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Explainable Artificial Intelligence (XAI) is targeted at understanding how models perform feature selection and derive their classification decisions. This paper explores post-hoc explanations for deep neural networks in the audio domain. Notably, we present a novel Open Source audio dataset consisting of 30,000 audio samples of English spoken digits which we use for classification tasks on spoken digits and speakers' biological sex. We use the popular XAI technique Layer-wise Relevance Propagation LRP to identify relevant features for two neural network architectures that process either waveform or spectrogram representations of the data. Based on the relevance scores obtained from LRP, hypotheses about the neural networks' feature selection are derived and subsequently tested through systematic manipulations of the input data. Further, we take a step beyond visual explanations and introduce audible heatmaps. We demonstrate the superior interpretability of audible explanations over visual ones in a human user study. • We present a novel audio dataset consisting of 30,000 audio samples of spoken digits. • We use LRP to explain predictions of two different models in the audio domain. • We confirm hypotheses about the neural networks' use of features from explanations. • We present audible explanations and demonstrate their superior interpretability. [ABSTRACT FROM AUTHOR]

This paper researches the finite-time master–slave synchronization for implicit hybrid neural networks under event-triggered guaranteed cost control and random deception attacks. By developing Lyapunov–Krasovskii functional and exploiting singular value decomposition technique, the finite-time boundedness of synchronization error closed-loop system is achieved under deception attacks and event-triggered scheme, then the finite-time master–slave synchronization of implicit Markovian jump neural networks is realized. The desired gains of state feedback synchronization guaranteed cost controller are designed by solving a set of linear matrix inequalities. The availability of the given approach is confirmed by an artificial neural network and a numerical example. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to advancing artificial intelligence (AI) through the development of the Complex Recurrent Spectral Network (ℂ -RSN), an innovative variant of the Recurrent Spectral Network (RSN) model. The ℂ -RSN model introduces localized non-linearity, complex fixed eigenvalues, and a distinct separation of memory and input processing functionalities. These features enable the ℂ -RSN to evolve towards a dynamic, oscillating final state that bear some degree of similarity with biological cognition. The model's ability to classify data through a time-dependent function, and the localization of information processing, is demonstrated by using the MNIST dataset. Remarkably, distinct items supplied as a sequential input yield patterns in time which bear the indirect imprint of the insertion order (and of the separation in time between contiguous insertions). • The C-RSN overcomes some limitations in existing neural network models. • C-RSN introduces localized non-linearity, complex eigenvalues and a separated memory. • The network evolves towards a dynamic, oscillating final state. • The model's efficacy is demonstrated through empirical evaluation. • C-RSN is able to process sequential inputs keeping track of the insertion order. [ABSTRACT FROM AUTHOR]

In this paper, a neural network approach is suggested to the product design analysis. Namely, fractional-order neural network models are proposed as more flexible mechanism to study product form design. Since control and stability methods are fundamental in the construction and practical significance of a neural network model, appropriate controllers are designed and practical stability criteria are proposed for the fractional-order model under consideration. The stability and control analysis are based on the Lyapunov function method. Examples are elaborated to demonstrate the established results. The proposed modeling approach and the stability results are also applicable to numerous industrial design tasks. • Fractional-order neural network modeling approach is introduced to the product form design. • Appropriate controllers are utilized. • The practical stability notion is adopted to the introduced model. • Practical stability criteria are established using the Lyapunov function technique. • Examples and discussion are also offered to verify and justify the proposed results. [ABSTRACT FROM AUTHOR]

Quantile regression is widely applied in various fields such as economy, energy, meteorological prediction research in recent years since it does not require distribution assumptions, has relatively loose conditions, and can effectively estimate the uncertainty of time series forecasting. In this paper, a monotone quantile regression neural network (MQRNN) framework is constructed for time series quantile forecasting. The proposed approach takes the monotonicity of quantile into consideration and handles the quantile crossing problem by adding the quantile information into the input structure and using the gradient based point-wise loss function. Aiming at the complex characteristics of time series, such as time-varying and asymmetric heavy-tailed features, a new quantile function is utilized to describe the complete conditional distribution information of data. Under this model framework, non-crossing multiple quantiles can be predicted simultaneously. The proposed approach is implemented based on artificial neural networks, and the constructed model is applied to actual data in different fields. The experimental results demonstrate that the proposed method combined with long short-term memory (LSTM) can provide accurate and reliable multi-quantile prediction, and alleviate the problem of quantile crossing. [ABSTRACT FROM AUTHOR]