Abstract
Background: Stress is a complex process with an impact on health and performance. The use of wearable sensor-based monitoring systems offers interesting opportunities for advanced health care solutions for stress analysis. Considering the stressful nature of firefighting and its importance for the community’s safety, this study was conducted for firefighters. Objectives: A biomonitoring platform was designed, integrating different biomedical systems to enable the acquisition of real time Electrocardiogram (ECG), computation of linear Heart Rate Variability (HRV) features and collection of perceived stress levels. This platform was tested using an experimental protocol, designed to understand the effect of stress on firefighter’s cognitive performance, and whether this effect is related to the autonomic response to stress. Method: The Trier Social Stress Test (TSST) was used as a testing platform along with a 2-Choice Reaction Time Task. Linear HRV features from the participants were acquired using an wearable ECG. Self-reports were used to assess perceived stress levels. Results: The TSST produced significant changes in some HRV parameters (AVNN, SDNN and LF/HF) and subjective measures of stress, which recovered after the stress task. Although these short-term changes in HRV showed a tendency to normalize, an impairment on cognitive performance was found after performing the stress event. Conclusion: Current findings suggested that stress compromised cognitive performance and caused a measurable change in autonomic balance. Our wearable biomonitoring platform proved to be a useful tool for stress assessment and quantification. Future studies will implement this biomonitoring platform for the analysis of stress in ecological settings. © 2018 Rodrigues et al.

Abstract
Head-Mounted Displays are useful to place users in virtual reality (VR). They do this by totally occluding the physical world, including users' bodies. This can make self-awareness problematic. Indeed, researchers have shown that users' feeling of presence and spatial awareness are highly influenced by their virtual representations, and that self-embodied representations (avatars) of their anatomy can make the experience more engaging. On the other hand, recent user studies show a penchant towards a third-person view of one's own body to seemingly improve spatial awareness. However, due to its unnaturality, we argue that a third-person perspective is not as effective or convenient as a first-person view for task execution in VR. In this paper, we investigate, through a user evaluation, how these perspectives affect task performance and embodiment, focusing on navigation tasks, namely walking while avoiding obstacles. For each perspective, we also compare three different levels of realism for users' representation, specifically a stylized abstract avatar, a mesh-based generic human, and a real-time point-cloud rendering of the users' own body. Our results show that only when a third-person perspective is coupled with a realistic representation, a similar sense of embodiment and spatial awareness is felt. In all other cases, a first-person perspective is still better suited for navigation tasks, regardless of representation.

Abstract
Social media are becoming an increasingly important source of information about the public mood regarding issues such as elections, Brexit, stock market, etc. In this paper we focus on sentiment classification of Twitter data. Construction of sentiment classifiers is a standard text mining task, but here we address the question of how to properly evaluate them as there is no settled way to do so. Sentiment classes are ordered and unbalanced, and Twitter produces a stream of time-ordered data. The problem we address concerns the procedures used to obtain reliable estimates of performance measures, and whether the temporal ordering of the training and test data matters. We collected a large set of 1.5 million tweets in 13 European languages. We created 138 sentiment models and out-of-sample datasets, which are used as a gold standard for evaluations. The corresponding 138 in-sample data-sets are used to empirically compare six different estimation procedures: three variants of cross-validation, and three variants of sequential validation (where test set always follows the training set). We find no significant difference between the best cross-validation and sequential validation. However, we observe that all cross-validation variants tend to overestimate the performance, while the sequential methods tend to underestimate it. Standard cross-validation with random selection of examples is significantly worse than the blocked cross-validation, and should not be used to evaluate classifiers in time-ordered data scenarios.

Abstract
Cutting raw-material into smaller parts is a fundamental phase of many production processes. These operations originate raw-material waste that can be minimised. These problems have a strong economic and ecological impact and their proper solving is essential to many sectors of the economy, such as the textile, footwear, automotive and shipbuilding industries, to mention only a few. Two-dimensional (2D) nesting problems, in particular, deal with the cutting of irregularly shaped pieces from a set of larger containers, so that either the waste is minimised or the value of the pieces actually cut from the containers is maximised. Despite the real-world practical relevance of these problems, very few approaches have been proposed capable of dealing with concrete characteristics that arise in practice. In this paper, we propose a new general heuristic (H4NP) for all 2D nesting problems with limited-size containers: the Placement problem, the Knapsack problem, the Cutting Stock problem, and the Bin Packing problem. Extensive computational experiments were run on a total of 1100 instances. H4NP obtained equal or better solutions for 73% of the instances for which there were previous results against which to compare, and new benchmarks are proposed.

Abstract
Despite recent advances, always-on broadband Internet connectivity is still not available in Temporary Crowded Events (TCEs). To solve this problem, this paper envisions a novel concept named Traffic-Aware Multi-Tier Flying Network (TMFN). A TMFN consists of a mobile and physically reconfigurable network of Flying Mesh Access Points (FMAPs) and Gateways, which is able to dynamically reconfigure its topology according to the users' traffic demands - characterized by the users' positions and offered traffic. To implement this concept, a novel traffic-aware Network Planning (NetPlan) algorithm is proposed, which dynamically determines the FMAPs' coordinates and Wi-Fi cell ranges according to the users' traffic demands, in order to improve the TMFN's aggregate throughput, without compromising the overall coverage. Simulation results obtained in scenarios typically observed in TCEs demonstrate improved Quality of Service metrics, specifically the mean throughput, thus validating the proposed NetPlan algorithm.

Abstract
Most distributed photovoltaic systems (DPVSs) are normally located behind the meter and are thus invisible to utilities and retailers. The accurate information of the DPVS capacity is very helpful in many aspects. Unfortunately, the capacity information obtained by the existing methods is usually inaccurate due to various reasons, e.g., the existence of unauthorized installations. A two-stage DPVS capacity estimation approach based on support vector machine with customer net load curve features is proposed in this paper. First, several features describing the discrepancy of net load curves between customers with DPVSs and those without are extracted based on the weather status driven characteristic of DPVS output power. A one-class support vector classification (SVC) based DPVS detection (DPVSD) model with the input features extracted above is then established to determine whether a customer has a DPVS or not. Second, a bootstrap-support vector regression (SVR) based DPVS capacity estimation (DPVSCE) model with the input features describing the difference of daily total PV power generation between DPVSs with different capacities is proposed to further estimate the specific capacity of the detected DPVS. A case study using a realistic dataset consisting of 183 residential customers in Austin (TX, U.S.A.) verifies the effectiveness of the proposed approach.