738 research outputs found

    Making Open Resources Discoverable: Collaborative Approaches for Enhanced Access

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    This article explores collaborative efforts to enhance the discoverability of open access resources. It highlights the pivotal role of librarians, educators, library system platform vendors, and publishers in improving access to these valuable resources. Through collective expertise and cooperation, these collaborative approaches aim to unlock the potential of open resources, benefiting researchers, students, and the broader academic community. By working together and leveraging their collective knowledge, these collaborative efforts promise to tap into the wealth of open resources, making them more accessible for professors, students, and the broader academic community

    Cognitive Machine Individualism in a Symbiotic Cybersecurity Policy Framework for the Preservation of Internet of Things Integrity: A Quantitative Study

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    This quantitative study examined the complex nature of modern cyber threats to propose the establishment of cyber as an interdisciplinary field of public policy initiated through the creation of a symbiotic cybersecurity policy framework. For the public good (and maintaining ideological balance), there must be recognition that public policies are at a transition point where the digital public square is a tangible reality that is more than a collection of technological widgets. The academic contribution of this research project is the fusion of humanistic principles with Internet of Things (IoT) technologies that alters our perception of the machine from an instrument of human engineering into a thinking peer to elevate cyber from technical esoterism into an interdisciplinary field of public policy. The contribution to the US national cybersecurity policy body of knowledge is a unified policy framework (manifested in the symbiotic cybersecurity policy triad) that could transform cybersecurity policies from network-based to entity-based. A correlation archival data design was used with the frequency of malicious software attacks as the dependent variable and diversity of intrusion techniques as the independent variable for RQ1. For RQ2, the frequency of detection events was the dependent variable and diversity of intrusion techniques was the independent variable. Self-determination Theory is the theoretical framework as the cognitive machine can recognize, self-endorse, and maintain its own identity based on a sense of self-motivation that is progressively shaped by the machine’s ability to learn. The transformation of cyber policies from technical esoterism into an interdisciplinary field of public policy starts with the recognition that the cognitive machine is an independent consumer of, advisor into, and influenced by public policy theories, philosophical constructs, and societal initiatives

    Asking Clarifying Questions:To benefit or to disturb users in Web search?

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    Modern information-seeking systems are becoming more interactive, mainly through asking Clarifying Questions (CQs) to refine users’ information needs. System-generated CQs may be of different qualities. However, the impact of asking multiple CQs of different qualities in a search session remains underexplored. Given the multi-turn nature of conversational information-seeking sessions, it is critical to understand and measure the impact of CQs of different qualities, when they are posed in various orders. In this paper, we conduct a user study on CQ quality trajectories, i.e., asking CQs of different qualities in chronological order. We aim to investigate to what extent the trajectory of CQs of different qualities affects user search behavior and satisfaction, on both query-level and session-level. Our user study is conducted with 89 participants as search engine users. Participants are asked to complete a set of Web search tasks. We find that the trajectory of CQs does affect the way users interact with Search Engine Result Pages (SERPs), e.g., a preceding high-quality CQ prompts the depth users to interact with SERPs, while a preceding low-quality CQ prevents such interaction. Our study also demonstrates that asking follow-up high-quality CQs improves the low search performance and user satisfaction caused by earlier low-quality CQs. In addition, only showing high-quality CQs while hiding other CQs receives better gains with less effort. That is, always showing all CQs may be risky and low-quality CQs do disturb users. Based on observations from our user study, we further propose a transformer-based model to predict which CQs to ask, to avoid disturbing users. In short, our study provides insights into the effects of trajectory of asking CQs, and our results will be helpful in designing more effective and enjoyable search clarification systems.This study is supported under the RIE2020 Industry Alignment Fund – Industry Collaboration Projects (IAF-ICP) Funding Initiative, as well as cash and in-kind contribution from Singapore Telecommunications Limited (Singtel), through Singtel Cognitive and Artificial Intelligence Lab for Enterprises (SCALE@NTU). This study is also supported by the NWO Smart Culture - Big Data/Digital Humanities (314-99-301), the NWO Innovational Research Incentives Scheme Vidi (016.Vidi.189.039), and the H2020- EU.3.4. - SOCIETAL CHALLENGES - Smart, Green, And Integrated Transport (814961)

    Evaluation of Citation Graph Thematic Dataset Construction and Paper Filtering Methods for Research Literature Recommendation

