5,422 Result(s)
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Article
ConDA: state-based data augmentation for context-dependent text-to-SQL
The context-dependent text-to-SQL task has profound real-world implications, as it facilitates users in extracting knowledge from vast databases, which allows users to acquire the information interactively for...
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Combining core points and cluster-level semantic similarity for self-supervised clustering
Contrastive learning utilizes data augmentation to guide network training. This approach has attracted considerable attention for clustering, object detection, and image segmentation. However, previous studies...
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Dual flow fusion graph convolutional network for traffic flow prediction
In recent decades, motor vehicle ownership has increased worldwide year by year, which causes that the accurate prediction of traffic flow on urban road networks becomes more important. However, the dual depen...
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Online distributed optimization with stochastic gradients: high probability bound of regrets
In this paper, the problem of online distributed optimization subject to a convex set is studied via a network of agents. Each agent only has access to a noisy gradient of its own objective function, and can c...
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A general framework for improving cuckoo search algorithms with resource allocation and re-initialization
Cuckoo search (CS) has currently become one of the most favorable meta-heuristic algorithms (MHAs). In this article, a simple yet effective framework is proposed for CS algorithms to reinforce their performanc...
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Deep bilinear Koopman realization for dynamics modeling and predictive control
The data-driven approaches based on the Koopman operator theory have promoted the analysis and control of the nonlinear dynamics by providing an equivalent Koopman-based linear system associated with nonlinear...
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Dual stage black-box adversarial attack against vision transformer
Relying on wide receptive fields, Vision Transformers (ViTs) are more robust than Convolutional Neural Networks (CNNs). Consequently, some transfer-based attack methods that perform well on CNNs perform poorly...
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Fast Shrinking parents-children learning for Markov blanket-based feature selection
High-dimensional data leads to degraded performance of machine learning algorithms and weak generalization of models, so feature selection is of great importance. In a Bayesian network (BN), the Markov blanket...
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Novel multi-label feature selection via label enhancement and relative maximal discernibility pairs
Multi-label feature selection is an effective solution to the multi-label data dimensionality disaster problem. However, there are few studies on multi-label feature selection considering label enhancement met...
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An evolutionary feature selection method based on probability-based initialized particle swarm optimization
Feature selection is a common data preprocessing technique that aims to construct better models by selecting the most predictive features. Existing particle swarm optimization-based feature selection algorithm...
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Article
Open AccessTransferable preference learning in multi-objective decision analysis and its application to hydrocracking
Hydrocracking represents a complex and time-consuming chemical process that converts heavy oil fractions into various valuable products with low boiling points. It plays a pivotal role in enhancing the quality...
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Open AccessA novel bayesian network-based ensemble classifier chains for multi-label classification
In this paper, we address the challenges of random label ordering and limited interpretability associated with Ensemble Classifier Chains (ECC) by introducing a novel ECC method, ECC-MOO&BN, which integrates B...
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Open AccessA distributed adaptive policy gradient method based on momentum for multi-agent reinforcement learning
Policy Gradient (PG) method is one of the most popular algorithms in Reinforcement Learning (RL). However, distributed adaptive variants of PG are rarely studied in multi-agent. For this reason, this paper pro...
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An expert method for defining the adaptation conditions of irrigated crops with the ecosystem of Northwestern China
The uncertain impacts of climate changes on the crop water requirements disarrange the on-farm balance of water supply and demand, thus here needs to adapt a crop** pattern to sustain the Northwestern China ...
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Industrial product surface defect detection via the fast denoising diffusion implicit model
In the age of intelligent manufacturing, surface defect detection plays a pivotal role in the automated quality control of industrial products, constituting a fundamental aspect of smart factory evolution. Con...
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Joint features-guided linear transformer and CNN for efficient image super-resolution
Integrating convolutional neural networks (CNNs) and transformers has notably improved lightweight single image super-resolution (SISR) tasks. However, existing methods lack the capability to exploit multi-lev...
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Open AccessAttention-based RNN with question-aware loss and multi-level copying mechanism for natural answer generation
Natural answer generation is in a very clear practical significance and strong application background, which can be widely used in the field of knowledge services such as community question answering and intel...
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Analysis of wall thickness evolution and forming quality of sheet metal manufactured by wrinkles-free forming method
Sheet metal wrinkles-free forming method is a novel sheet metal forming technology which deforms a blank to the target part using low-melting point alloy (LMA) without the blank holder, increasing materials ut...
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Article
Computational intelligence and its dynamic development: statistical exploration, comprehensive evaluation and prospect expansion
Computational intelligence (CI) has become one of the most useful and successful tools for dealing with uncertainties and complex problems in many fields, such as neural networks, genetic algorithms, and swarm...
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Optimization of music education strategy guided by the temporal-difference reinforcement learning algorithm
To make up for the shortcomings of traditional music teaching strategies and improve the intelligence of music teaching, this study uses a reinforcement learning (RL) algorithm to conduct an intelligent explor...
