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In order to efficiently construct a multimodal subject knowledge graph in the field of education, a textbook text entity relationship extraction algorithm based on large model knowledge distillation and multi-model collaborative reasoning is proposed. During the model training phase, this paper uses a closed source model with 100 billion parameters to annotate text data and achieve implicit knowledge distillation. Then, this paper fine-tunes the domain data instructions for the open-source billion scale parameter model to enhance the instruction compliance ability of the entity relationship extraction task of the open-source model. In the model inference stage, the closed source model serves as the guiding model, and the open-source billion scale parameter model serves as the execution model. Experimental results show that knowledge distillation, multi-model collaboration, and domain data instruction fine-tuning are effective, significantly improving the effectiveness of textbook text entity relationship extraction tasks based on instruction prompts. A multimodal named entity recognition algorithm for textbook diagrams with explicit and implicit knowledge enhancement has been proposed. Firstly, this paper uses techniques such as image OCR (optical character recognition) and visual language modeling to extract textual information and global content description information from textbook diagrams. Then, by using explicit knowledge base retrieval and implicit LLM hint enhancement methods, auxiliary knowledge that may be associated with image title pairs is obtained. The knowledge obtained from explicit knowledge base and implicit LLM is further fused to form the final auxiliary knowledge. Finally, the auxiliary knowledge of the schematic diagram is combined with the schematic diagram title to achieve multimodal named entity recognition of the textbook schematic diagram title. Experimental results show that the algorithm is advanced and the interpretability of the algorithm is enhanced.

In order to address the challenge of the small-scale, small-target, and complex scenes often encountered in offshore remote sensing image datasets, this paper employs an interpolation method to achieve super-resolution-assisted target detection. This approach aligns with the logic of popular GANs and generative diffusion networks in terms of super-resolution but is more lightweight. Additionally, the image count is expanded fivefold by supplementing the dataset with DOTA and data augmentation techniques. Framework-wise, based on the Faster R-CNN model, the combination of a residual backbone network and pyramid balancing structure enables our model to adapt to the characteristics of small-target scenarios. Moreover, the attention mechanism, random anchor re-selection strategy, and the strategy of replacing quantization operations with bilinear interpolation further enhance the model's detection capability at a low cost. Ablation experiments and comparative experiments show that, with a simple backbone, the algorithm in this paper achieves a mAP of 71.2% on the dataset, an improvement in accuracy of about 10% compared to the Faster R-CNN algorithm. [ABSTRACT FROM AUTHOR]

In order to solve the problems of the small capacity of structured data and uneven distribution among classes in machine learning tasks, a supervised generation method for structured data called WAGAN and a cyclic sampling method named SACS (Semi-supervised and Active-learning Cyclic Sampling), based on semi-supervised active learning, are proposed. The loss function and neural network structure are optimized, and the quantity and quality of the small sample set are enhanced. To enhance the reliability of generating pseudo-labels, a Semi-supervised Active learning Framework (SAF) is designed. This framework redistributes class labels to samples, which not only enhances the reliability of generated samples but also reduces the influence of noise and uncertainty on the generation of false labels. To mine the diversity information of generated samples, an uncertain sampling strategy based on spatial overlap is designed. This strategy incorporates the idea of spatial overlap and uses global and local sampling methods to calculate the information content of generated samples. Experimental results show that the proposed method performs better than other data enhancement methods on three different datasets. Compared to the original data, the average F 1 m a c r o value of the classification model is improved by 11.5%, 16.1%, and 19.6% relative to compared methods. [ABSTRACT FROM AUTHOR]

In the big data era, distributed computation is becoming a preferred solution for iterative graph analysis. However, graphs are rapidly growing in size and more importantly, there exist a lot of messages across iterations. For better scalability, many distributed systems keep graph data and message data on disk. Now these systems solely employ either pushing or pulling mode to manage data, but neither can always work well during the entire computation. This is mainly because I/O access patterns are dynamic and complex. This article proposes a hybrid solution. It achieves the optimal performance in different scenarios by dynamically and adaptively switching modes between pushing and pulling. Specifically, we first devise a new block-centric pulling technique. It pulls messages much more I/O-efficiently than the existing vertex-centric pulling mode. We then combine pushing and pulling. For general-purpose, we categorize graph algorithms and accordingly present two seamless switching frameworks. We also design performance prediction components specialized to the two frameworks, to decide how and when we can switch modes. Some optimization strategies are also given to further enhance performance, such as priority scheduling and lightweight fault-tolerance. Extensive experiments against state-of-the-art solutions confirm the effectiveness of our proposals. [ABSTRACT FROM AUTHOR]