[Purpose/Significance] Computational reproducibility is the cornerstone of reliable and credible research. To investigate and analyze data policies, data availability and reproducibility methods of journals and conferences in computing field can provide references for promoting data sharing and solving computational reproducibility issues. [Method/Process] With web survey research and content analysis method, it analyzed journal data policies and conference data policies. It used web crawler to obtain data availability statement of journal article and analyze the current situation of data availability. Then, it summarized computational reproducibility methods. [Result/Conclusion] Most journals and more than half of the conferences in computing field have data policies, but the intensity of data sharing attitudes still needs to be improved. Journals or conferences with higher level are more likely to have data policies. Compared with journals focusing on data sharing issues, conferences pay more attention to the problem of computational reproducibility. The data availability statement promotes data sharing, but there is still a gap between data sharing practices and data sharing policy requirements. Computational reproducibility methods include data sharing, expert review, setting rewards, paper submission checklist, calling for reproducibility papers and so on.

[Purpose/Significance] Through an analysis of typical cases such as the American Authors Guild v. Hathi Trust case and Cambridge University Press v. Albert case, this paper explores the implications of American jurisprudence in determining the fair use in controlled digital lending in libraries. [Method/Process] Utilizing a case study approach, this research delves into the primary considerations of American courts in adjudicating cases involving CDL in libraries. These considerations include the purpose and nature of use, the nature of the work, the quantity and substantiality of the portion used, as well as market effects. Furthermore, by comparing how the courts interpret and apply the fair use principle in different cases, we aim to uncover patterns and trends. [Result/Conclusion] The study finds that American courts tend to consider multiple factors when determining whether CDL in libraries constitutes fair use. While upholding copyright protection, the courts also strive to satisfy the public’s legitimate need for information access. These precedents not only provide significant guidance for libraries in the United States to conduct CDL, but also offer valuable references for libraries in China when dealing with similar issues.

[Purpose/Significance] General information literacy competitions have gained widespread attention due to their competitiveness and interactivity. This study explores the current situation and development models of information literacy competitions, and provides reference and guidance for the development of such competitions. [Method/Process] It conducted a literature review and online research to collect the data and analyzed information literacy competitions from 2020 to 2022 with a multi-dimensional statistical analysis approach. It focused on organisational structure, competition models and other aspects with the tag analysis method. [Result/Conclusion] This study reveals the characteristics of domestic information literacy competitions in terms of overall quantity, competition scale, organizational structure, target audience, competition themes, disciplinary subjects, competition schedules and formats, and competition outcomes. It proposes a comprehensive competition model of “teaching-training-competition-evaluation” under the broad background of information literacy.

[Purpose/Significance] Exploring the patent information services is crucial for applied university libraries to enhance their academic and professional support for the university scientific and technological innovation and local economic development. [Method/Process] Taking one university as an example, it investigated the needs of different types of scientific research users through questionnaires and constructed a user needs framework. It compared the characteristics of patent output, patent distribution and patent inventors in applied universities with those in research universities, and clarified the key points and difficulties of services. Finally, it analyzed its advantages and disadvantages, and put forward the corresponding service strategy and path design. [Result/Conclusion] It proposes that a “small but sophisticated, specialized but excellent” service strategy is more suitable for patent information services in applied university libraries. It can adopt different service paths for distinct user types respectively, including leveraging cooperation, embedded integration, tracking and promoting patents, and cultivating user literacy.

