Abstract
Improving the safety of public roads and industrial factories requires more reliable and robust computer vision-based approaches for monitoring the eye state (open or closed) of human operators. Getting this information in real time when humans are driving cars or using hazardous machinery will help to prevent accidents and deaths. This paper proposes a new framework called EyeLSD to localize the eyes and detect their states without face detection step. For EyeLSD aims, two novel descriptors are proposed: enhanced Pyramidal Local Binary Pattern Histogram (ePLBPH) and Multi-Three-Patch LBP histogram (Multi-TPLBP). The performance of EyeLSD with ePLBPH and Multi-TPLBP is evaluated and compared against other approaches. For this evaluation three independent and public datasets were used: BioID, CAS-PEAL-R1 and ZJU datasets. The set EyeLSD, ePLBPH and Multi-TPLBP have a greater performance when compared against the state-of-the-art algorithms. The proposed approach is very stable under large range of eye appearances caused by expression, rotation, lighting, head pose, and occlusion.

Abstract
Nowadays, not only the number of multimedia resources available is increasing exponentially, but also the crowd-sourced feedback volunteered by viewers generates huge volumes of ratings, likes, shares and posts/reviews. Since the data size involved surpasses human filtering and searching capabilities, there is the need to create and maintain the profiles of viewers and resources to develop recommendation systems to match viewers with resources. In this paper, we propose a personalised viewer profiling technique which creates individual viewer models dynamically. This technique is based on a novel incremental learning algorithm designed for stream data. The results show that our approach outperforms previous approaches, reducing substantially the prediction errors and, thus, increasing the accuracy of the recommendations. © Springer International Publishing AG, part of Springer Nature 2018.

Abstract
5G heterogeneity will cover a huge diversity of use cases, ranging from enhanced-broadband to low-throughput and low-power communications. To address such requirements variety, this paper proposes a parallel-pipelined architecture for an OFDM baseband modulator with clock frequency run-time adaptation through dynamic frequency scaling (DFS). It supports a set of OFDM numerologies recently proposed for 5G communication systems. The parallel-pipelined architecture can achieve high throughputs at low clock frequencies (up to 520.3 MSamples/s at 160 MHz) and DFS allows for the adjustment of baseband processing clock frequency according to immediate throughput demands. The application of DFS increases the system’s power efficiency by allowing power savings up to 62.5%; the resource and latency overhead is negligible. © Springer International Publishing AG, part of Springer Nature 2018.

Abstract
In a world where many human lives depend on the correct behavior of software systems, program verification assumes a crucial role. Many verification tools rely on an algorithm that generates verification conditions (VCs) from code annotated with properties to be checked. In this paper, we revisit two major methods that are widely used to produce VCs: predicate transformers (used mostly by deductive verification tools) and the conditional normal form transformation (used in bounded model checking of software). We identify three different aspects in which the methods differ (logical encoding of control flow, use of contexts, and semantics of asserts), and show that, since they are orthogonal, they can be freely combined. This results in six new hybrid verification condition generators (VCGens), which together with the fundamental methods constitute what we call the VCGen cube. We consider two optimizations implemented in major program verification tools and show that each of them can in fact be applied to an entire face of the cube, resulting in optimized versions of the six hybrid VCGens. Finally, we compare all VCGens empirically using a number of benchmarks. Although the results do not indicate absolute superiority of any given method, they do allow us to identify interesting patterns. © 2018 IEEE.

Abstract
Demand response (DR) provides enormous opportunities to distribution system operators so as to conduct the power system in a sustainable manner. Due to the increasing penetration of electric vehicles (EV) in the power system, the necessity of enhancing flexibility has gained importance in the charging operation process. With the aid of the smart grid concept and DR programs, more flexible grid operations are provided. In this study, an optimal day-ahead EV charging strategy through electric vehicle parking lots (EVPL) aggregators is intended for the purpose of maximizing the load factor during daily operation. Furthermore, the behavioral uncertainty of EVs and peak load limitation based DR programs are also taken into account in the devised model. In order to reveal the effectiveness of the proposed EVPL aggregator energy management strategy, various case studies are performed, and credible results are reported.

Abstract
Understanding the role of the climate on the wine production is one of the major concerns of this sector since the environment usually determines the output of this industry. There are only a few previous studies that attempted to compile these environmental effects as an index, usually considering the temperature and the precipitation as their core variables. The present study suggests a new climate index which is based on descriptive statistics. Our index tries to mimic the target region characteristics and avoid the past studies premise of imposing previously conceived restrictions such as a fixed optimal climate. We then used yearly production and daily temperature data (1950-2016) from the Portuguese Minho wine region to test our proposed index and compare it with Ribéreau-Gayon and Peynaud (RGP, Ribéreau-Gayon et al., 2003) and Growing Degree-Days (GDD, Winkler et al., 1974) indexes. Our results showed that the newly proposed index may outperform the explanatory power of the other indexes and, in addition, may output interesting and unknown characteristics such as the different ideal temperatures regarding the studied region. © The Authors, published by EDP Sciences, 2018.