bluuebunny/crossref_metadata_2025 huggingface dataset:
The Impacts of Changes in Journal Data Policies: A Cross‐disciplinary Survey
Authors: Ikeuchi Ui
Published: October 2023

Journal policies play a crucial role in facilitating the sharing of research data. This study examined the Data Sharing Policies (DSP) and Supplemental Materials Policies (SMP) of ten journals from 22 disciplines in 2023, comparing the findings to those of previous surveys conducted in 2014 and 2019. Among the 220 journals analyzed, 91.4% implemented DSP, whereas 95.0% adopted SMP. Compared with the surveys conducted in 2014 and 2019, a greater number of journals exhibited more stringent requirements for DSP, particularly in the fields of Engineering, Economics & Business, and Computer Science. Although the percentage of journals that accepted supplemental materials increased, only 12.7% strongly required their submission. Furthermore, this study compared the changes in Journal Impact Factors (JIF) between journals that adopted DSP (either requiring or recommending it) and those that did not have DSP in 2019. These findings suggest that DSP has a significant effect on JIF in the Economics & Business and Psychiatry/Psychology fields.
AI or Authors?: A Comparative Analysis of BERT and ChatGPT's Keyword Selection in Digital Divide Studies
Authors: Kang Woojin , Lee Myeong , Lee Jongwook , Oh Sanghee
Published: October 2023

Author keywords attached to academic papers are often used in intellectual structure analysis. However, the length and selection criteria for keywords vary across publications and, even some publishers do not require keywords for their articles. To explore the opportunity to overcome such keyword inconsistency issues, this study compared author keywords from papers focused on the digital divide with those extracted using the language models, BERT and ChatGPT. Preliminary findings reveal structural variations across the keyword networks and suggest a potential need to revisit keyword‐based research. Future research will expand the scope of the dataset and conduct an in‐depth analysis of keyword patterns across the language models.
Reading Comprehension in Information Retrieval (RCIR) for Personalized Results
Authors: Kim Yumi , Kim Heesop
Published: October 2023

Recent research on personalized retrieval technology has been actively conducted to meet the needs of users for seeking adequate information. To refine the retrieval, researchers are considering user behavior patterns in a variety of ways. In this study, we use eye‐tracking metadata to predict users' levels of comprehension as textual evidence for IR processes. Furthermore, we incorporated eye‐tracking metadata on the Automated Readability Index (ARI), a readability assessment tool of an English text. Our research is largely divided into two tasks: i) comprehension evaluation task (CET) and ii) comprehension‐based retrieval task (CRT). In the CET task, for predicting the comprehension level, we applied various regression models. Among them, the Voting regressor demonstrated the highest performance with a Spearman's 𝜌 of 0.68. In the CRT task, we incorporated the level of comprehension predicted in the CET task and ARI score into the ranking results. We derived a sentenceBERT to find the relevant text for a query and the Normalized Discounted Cumulative Gain (nDCG) for evaluating the CRT task. The nDCG score for Comprehension Level only and that with ARI together were 0.65 and 0.78, respectively. Thus, applying ARI resulted in a higher nDCG score compared to comprehension level only.
Usability Evaluation of Kiosks for Visually Impaired College Students
Authors: Kim Yumi , Kim Kyounghoon , Lee Jongwook
Published: October 2023

In the rapid flow of digital transformation, kiosks have naturally become part of our daily lives. In this study, we targeted visually impaired college students in their 20s, the primary user group of kiosks. We evaluated the usability of a self‐service certificate issuance and fast‐food restaurant kiosks. Based on Nielsen's five usability evaluation criteria, we presented experimental tasks to the visually impaired college students, monitored their performance, and conducted interviews to assess usability. Through this process, we aimed to understand the usage difficulties of visually impaired people when using kiosks and identify their specific requirements. Furthermore, we aimed to provide insights into improving the accessibility and usability of kiosks for this population and offer practical implications for developing kiosk education programs.
Toward the Conceptualization of Data Quality Assurance in Research Data Repositories
Authors: Lee Dong Joon , Ali Faizan , Stvilia Besiki , Pang Yuanying , Gonthina Karthik
Published: October 2023

Data quality assurance (DQA) is critical to research data sharing and reuse. There has been a growing recognition of data transparency, reproducibility, credibility, and validity in research. Although the research data curation literature is large, it lacks data quality theory‐guided examinations of DQA practices in research data repositories. This poster paper reports on the preliminary findings of a larger study that examines DQA practices in research data repositories, including their use of DQA ontologies, standards, and metadata vocabularies. In particular, the paper examines two quality standards and an ontology for their conceptualization of DQA activities and their structure. The authors used the findings of the analysis and the data quality literature to synthesize an initial model of a DQA process in research data repositories that conceptualizes three DQA activities: evaluation, intervention, and communication. This paper can inform the development of ontologies and best practice guides for designing and evaluating DQA workflows in research data repositories.
Analysis of the Dissemination Characteristics of Papers on WeChat Official Accounts of Chinese Academic Journals
Authors: Li Lei , Wang Xuyan
Published: October 2023

