Time-Delay Neural Networks (TDNN) have been shown by Waibel et al. [1] to be a good method for the classification of dynamic speech sounds such as voiced stop consonants. In this paper we discuss key issues in the design and training of a TDNN, based on a Multi-Layer Perceptron (MLP), when used for classification of the sets of voiced stop consonants (/b/, /d/, and /g/) and unvoiced stop conson...

We present a new type of probabilistic model which we call DISsimilarity COefficient Networks (DISCO Nets). DISCO Nets allow us to efficiently sample from a posterior distribution parametrised by a neural network. During training, DISCO Nets are learned by minimising the dissimilarity coefficient between the true distribution and the estimated distribution. This allows us to tailor the training...

Social responsibility is a key factor that could result in success and achieving great benefits for supply chains. Responsiveness and reliability are important social responsibility measures for consumers and all stakeholders that strategists and company managers should be concerned about them in long-term planning horizon. Although, presence of uncertainties as an intrinsic part of supply chai...

This paper uses nonlinear regression, Artificial Neural Network (ANN) and Genetic Programming (GP) approaches for predicting an important tangible issue i.e. scours dimensions downstream of inverted siphon structures. Dimensional analysis and nonlinear regression-based equations was proposed for estimation of maximum scour depth, location of the scour hole, location and height of the dune downs...

Deep neural networks achieve remarkable performance in many computer vision tasks. Most state-of-the-art (SOTA) semantic segmentation and object detection approaches reuse network architectures designed for image classification as the backbone, commonly pre-trained on ImageNet. However, gains can be achieved by designing specifically segmentation, shown recent architecture search (NAS) research...

An inspection of signal processing approach in order to estimate underwater network cardinalities is conducted in this research. A matter of key prominence for underwater network is its cardinality estimation as the number of active cardinalities varies several times due to numerous natural and artificial reasons due to harsh underwater circumstances. So, a proper estimation technique is mandat...

Classical models of customer decision making in unobservable queues assume acquiring queue-length information is too costly. However, due to recent advancements in communication technology, various services now make this kind of information accessible to customers at a reasonable cost. In our model, which reflects this new opportunity, customers choose among three options: join the queue, balk,...

We study the scenario where a batch of transient faults hits an asynchronous distributed system by corrupting the state of some f nodes. We concentrate on the basic majority consensus problem, where nodes are required to agree on a common output value which is the input value of the majority of them. We give a fully self-stabilizing adaptive algorithm, i.e., the output value stabilizes in O(f) ...