[Objective/Significance] Through fine-grained emotion analysis of domestic and foreign tourists’ comments on museum services, this paper explores tourists’ needs and preferences, and compares the differences in domestic and foreign tourists’ image perception and satisfaction towards domestic history museums so as to provide references for museum managers to formulate more targeted service strategies. [Method/Process] Firstly, it collected online reviews of museums from both domestic and foreign tourists, and extracted attribute words of museum services and attribute-level statements. Then, by fine-tuning multiple large language models and comparing the effectiveness in extracting museum users’ fine-grained reviews, it identified GPT-3 and Llama2 as the optimal classification effect for Chinese and foreign reviews, respectively. Then it employed the optimal large language model to conduct fine-grained sentiment analysis of attribute-level statements. Finally, based on the results, it analyzed the satisfaction and differences between the two groups. [Results/Conclusion] The categories of museum services reviewed by domestic tourists include cultural creation, service facilities, guided explanation, museum staff, ticket security, online service. Foreign tourists, on the other hand, focus on guided explanation, in-museum service, ticket security, online service, and shopping. The analysis reveals that both domestic and foreign tourists show the highest levels of attention and satisfaction with guided explanation. Domestic tourists have the lowest satisfaction on the staff service, while foreign tourists express the least satisfaction with the ticket security. It summarizes the key factors influencing tourist satisfaction in each service category.

[Purpose/Significance] Technological innovation can be regarded as a complex systematic project involving the synergistic effect of multiple technological innovation elements. Extracting technological innovation elements and their associations from technical texts, analyzing their temporal dynamic changes and cross-domain interactions can reveal the process and law of technological innovation at the micro level, and then predict innovation opportunities. One of the key issues is how to identify technological innovation elements and their associations from technical texts. Specifically, this includes what technological innovation elements and their associations are to be identified and what methods are used for identification. [Method/Process] In order to solve the problem of object identification, the authors previously constructed a conceptual model of elemental associations oriented to the description of the technological innovation process. In order to solve the problem of identification method, this paper took the field of synthetic biology as an example, oriented to the text features of the elements and their associations in the conceptual model, and proposed a method for identifying the technological innovation elements and their associations. Based on patents and supplemented by papers, the method synthesized by textual content mining and citation analysis. which It revealed the interactions between technological innovation elements originated from the same or different fields in the technological innovation process, as well as the role of different technological innovation elements in different technological innovation events. [Result/Conclusion] Applying the identification framework to the field of synthetic biology, it identifies 7 633 technical object elements, 3 420 technical method elements, 3 919 technical application elements, 5 393 technical theory elements and their domain and time attributes, as well as 25 647 associative relationships of six different elements in 6 435 patents and their associated papers. This method can lay the foundation for revealing the process of technological innovation based on elemental correlation, analyzing the process and law of technological innovation in frontier cross-cutting fields, and predicting the opportunities of convergent innovation.

[Purpose/Significance] Given the significant role of the move structure in academic papers for enabling readers to deeply understand the content and rapidly locate key information, this study aims to investigate methods for full-text move recognition, to quickly capture the core content of academic papers, thereby advancing intelligent semantic retrieval. [Method/Process] The article reviewed current studies on move recognition methods and, on this basis, proposed a fine-grained move recognition model, the SciBERT-HAMI, which integrated ChatGPT data augmentation and a pre-trained language model. This model employed original texts and corpus augmentation via the ChatGPT large model, to enhance the variety and volume of the training data. A hierarchical neural network model was adopted to learn the paper’s semantic feature representations at the “word-sentence-section” levels, to capture semantic information at varied levels. The SciBERT word embedding representations were inputted, and the model was trained using a hierarchical neural network with the FocalLoss loss function for fine-grained move recognition. [Result/Conclusion] Integrating ChatGPT data augmentation strategies, the SciBERT-HAMI-DA model achieve F1 scores of 73.1% and 74.1% on the CoreSC and AZ datasets, respectively. Comparative experiments demonstrate that the proposed model shows effective performance improvement in the task of fine-grained move recognition in full-text academic papers, and its effectiveness is verified through ablation experiments. By integrating pre-trained language models and ChatGPT data augmentation, the prediction effect of the full-text move recognition model is effectively improved, which helps to promote the automation and intelligence of academic research.