New media platforms have enhanced the efficiency and diversity of information dissemination, providing new possibilities for the dissemination and promotion of academic papers. Currently, a large number of Chinese academic journals from different disciplines have established WeChat official accounts to promote their papers. This study examines WeChat official accounts from three disciplines: social sciences, natural sciences, and medicine. We analyze the existing paper promotion methods employed by these academic journal official accounts from four dimensions: content presentation format, number of papers promoted in a single post, interactive forms, and publishing time. The findings reveal that the current promotion methods for academic papers on WeChat official accounts are relatively limited, with low utilization of multimedia content. Therefore, there is a need for further improvement in new media promotion for academic papers.
Humanities Scholars' Understanding of Data and the Implications for Humanities Data Curation
Authors: Li Wenqi , Zhang Pengyi , Wang Jun
Published: October 2023

This study addresses the need for a shared understanding of humanities data to enhance data curation. Through interviews with 27 scholars, it identifies two ways scholars conceptualize data ‐ by format or role in research. It highlights three unique aspects: diverse requirements of materiality and processing levels, significance of authorship and perspective, and the dual role of tertiary sources. The study suggests prioritizing provenance, facilitating data documentation, curating tertiary sources for wider use, and establishing scholarly communication mechanisms for effective data curation.
A Cognitive Model of Data Retrieval Interaction
Authors: Li Xueyi , Li Qiao , Wang Ping , Hou Jingrui
Published: October 2023

This study introduces and examines the “Multi‐dimensional Interaction‐Attitude‐Usage Model” (MIAU Model), drawing from cross‐disciplinary theories. The MIAU Model proposes dimensions of interactive data retrieval, including resource, technology, context, and dual‐process‐based cognition, and explores their relationships with user attitude toward systems and usage intention. To test the MIAU Model, a structural equation modeling analysis was conducted on the questionnaire data. The results support the proposed model. The MIAU Model suggests that resources, technology, contexts, and individual characteristics directly impact data searchers' dual‐process‐based cognition. System 1‐based cognition has a direct influence on system 2‐based cognition. Moreover, dual‐process‐based cognition and contexts directly affect attitude and usage intention. Additionally, resources, technology, contexts, and individual characteristics indirectly affect system 2‐based cognition through system 1‐based cognition, and similarly, these factors indirectly influence attitude and usage intention through dual‐process‐based cognition.
Informal Learning Trends on YouTube during the COVID‐19 Pandemic: A Topic Modeling Analysis
Authors: Lim Kok Khiang , Lee Chei Sian
Published: October 2023

This study explores the impact of the COVID‐19 pandemic on informal online learning behavior through the lens of online video tutorials hosted on YouTube. Over 40,000 YouTube comments were collected for topic modeling analysis and uncovered thirteen latent topics related to three learning behavior types, that is, environment structuring, help‐seeking, and self‐evaluation. These learning behaviors underwent varying degrees of change after the pandemic declaration and normalized after that, highlighting the importance of adapting teaching methods to meet the changing needs of learners in response to any learning disruptions.
A New Ontology for Restaurant Review Sentiment Analysis
Authors: Luo Manman , Mu Xiangming
Published: October 2023

Understanding the aspects of a restaurant that contribute to people's sentiment is crucial for restaurant owners and marketers to improve customer experiences. A sentiment ontology for restaurant review sentiment analysis was proposed in this study. The proposed ontology identified six aspects of a restaurant evaluation: food, service, ambience, cleanliness, location, and price, based on 1,000 annotated Yelp data. Applying the proposed ontology in the annotated Yelp data showed that it helped improve the sentiment analysis accuracy by 11.2% on average. We believe that this study provides insights into the key factors that contribute to restaurant review sentiments.
Expanded Model of Everyday Information Practices with Information Avoidance in Digital Environments
Authors: Matsubayashi Mamiko
Published: October 2023

Until recently, research on information behavior and practices has focused on a series of actions, including having information needs, seeking information to satisfy those needs, and using the acquired information to varying degrees. However, in digital environments with an enormous distribution of information, it is necessary to consider information practices by focusing on their relationship with negative behaviors, such as information avoidance. Based on the discourses by Japanese Canadian seniors on information behavior during the COVID‐19 pandemic, this study attempted to construct an expanded model of everyday information practices (EIP) that incorporates the concept of information avoidance into the EIP model proposed by Savolainen. Findings suggest that information avoidance is likely related to an individual's social context and that, as a result of information avoidance, different means of information acquisition are chosen from a person's stock of knowledge, resulting in different aspects of the individual's information practices.
Do Black‐Owned Restaurants Matter?: Yelp and Platform Visibility amid a Racial Reckoning
Authors: Moy Cameron , Bui Matthew
Published: October 2023