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    One of the main challenges faced by new researchers is immersing themselves in the existing literature relevant to their field of interest. The vastness and continuous growth of knowledge in their field can be overwhelming, making it difficult to identify the most pertinent research papers within their research themes. To address this issue, research paper recommender systems have emerged as valuable tools. These systems allow researchers to find relevant papers based on their specific interests or research themes by analyzing various aspects such as titles, abstracts, and full texts. The quality of the dataset used is crucial for the development, testing, and refinement of these systems to ensure optimal results. Dataset quality directly impacts the accuracy and reliability of a recommender system. In this thesis, I propose a novel approach for constructing datasets using citation graph networks. These networks consist of nodes representing research papers and edges representing citations between them. By leveraging citation graph networks, we gain a more comprehensive understanding of the relationships and influences among different papers compared to traditional methods that rely solely on keyword searches. To evaluate the effectiveness of the citation graph network method, I compared it with the traditional keyword search approach for dataset construction. Additionally, I assessed the effectiveness of three recommender system algorithms: user-based collaborative filtering, combined with PageRank and personalized PageRank algorithms. The experimental findings provide clear evidence that utilizing citation graph network datasets significantly enhances the efficacy of research paper recommender systems. This improvement simplifies the process of finding relevant literature for researchers, potentially accelerating scientific discovery

    Identifying effective criteria for author matching in bioinformatics

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    With the increasing development of information and scientific databases, scientific collaboration has expanded in health sciences. This study aims to prioritize the criteria that affect finding potential author matches in bioinformatics using fuzzy Multiple Criteria Decision Making (MCDM) methods such as Analytical Hierarchy Process (AHP), Fuzzy Delphi Method (FDM), and Triangular Fuzzy Numbers (TFN). To answer the research questions, a mix of documentary analysis and fuzzy methods is utilized. The documentary analysis stage involves collecting relevant documents and resources using the purposive sampling approach and ranking the effective criteria. The subsequent step involves experts determining the priorities of the effective criteria using pairwise comparisons and the Delphi questionnaire. The final weights are obtained based on the research purpose. The study shows that 79 criteria related to the research purpose can be grouped into three general categories: behavioral, topological, and content-based criteria. The most effective criteria in finding and recommending a potential author match are “journal titles”, “citations”, “paper titles”, “affiliations”, “keywords”, and “abstracts”. Among these criteria, citation and paper titles have a higher priority compared to others. The results indicate that contentbased criteria have the most significant impact on finding potential author matches in static scholar networks and networks with text information. Furthermore, among the content-based criteria, the number of publications in common specialized journals and the number of common citations are the most sought-after criteria for finding a potential author match with the highest similarity

    Recent Developments in Recommender Systems: A Survey

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    In this technical survey, we comprehensively summarize the latest advancements in the field of recommender systems. The objective of this study is to provide an overview of the current state-of-the-art in the field and highlight the latest trends in the development of recommender systems. The study starts with a comprehensive summary of the main taxonomy of recommender systems, including personalized and group recommender systems, and then delves into the category of knowledge-based recommender systems. In addition, the survey analyzes the robustness, data bias, and fairness issues in recommender systems, summarizing the evaluation metrics used to assess the performance of these systems. Finally, the study provides insights into the latest trends in the development of recommender systems and highlights the new directions for future research in the field