[Purpose/Significance] As the core driving force of industrial revolution, disruptive technologies can rapidly overturn the development trajectory of existing technologies. Identifying the application of disruptive technologies to the field of hydrogen energy is of great significance for improving the country’s hydrogen energy technology strength and promoting the development of clean energy. [Method/Process] Based on multi-source heterogeneous data, the paper constructed an early identification model of disruptive technologies from four dimensions: novelty, foresight, feasibility, and high attention. The study calculated the indicator weights and comprehensive evaluation scores of technologies by Entropy-TOPSIS method and adopted an empirical study in the field of hydrogen energy. [Result/Conclusion] The study indicates that solar decomposition water hydrogen production technology, hydrogen adsorption storage technology, and proton exchange membrane fuel cells are the most disruptive technologies in the field of hydrogen energy.

[Purpose/Significance] This article selects highly cited researchers in chemistry at home and abroad as samples, analyzes their characteristics, and provides reference for understanding the growth pattern of scientists. [Method/Process] Taking scientists selected as “Global Highly Cited Researchers” by Clarivate in chemistry China and the United States from 2019 to 2021 as the research object, the article constructed a database of the highly cited researchers from multi-source information mining, and conducted a comparative analysis on them from age characteristics, education experience, and mobility experience. [Result/Conclusion] Compared with the United States, the average age of Chinese highly cited researchers in chemistry is 8.74 years younger, but the continuity of their influence is 1.53 years lower than that of the U.S. scientists. The higher education of these researchers lays the foundation for their research, 98.4% of the have a doctoral degree, and most of them have graduated from well-known colleges and universities at home and abroad, 46.07% of them have never changed their work institutions, with a certain “fixed” characteristics.

[Purpose/Significance] With the graph model framework, the representation of summary knowledge is an important technical node in the automatic text summarization process. To address the issue of insufficient depth of semantic disclosure of summary content, this paper proposes a model for automatic summarization of news articles, providing a reference for practical research in related fields using summarized web reportable news text data. [Method/Process] With ETM (Embedded Topic Model), a topic model analysis tool integrating word vectors, this paper introduced topic importance and semantic relevance features into the topic construction link of the target summary sentence in the graph model framework. And it redesigned the statistical features between reportable news sentences to mine and filter the in-depth topic semantic information of the texts. Based on this, it formed the automatic summary extraction model for reportable news under the method proposed in this paper. Subsequently, according to the main functional requirement, it proposed a quantitative index system of the summary topic measurement function, and established the corresponding relationship between the measurement standard and the quantitative method to optimize and adjust the proposed model of reportable news. [Result/Conclusion] Using the graph model framework, the automatic summarization method for reportage news specifically selects the summarization process of agricultural science and technology dynamic reportage news for empirical research, establishes a measurement standard for automatic summarization of reportage news, and further obtains an optimized reportage news summarization model scheme. The results show that it performs better than the comparative method in terms of external reportage function and internal ROUGE evaluation, which can effectively improve the accuracy of automatic summarization extraction for reportage news.

[Purpose/Significance] Research on the applications, risks, and governance of Artificial Intelligence Generated Content (AIGC) contributes to understanding the future development direction of artificial intelligence. It provides references for relevant emerging thematic studies. [Method/Process] Using CNKI (China National Knowledge Infrastructure), Wanfang, Web of Science, ProQuest, and ScienceDirect as data sources, this study collected literature related to AIGC, analyzed the current applications and associated risks of AIGC across various fields, and proposed governance objective for AIGC. [Result/Conclusion] AIGC has achieved certain accomplishments in fields such as healthcare, journalism, academia, education, and the arts, while concurrently facing interrelated risks from the technical, individual, and societal levels. This paper constructs a “Human-centered” AIGC service system model and proposes specific governance measures for technical control and personal enhancement at the internal cyclical symbiosis level, as well as specific measures for policy formulation and social guidance at the external control guidance level, to guide the design, application, and development of generative artificial intelligence.