In Spring 2020, the murder of George Floyd catalyzed calls for racial justice across the United States, ushering in a series of organizational and institutional responses. One response from Yelp, a popular review‐based platform, included its addition of a searchable Black‐owned attribute, a tag intended to signal its solidarity with Black businesses and communities. Analyzing a repository of over 300,000 Yelp reviews from Black and non‐Black‐owned restaurants in Los Angeles, CA, USA and Detroit, MI, USA, we pose the question: How did the addition of the Black‐owned tag impact the online reputation of Los Angeles and Detroit Black‐owned restaurants on Yelp? To examine this, we use restaurant review counts and average star ratings as proxy measures for online reputation and track these metrics over the year following the platform design intervention. We find that the addition of the Black‐owned tag did not create positive, sustainable change for Los Angeles and Detroit Black‐owned restaurants. In all, we call attention to the disparate impacts of platform design features on users and communities, especially for Yelp and digital platforms seeking to effect racial justice through novel platform design interventions.
Exploring the Information Privacy Practices of Persons with Diverse Sexual Orientation and Gender Identities (SOGI)
Authors: Nobbs Breanna
Published: October 2023

This poster describes the research design to study the information behaviours of individuals with diverse sexual orientation and gender identities (SOGI) and how they negotiate between their personal privacy and their social and public identities in the digital world. In Australia, there is no legal right to privacy, and our increasing reliance on new technologies has created legal and regulatory challenges, especially for vulnerable populations such as persons with diverse SOGI. The poster will outline the literature review, research questions, aims, and methods guiding the research. Methods proposed for this study include an analysis of legal frameworks and policies, a survey of approximately 100 people with diverse SOGI about their use of digital social technologies, and in‐depth interviews with a sub‐set of self‐selected survey participants. Information behaviour theories and privacy literacy theories will be used to understand participants' behaviours and experiences regarding how they use digital social media, and the practices they have in place to manage their privacy.
Investigating the Intersections of Ethics and Artificial Intelligence in the Collections as Data Position Papers
Authors: Osti Giulia , Cushing Amber , Little Suzanne
Published: October 2023

A paradigm shift is currently underway with the emergence of the Collections as Data movement, which advocates the creation and dissemination of cultural heritage collections that are amenable to large‐scale computation to empower both collection managers and users. Although this discourse is beginning to gain some traction in the literature, critical evidence‐based assessments of the opportunities and risks of this process are underexplored. This paper presents the results of a content analysis of the official position statements (n = 83) produced in the Collections as Data forums and written by international professionals working with digital collections. Although preliminary, the analysis presented and discussed here sheds light on the initial reception of the idea of Collections as Data and its articulation in practice. The study represents the first systematic attempt to explore the complexities of the intersection of ethics and artificial intelligence in the context of cultural heritage, aiming at providing a valuable precedent for further elaboration and discussion.
Exploring an Online Community of Blind Programmers by Using Topic Modeling and Network Analysis
Authors: Park Jaihyun , Seo JooYoung , Lee Jae Young
Published: October 2023

Much work has been carried out to highlight the accessibility challenges of blind programmers. Yet, relatively little has been known about how blind programmers help each other to solve problems. We present a data‐driven approach to explore collaborative problem‐solving of users in the Program‐l community of, by, and for blind programmers. We collected 8,344 longitudinal email threads from 778 users from 2004 through 2022 to observe the dynamics of collaborative problem‐solving among blind programmers. Our embedding‐based topic modeling and assortativity network analysis reveal that the knowledge of blind programmers diverges between when asking and answering questions. Our findings also suggest that users who have a high cluster level in the first year of activity and members are more likely to interact with other members with different roles. Our paper contributes to the field of social computing by introducing the first large‐scale study of a unique community of blind programmers.
From Digitization and Images to Text and Content: Transkribus as a Case Study
Authors: Prebor Gila
Published: October 2023

This poster explores the potential of using technological tools, specifically the Transkribus platform, for the transcription of Hebrew manuscripts. The digitization of historical resources has made them accessible, but the textual content of the scanned images remains inaccessible. Transkribus, an AI‐powered platform, offers tools for text recognition, transcription, and search of historical documents. The poster discusses the process of automatic text recognition (ATR) and the challenges it faces, particularly in handling handwritten texts and Hebrew letters. It provides an overview of the Transkribus platform, its functionalities, and the training process for creating transcription models. The author presents a case study of transcribing a 15th‐century Sephardic semi‐cursive Hebrew manuscript using the Transkribus platform and evaluates the performance of different models. The poster concludes by discussing the implications and possibilities of using Transkribus for automatic transcription of historical Hebrew manuscripts. While the results show promising improvements in accuracy, further challenges and solutions are also discussed. Overall, Transkribus offers significant potential for the study and transcription of Hebrew manuscripts, revolutionizing the field of Jewish studies and historical research.
Knowledge Work Activities of a Systematic Review Task
Authors: Segura‐Rodas Stephanie , Griffin Martyn , Simpson Andrew , Toms Elaine
Published: October 2023

