Abstract
The class imbalance problem is a key issue that has received much attention. This attention has been mostly focused on two-classes problems. Fewer solutions exist for the multi-classes imbalance problem. From an evaluation point of view, the class imbalance problem is challenging because a non-uniform importance is assigned to the classes. In this paper, we propose a relevance-based evaluation framework that incorporates user preferences by allowing the assignment of differentiated importance values to each class. The presented solution is able to overcome difficulties detected in existing measures and increases discrimination capability. The proposed framework requires the assignment of a relevance score to the problem classes. To deal with cases where the user is not able to specify each class relevance, we describe three mechanisms to incorporate the existing domain knowledge into the relevance framework. These mechanisms differ in the amount of information available and assumptions made regarding the domain. They also allow the use of our framework in common settings of multi-class imbalanced problems with different levels of information available. © 2017, Springer International Publishing AG.

Abstract
Augmented Reality (AR) is no longer just a gimmick. 50 years after the development of the first head-mounted display, and approaching the 20th anniversary of the first conference dedicated to AR, it is time for a new review on the theme. As such, we present a bibliometric analysis of scientific literature since 1997, using as database the Web of Science. This allowed identifying the most relevant authors, their distribution by subjects, the evolution of publishing by year and the most frequent publications.

Abstract
In this paper we analyse the effects of the Doppler shift on the optical response of a nanoplasmonic system. Through the development of a simplified model based on the Hydrodynamic Drude model we analyse the response of a quantum dot embed in a moving fluid, predicting the Doppler broadening and the shift of the spectral line.

Abstract
This paper briefly presents a new approach for helping developers identify energy hot spots in their applications. Using tests cases, and statistical methods based on Spectrum-based Fault Localization, high energy consumption is related to the system's source code and a ranking of possible energy leaks are pointed. This technique is both language independent, and context level independent. Initial studies have also shown that using this technique helped developers identify and optimize energy problems in half the time while improving the energy efficiency by 18%. © 2017 IEEE.

Abstract
Classification datasets, which feature a skewed class distribution, are said to be class imbalance. Traditional methods favor the larger classes. We propose pairwise ranking as a method for imbalance classification so that learning compares pairs of observations from each class, and therefore both contribute equally to the decision boundary. In previous work, we suggested treating the binary classification as a ranking problem, followed by a threshold mapping to convert back the ranking score to the original classes. In this work, the method is extended to multi-class ordinal classification, and a new mapping threshold is proposed. Results are compared with traditional and ordinal SVMs, and ranking obtains competitive results.

Abstract
This paper presents a low-power binary phase shift keying (BPSK) pulse generator for ultra-wide-band transmitters. The circuit has been designed based on LC-tank resonators using 0.13 um CMOS technology. Simulation shows -10dB bandwidth of around 3 GHz and power consumption of 2 mW at 100 MHz PRF. Peak-peak amplitude voltage for both symbols '1' and '0' are approximately as large as 1.2V supply voltage and can radiate enough energy to satisfy the FCC mask only by one pulse. Thus, the energy consumption is 20 pJ/pulse/bit. Pulse duration is 1.5 ns and the transmitter can reach data rates of 660 Mbps