Two hundred and sixty grade 9 through 12 students completed questionnaires designed to examine relations among social support, perception of future opportunity, and education and career aspirations and expectations. Path analyses showed that for both males and females, perception of opportunity predicts educational expectations, which, in turn, predict educational aspirations and career expectations. For females, peer, family and teacher supports predict perception of opportunity, whereas for males only family support is predictive of perception of opportunity. ANOVAs demonstrated that females perceive more teacher and peer support than do males, and that compared to their male peers, females have greater perceived future opportunity, educational aspirations and expectations, and career expectations. Both males and females indicate a greater gap between career aspirations and expectations than between education aspirations and expectations. The possible contributions of socioeconomic conditions and gendered socialization are discussed.

In recent years, 'Cyber Security' has emerged as a widely-used term with increased adoption by practitioners and politicians alike. However, as with many fashionable jargon, there seems to be very little understanding of what the term really entails. Although this is may not be an issue when the term is used in an informal context, it can potentially cause considerable problems in context of organizational strategy, business objectives, or international agreements. In this work, we study the existing literature to identify the main definitions provided for the term 'Cyber Security' by authoritative sources. We then conduct various lexical and semantic analysis techniques in an attempt to better understand the scope and context of these definitions, along with their relevance. Finally, based on the analysis conducted, we propose a new improved definition that we then demonstrate to be a more representative definition using the same lexical and semantic analysis techniques.

Recent speech enhancement research has shown that deep learning techniques are very effective in removing background noise. Many deep neural networks are being proposed, showing promising results for improving overall speech perception. The Deep Multilayer Perceptron, Convolutional Neural Networks, and the Denoising Autoencoder are well-established architectures for speech enhancement; however, choosing between different deep learning models has been mainly empirical. Consequently, a comparative analysis is needed between these three architecture types in order to show the factors affecting their performance. In this paper, this analysis is presented by comparing seven deep learning models that belong to these three categories. The comparison includes evaluating the performance in terms of the overall quality of the output speech using five objective evaluation metrics and a subjective evaluation with 23 listeners; the ability to deal with challenging noise conditions; generalization ability; complexity; and, processing time. Further analysis is then provided while using two different approaches. The first approach investigates how the performance is affected by changing network hyperparameters and the structure of the data, including the Lombard effect. While the second approach interprets the results by visualizing the spectrogram of the output layer of all the investigated models, and the spectrograms of the hidden layers of the convolutional neural network architecture. Finally, a general evaluation is performed for supervised deep learning-based speech enhancement while using SWOC analysis, to discuss the technique’s Strengths, Weaknesses, Opportunities, and Challenges. The results of this paper contribute to the understanding of how different deep neural networks perform the speech enhancement task, highlight the strengths and weaknesses of each architecture, and provide recommendations for achieving better performance. This work facilitates the development of better deep neural networks for speech enhancement in the future.

In this paper, a spiking neural network (SNN) architecture to simulate the sound localization ability of the mammalian auditory pathways using the interaural intensity difference cue is presented. The lateral superior olive was the inspiration for the architecture, which required the integration of an auditory periphery (cochlea) model and a model of the medial nucleus of the trapezoid body. The SNN uses leaky integrate-and-fire excitatory and inhibitory spiking neurons, facilitating synapses and receptive fields. Experimentally derived head-related transfer function (HRTF) acoustical data from adult domestic cats were employed to train and validate the localization ability of the architecture, training used the supervised learning algorithm called the remote supervision method to determine the azimuthal angles. The experimental results demonstrate that the architecture performs best when it is localizing high-frequency sound data in agreement with the biology, and also shows a high degree of robustness when the HRTF acoustical data is corrupted by noise.

This is the accepted version of the paper.This version of the publication may differ from the final published version. Permanent ABSTRACTThis paper presents a strategy for enabling speech recognition to be performed in the cloud whilst preserving the privacy of users. The approach advocates a demarcation of responsibilities between the client and server-side components for performing the speech recognition task. On the client-side resides the acoustic model, which symbolically encodes the audio and encrypts the data before uploading to the server. The server-side then employs searchable encryption to enable the phonetic search of the speech content. Some preliminary results for speech encoding and searchable encryption are presented.