Most workers' activities in the workplace can be classed as knowledge work (KW). Previous research has described workers' tasks very broadly, making it difficult to know the actual work. We hypothesise that deconstructing what goes on inside a task could reveal many subtasks and activities at levels that are, as yet, unexamined and in need of deeper understanding. To explore this further, a data set collected from research fellows who described a specific task was examined. The task was broken down into many subtasks and activities, showing that one single task contains many levels of tasks. This understanding may allow for the development of tools to support the worker.
How Do Open Patents Affect Follow‐On Innovation? Evidence from Tesla
Authors: Shi Jing , Kang Lele , Chen Ye , Sun Jianjun
Published: October 2023

In the past, high‐tech enterprises aimed to apply for patents to protect their innovation and obtain a competitive advantage. However, Open Innovation models encourage enterprises to open and exchange knowledge to accelerate technology development because of higher innovation complexity. Nonetheless, the impacts of open knowledge on follow‐on innovation are unclear. Tesla's patent openness event provides a better opportunity to observe a causal relationship between open knowledge and follow‐on innovation as it is an exogenous shock for the whole Electric Vehicle (EV) field. Against this backdrop, this poster uses patent data of Tesla and EVs before and after the open‐patent events from Derwent Innovations Index (DII) between 2003 and 2020. The difference‐in‐difference regression results show that patent openness has an inhibition effect on follow‐on patents and patentees and the high‐value open patents will strengthen this inhibition effect. Surprisingly, once technologies were shocked by openness, more open patents would stimulate them to boost in the future.
Automated Metadata Enhancement for Physical Sample Record Aggregation in the iSamples Project
Authors: Song Hyunju , Cui Hong , Vieglais Dave , Mandel Danny , Thomer Andrea K.
Published: October 2023

Large amounts of samples have been collected and stored by different institutions and collections across the world. However, even the most carefully curated collections can appear incomplete when aggregated. To solve this problem and support the increasing multidisciplinary science conducted on these samples, we propose a method to support the FAIRness of the aggregation by augmenting the metadata of source records. Using a pipeline that is a combination of rule‐based and machine learning‐based procedures, we predict the missing values of the metadata fields of 4,388,514 samples. We use these inferred fields in our user interface to improve the reusability.
Result Assessment Tool: A Software Toolkit for Conducting Studies Based on Search Results
Authors: Sünkler Sebastian , Yagci Nurce , Sygulla Daniela , von Mach Sonja , Schultheiß Sebastian , Lewandowski Dirk
Published: October 2023

The Result Assessment Tool (RAT) is a software toolkit for conducting research using results from commercial search engines and other information retrieval (IR) systems. This software combines modules used for the design and management of studies, the automatic collection of search results through web scraping, and the assessment of search results by jurors using different scales in an assessment interface. Due to the flexibility of RAT, several types of studies can be implemented, for example, classification studies and qualitative content analyses in addition to classic retrieval tests. Therefore, RAT is a versatile tool and useful in various disciplines.
Who Communicates Well with Data: Examining Data Literacy among Pre‐service Secondary Teachers
Authors: Tsai Tien‐I , Lee San
Published: October 2023

The current study developed a data literacy instrument with 20 contextual questions and conducted a multi‐mode survey with 232 pre‐service secondary teachers in a university system. Three‐way ANOVA was used to test whether pre‐service teachers with different characteristics (i.e., field of study, library use, and Internet use) performed differently on data literacy. Pre‐service teachers got a decent overall percentage score. They performed better in “identifying problems” and “analyzing data” than in “transforming data” and “collecting data.” While pre‐service teachers in humanities and social sciences performed better in “identifying problems,” those in sciences performed better in “analyzing data.” Humanities pre‐service teachers with high Internet use scored significantly lower; social science pre‐service teachers with high Internet use and low library use scored significantly lower; science pre‐service teachers with low library use scored higher. Implications for future data literacy education and instrument design are discussed based on the preliminary findings.
Which Information Behavior Concepts Bridge the Gap from Research to Reference Practice?
Authors: VanScoy Amy , Merčun Tanja , Hands Africa , Švab Katarina , Kuhar Maja
Published: October 2023

