There are many electronic documents salient to read for each given topic; however, finding a suitable reading order for pedagogical purposes has been underserved historically by the text analytics community. In this research, we propose an automatic reading order generation technique that can suggest a suitable and optimal reading order for curriculum generation quantitatively. It is necessary to read the relevant documents in some logical order to understand the topics clearly. There are many learning pedagogies advanced, so for our purposes we use the author-supplied reading orders of salient content sets for ground truth. Our method suggests the best reading order automatically by checking the relevant topics, document distances, and semantic structure of the given documents. The system will generate a suitable and efficient reading sequence by analyzing the information, similarity, overlap of contents, and distances using word frequency, and topic sets. We measure the similarity, relevance, distance, and overlap of different documents using cosine similarity, entropy relevance, Euclidean distances, and Jaccard similarities respectively. We propose an algorithm that will generate the best possible reading order for a set of given documents. We evaluated the performance of our system against the ground truth reading order using different kinds of textbooks and generalized the finding for any given set of documents.
In this paper, a novel framework for semi-supervised learning based on graphs is introduced. We present an innovative approach for concurrently estimating label inference and performing a linear transformation. This specific linear transformation is directed towards achieving a discriminant subspace, which effectively reduces the dimensionality of the data. To enhance the semisupervised learning process, our framework places a strong emphasis on leveraging the inherent data structure and incorporating the information provided by soft labels from the available unlabeled samples. The method we propose ultimately results in an improved discriminative linear transformation. The effectiveness of our approach is verified through a series of experiments conducted on real image datasets. These experiments not only confirm the efficacy of our proposed method but also demonstrate its superior performance when compared to semi-supervised methods that simultaneously incorporate integration and label inference.
In the dynamic realm of image processing, coordinate-based neural networks have made significant strides, especially in tasks such as 3D reconstruction, pose estimation, and traditional image/video processing. However, these Multi-Layer Perceptron (MLP) models often grapple with computational and memory challenges. Addressing these, this study introduces an innovative approach using Tensor-Product B-Spline (TPB), offering a promising solution to lessen computational demands without sacrificing accuracy. The central objective was to harness TPB’s potential for image denoising and super-resolution, aiming to sidestep computational burdens of neural fields. This was achieved by replacing iterative processes with deterministic TPB solutions, ensuring enhanced performance and reduced load. The developed framework adeptly manages both super-resolution and denoising, utilizing implicit TPB functions layered to optimize image reconstruction. Evaluation on the Set14 and Kodak datasets showed the TPB-based approach to be comparable to established methods, producing high-quality results in both quantitative metrics and visual evaluations. This pioneering methodology, emphasizing its novelty, offers a refreshed perspective in image processing, setting a promising trajectory for future advancements in the domain.