    Enhancing Online Reading Lists for Tertiary Education

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    Reading lists are a pedagogical tool widely used in tertiary education. Reading lists may provide pedagogical ‘scaffolding’ in which academics offer support to students through signposting and rich annotation on required readings. They thus have a critical role to play in transforming students into autonomous learners. However, it has been observed that existing Reading Lists software solutions support student learning in a partial, fractured way and are under-used in their role as a pedagogical tool. Therefore, this thesis examines the barriers to uptake of Reading Lists (i.e., reading lists that has been created using a special reading lists management software application) in universities, and, in particular, to explore possible interventions to improve academics’ experience with Reading Lists. We employed a mixed-methods approach in which we performed user studies (for academics, librarians, and students), content analyses and prototype designs. The results of these studies have been published across six research papers comprising Chapters 2–7 in this thesis. The first paper explores the types of resources that are linked in Reading Lists, in particular the inclusion of electronic materials. We identified that many academics struggle with successfully linking resources, and do not perceive the process to be user friendly. We recommended a number of interventions to improve the reading list experience for academics. In the second paper, we examined in greater detail the make-up of Reading Lists at the University of Waikato. We investigated the experience of academics and librarians when creating Reading Lists and found that uptake of Reading Lists varies widely between different academic disciplines. We recommended developing discipline-specific support to increase Reading Lists numbers and to integrate pedagogical features to increase academic buy-in. The third paper explores the students’ experience with the Reading Lists, in particular, when accessing electronic materials. The results of our analysis found that the students appreciate the way that Reading Lists help in their learning and perceive Reading Lists to be a useful tool for their learning process. However, their use of Reading Lists features varied due to the lack of awareness, visibility and interaction difficulties. We recommend enhancing the usability and the pedagogical features of Reading Lists to increase students' engagement. In the fourth paper, we explore in greater detail the pedagogical support that is offered in Reading List systems designed for tertiary teaching in a comparative study. The results of our comparative analysis identified a need for Reading List systems’ features that provide pedagogical support to better integrate into academic teaching. For these features to be truly beneficial, we identified a need to assist teachers to effectively use these tools in their daily practice. In our fifth paper, we explore the academics' experiences with Reading Lists by focusing on their engagement with the specific aspects, such as creating a reading list, linking resources and the reading lists’ notes. We found a need for streamlining the user workflow, improved usability, and better synchronization with other teaching support systems. We recommend improving the systems’ usability by re-engineering the user workflows and to better integrate “the notes feature” into academic teaching. In our final paper, we explore the academics’ feedback for prototype design, in which we introduce a redesigned interface for Reading Lists system, in comparison to the existing interfaces of the Waikato Reading Lists system. The results of our analysis identify that our new prototype design is better than the existing interfaces and our design has been accepted by the majority of academics. In conclusion, the research presented in this thesis has contributed to our understanding of the experiences of academics, librarians, and students as they engage with Reading List systems. Our investigation has identified obstacles that hinder the adoption of Reading List systems, shedding light on the challenges faced by users in embracing these platforms. Additionally, our findings have resulted in a proposed enhanced interface aimed at simplifying and streamlining the use of Reading List systems. This suggested improvement seeks to create a user experience for all individuals involved, encouraging wider acceptance and integration of Reading List systems into the higher education landscape. By deepening our understanding of the interactions between users and Reading List systems this research provides insights that can guide advancements in educational technology. Ultimately, we hope that these contributions will pave the way for efficient engagement with Reading List systems thus enhancing the overall teaching and learning experience

    The Relevance of Algorithm Skills for Digital Inequality

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    EVALUATING MICROLEARNING STRATEGIES IN THE CORPORATE ENVIRONMENT: A COMPARATIVE MIXED METHODS STUDY USING THE KIRKPATRICK MODEL

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    Corporate training and development, despite their significance as primary learning strategies for adults, have received limited research attention. This study addresses this gap by evaluating and comparing the effectiveness of video-based and digital job aid procedural-based microlearning in a corporate setting, utilizing most of the Kirkpatrick Model: learner satisfaction, knowledge retention, and behavior change. Employing a mixed methods approach to gain a comprehensive understanding of microlearning’s application and potential for improving business outcomes, the study incorporates pre- and post-tests, participant interviews, and self-reported questionnaires. Thirty participants completed the study, with equal representation (15 participants each) for both modalities, and interviews were conducted with 10 participants from each group. The results reveal that the type of microlearning treatment does not significantly impact knowledge retention, while the time elapsed since learning does influence retention. Additionally, the modality of the microlearning treatment may impact behavior change, although further investigations are necessary to examine the role of bias related to treatment preference and individual roles. Further findings indicate that participants favored a mixed modality microlearning approach for corporate training needs, involving initial training through videos and follow-up reference material through job aids. Furthermore, participants preferred knowledge evaluation methods such as quizzes or application-based assessments to apply and evaluate their understanding of the content. Future research should explore the impact of microlearning strategies on Key Performance Indicators (KPIs) in a business context, as well as the relationship between application-based training and knowledge transfer. Additionally, investigating the influence of peers and supervisors on behavioral change, as well as the impact of content management on cognitive load, would be valuable. Ultimately, this research seeks to bridge the gap between educational theory and practical implementation for instructional or learning designers and training, development, and/or enablement leaders, empowering them to make informed decisions regarding business practices and learning modalities

    Video Recommendation Using Social Network Analysis and User Viewing Patterns

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    With the meteoric rise of video-on-demand (VOD) platforms, users face the challenge of sifting through an expansive sea of content to uncover shows that closely match their preferences. To address this information overload dilemma, VOD services have increasingly incorporated recommender systems powered by algorithms that analyze user behavior and suggest personalized content. However, a majority of existing recommender systems depend on explicit user feedback in the form of ratings and reviews, which can be difficult and time-consuming to collect at scale. This presents a key research gap, as leveraging users' implicit feedback patterns could provide an alternative avenue for building effective video recommendation models, circumventing the need for explicit ratings. However, prior literature lacks sufficient exploration into implicit feedback-based recommender systems, especially in the context of modeling video viewing behavior. Therefore, this paper aims to bridge this research gap by proposing a novel video recommendation technique that relies solely on users' implicit feedback in the form of their content viewing percentages
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