Although there is much discussion of the theory/practice gap in library and information science, there is little research about how librarians use formal theory in their practice. Using a card sort and interviews, we explore the extent to which public librarians encounter or use theoretical concepts and models from information behavior in their reference practice. Results from our pilot study show that our procedures, including plain language descriptions of theoretical concepts and models printed on cards, are effective for eliciting examples of theory in practice. Librarians are clear about which concepts are relevant to their practice and can easily provide examples of real‐world application. Results of the study will suggest which theoretical concepts might be most important for instructors to teach to students in reference courses and which translate best from the research to the practice environment. In addition, the examples provided by librarians of how they use the concepts in their practice will be useful for instructors as they try to engage students in learning.
Between Realities: Information Sharing Practices of Deepfake Creators
Authors: Vera Nick
Published: October 2023

Deepfakes are AI‐generated multimedia that convincingly graft or replace one person's likeness onto another, resulting in remarkably realistic yet fabricated content. These sophisticated manipulations challenge users' ability to discern factual visual information as they portray individuals engaging in actions and uttering words that never occurred. This research takes a unique approach by delving into the perspective of deepfake creators through the analysis of 15 tutorial videos on YouTube. By examining creators' information‐sharing practices, this study aims to identify creators' motivations for disseminating knowledge on deepfake creation and their understanding or lack of understanding of the ethical implications of sharing such information. Achieving such an understanding can play a vital role in informing the development of regulatory measures. By examining creators' intentions and comprehending their societal impact, patterns, and characteristics, this research can contribute to the creation of effective detection technologies and strengthen content platform guidelines. Analyzing deepfake creation tutorials can facilitate the implementation of targeted and comprehensive regulatory measures, enhancing protection against potential risks and misuse.
Nonsuicidal Self‐Injury and Content Moderation on TikTok
Authors: Vera Valerie
Published: October 2023

Online nonsuicidal self‐injury communities commonly create and share information on harm reduction strategies and exchange social support on social media platforms, including the short‐form video sharing platform TikTok. While TikTok's Community Guidelines permit users to share personal experiences with mental health topics, TikTok explicitly bans content depicting, promoting, normalizing, or glorifying activities that could lead to self‐harm. As such, TikTok may moderate user‐generated content, leading to exclusion and marginalization in this digital space. Through semi‐structured interviews with eight TikTok users with a history of nonsuicidal self‐injury, this pilot study explores how users experience TikTok's algorithm to create and engage with content on nonsuicidal self‐injury. Findings demonstrate that users understand how to circumnavigate TikTok's algorithm through algospeak (i.e., codewords or turns of phrases) and signaling to maintain visibility on the platform. Further, findings emphasize that users actively engage in self‐surveillance and self‐censorship to create a safe online community. In turn, content moderation can ultimately hinder progress toward the destigmatization of nonsuicidal self‐injury and restrict social support exchanged within online nonsuicidal self‐injury communities.
Beyond Boundaries: Unraveling the Interactions between Science and Technology through Linked Topics
Authors: Wang Jiajie , Hou Wanfang , Wu Keye , Sun Jianjun
Published: October 2023

Understanding the interactions between science and technology (S&T) is crucial for driving major innovations. Previous studies have typically focused on identifying scientific and technical topics separately and analyzing their association through semantic or citation. In this study, we propose a novel approach to identifying linked topics that directly reflect the interactions within the S&T domain. Our approach integrates semantic characteristics and citation relationships, allowing for a comprehensive analysis of the specific content and structure of these interactions. We test our approach using a dataset of 2,821 patents and 4,626 papers from the field of genetic engineering vaccines, spanning the years 1980 to 2020. The results demonstrate that our approach provides a more direct and detailed understanding of the content and structural characteristics of S&T interactions. This research contributes to the methodology of linked topics identification in the field of S&T, offering new insights and analytical perspectives for related studies.
Differences in Open Government Information among Departments with Different Responsibilities
Authors: Wei Jingzhu , Zhang Tongrui
Published: October 2023

Although open government information awareness has enhanced since China implemented the Open Government Information Regulation, the complaints about the information disclosure work also increased. Previous research has ignored the impact of the department's responsibilities on information disclosure, which are essential as some departments handle information that should not be disclosed. From this standpoint, this paper is the first to analyze the differences in departments' open government information performance from their annual information openness reports. Variance and correlation analysis show that the potential harm of disclosure to third parties' legitimate rights and interests, the availability of relevant information, and duplicate requests can explain their differences in government information disclosure on request. Besides, this paper calls for an elastic criterion for openness evaluation and finds that the benchmark effect in previous research does not apply to departments with different responsibilities.
Multilingual Knowledge Organization of Cultural Routes: The Case of the Grand Canal
Authors: Wen Xinyue , Liang Shaobo , Wu Dan
Published: October 2023

This study collects heterogeneous information from multiple sources related to the Grand Canal, a cultural route in China, to construct a multilingual ontology of the Grand Canal. The research designs a data translation, optimization, and multilingual ontology construction method to reveal the internal and external characteristics and associations of the Grand Canal, which can provide users with multilingual and ordered knowledge of the cultural route, providing a feasible solution for digital preservation and global sharing of the cultural routes. On this basis, a multilingual knowledge service system is built.
Novels and the NSTC: A Quantitative Study of Legal Deposit
Authors: Wingate Alexandra
Published: October 2023

