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Personalized document-level sentiment analysis (PDSA) plays an important role in many real-world applications. So far, various deep learning models for PDSA have been proposed. However, there has been no systematic survey in this area. To address ...

• To the best of our knowledge, this is the first survey of PDSA.
• We propose a PDSA taxonomy, and summarize the ideas of existing works for each group.
• We propose a model for PDSA based on user correlation to fill the existing ...

With the rapid development of information technology, artificial intelligence technology has been applied to the selection and recommendation of university textbooks to some extent, but it still faces the problem of low recommendation accuracy. To solve ...

Anchor link prediction (ALP) aims to align the accounts of the same natural person on different social networks, which is essential for cross-platform recommendations and comprehensive characterization of user characteristics. In recent years, the ...

Graph Transformers (GTs) have significantly advanced the field of graph representation learning by overcoming the limitations of message-passing graph neural networks (GNNs) and demonstrating promising performance and expressive power. However, the ...

This paper explores how to optimize university textbook recommendation systems using clustering analysis methods based on the ISODATA algorithm. It first introduces the basic principles of the ISODATA algorithm and its application in data clustering. ...

The system architecture of contemporary supercomputers is growing increasingly intricate with the ongoing evolution of system-wide network and storage technologies, making it challenging for application developers and system administrators to manage and ...

This paper aims to explore the application of deep meta-learning in urban traffic flow prediction, analyzing its advantages over traditional methods and the challenges it faces. Firstly, we introduce the background and importance of urban traffic flow ...

Multilayer perceptron (MLP) networks have become a popular alternative to convolutional neural networks and transformers because of fewer parameters. However, existing MLP-based models improve performance by increasing model depth, which adds ...

• The double-spatial-shift MLP and multi-spatial-shift external attention module are used.
• We integrate DSS-MLP and MSSEA into a convolution-based encoder–decoder architecture.
• Extensive experiments are conducted on three skin lesion ...

Current captioning approaches tend to generate correct but "generic" descriptions that lack real-world knowledge, e.g., named entities and contextual information. Considering that Vision-Language Pre-Training (VLP) models master massive such knowledge ...

Giving machines the ability to understand the intent of human actions is a basic goal of Natural Language Understanding. In the context of that, a task called the Multi-axis Event Processes Typing is proposed, which aims to comprehend the overall ...

Cross-social network anchor link prediction plays a pivotal role in downstream tasks, such as comprehensively portraying user characteristics, user friend recommendations, and online public opinion analysis, which aims to find accounts that belong ...

Humans are accustomed to autonomously associating prior knowledge with the text in a query when answering questions. However, for machines lacking cognition and common sense, a query is merely a combination of some words. Although we can enrich the ...

Graph Transformer is gaining increasing attention in the field of machine learning and has demonstrated state-of-the-art performance on benchmarks for graph representation learning. However, as current implementations of Graph Transformer primarily focus ...

In recent years, graph Transformers (GTs) have been demonstrated as a robust architecture for a wide range of graph learning tasks. However, the quadratic complexity of GTs limits their scalability on large-scale data, in comparison to Graph Neural ...

Graph Transformer has recently received wide attention in the research community with its outstanding performance, yet its structural expressive power has not been well analyzed. Inspired by the connections between Weisfeiler-Lehman (WL) graph ...

Training Graph Neural Networks (GNNs) incrementally is a particularly urgent problem, because real-world graph data usually arrives in a streaming fashion, and inefficiently updating of the models results in out-of-date embeddings, thus degrade its ...

In this paper, we consider a new family of multivariate copulas described by a sequence of functions, named as AMO copula. The set of AMO copulas corresponds to a class of multivariate shock models with the Archimedean type of dependence. ...

Urgent need for driving range of lightweight electric vehicles has given birth to module-free lithium-ion batteries with high efficiency and low costs. Conventional module-based design methodology is not suitable for module-free battery thermal ...

Multimodal models have been proven to outperform text-based models on learning semantic word representations. According to psycholinguistic theory, there is a graphical relationship among the modalities of language, and in recent years,...

When HDFS stores a large number of small files, the NameNode will have insufficient memory space and high read latency, making NameNode a system bottleneck and seriously affecting the file processing capabilities and user experience of HDFS. This article ...