Neurocomputing

The rapid adoption of Industry 4.0 has seen Information Technology (IT) networks increasingly merged with Operational Technology (OT) networks, which have traditionally been isolated on air-gapped and fully trusted networks. This increased attack ...

• Anomaly detection in Cyber–Physical Systems.
• Internet of Things (IoT) Security.
• One-class machine learning.

Incomplete phase encoding with few phases is an effective under-sampling manner of fast Magnetic Resonance (MR) scan. The key is how to choose important slice-specific phases. Reinforcement Learning (RL) is powerful for sequential decision-making ...

Predicting stock market trends is the basic daily routine task that investors should perform in the stock trading market. Traditional market trends prediction models are generally based on hand-crafted factors or features, which heavily rely on ...

The efficiency of deep neural networks is increasing, and so is the amount of annotated data required for training them. We propose a solution improving the learning process of a classification network with less labeled data. Our approach is to ...

• We propose a solution to learn a classification network with less labeled data.
• The principle is to provide the classifier a binary mask as a simple shape prior.
• We resort to a Siamese architecture and feed it with images and shape ...

Affective Computing systems can detect the emotional state and mindset of an individual. Speech Emotion Recognition (SER) is a unimodal affect computing system based on emotional speech data. It is an active area of research in pattern ...

A novel hybrid neural network based learning control method is proposed to improve trajectory tracking accuracy for complex robot manipulators in this paper. Firstly, a hybrid neural network is presented to improve the model accuracy and data ...

In this paper, a novel decentralized control method is proposed for nonlinear mismatched large-scale interconnected systems subjected to partially unknown dynamics by designing auxiliary control for each subsystem. It is demonstrated that the ...

• Edge dynamic event-triggered control is first time combined with IRL algorithms.
• The IRL algorithm relaxes the requirement of the system drift dynamics.
• Edge dynamic event-triggered method has better performance than static ...

In this paper, a novel discrete-time interconnected model composed of nodes and edges subsystems is proposed, to depict the complex dynamical networks (CDNs) consisted of nodes and dynamic edges which are coupled mutually. Firstly, we propose a ...

Thanks to their ease of implementation, multilayer perceptrons (MLPs) have become ubiquitous in deep learning applications. The graph underlying an MLP is indeed multipartite, i.e. each layer of neurons only connects to neurons belonging to the ...

Hyperbolic graph embedding has garnered significant attention in a wide range of downstream applications. This is because hyperbolic geometry provides a valuable mapping tool, whereby the scale-free or hierarchical properties of complex networks ...

Every day, enormous amounts of biomedical texts discussing various biomedical topics are produced. Revealing strong semantic connections hidden in those unstructured data is essential for many interesting applications such as knowledge base ...

Many feature extraction components have been proposed for image deraining tasks, aiming to improve feature learning. However, few models have addressed the integration of multi-scale features from derain images. The fusion of multiple features at ...

• Introduce detail scaling module to extract generalized features from rainfall image.
• Improved Transform block was introduced to enhance the model’s generalized ability.
• Scale-mixing strategy is proposed for capturing more multi-...

Active Inference is a computational framework used in neuroscience and cognitive science that characterises perception, planning and action in terms of probabilistic inference and the minimisation of variational free energy. cpp-AIF is a header-...

Learning-based correspondence matching methods have become the mainstream techniques in many computer vision and robotics applications due to their robustness to large illumination and viewpoint changes. However, it is difficult for conventional ...

Simion Zoo is a Reinforcement Learning (RL) workbench developed for training of novel users that can be deployed over computer farms allowing extensive experimentation over distributed resources. In this paper, we present this software platform ...

In this paper, the stability of tracking error dynamics under the data-based discounted iterative Q-learning is investigated. First, a novel performance index with a discount factor is introduced into the iterative Q-learning-based tracking ...

Position encoding has recently been shown to be effective in transformer architecture. It enables valuable supervision for dependency modeling between elements at different positions of the sequence. In this paper, we first investigate various ...

This article proposed a novel adaptive decentralized control (ADC) method for the continuous-time state-constrained and input-constrained large-scale unmatched interconnection system by the means of the adaptive critic design in the edge dynamic ...

• The state-constrained and input-constrained problems are simultaneously considered.
• In online IRL algorithm, the constrained system dynamic can be partially unknown.
• The EDET mechanism is devised to replace the traditional SET ...

Official gazettes are documents published by governments to publicize their actions, spanning long periods of time and making an important transparency mechanism. These documents have information on laws, contracts, and bidding processes, as well ...

Naturally inspired designs of training environments for reinforcement learning (RL) often suffer from highly skewed encounter probabilities, with a small subset of experiences being encountered frequently, while extreme experiences remain rare. ...