The Nineteenth‐Century Short Title Catalog (NSTC) attempts to provide comprehensive coverage of the print record in the English‐speaking world from 1801 to 1918 based primarily on the catalog records of Britain's five legal deposit libraries during the 19th century. Previous studies have used the NSTC to quantitatively study broad trends in Victorian‐era British publishing, but they have not sufficiently acknowledged the NSTC's limitations. This study works within the NSTC's limitations by using it to quantitatively study the phenomena of legal deposit. Analysis of two, 90‐volume random samples of first edition English novels reveals the impact of changing library acquisition practices and legal deposit legislation on the comprehensiveness of Britain's legal deposit libraries, as well as the NSTC's comprehensiveness and how it can be utilized for quantitative book history despite its deficiencies.
Measuring Unequal Knowledge Distance by Network Embedding and Multiple Relationships
Authors: Wu Keye , Kang Lele , Xie Ziyue , Du Jia Tina , Sun Jianjun
Published: October 2023

Knowledge distance, representing the dissimilarity between different knowledge units, has been considered as an important dimension of recombination novelty and technological innovation. Previous measurements merely rely on the citation relationship and ignore their directions and weights. To fill this gap, this study proposes a new measurement which not only captures the unequal citation relationship but also integrates multiple information to depict knowledge distance. The results show that our method can accurately portray the knowledge distance in both scientific areas and technical fields.
Untangle the Characteristics of Disruptive and Consolidating Citations of Nobel‐winning Papers
Authors: Yang Alex Jie , Zhao Yuehua , Wang Hao , Deng Sanhong
Published: October 2023

Scientific breakthroughs have the potential to revolutionize the course of research and shape the trajectory of scientific knowledge. This study investigates the characteristics of Disruptive Citing Papers (DCP) and Consolidating Citing Papers (CCP) associated with Nobel‐winning scientific breakthroughs, aiming to provide insights into the mechanisms of knowledge creation and dissemination. By analyzing a dataset of Nobel‐winning papers and their citation networks, we find that Nobel‐winning papers tend to attract a higher proportion of DCP compared to CCP. However, CCP exhibit a higher impact, as evidenced by their citation counts and likelihood of becoming hit papers. Furthermore, DCP are associated with larger research teams, highlighting the collaborative nature of disruptive research, while CCP employ a higher degree of professional language style characterized by shorter titles and specialized jargon. These findings deepen our understanding of the role played by disruptive and consolidating impact in scientific breakthroughs, shedding light on the dynamics of knowledge creation and dissemination in the scientific community. This research contributes to the broader understanding of scientific progress and provides valuable insights for researchers, policymakers, and stakeholders in the scientific ecosystem.
Female College Students' Perceptions on the Internet Use for Reproductive Health Information
Authors: Yoon Hyunsoo , Oh Sanghee
Published: October 2023

This study aims to explore the factors related to young women's Internet use for reproductive health information, specifically focusing on female college students. The research model is based on the Health Belief Model (HBM) and the Theory of Planned Behavior (TPB). A convenience sample of female college students who have recently used the Internet for reproductive health information will participate in an online survey. The survey will collect demographic information, background details on Internet use for reproductive health, and perceptions of reproductive health behaviors. Data analysis will examine correlations among the perceptions and their association with the intention to continue using the Internet for reproductive health information. The findings will contribute to understanding female college students' influential factors and information behaviors, informing the development of educational programs, intervention strategies, and online resources to improve reproductive health outcomes and empower young women.
Real or Fake: Eliciting Deepfake Identification Strategies through a Diary Study
Authors: Zeng Ruoyao , Song Siyi , Guo Zhengxi , Goh Dion Hoe‐Lian , Lee Chei Sian
Published: October 2023

The prevalence of deepfake technology has led to concerns about the ability of people to spot real videos from fakes. We address these concerns through a diary study to elicit strategies people use to identify deepfake and real videos. Results indicate that different strategies were associated with deepfake and real video identification. Interestingly, popular strategies were often not as effective as less popular ones. Implications of our work are discussed.
The Spatial‐Temporal Trajectories of the Poets and the Sociocultural Environment of the Age
Authors: Zhan Yiying , Wang Xi
Published: October 2023

This research in progress presents a deep learning‐based approach to identifying named entities, including geographical locations and poetic imagery in ancient Chinese poetry. By leveraging association rule mining, this study establishes a connection between historical events, spatial‐temporal trajectories of poets, and sociocultural phenomena of the age. From the perspective of digital humanities, this study hopes to be able to provide new evidence for socioeconomic status, cultural openness, or historical events at different ages and to picture a better view of the development of classical Chinese.
Exploring Laypeople's Engagement with AI Painting: A Preliminary Investigation into Human‐AI Collaboration
Authors: Zhang Xiaoyu , Zhu Sicheng , Zhao Yuxiang (Chris) , Hansen Preben , Zhu Qinghua
Published: October 2023

