A Video Analysis of Eye Movements during Typing: How Effective is Handwriting during Note-Taking Tasks?

Keyboard input for non-alphabetical languages, such as Chinese and Japanese, is problematic because it is labor intensive and imposes a high cognitive load. In our previous work, we measured the effectiveness of handwriting during a note-taking task in Japanese, and found that the input speed during note-taking was higher by hand than by keyboard. The results also showed that the quality of notes taken by hand was higher than that of notes taken by keyboard, and this might have been due to the higher cognitive load during typing. In addition, observation during the experiment revealed several problems subjects faced in the keyboard input task. To evaluate the significance of these observations, we had to obtain quantitative evidence through further study of participant behavior. Therefore, we repeated the experiment, this time with video analysis of the keyboard subtask. By analyzing the participants’ eye movements and their behavior throughout the keyboard subtask we obtained quantitative evidence to support our findings from the previous study. Here, we describe this experiment and our findings in detail

Character Recognition

Character recognition is one of the pattern recognition technologies that are most widely used in practical applications. This book presents recent advances that are relevant to character recognition, from technical topics such as image processing, feature extraction or classification, to new applications including human-computer interfaces. The goal of this book is to provide a reference source for academic research and for professionals working in the character recognition field

Proceedings from NordiCHI 2008 Workshop Sunday October 19, 2008

This paper raises themes that are seen as some of the challenges facing the emerging practice and research field of Human Work Interaction Design. The paper has its offset in the discussions and writings that have been dominant within the IFIP Working Group on Human Work Interaction Design (name HWID) through the last two and half years since the commencement of this Working Group. The paper thus provides an introduction to the theory and empirical evidence that lie behind the combination of empirical work studies and interaction design. It also recommends key topics for future research in Human Work Interaction Design

Advances in Character Recognition

This book presents advances in character recognition, and it consists of 12 chapters that cover wide range of topics on different aspects of character recognition. Hopefully, this book will serve as a reference source for academic research, for professionals working in the character recognition field and for all interested in the subject

The Practice of Basic Informatics 2020

Version 2020/04/02Kyoto University provides courses on 'The Practice of Basic Informatics' as part of its Liberal Arts and Sciences Program. The course is taught at many schools and departments, and course contents vary to meet the requirements of these schools and departments. This textbook is made open to the students of all schools that teach these courses. As stated in Chapter 1, this book is written with the aim of building ICT skills for study at university, that is, ICT skills for academic activities. Some topics may not be taught in class. However, the book is written for self-study by students. We include many exercises in this textbook so that instructors can select some of them for their classes, to accompany their teaching plans. The courses are given at the computer laboratories of the university, and the contents of this textbook assume that Windows 10 and Microsoft Office 2016 are available in these laboratories. In Chapter 13, we include an introduction to computer programming; we chose Python as the programming language because on the one hand it is easy for beginners to learn, and on the other, it is widely used in academic research. To check the progress of students' self-study, we have attached assessment criteria (a 'rubric') of this course as an Appendix. Current ICT is a product of the endeavors of many people. The "Great Idea" columns are included to show appreciation for such work. Dr. Yumi Kitamura and Dr. Hirohisa Hioki wrote Chapters 4 and 13, respectively. The remaining chapters were written by Dr. Hajime Kita. In revision for 2018 edition and after, Dr. Hiroyuki Sakai has participated in the author group, and Dr. Donghui Lin has also joined for English edition 2019. The authors hope that this textbook helps you to improve your academic ICT skill set. The content included in this book is selected based on the reference course plan discussed in the course development team for informatics at the Institute for Liberal Arts and Sciences. In writing this textbook, we obtained advice and suggestions from staffs of the Network Section, Information Infrastructure Division, Department of Planning and Information Management Department, Kyoto University on Chapters 2 and 3, from Mr. Sosuke Suzuki, NTT Communications Corporation also on Chapter 3, Rumi Haratake, Machiko Sakurai and Taku Sakamoto of the User Support Division, Kyoto University Library on Chapter 4. Dr. Masako Okamoto of Center for the Promotion of Excellence in Higher Education, Kyoto University helped us in revision of 2018 Japanese Edition. The authors would like to express their sincere gratitude to the people who supported them

Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias

Large language models (LLMs) have been recently leveraged as training data generators for various natural language processing (NLP) tasks. While previous research has explored different approaches to training models using generated data, they generally rely on simple class-conditional prompts, which may limit the diversity of the generated data and inherit systematic biases of LLM. Thus, we investigate training data generation with diversely attributed prompts (e.g., specifying attributes like length and style), which have the potential to yield diverse and attributed generated data. Our investigation focuses on datasets with high cardinality and diverse domains, wherein we demonstrate that attributed prompts outperform simple class-conditional prompts in terms of the resulting model's performance. Additionally, we present a comprehensive empirical study on data generation encompassing vital aspects like bias, diversity, and efficiency, and highlight three key observations: firstly, synthetic datasets generated by simple prompts exhibit significant biases, such as regional bias; secondly, attribute diversity plays a pivotal role in enhancing model performance; lastly, attributed prompts achieve the performance of simple class-conditional prompts while utilizing only 5\% of the querying cost of ChatGPT associated with the latter. We release the generated dataset and used prompts to facilitate future research. The data and code will be available on \url{https://github.com/yueyu1030/AttrPrompt}.Comment: Work in progress. A shorter version is accepted to the ICML DMLR worksho

Text Augmentation: Inserting markup into natural language text with PPM Models

This thesis describes a new optimisation and new heuristics for automatically marking up XML documents. These are implemented in CEM, using PPMmodels. CEM is significantly more general than previous systems, marking up large numbers of hierarchical tags, using n-gram models for large n and a variety of escape methods. Four corpora are discussed, including the bibliography corpus of 14682 bibliographies laid out in seven standard styles using the BIBTEX system and markedup in XML with every field from the original BIBTEX. Other corpora include the ROCLING Chinese text segmentation corpus, the Computists’ Communique corpus and the Reuters’ corpus. A detailed examination is presented of the methods of evaluating mark up algorithms, including computation complexity measures and correctness measures from the fields of information retrieval, string processing, machine learning and information theory. A new taxonomy of markup complexities is established and the properties of each taxon are examined in relation to the complexity of marked-up documents. The performance of the new heuristics and optimisation is examined using the four corpora