Recent advancements in AI and data science have led to the increased use of Jupyter notebooks. As such, various AI-Based automated tools have been also developed to automatically document notebooks. However, a key challenge is the absence of suitable datasets for training AI models. In this paper, we outline a valuable roadmap for developing a dataset of (markdown, code) pairs centered on functions in Jupyter notebooks. The roadmap encompasses four high-level steps: structural filtering, structural processing, conceptual filtering, and conceptual processing. Our proposed roadmap leads to providing a quality dataset for training AI models on Jupyter notebooks.

Computational notebooks, like Kaggle notebooks, offer an integrated platform for coding and documentation, yet the latter's quality often falls short as scientists may neglect this crucial aspect. This paper addresses the need for improved and efficient code documentation generation in computational notebooks. As recent literature emphasizes integrating code's inherent structure into documentation generation models, our research explores unutilized structural characteristics, incorporating metrics from code sequences to enable better code documentation suggestions. Evidenced by the improved BLEU scores, our proposed method significantly outperforms the conventional model in a preliminary 10-fold cross-validation experiment and further provides a flexible foundation for integrating source code metrics into diverse code documentation generation models.

In software development, code documentation is crucial for collaboration and maintenance, especially as projects become more complex. However, it is often neglected due to the tedious effort it requires. This paper explores automating documentation generation for computational notebooks, focusing on the impact of code metrics such as lines of code, API popularity, and complexity on this task. Using a dataset of 22K code-documentation pairs, we compare deep learning models with and without code metric augmentation. The results show that incorporating these metrics significantly improves the accuracy of documentation generation, underscoring the connection between code metrics and quality documentation.

The most important part of the common algorithms for multi‐exposure image fusion (MEF) is the selection of features and metrics that are appropriate for weight map extraction. This study presents a structure‐based multi‐exposure image fusion by employing the phase congruency (PC) of the input image. The main idea behind PC‐based analysis is that the locations of image key attributes are at points where frequency components are maximally in phase. PC detects the details of an image invariant to its contrast and also emphasises on the texture‐ or structure‐based features. In this work, alongside intensity‐based maps, the extracted PC‐based map is utilised for MEF in a pyramidal manner. Several experiments conducted on the benchmark dataset including a variety of natural multi‐exposed image sequences to evaluate the proposed algorithm. Quantitative evaluations in terms of MEF structural similarity index and visual quality assessments show that the proposed method achieves better performance and produces comparable fused images in comparison to other approaches.

A 1.8V, 10-bit, 40 MS/s pipeline ADC using MOS capacitors in 0.18/spl mu/m CMOS technology is presented. The converter uses compensated depletion mode MOS capacitors in all 1.5-bit stages. HSpice simulations confirm that the MOSFET-only ADC achieves 51 dB SNDR and 55.5 dB SFDR. The effect of capacitor nonlinearity is also discussed in a conventional 1.5-bit stage.