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With a mass of electronic multi-topic documents available, there is an increasing need for evaluating emerging analysis tools to help users and digital libraries analyze these documents better. The purpose of this paper is to evaluate the effectiveness, efficiency and user satisfaction of THC-DAT, a within-document analysis tool, in reading a multi-topic document.

The authors reviewed related literature first, then performed a user-centered, comparative evaluation of two within-document analysis tools, THC-DAT and BOOKMARK. THC-DAT extracts a topic hierarchy tree using hierarchical latent Dirichlet allocation (hLDA) method and takes the context information into account. BOOKMARK provides similar functionality to the Table of Contents bookmarks in Adobe Reader. Three novel kinds of tasks were devised for participants to finish on two tools, with objective results to assess reading effectiveness and efficiency. And post-system questionnaires were employed to obtain participants’ subjective judgments about the tools.

The results confirm that THC-DAT is significantly more effective than BOOKMARK, while not inferior in efficiency. There is some evidence that suggests THC-DAT can slow down the process of approaching cognitive overload and improve users’ willingness to undertake difficult task. Based on qualitative data from questionnaires, the results indicate that users were more satisfied when using THC-DAT than BOOKMARK.

Adopting THC-DAT in digital libraries or electrical document reading systems contributes to promoting users’ reading performance, willingness to undertake difficult task and general satisfaction. Moreover, THC-DAT is of great value to addressing cognitive overload problem in the information retrieval field.

This paper evaluates a novel within-document analysis tool in analyzing a multi-topic document, and proved that this tool is superior to the benchmark in effectiveness and user satisfaction, and not inferior in efficiency.

The purpose of this paper is to propose a novel within-document analysis tool (DAT) topic hierarchy and context-based document analysis tool (THC-DAT) which enables users to interactively analyze any multi-topic document based on fine-grained and hierarchical topics automatically extracted from it. THC-DAT used hierarchical latent Dirichlet allocation method and took the context information into account so that it can reveal the relationships between latent topics and related texts in a document.

The methodology is a case study. The authors reviewed the related literature first, then utilized a general “build and test” research model. After explaining the model, interface and functions of THC-DAT, a case study was presented using a scholarly paper that was analyzed with the tool.

THC-DAT can organize and serve document topics and texts hierarchically and context based, which overcomes the drawbacks of traditional DATs. The navigation, browse, search and comparison functions of THC-DAT enable users to read, search and analyze multi-topic document efficiently and effectively.

It can improve the document organization and services in digital libraries or e-readers, by helping users to interactively read, search and analyze documents efficiently and effectively, exploringly learn about unfamiliar topics with little cognitive burden, or deepen their understanding of a document.

This paper designs a tool THC-DAT to analyze document in a THC way. It contributes to overcoming the coarse-analysis drawbacks of existing within-DATs.

Since researchers have utilized text signals to develop a mass of within-document visualization analysis tools for reading aid in a long document, there is an increasing need to study the relationship between readers’ behavior of using text signals for navigation and their reading performance in the tools. The purpose of this paper is to combine the text signals using behavior and reading performance in two kinds of analysis tools to verify their relationship and discover whether there is any efficient reading strategy when using text signals to navigate a long document.

The methodology is a case study. The authors reviewed related literature first. After explaining the design ideas, interface and functions of THC-DAT and BOOKMARK, which are two reading tools utilizing two main kinds of text signals, one utilizing topics and the other utilizing headings for reading aid, a case study was presented to collect click data on the text signals of participants and their reading effectiveness (score) and efficiency (time).

The results confirm that the text signals using behavior for navigation has a significant impact on reading efficiency and no impact on reading effectiveness in both BOOKMARK and THC-DAT. The discrete degree of clicks behavior on text signals has an impact on reading efficiency. The using behavior of different types of text signals has different impacts on reading efficiency.

Using text signals for navigation time evenly can help improve reading efficiency. And a basic strategy suggested to readers is focusing on reducing their time to find answers when using text signals for navigation in a long document. As to utilizing the two different kinds of text signals, readers can have different strategies. Accordingly, personalized recommendation based on interval of adjacent clicks will help to improve computer-aided reading tools.

This paper combines the text signals using behavior for navigation and reading performance in two kinds of visual analysis tools, studied the relationship between them and discovers some efficient reading strategies when using text signals for navigation to read a long document.

The purpose of this paper was to identify whether artificial intelligence (AI) products can possess human rights, how to define their rights and obligations and what ethical standards they should follow. In this study, the human rights ethical dilemma encountered in the application and development of AI technology has been focused on and analyzed in detail in the light of the existing research status of AI ethics.

In this study, first of all, the development and application of AI technology, as well as the concept and characteristics of human rights ethics, are introduced. Second, the human rights ethics of AI technology are introduced in detail, including the human rights endowment of AI machines, the fault liability of AI machines and the moral orientation of AI machines. Finally, the approaches to human rights ethics are proposed to ensure that AI technology serves human beings. Every link of its research, production and application should be strictly managed and supervised.

The results show that the research in this study can provide help for the related problems encountered in AI practice. Intelligent library integrates human rights protection organically so that readers or users can experience more intimate service in this system. It is a kind of library operation mode with more efficient and convenient characteristics, which is based on digital, networked and intelligent information science. It aims at using the greenest way and digital means to realize the reading and research of human rights protection literature in the literature analysis method.

Intelligent library is the future development mode of new libraries, which can realize broad interconnection and sharing. It is people-oriented and can make intelligent management and service and establish the importance of the principle of human rights protection and the specific idea of the principle. The development of science and technology brings not only convenience to people's social life but also questions to be thought. People should reduce its potential harm, so as to make AI technology continue to benefit humankind.

The purpose of this paper is to present a novel approach for mining scientific trends using topics from Call for Papers (CFP). The work contributes a valuable input for researchers, academics, funding institutes and research administration departments by sharing the trends to set directions of research path.

The authors procure an innovative CFP data set to analyse scientific evolution and prestige of conferences that set scientific trends using scientific publications indexed in DBLP. Using the Field of Research code 804 from Australian Research Council, the authors identify 146 conferences (from 2006 to 2015) into different thematic areas by matching the terms extracted from publication titles with the Association for Computing Machinery Computing Classification System. Furthermore, the authors enrich the vocabulary of terms from the WordNet dictionary and Growbag data set. To measure the significance of terms, the authors adopt the following weighting schemas: probabilistic, gram, relative, accumulative and hierarchal.

The results indicate the rise of “big data analytics” from CFP topics in the last few years. Whereas the topics related to “privacy and security” show an exponential increase, the topics related to “semantic web” show a downfall in recent years. While analysing publication output in DBLP that matches CFP indexed in ERA Core A* to C rank conference, the authors identified that A* and A tier conferences not merely set publication trends, since B or C tier conferences target similar CFP.

Overall, the analyses presented in this research are prolific for the scientific community and research administrators to study research trends and better data management of digital libraries pertaining to the scientific literature.