Power disaggregation is aimed at determining appliance-by-appliance electricity consumption, leveraging upon a single meter only, which measures the entire power demand. Data-driven procedures based on Factorial Hidden Markov Models (FHMMs) have produced remarkable results on energy disaggregation. Nevertheless, these procedures have various weaknesses; there is a scalability problem as the number of devices to observe rises, and the inference step is computationally heavy. Artificial neural networks (ANNs) have been demonstrated to be a viable solution to deal with FHMM shortcomings. Nonetheless, there are two significant limitations: A complicated and time-consuming training system based on back-propagation has to be employed to estimate the neural architecture parameters, and large amounts of training data covering as many operation conditions as possible need to be collected to attain top performances. In this work, we aim to overcome these limitations by leveraging upon the unique and useful characteristics of the extreme learning machine technique, which is based on a collection of randomly chosen hidden units and analytically defined output weights. We find that the suggested approach outperforms state-of-the-art solutions, namely FHMMs and ANNs, on the UK-DALE corpus. Moreover, our solution generalizes better than previous approaches for unseen houses, and avoids a data-hungry training scheme.

Model adaptation is a key technique that enables a modern automatic speech recognition (ASR) system to adjust its parameters, using a small amount of enrolment data, to the nuances in the speech spectrum due to microphone mismatch in the training and test data. In this brief, we investigate four different adaptation schemes for connectionist (also known as hybrid) ASR systems that learn microphone-specific hidden unit contributions, given some adaptation material. This solution is made possible adopting one of the following schemes: 1) the use of Hermite activation functions; 2) the introduction of bias and slope parameters in the sigmoid activation functions; 3) the injection of an amplitude parameter specific for each sigmoid unit; or 4) the combination of 2) and 3). Such a simple yet effective solution allows the adapted model to be stored in a small-sized storage space, a highly desirable property of adaptation algorithms for deep neural networks that are suitable for large-scale online deployment. Experimental results indicate that the investigated approaches reduce word error rates on the standard Spoke 6 task of the Wall Street Journal corpus compared with unadapted ASR systems. Moreover, the proposed adaptation schemes all perform better than simple multicondition training and comparable favorably against conventional linear regression-based approaches while using up to 15 orders of magnitude fewer parameters. The proposed adaptation strategies are also effective when a single adaptation sentence is available.

No abstract

This paper introduces a fuzzy-based method that, according to the ratio of Throughput to Workload and the battery level, manages the sleeping time of devices in Wireless Sensor Networks (WSNs) for smart homes. The purpose of this work is a system that can be executed on off-the-shelf hardware and offers enhanced performance confronted with other approaches. The challenge here is to achieve a practical method that reaches the target while bypassing complex and computationally expensive solutions, which would diminish the possible applicability of the method in real scenarios. The retrieved results prove that the proposed approach outperforms other solutions, significantly prolonging the life of battery-powered wireless devices with also satisfactory values of the ratio Throughput to Workload. Besides, a proof-of-concept implementation on off-the-shelf devices confirms that the proposed method does not expect powerful hardware and can be surely implemented on a low-cost device.

No abstract

We report here the reaction of in situ prepared PhSeZnCl with steroid derivatives having an epoxide as an electrophilic functionalization. The corresponding ring-opening reaction resulted to be regio- and stereoselective affording to novel phenylselenium-substituted steroids. Assessment of their antibacterial properties against multidrug-resistant bacteria, such as Pseudomonas aeruginosa Xen 5 strain, indicates an interesting bactericidal activity and their ability to prevent bacterial biofilm formation.

We have recently proposed a universal acoustic characterisation to foreign accent recognition, in which any spoken foreign accent was described in terms of a common set of fundamental speech attributes. Although experimental evidence demonstrated the feasibility of our approach, we belive that speech attributes, namely manner and place of articulation, can be better modelled by a deep neural network. In this work, we propose the use of deep neural network trained on telephone bandwidth material from different languages to improve the proposed universal acoustic characterisation. We demonstrate that deeper neural architectures enhance the attribute classification accuracy. Furthermore, we show that improvements in attribute classification carry over to foreign accent recognition by producing a 21% relative improvement over previous baseline on spoken Finnish, and a 5.8% relative improvement on spoken English.

Bottleneck features (BNFs) generated with a deep neural network (DNN) have proven to boost spoken language recognition accuracy over basic spectral features significantly. However, BNFs are commonly extracted using language-dependent tied-context phone states as learning targets. Moreover, BNFs are less phonetically expressive than the output layer in a DNN, which is usually not used as a speech feature because of its very high dimensionality hindering further post-processing. In this article, we put forth a novel deep learning framework to overcome all of the above issues and evaluate it on the 2017 NIST Language Recognition Evaluation (LRE) challenge. We use manner and place of articulation as speech attributes, which lead to low-dimensional "universal" phonetic features that can be defined across all spoken languages. To model the asynchronous nature of the speech attributes while capturing their intrinsic relationships in a given speech segment, we introduce a new training scheme for deep architectures based on a Maximal Figure of Merit (MFoM) objective. MFoM introduces non-differentiable metrics into the backpropagation-based approach, which is elegantly solved in the proposed framework. The experimental evidence collected on the recent NIST LRE 2017 challenge demonstrates the effectiveness of our solution. In fact, the performance of speech language recognition (SLR) systems based on spectral features is improved for more than 5% absolute Cavg. Finally, the F1 metric can be brought from 77.6% up to 78.1% by combining the conventional baseline phonetic BNFs with the proposed articulatory attribute features.