A smart grid is an intelligent electricity grid that optimizes the generation, distribution and consumption of electricity through the introduction of Information and Communication Technologies on the electricity grid. In essence, smart grids bring profound changes in the information systems that drive them: new information flows coming from the electricity grid, new players such as decentralized producers of renewable energies, new uses such as electric vehicles and connected houses and new communicating equipments such as smart meters, sensors and remote control points. All this will cause a deluge of data that the energy companies will have to face. Big Data technologies offers suitable solutions for utilities, but the decision about which Big Data technology to use is critical. In this paper, we provide an overview of data management for smart grids, summarise the added value of Big Data technologies for this kind of data, and discuss the technical requirements, the tools and the main steps to implement Big Data solutions in the smart grid context.

In this paper, we give an overview of the AMIDA systems for transcription of conference and lecture room meetings. The systems were developed for participation in the Rich Transcription evaluations conducted by the National Institute for Standards and Technology in the years 2007 and 2009 and can process close talking and far field microphone recordings. The paper first discusses fundamental properties of meeting data with special focus on the AMI/AMIDA corpora. This is followed by a description and analysis of improved processing and modeling, with focus on techniques specifically addressing meeting transcription issues such as multi-room recordings or domain variability. In 2007 and 2009, two different strategies of systems building were followed. While in 2007 we used our traditional style system design based on cross adaptation, the 2009 systems were constructed semi-automatically, supported by improved decoders and a new method for system representation. Overall these changes gave a 6%-13% relative reduction in word error rate compared to our 2007 results while at the same time requiring less training material and reducing the real-time factor by five times. The meeting transcription systems are available at www.webasr.org.

We are all exposed to a risk of voice disorders at some point in our life, as we use our voice daily for speaking, singing, laughing and more. Voice disorders are often characterized by difficulty when breathing, a vocal fatigue, a sore throat, changes in voice such as hoarseness, repetitive cough, and difficulty when swallowing. When these disorders occur, a change in the acoustic characteristics can be noticed. It is then possible to diagnose these diseases based on the automatic analysis of the acoustic quality of the voice signal. In this paper, we present the National School of Applied Sciences of El Jadida - University of Chouaib Doukkali (UCD) system for automatic voice disorder classification. The system was developed for participation in the 2018 FEMH Voice Data Challenge. For this task, we used audio recorded data from the patients of the Far Eastern Memorial Hospital (FEMH). Different types of features are investigated; the Mel Frequency Cepstral Coefficients, the zero-crossing rate, the roll-off frequency, the spectral centroid, the spectral bandwidth and the spectral contrast. All of these features are normalized using summary statistics such as mean, standard deviation, skewness, maximum, minimum and the median. Experimental results show that the Support Vector Machine outperforms the other classifiers and that the best performance of our classifier is reached when using MFCC with spectral features.