As advancements in artificial intelligence (AI) technology have opened new avenues for artistic expression and creation, human‐AI collaboration in creative activities has garnered increasing attention. Through semi‐structured interviews with 15 participants, we investigate the intrinsic and extrinsic factors that motivate laypeople's engagement with AI painting, as well as challenges and concerns faced by users. Our findings reveal that laypeople engage with AI painting for emotional needs like entertainment, aesthetics, surprise, and curiosity, personal utilitarian needs such as self‐expression and customization, and social interaction through sharing and communication. Despite the appeal of novelty and unpredictability they also encountered challenges related to technical and system functionality, personal and environmental factors, as well as concerns about algorithm bias, pornography misuse, employment risks, copyright disputes and ethical implications. Findings provide preliminary evidence of the potential and limitations of AI in democratizing creative activities, and offer implications for designing and developing of AI assistance tools.
Understanding the Motivation of Participants in Innovation Open Data Contests: A Task Presentation Affordance Perspective
Authors: Zhang Yan , Liu Zhouying , Zhao Yuxiang (Chris) , Wu Dawei
Published: October 2023

Innovation Open Data Contest (IODC) is an effective way to take advantage of public efforts to realize the great potential value of open data in the field of digital humanities. Previous literature focusses more on the challenge open data contest rather than innovation open data contest. Given this, understanding the underlying factors motivating participants to actively engage in the contest is necessary. Based on the task affordance theory and self‐determination theory (SDT), this study aims to identify and examine how task presentation affordances of IODC influence participants' motivations and thereby shape their level of effort. We employ partial least squares structural equation modeling (PLS‐SEM) techniques to analyze the responses from 215 individuals who have previously participated in the IODC. The findings indicate that participants' level of effort in the IODC is contingent upon their perception of relatedness and competence. Moreover, hedonic affordance and connective affordance positively influence participants' perceptions of relatedness and competence. Our findings contribute to the extant literature by proposing a theoretical model to understand the participants' motivation and have practical implications for IODC's organizers.
Effect of liquid ammonia and protease on surface modification and shrink resistance of wool
Authors: Shang Xianqin , Wang Qiang , Jiang Zhe , Ma Haitao
Published: October 2023

In order to improve the surface properties of wool fiber, the wool fabric was treated with liquid ammonia and protease treatments successively. The results showed that liquid ammonia pretreatment could cause certain damage to the surface structure and disulfide bonds in keratin, which enhanced the action of protease on wool surface and catalytic hydrolysis of the cuticle. The joint treatment of liquid ammonia and protease changed the secondary structure of wool. The content of α‐helix and random crimp increased, while the content of β‐sheet decreased. In addition, the shrink resistance of wool fabric was significantly improved without obvious losses in tensile strength and elongation. The combination of liquid ammonia and protease treatments provides a potential new method for the surface modification and eco‐friendly shrink‐resistant finishing of wool and further widens the application of liquid ammonia in textile industry.
Refining autonomous vehicle situational awareness due to varying sensor error
Authors: Costello Donald , Hanlon Nicholas , Xu Huan
Published: October 2023

Pilots use their senses and training to generate situational awareness (SA). They then use this SA to make sound aeronautical decisions. Autonomous vehicles, by contrast, cannot rely on pilot expertise in off‐nominal situations. They must rely on their onboard sensors to build SA of the environment. As these sensors degrade, it is hypothesized that a point exists where the SA generated by these sensors is inadequate to allow the autonomous vehicle to make sound aeronautical decisions. In previous work, a point was defined based on broad assumptions within a modeling and simulation environment (i.e., the error within each sensor was known and not random). This research used a larger data set that contained random errors within the sensors. The data was then used to build predictive equations through a Monte Carlo simulation in the same simulation environment as previous work. While the data showed there was a statistically significant relationship between the error values in each sensor and the fused distance value, the resulting predictive equations were not able to provide adequate SA to make sound aeronautical decisions. This research highlights multiple issues the test and evaluation community will face when trying to develop new techniques for the verification and validation of autonomous systems.
Quality Performance Indicators for the Surgical Management of Oesophageal Cancer: A Systematic Literature Review
Authors: Kulasegaran Suheelan , Wang Yijiao , Woodhouse Braden , MacCormick Andrew , Srinivasa Sanket , Koea Jonathan
Published: October 2023

Background

The objective of this systematic review was to identify pre‐existing quality performance indicators (QPIs) for the surgical management of oesophageal cancer (OC). These QPIs can be used to objectively measure and compare the performance of individual units and capture key elements of patient care to improve patient outcomes.

