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In practical multi-view applications, some data in each view are missing. Although recent incomplete multi-view clustering (IMC) approaches have achieved encouraging performance, two challenges remain. They utilize the tensor nuclear norm to ...

• Improving two defects of the tensorized IMC method based on missing-view recovery.
• Enhancing multi-view high-order consistency by refining view-specific similarity.
• Recovering complete multi-view data with flexible cluster ...

Influence maximization (IM) aims to strategically select influential users to maximize information propagation in social networks. Most of the existing studies focus on IM in single-layer networks. However, we have observed that individuals often ...

• A new diffusion model is designed to simulate the information diffusion in multilayer network.
• A new centrality metric is proposed to select candidate nodes.
• The discrete particle swarm algorithm is first introduced to select seed ...

Lane change risk identification and prediction research is important for traffic safety, development of advanced driver assistance systems, traffic flow optimization. Among them, vehicle continuous‐lane‐change (CLC) risk identification and ...

Lifelong Vehicle Re-Identification (LVReID) aims to match a target vehicle across multiple cameras, considering non-stationary and continuous data streams, which fits the needs of the practical application better than traditional vehicle re-...

Transformer-based language models have achieved significant success in various domains. However, the data-intensive nature of the transformer architecture requires much labeled data, which is challenging in low-resource scenarios (i.e., few-shot ...

• A simple and modular framework, ”Mask-BERT”, for few-shot text classification.
• Mask-BERT itilizes selective masking of text inputs to filter out irrelevant information.
• Mask-BERT guides the model towards relevant information to ...

The scarcity of medical images significantly hampers the advancement of artificial intelligence techniques in the medical field. Medical images face the issue of inferior training accuracy and efficiency in classification tasks due to insufficient ...

Recent research has uncovered that CSS style sheets can be leveraged for user fingerprinting by probing various characteristics, thereby circumventing existing detection mechanisms and mitigation strategies-posing serious privacy concerns. In ...

We present Follow-Your-Emoji, a diffusion-based framework for portrait animation, which animates a reference portrait with target landmark sequences. The main challenge of portrait animation is to preserve the identity of the reference portrait and ...

Predicting the next Point-of-Interest (POI) is crucial for location-based services, offering personalized recommendations and enhancing user experiences. However, existing methods often fail to adequately capture the temporal dynamics of user ...

Text guided image diffusion model has demonstrated remarkable ability in consistent image generation. In this paper, we introduce a training free image composition framework that realizes the non-rigid objects composition based on a pair of source ...

Contrastive Learning (CL) has recently achieved remarkable performance in recommendation systems, especially in Graph Collaborative Filtering (GCF), due to its effective handling of data sparsity issues by comparing positive and negative sample ...

Artificial Intelligence (AI) is a key driver of the Industry 4.0 revolution. In industrial automation systems, data points of assets are represented by globally unique identifiers known as “Tags,” which often contain abbreviated asset and ...

The recognition of hand-drawn chemical molecular formulas is crucial for applications such as electronic note-taking and automated grading. Despite the challenges posed by stylistic variations in hand-drawn chemical structure diagrams, we ...$\mathrm{}\mathrm{}$

This paper is concerned with the optimality and duality for a class of nonconvex fuzzy optimization problems under granular differentiability. We first derive optimality conditions for fuzzy optimization problems with fuzzy inequality constraints ...

The elastic task model is a flexible paradigm for representing recurring real-time workloads, in which the frequencies of periodic/sporadic tasks are allowed to be adjusted within their specified ranges. This paper introduces a novel approach to identify ...

Uncertainty-aware multi-view deep classification methods have markedly improved the reliability of results amidst the challenges posed by noisy multi-view data, primarily by quantifying the uncertainty of predictions. Despite their efficacy, these ...

With the rise of "Metaverse" and "Web 3.0", Non-Fungible Token (NFT) has emerged as a kind of pivotal digital asset, garnering significant attention. By the end of March 2024, more than 1.7 billion NFTs have been minted across various blockchain ...

Deep Learning (DL) has emerged as a promising means for vulnerability detection due to its ability to automatically derive features from vulnerable code. Unfortunately, current solutions struggle to focus on vulnerability-related parts of vulnerable ...

The performance of repository-level code completion depends upon the effective leverage of both general and repository-specific knowledge. Despite the impressive capability of code LLMs in general code completion tasks, they often exhibit less ...

Due to the easy access, implicit feedback is often used for recommender systems. Compared with point-wise learning and pair-wise learning methods, list-wise rank learning methods have superior performance for top-\(N\) recommendation. Recent solutions, ...