Reviewerly/exHarmony huggingface dataset:
Pengembangan Media Pembelajaran Interaktif Berbasis Android dalam Pengenalan Huruf, Angka, Warna, Hewan, Buah, dan Huruf Hijaiyah

The development of information technology supports children to learn more optimally, so more optimally children need intermediaries or what is commonly referred to as learning media, where the existence of learning media can divert children's attention does not get bored quickly or be able to concentrate in an activity with a long enough time compared to other activities by not using learning media. In this study, the learning media in question is an application that can provide knowledge to children about recognizing letters, numbers, colours, animals, and fruits around them. Making learning media, cannot be separated from the exercises or questions given to measure how much material is absorbed. The purpose of this study is to help foster children's interest in learning the introduction of letters, numbers, colours, hijaiyah letters, animals, and fruits through a learning application. The type of research used in this research is Research and Development (R&amp;D). This data collection technique includes media feasibility validation and response questionnaires. The Validation of the media that has been developed is carried out by two validators in the field of education. Response questionnaires were given to 2 teachers and 1st grade students at 4 Banda Sakti elementary school. Application development is carried out using the Systems Development Life Cycle (SDLC) method. The results of the study are in the form of an android application that presents materials about recognizing letters, numbers, colours, animals, fruits, and hijaiyah letters as well as several practice questions that are presented randomly. The result shows that the media’s validation obtained 95% which means the media is feasible to be tested. Meanwhile, the teacher and student’s response questionnaires were 95.8% and 94%, respectively, which indicated that the media could be used as a learning support.
<scp>Person‐Oriented</scp> Ontologies Analysis for Digital Humanities Collections from a Metadata Crosswalk Perspective

ABSTRACT Mapping between different representations of similar data is a common challenge in digital humanities (DH). In practical DH collections, the ‘person’ is an essential and centric unit and other parts could link to the ‘person’ to form the knowledge base. However, there is still no general and useful person‐oriented ontology in DH community. Many practical DH projects have developed their own ontologies by DH experts, but these ontologies are not interoperable. Therefore, it is important to explore existing biographical ontologies and develop a comprehensive person‐oriented ontology for DH. Using the metadata crosswalk method, we examined the ontologies provided for persons in three DH collections to analyze how they map onto standard ontologies such as FOAF (friend of a friend). This paper uncovers a significant and consistent gap between standard biographical ontologies and those used in practical DH collections, arriving at a set of heterogeneous problems, including different granularities of metadata. Consequently, we propose three key person‐oriented ontological types of elements, drawing on this metadata crosswalk: basic biographical elements, relational elements, and explanatory elements (such as career, connected with role and time). This metadata crosswalk provides a foundation for future matching between person‐oriented ontologies and facilitates semantic interoperability between DH collections.
Multi-ant colony algorithm based on cooperative game and dynamic path tracking

For the problems of slow convergence and low stability in traditional ant colony algorithm when solving large-scale Traveling Salesman Problem (TSP), a multi-ant colony algorithm based on cooperative game and dynamic path tracking (CDMACA) is proposed. Firstly, a novel heterogeneous multi-population is formed by Ant Colony System (ACS) and Max–Min Ant System (MMAS). Secondly, a cooperative game is introduced. In the isomorphic populations, all the populations cooperate and then the pheromone income will be distributed to the participants based on the contribution. Populations with higher contribution will obtain more income, which can improve the accuracy of the solutions; On the other hand, in the heterogeneous populations, a learning mechanism is introduced to enable heterogeneous populations to learn the optimal solution from each other to further improve the accuracy. Thirdly, a dynamic path tracking mechanism is proposed to reward or punish the public paths with pheromone based on the similarity of the optimal solutions, which can enhance the concentration of pheromone on the optimal path, thus improving the convergence. Finally, when the algorithm stalls, a pheromone balance mechanism is introduced. By dynamically cutting the pheromone on the current optimal path, the probability of the optimal path being chosen will decrease, which can help the algorithm jump out of the local optimum. Through a large number of TSP instances, the proposed algorithm in this paper can further improve the accuracy when solving large-scale TSP compared with other improved algorithms.
Messages and Calls Ignored? A Survey of Four Thousand Smartphone Users on <scp>Non‐Response</scp> Behavior

