivated by the limitations associated with the cooperative or non-cooperative solutions, we propose a new approach from basic definitions of representativeness and equal treatment. The result is a unique asymptotic allocation rule for any number of individuals. We show that it satisfies a set of desirable properties. © 2016, Springer Science+Business Media Dordrecht.

Abstract
Recent advances in visual motion detection and interpretation have made possible the rising of new robotic systems for autonomous and active surveillance. In this line of research, the current work discusses motion perception by proposing a novel technique that analyzes dense flow fields and distinguishes several regions with distinct motion models. The method is called Wise Optical Flow Clustering (WOFC) and extracts the moving objects by performing two consecutive operations: evaluating and resetting. Motion properties of the flow field are retrieved and described in the evaluation phase, which provides high level information about the spatial segmentation of the flow field. During the resetting operation, these properties are combined and used to feed a guided segmentation approach. The WOFC requires information about the number of motion models and, therefore, this paper introduces a model selection method based on a Bayesian approach that balances the model's fitness and complexity. It combines the correlation of a histogram-based analysis with the decay ratio of the normalized entropy criterion. This approach interprets the flow field and gives an estimative about the number of moving objects. The experiments conducted in a realistic environment have proved that the WOFC presents several advantages that meet the requirements of common robotic and surveillance applications: is computationally efficient and provides a pixel-wise segmentation, comparatively to other state-of-the-art methods.

Abstract
The complexity and subjectivity of the judgement task conceals the existence of biases that undermines the quality of the process. This paper presents a weighted aggregation function that attempts to reduce the influence of biased judgements on the final score. We also discuss a set of desirable properties. The proposed weighted aggregation function is able to correct the “nationalism bias” found by Emerson et al. (Am Stat 63(2):124–131, 2009) in the 2000 Olympic Games diving competition and suggest the possibility of a “reputation bias”. Our results can be applied to judgement sports and other activities that require the aggregation of several personal evaluations. © 2016, Springer Science+Business Media New York.

Abstract
Traditional imageryprovided, for example, by RGB and/or NIR sensorshas proven to be useful in many agroforestry applications. However, it lacks the spectral range and precision to profile materials and organisms that only hyperspectral sensors can provide. This kind of high-resolution spectroscopy was firstly used in satellites and later in manned aircraft, which are significantly expensive platforms and extremely restrictive due to availability limitations and/or complex logistics. More recently, UAS have emerged as a very popular and cost-effective remote sensing technology, composed of aerial platforms capable of carrying small-sized and lightweight sensors. Meanwhile, hyperspectral technology developments have been consistently resulting in smaller and lighter sensors that can currently be integrated in UAS for either scientific or commercial purposes. The hyperspectral sensors' ability for measuring hundreds of bands raises complexity when considering the sheer quantity of acquired data, whose usefulness depends on both calibration and corrective tasks occurring in pre- and post-flight stages. Further steps regarding hyperspectral data processing must be performed towards the retrieval of relevant information, which provides the true benefits for assertive interventions in agricultural crops and forested areas. Considering the aforementioned topics and the goal of providing a global view focused on hyperspectral-based remote sensing supported by UAV platforms, a survey including hyperspectral sensors, inherent data processing and applications focusing both on agriculture and forestrywherein the combination of UAV and hyperspectral sensors plays a center roleis presented in this paper. Firstly, the advantages of hyperspectral data over RGB imagery and multispectral data are highlighted. Then, hyperspectral acquisition devices are addressed, including sensor types, acquisition modes and UAV-compatible sensors that can be used for both research and commercial purposes. Pre-flight operations and post-flight pre-processing are pointed out as necessary to ensure the usefulness of hyperspectral data for further processing towards the retrieval of conclusive information. With the goal of simplifying hyperspectral data processingby isolating the common user from the processes' mathematical complexityseveral available toolboxes that allow a direct access to level-one hyperspectral data are presented. Moreover, research works focusing the symbiosis between UAV-hyperspectral for agriculture and forestry applications are reviewed, just before the paper's conclusions.

Abstract
Peer Observation of Teaching has raised a lot of interest as a device for quality enhancement of teaching. While much research has focused on its models, implementation schemes and feedback to the observed, little attention has been paid to what the observer actually sees and can learn from the observation. A multidisciplinary peer observation of teaching program is described, and its data is used to identify the pedagogical aspects to which lecturers pay more attention to when observing classes. The discussion addresses the valuable learning opportunities for observers provided by this program, as well as its usefulness in disseminating, sharing and clarifying quality teaching practices. The need for further research concerning teacher-student relationships and students' engagement is also suggested.

Abstract
Dealing with missing data is a crucial step in the preprocessing stage of most data mining projects. Especially in healthcare contexts, addressing this issue is fundamental, since it may result in keeping or loosing critical patient information that can help physicians in their daily clinical practice. Over the years, many researchers have addressed this problem, basing their approach on the implementation of a set of imputation techniques and evaluating their performance in classification tasks. These classic approaches, however, do not consider some intrinsic data information that could be related to the performance of those algorithms, such as features’ distribution. Establishing a correspondence between data distribution and the most proper imputation method avoids the need of repeatedly testing a large set of methods, since it provides a heuristic on the best choice for each feature in the study. The goal of this work is to understand the relationship between data distribution and the performance of well-known imputation techniques, such as Mean, Decision Trees, k-Nearest Neighbours, Self-Organizing Maps and Support Vector Machines imputation. Several publicly available datasets, all complete, were selected attending to several characteristics such as number of distributions, features and instances. Missing values were artificially generated at different percentages and the imputation methods were evaluated in terms of Predictive and Distributional Accuracy. Our findings show that there is a relationship between features’ distribution and algorithms’ performance, although some factors must be taken into account, such as the number of features per distribution and the missing rate at state.