Neurocomputing

The multi-robot task allocation problem has garnered significant attention in research and development. This paper addresses the multi-robot task allocation problem by introducing a unified model that seamlessly transforms into four popular ...

Although various machine learning methods have been proposed for industrial applications, there are not many examples of their application in some industrial sectors. Data collected within organizations are inconsistently formatted and expensive ...

• This method combines DNN and evaluation functions. It is trainable without datasets.
• One of the networks approximates evaluation functions to train the other network.
• This method has advantages over EA for complex problems due to ...

Abstractive Text summarization aims to generate a short summary for a document while preserving salient information. Recently, contrastive learning has been extended from visual representation to summarization tasks. At present, the methods of ...

• We propose a contrastive learning method for abstractive text summarization.
• We construct contrastive samples in the source sentences.
• We incorporate the salient distance-aware weightsinto the contrastive loss.
• Experiments show ...

As one of the most representative detectors, feature pyramid network (FPN) has achieved remarkable improvement in object detection. Thanks to its distinctive top-down feature fusion path and multi-scale detection paradigm, FPN has become an ...

Boundary detection, as a fundamental task for computer vision applications, plays an important role in many tasks such as image deblurring, semantic segmentation, camouflaged object detection, and salient object detection. The thickness problem ...

Deep residual network (ResNet) had shown remarkable performance in image recognition tasks, due to its shorter connections between layers close to the input and those close to the output. And densely connected convolutional network (DenseNet) had ...

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• Our network improves the residual units, inside which the features are reused.
• Our network reuses all residuals in the residual reuse path outside the units.
• The output of residual reuse path is used as the final feature maps.
• ...

Research on neuromorphic computing with spiking neural networks and in-memory computing to achieve low-power consumption and high-speed operation has received great attention. In this study, we proposed a new synaptic device called a fusion ...

• A fusion synapse that consists of a memristor and a capacitor has been proposed.
• Our proposed synapse represents the weight using the time constant.
• A spiking neural network was designed with our proposed synapses.
• HSPICE ...

Link prediction is a crucial aspect of graph machine learning, with applications as diverse as disease prediction, social network recommendations, and drug discovery. It involves the prediction of potential new links between nodes within a ...

• Introduced Node Centrality and Similarity based Parameterised Model (NCSM) for link prediction.
• NCSM uses node centrality and similarity as edge features in a custom Graph Neural Network.
• Evaluated NCSM on five benchmark datasets, ...

Unlike the earlier 3D object detection approaches that formulate hand-crafted dense (in thousands) object proposals by leveraging anchors on dense feature maps, we formulate n p (in hundreds) number of learnable sparse object proposals to predict ...

The development of explainability methods for Convolutional Neural Networks (CNNs), under the growing framework of explainable Artificial Intelligence (xAI) for image understanding, is crucial due to neural networks success in contrast with their ...

• Design a novel topology-based explanatory method for Convolutional Neural Networks.
• The topology of latent spaces is related to the variety of encoded information.
• Discussing in detail the learning process and its relationship to ...

Nearest neighbor (NN) sampling provides more semantic variations than predefined transformations for self-supervised learning (SSL) based image recognition problems. However, its performance is restricted by the quality of the support set, which ...

• Pseudo nearest neighbors improves learning in nearest neighbor contrastive methods.
• A pNN based method called pNNCLR is proposed to improve upon baseline NNCLR.
• Addition of noise to the sampling algorithm increases the semantic ...

In response to problems in using deep reinforcement learning to perform the stair climbing task for the crawler robot, such as the lack of theoretical values for stair size range, the inaccurate force on the model using wheel to simulate track ...

• We propose a dynamics parameter settings method to accurately simulate the track.
• We study kinematics and dynamics models to calculate the theoretical climbing ability.
• We propose a training course that reduces the impact of bad ...

To alleviate the reliance on expensive annotations, contrastive learning techniques have been applied to diagnose diseases from various types of medical images. However, popular contrastive learning methods, which generate positive pairs by ...

Motor imagery brain–computer interfaces (MI–BCIs) that use electroencephalogram (EEG) signals are essential for motor rehabilitation. One of the most important problems in MI–BCIs is intersubject variability, as brain signals can vary ...

Answering questions about hybrid data combining tables and text is challenging. Recent research has employed encoder-tree decoder frameworks to simulate the reasoning process of arithmetic expressions for generating answers. However, this ...

In recent years, Transformer has been widely used in the crossing task of computer vision (CV) and natural language processing (NLP), e.g., image captioning. The prior works of image captioning based on Transformer have achieved remarkable ...

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In this article, an evolution-guided value iteration (EGVI) algorithm is established to address optimal tracking problems for nonlinear nonaffine systems. Conventional adaptive dynamic programming algorithms rely on gradient information to ...

This article devises an adaptive quantized control scheme for full-state constrained Markov jumping nonlinear systems with incomplete transition probabilities and unknown control directions. First, the barrier Lyapunov functions were utilized to ...

Generating initial seeds is an important step in weakly supervised semantic segmentation (WSSS). Our approach concentrates on generating and refining initial seeds. The convolutional neural networks (CNNs)–based initial seeds focus only on the ...

This paper presents two kinds of novel results about semiglobal synchronization in probability for stochastic systems. To this end, new criteria of semiglobal fixed-time synchronization in probability (SFXTSp) and semiglobal preassigned-time ...

• Two novel semiglobal synchronization in probability results are proposed.
• Our conclusions have wider applicability and address the existing challenges.
• Various stochastic factors are considered, making our results more general.