Methods

A systematic literature search of PubMed, MEDLINE, Scopus and Embase was conducted. Articles reporting on the quality of healthcare in relation to oesophageal neoplasm or cancer and the surgical treatment of OC available until the 1st of March 2022 were included.

Results

The final list of articles included retrospective reviews (n = 13), prospective reviews (n = 8), expert guidelines (n = 1) and consensus (n = 1). The final list of QPIs was categorized as process, outcome or structural measures. Process measures included multidisciplinary involvement, availability of multimodality diagnostic and treatment pathways and surgical metrics. Outcome measures included reoperation and readmission rates, the achievement of RO resection and length of hospital stay. Structural measures include multidisciplinary meetings.

Conclusions

This systematic review summarizes QPIs for the surgical treatment of OC. The data will serve as an introduction to establishing a quality initiative project for OC resections.
The Association between Mindful Parenting, and Internalizing and Externalizing Symptoms in Adolescence
Authors: Royuela-Colomer Estíbaliz , Orue Izaskun , Visu-Petra Laura , Fernández-González Liria
Published: October 2023

Internalizing and externalizing symptoms are common among adolescents, and mindful parenting can play a pivotal role in their prevention. Study 1 explores the association between internalizing and externalizing symptoms and the moderating role of mindful parenting. Study 2 explores the longitudinal association between mindful parenting and externalizing symptoms and whether mindful parenting moderates the longitudinal stability of externalizing symptoms. In Study 1, 420 parents (81.9% mothers) of children aged 12 to 17 (M = 14) completed measures of mindful parenting and their children’s emotional symptoms and conduct problems. In Study 2, 151 adolescents (65.6% girls) aged 12 to 17 (M = 13.9) from Study 1 completed a measure of antisocial behavior two times one year apart. Study 1 found an association between adolescents’ emotional symptoms and conduct problems, and this association was moderated by mindful parenting. The association was significant only in adolescents whose parents reported low levels of the mindful parenting factor called “being in the moment with the child.” Study 2 found an association between antisocial behavior at Wave 1 and 2, and this longitudinal association was moderated by mindful parenting. The longitudinal relationship was statistically significant exclusively among adolescents whose parents reported low levels of “mindful discipline.” Mindful parenting seems to be a protective factor for adolescents’ externalizing symptoms. Specifically, “being in the moment with the child” can diminish the association between emotional symptoms and conduct problems, while “mindful discipline” can be beneficial for reducing the stability of antisocial behavior over time.
Fully metallic frequency selective surface (FSS) circular polarizer based on cost‐effective chemical etching manufacturing technique
Authors: Yong Wai Yan , Velkers Arjen , Glazunov Andrés Alayón
Published: October 2023

This letter presents a fully metallic circular polarizer based on a bandpass frequency selective surface (FSS) that converts linear polarization (LP) to circular polarization (CP) in the Ka‐band. The proposed FSS circular polarizer consists of three cascaded identical perforated metal screens. The Jerusalem slot is used as the element of the FSS circular polarizer to produce an entirely metallic screen with a compact footprint. The Jerusalem slot allows the all‐metallic design to be realized without mechanical issues. A 35 × 35 elements prototype has been designed and manufactured using a cost‐effective chemical metallic etching technique, and performed measurements agree well with the numerical simulations. At broadside direction (), the proposed polarizer operates from 27.5–30.1 GHz, while at the oblique angle of incidence , the operating frequency is reduced to 27.7–29.4 GHz. The manufactured prototype has a low insertion loss (IL), i.e.  dB and a good axial ratio performance, i.e. AR ⩽ 2 dB at the desired operating frequency band. Thus, based on a cost‐effective manufacturing technique, the proposed circular polarizer demonstrates an excellent approach to devising all‐metallic FSS polarizers with good IL.
Depression recognition from facial videos: Preprocessing and scheduling choices hide the architectural contributions
Authors: Lage Cañellas Manuel , Álvarez Casado Constantino , Nguyen Le , Bordallo López Miguel
Published: October 2023

Deep learning models have been widely applied in video‐based depression detection. It is observed that the diversity of preprocessing, data augmentation, and optimization techniques makes it difficult to fairly compare model architectures. In this study, the typical ResNet‐50 model is enhanced by using specific face alignment methods, improved data augmentation, optimization, and scheduling techniques. The extensive experiments on two popular benchmark datasets (AVEC2013 and AVEC2014) obtained competitive results, compared to sophisticated spatio‐temporal models for single streams. Moreover, the score‐level fusion approach based on two texture streams outperformed the state‐of‐the‐art methods. It achieved mean square errors of 5.82 and 5.50 on AVEC2013 and AVEC2014, respectively. These findings suggest that the preprocessing and training configurations result in noticeable improvements, which have been originally attributed to the network architectures.