ABSTRACT We are living in a world where a lot of people maintain constant connectivity through sending and receiving messages and calls. Contemporary research has focused on how smartphones disconnect us from our physical environment. Yet, in a digital disconnect, almost daily, people choose not to respond to certain messages or calls, which can make the sender anxious and adversely affect their communication. The first phase of this project consisted of an interview study of 24 participants which led to publications covering the sender and receiver perspectives. A follow‐up large‐scale survey of 4,001 respondents was carried out to investigate the receiver's reasons for not responding including the role of power, the psychological impact on the sender, and other communication. Theories on information avoidance, power, interpersonal deception, and emotions inform this study. Preliminary findings are shared here. The findings will inform the fields of information science, communication, psychology, and mental health, and should help software designers build tools to help users communicate their context to each other.
Real or Fake: Eliciting Deepfake Identification Strategies through a Diary Study

ABSTRACT 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.
Deep fit_predic: a novel integrated pyramid dilation EfficientNet-B3 scheme for fitness prediction system

This study introduces novel deep learning (DL) techniques for effective fitness prediction using a person's health data. Initially, pre-processing is performed in which data cleaning, one-hot encoding and data normalization are performed. The pre-processed data are then fed into the feature selection stage, where the useful features are extracted using the enhanced chameleon swarm (ECham-Sw) optimization technique. Then, a clustering process is performed using Minkowski integrated gravity center clustering (Min-GCC) to cluster the health profiles of each individual. Finally, the Pyramid Dilated EfficientNet-B3 (PyDi-EfficientNet-B3) technique is proposed to predict the fitness of each individual efficiently with enhanced accuracy of 99.8%.
Knowledge Organisation Systems in the Humanities—Semantic Interoperability in Practice

ABSTRACT We observe a growing universe of machine‐readable knowledge organisation systems (KOS) or even wider ‘semantic artifacts. We see at the same time, various attempts to bring semantic artifacts together via registries, catalogues and via cross‐walks among ontologies. This poster reflects how newest research on semantic interoperability informs current practice for research data repositories and registry service providers. We focus on the domain of humanities and cultural heritage, using different examples from Europe and the Netherlands: The European Archaeological Data Infrastructure Ariadne, services of the Dutch Cultural Heritage Network and two Data Stations (for Archaeology and for SSH) hosted at DANS‐KNAW. For those cases we report on current efforts to include ‘semantic artifacts’, together with opportunities and challenges. This poster is a practice report combined with an invitation to reflect about the context of semantic interoperability itself.
Digital Authentication System in Avatar Using DID and SBT

Anonymity forms the basis of decentralized ecosystems, leading to an increase in criminal activities such as money laundering and illegal currency trading. Especially in blockchain-based metaverse services, activities such as preventing sexual crimes and verifying the identity of adults are becoming essential. Therefore, avatar authentication and the KYC (Know Your Customer) process have become crucial elements. This paper proposes a mechanism to achieve the KYC process by verifying user identity using smart contracts. Users obtain an SBT (Soul Bound Token) from the metaverse service provider through the DID (Decentralized Identity) credential issued during the KYC process. The identity verification of avatars occurs within smart contracts, ensuring user privacy and protection through ZKP (Zero Knowledge Proof). Tools for generating ZKP are also provided, enabling users, even those who are unfamiliar with ZKP, to use them conveniently. Additionally, an integrated wallet is offered to seamlessly manage DID credentials and SBTs. Furthermore, in case of avatar identity issues, users can request an audit by the issuer through the associated DID tokens.
Translating Research into Practice: Plain Language and Writing for Machine Translation Guidelines

