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People tend to embrace emerging technologies to make their lives easier in every possible aspect. This has its impact in the field of education as well in terms of pedagogies, interactive tools, learning management systems, and more specifically instructional materials. Amongst the instructional materials, e-books have recently become popular in the sphere of education. It gained popularity during the pandemic situation when the shops were shut to get printed books which reinforced students to adopt e-books. This study intends to find the preference of the students of Velammal College of Engineering & Technology in Madurai with respect to e-books (not the interactive ones) and printed books so that the results can facilitate in decision making as to what kind of sources the educationists should invest on. A survey was conducted using a structured fivepoint Likert scale questionnaire. It was shared with the students and their responses were recorded. Random sampling method was used for the choice of the participants. Cronbach alpha test was carried out to verify the reliability of the questionnaire. Having carried out the t-test to check whether the two groups vary from each other, the comparative analysis research methodology was used for data analysis. This study also deals with the pros and cons of both e-books and printed books based on the survey. The results of the study confirms that the students still seem to prefer printed books over e-books. The findings suggest that the educationists should prioritize student preferences and ensure the availability of light weight printed books everywhere. [ABSTRACT FROM AUTHOR]

Transfer learning approaches in natural language processing have been explored and evolved as a potential solution for solving many problems in recent days. The current research on aspect-based summarization shows unsatisfactory accuracy and low-quality generated summaries. Additionally, the potential advantages of combining language models with parallel processing have not been explored in the existing literature. This paper aims to address the problem of aspect-based extractive text summarization using a transfer learning approach and an optimization method based on map reduce. The proposed approach utilizes transfer learning with language models to extract significant aspects from the text. Subsequently, an optimization process using map reduce is employed. This optimization framework includes an in-node mapper and reducer algorithm to generate summaries for important aspects identified by the language model. This enhances the quality of the summary, leading to improved accuracy, particularly when applied to electrical power system documents. By leveraging the strengths of natural language models and parallel data processing techniques, this model presents an opportunity to achieve better text summary generation. The performance metric used is accuracy, measured with the ROUGE tool, incorporating precision, recall and f-measure. The proposed model demonstrates a 6% improvement in scores compared to state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

Presents results of a survey that examined the use of networks by librarians in New England. Data are reported on networks used, personnel access, exposure to networks, use of electronic mail, use of lists and bulletin boards, remote access of text files and software, problems, improvements, and future uses. The questionnaire is appended. (20 references) (MES)