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Node classification, seeking to predict the categories of unlabeled nodes, is a crucial task in graph learning. One of the most popular methods for node classification is currently Graph Neural Networks (GNNs). However, conventional GNNs assign ...

Previous studies on Multimodal Affective Computing (MAC) predominantly focus on leveraging language, acoustic, and facial information to identify human’s affective states, which largely ignore the dynamic and temporal action information, despite ...

Graph representation learning is a crucial area in machine learning, with widespread applications in social networks, recommendation systems, and traffic flow prediction. Recently, Graph Transformers have emerged as powerful tools for this ...

Multimodal Stance Detection aims to classify public opinions on specific targets in social media, incorporating both text and image data. However, prior studies have overemphasized the significance of images, neglecting the presence of irrelevant ...

Large language models (LLMs) have shown remarkable capabilities in understanding and generating language across a wide range of domains. However, their performance in advanced arithmetic calculation remains a significant challenge, especially for ...

Vision Transformer (ViT) has achieved unprecedented success in vision tasks with the assistance of abundant data. However, the lack of inductive bias in lightweight ViT makes learning locality challenging on small datasets, leading to poor ...

• The Lightweight Vision Transformer with knowledge distillation can excel on small datasets.
• Knowledge distillation combined with Curriculum Learning can enhance distillation efficiency.
• Feature-based knowledge distillation can ...

Graph self-supervised learning is an effective technique for learning common knowledge from unlabeled graph data through pretext tasks. To capture the interrelationships between nodes and their essential roles globally, existing methods use ...

Collaborative robotic arms in smart factories should ensure the safety and interactivity during their operation such as reaching and grasping objects. Especially, the advanced motion planner including the path planning and the motion control ...

Knowledge graph embedding (KGE) aims to embed entities and relations in knowledge graphs (KGs) into a continuous, low-dimensional vector space. It has been shown as an effective tool for integrating knowledge graphs to improve various intelligent ...

• This study highlights the importance of learning order for KGE.
• A similarity-based knowledge learning difficulty evaluation method is proposed to get a reasonable learning order.
• Experimental results show the proposed model ...

News recommendation is an intelligent technology that aims to provide users with matching news content based on their preferences and interests. Nevertheless, current methodologies exhibit significant limitations. Traditional models often rely on ...

Speech is among the main forms of communication between humans and robots in industrial settings, being the most natural way for a human worker to issue commands. However, the presence of pervasive and loud environmental noise poses significant ...

• Speech-command recognition optimized for industrial noise.
• End-to-end Conformer architecture for efficiency.
• Curriculum learning enhances model robustness.
• Runnable in real-time on embedded devices with high accuracy.

Open-vocabulary object detection aims to train a detector capable of recognizing various novel classes. Most existing studies exploit image-level weak supervision to generate pseudo object boxes for novel class training. However, the generated ...

• Open-vocabulary object detection without using box-annotated images of novel classes.
• Better exploitation of image-level weak supervision for novel class training.
• Proposed debiased curriculum self-training for accurate pseudo-...

Real-time power network dispatching and control (PDC) presents unique challenges that traditional methods cannot effectively address due to the consideration of temporal dynamic factors. Reinforcement learning (RL) has been introduced and proven ...

Universal domain adaptation (UniDA) aims to transfer knowledge between domains without prior knowledge of the label spaces. Category shift and domain shift are two primary challenges in UniDA, which require the method not only to distinguish ...

• The curriculum learning is introduced into universal domain adaptation.
• A curriculum strategy is proposed to solve negative transfer.
• A score mechanism is proposed to detect private samples.
• Graph neural networks are used to ...

Unmanned Aerial Vehicles (UAVs) are increasingly being used for data harvesting from Wireless Sensor Nodes (SNs). This study aims to minimize the Age of Information (AoI) during data collection, while also considering the energy sustainability of ...

• MARL with EPC minimizes AoI effectively.
• EPC algorithm enhances multi-agent learning.
• Energy-efficient trajectories vital for UAV data harvesting.
• AoI minimization constant crucial for real-time tasks.

Given a natural language query, code search aims to find matching code snippets from a codebase. Recent works are mainly designed for mainstream programming languages with large amounts of training data. However, code search is also needed for ...

Portfolio management (PM) is a popular financial process that concerns the occasional reallocation of a particular quantity of capital into a portfolio of assets, with the main aim of maximizing profitability conditioned to a certain level of ...

Sequential recommender systems (SRSs) aim to suggest next item for a user based on her historical interaction sequences. Recently, many research efforts have been devoted to attenuate the influence of noisy items in sequences by either assigning ...

Accurate depth estimation from monocular images is critical for various applications such as robotics, augmented reality, and autonomous navigation. However, achieving high accuracy while maintaining computational efficiency is a major challenge, ...

Lossy image coding techniques usually result in various undesirable compression artifacts. Recently, deep convolutional neural networks have seen encouraging advances in compression artifact reduction. However, most of them focus on the ...