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When large language models meet personalization: perspectives of challenges and opportunities
The advent of large language models marks a revolutionary breakthrough in artificial intelligence. With the unprecedented scale of training and model...
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Search and Harvesting across NFDI Consortia – Gaps and Challenges
Search and harvesting use cases on harmonised metadata play an important role in several activities on National Research Data Infrastructures (NFDI)....
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GPU-based butterfly counting
When dealing with large bipartite graphs, butterfly counting is a crucial and time-consuming operation. Graphics processing units (GPUs) are widely...
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DSPformer: discovering semantic parts with token growth and clustering for zero-shot learning
Transformers have achieved success in many computer vision tasks, but their potential in Zero-Shot Learning (ZSL) has yet to be fully explored. In...
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An aviation accidents prediction method based on MTCNN and Bayesian optimization
The safety of the civil aviation system has been of increasing concern with several accidents in recent years. It is urgent to put forward a precise...
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Deep reinforcement learning-based scheduling in distributed systems: a critical review
Many fields of research use parallelized and distributed computing environments, including astronomy, earth science, and bioinformatics. Due to an...
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Federated learning for supervised cross-modal retrieval
In the last decade, the explosive surge in multi-modal data has propelled cross-modal retrieval into the forefront of information retrieval research....
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Optimization-based convolutional neural model for the classification of white blood cells
White blood cells (WBCs) are one of the most significant parts of the human immune system, and they play a crucial role in diagnosing the...
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Latent side-information dynamic augmentation for incremental recommendation
The incremental recommendation involves updating existing models by extracting information from interaction data at current time-step, with the aim...
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An overview of semantic-based process mining techniques: trends and future directions
Process mining algorithms essentially reflect the execution behavior of events in an event log for conformance checking, model discovery, or...
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Adversarial attacks and defenses for large language models (LLMs): methods, frameworks & challenges
Large language models (LLMs) have exhibited remarkable efficacy and proficiency in a wide array of NLP endeavors. Nevertheless, concerns are growing...
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Multi-view Heterogeneous Graph Neural Networks for Node Classification
Recently, with graph neural networks (GNNs) becoming a powerful technique for graph representation, many excellent GNN-based models have been...
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Lcp-mixer: a lightweight model based on concept-level perception for NLP
Transformer-based models excel in natural language processing tasks but demand extensive computational resources and memory. To address this...
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DPHM-Net:de-redundant multi-period hybrid modeling network for long-term series forecasting
Deep learning models have been widely applied in the field of long-term forecasting has achieved significant success, with the incorporation of...
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A hybrid deep learning neural network for early plant disease diagnosis using a real-world Wheat–Barley vision dataset: challenges and solutions
Approximately 35% of India’s annual crop yield is lost due to plant diseases. Due to a lack of lab equipment and infrastructure, early diagnosis of...
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Exploring highly concise and accurate text matching model with tiny weights
In this paper, we propose a simple and general lightweight approach named AL-RE2 for text matching models, and conduct experiments on three...
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Twin neural network improved k-nearest neighbor regression
Twin neural network regression is trained to predict the difference between the regression targets of two data points rather than the individual...
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Comparing free reference extraction pipelines
In this paper, we compare the performance of several popular pre-trained reference extraction and segmentation toolkits combined in different...
