2,299 Result(s)
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Article
Robust consistency learning for facial expression recognition under label noise
Label noise is inevitable in facial expression recognition (FER) datasets, especially for datasets that collected by web crawling, crowd sourcing in in-the-wild scenarios, which makes FER task more challenging...
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Article
Open AccessKnowledge graph embedding closed under composition
Knowledge Graph Embedding (KGE) has attracted increasing attention. Relation patterns, such as symmetry and inversion, have received considerable focus. Among them, composition patterns are particularly import...
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Retraction Note: Application of empirical mode decomposition (EMD) for automated identification of congestive heart failure using heart rate signals
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An artificial neural network-source apportionment-based prediction model for carbon monoxide from total number of ships calling by ports in Malaysia
Air pollution has been a significant issue in recent years due to rising industrialization and maritime activity around the globe, making air pollution forecasting a crucial concept in environmental study. Thi...
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Open AccessDHR+S: distributed hybrid rendering with realistic real-time shadows for interactive thin client metaverse and game applications
Distributed hybrid rendering (DHR) is a real-time rendering approach that incorporates cloud-based ray tracing with locally rasterized graphics for interactive thin client metaverse and game applications. With...
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Optimal design and operation of battery energy storage systems in renewable power plants to reach maximum total electric sale revenues
This paper applies jellyfish search optimization algorithm (JSOA) to maximize electric sale revenue for renewable power plants (RNPPs) with the installation of battery energy storage systems (BESS). Wind turbi...
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Artificial Immune System of Secure Face Recognition Against Adversarial Attacks
Deep learning-based face recognition models are vulnerable to adversarial attacks. In contrast to general noises, the presence of imperceptible adversarial noises can lead to catastrophic errors in deep face r...
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Open AccessSeries2vec: similarity-based self-supervised representation learning for time series classification
We argue that time series analysis is fundamentally different in nature to either vision or natural language processing with respect to the forms of meaningful self-supervised learning tasks that can be define...
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High similarity controllable face anonymization based on dynamic identity perception
In the meta-universe scenario, with the development of personalized social networks, interactive behaviors such as uploading and sharing personal and family photographs are becoming increasingly widespread. Co...
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Open AccessSentiment analysis of Canadian maritime case law: a sentiment case law and deep learning approach
Historical information in the Canadian Maritime Judiciary increases with time because of the need to archive data to be utilized in case references and for later application when determining verdicts for simil...
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Open AccessAutomated asthma detection in a 1326-subject cohort using a one-dimensional attractive-and-repulsive center-symmetric local binary pattern technique with cough sounds
Asthma is a common disease. The clinical diagnosis is usually confirmed on a pulmonary function test, which is not always readily accessible. We aimed to develop a computationally lightweight handcrafted machi...
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Discriminative latent semantics-preserving similarity embedding hashing for cross-modal retrieval
Recently, there has been a significant increase in interest in cross-modal hashing technology. For hash code learning, most previous supervision methods use label information to create a similarity matrix in a...
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Sequential recommendation based on multipair contrastive learning with informative augmentation
To solve the recommendation accuracy degradation problem encountered in sequential recommendation cases caused by data sparsity—such as short historical user behaviour sequences and limited information—this pa...
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Open AccessDrivers behind the public perception of artificial intelligence: insights from major Australian cities
Artificial intelligence (AI) is not only disrupting industries and businesses, particularly the ones have fallen behind the adoption, but also significantly impacting public life as well. This calls for govern...
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Open AccessAn efficient full-size convolutional computing method based on memristor crossbar
Modern artificial intelligence systems based on neural networks need to perform a large number of repeated parallel operations quickly. Without hardware acceleration, they cannot achieve effectiveness and avai...
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Generative adversarial meta-learning knowledge graph completion for large-scale complex knowledge graphs
In the study of large-scale complex knowledge graphs, due to the incompleteness of knowledge and the existence of low-frequency knowledge samples, existing knowledge graph complementation methods are often lim...
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Open AccessThe methods for improving large-scale multi-view clustering efficiency: a survey
The diversity and large scale of multi-view data have brought more significant challenges to conventional clustering technology. Recently, multi-view clustering has received widespread attention because it can...
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Group sparse structural smoothing recovery: model, statistical properties and algorithm
In this paper, we propose an innovative group sparse structural smoothing recovery model and explore its statistical properties. Specifically, the nonconvex group norm and a new inner-group total variation reg...
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Real-time invasive sea lamprey detection using machine learning classifier models on embedded systems
Invasive sea lamprey (Petromyzon marinus) has historically inflicted considerable economic and ecological damage in the Great Lakes and continues to be a major threat. Accurately monitoring sea lampreys are criti...
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MSGNN: Multi-scale Spatio-temporal Graph Neural Network for epidemic forecasting
Infectious disease forecasting has been a key focus and proved to be crucial in controlling epidemic. A recent trend is to develop forecasting models based on graph neural networks (GNNs). However, existing GN...