ABSTRACT Scholars seek to translate research into practice through science communication, but the overwhelming use of English makes it challenging for some local community actors to access research findings. Machine translation (MT) tools can help, but the underlying data‐driven approach does not work equally well for all languages and domains. If plain language summaries can be rendered more MT‐friendly, this could make it easier for speakers of other languages to access research. This poster compares guidelines for plain language and guidelines for writing for MT to determine their compatibility and potential usefulness for creating reader‐ and MT‐friendly summaries.
Electronic Health Records and Cyber Hygiene: A Qualitative Study of the Awareness, Knowledge, and Experience of Physicians in Kuwait

ABSTRACT Threats against electronic medical and health records are on the rise. These threats include phishing attacks, malware and ransomware, encryption blind spots, cloud threats, and most important one is the internal threat caused by gaps in the level of awareness and knowledge of the employees and health practitioner of handling sensitive healthcare data. Cyber hygiene is a term used to describe both the technical and non‐technical threats. In the same way, personal hygiene practices are used to maintain one's own health and protect against diseases, cyber hygiene security practices are important in protecting and preserving sensitive electronic health information systems. In this paper, we report on the findings of a cyber hygiene study carried out in Kuwait with the objectives of assessing the level of awareness, knowledge and experience of physicians and healthcare professionals. The study identified seven different areas or barriers that impacted the cyber hygiene and the adoption of electronic health medical records in Kuwait. The seven areas include financial barriers, time, difficulty of using technology, lack of management support, negative attitude toward the use of electronic medical records, legal and ethical issues, as well as cultural barriers. Most of the physicians interviewed lacked the awareness and basic knowledge needed to practice cyber hygiene. Most physicians were not aware of regulation or standards pertaining to the use of electronic medical records.
Finding the Aha! Moment of Search: A Preliminary Examination of Insight Learning During Search

ABSTRACT Research in information behavior has examined search difficulties and how people learn during searches but has not fully examined how searchers solve the difficulties on their own and gain new knowledge during this process. This study introduced the concept of insight learning during search to provide a new perspective for the studies in Search as Learning (SAL) and to optimize searchers' experiences in a more efficient, innovative, and joyful way to combine search and learning. As a preliminary study, we conducted self‐reported interviews with 30 participants to collect cases of insight learning during the search process. Based on thematic analysis of the data, we summarized the benefits of insight learning during search, described the process of how aha! occurred after impasse, and identified the antecedent, key, and consequence of insight during the search process. We aimed to help generate more insights by providing three dimensions of key factors to think about. A preliminary understanding of the insights formed in this study could contribute to further discussion about learning during the search and could help design new search tools that support effective learning.
A Multiscale Neighbor-Aware Attention Network for Collaborative Filtering

Most recommender systems rely on user and item attributes or their interaction records to find similar neighbors for collaborative filtering. Existing methods focus on developing collaborative signals from only one type of neighbors and ignore the unique contributions of different types of neighbor views. This paper proposes a multiscale neighbor-aware attention network for collaborative filtering (MSNAN). First, attribute-view neighbor embedding is modeled to extract the features of different types of neighbors with co-occurrence attributes, and interaction-view neighbor embedding is leveraged to describe the fine-grained neighborhood behaviors of ratings. Then, a matched attention network is used to identify different contributions of multiscale neighbors and capture multiple types of collaborative signals for overcoming sparse recommendations. Finally, we make the rating prediction by a joint learning of multi-task loss and verify the positive effect of the proposed MSNAN on three datasets. Compared with traditional methods, the experimental results of the proposed MSNAN not only improve the accuracy in MAE and RMSE indexes, but also solve the problem of poor performance for recommendation in sparse data scenarios.
Comparative Analysis of <scp>AI</scp> Applications in Libraries: A Systematic Literature Review

ABSTRACT The application of AI(artificial intelligence) in libraries is not only the result of the development of technology, but also the choice of libraries to improve their service. However, how to better integrate libraries and AI still needs further exploration, and libraries also need guidance in implementing AI technology. This study uses a systematic literature review method to analyze the literature on the application of AI in libraries published before 2023. Based on sorting out the application of AI in libraries, this paper summarizes and analyzes the practice and attitudes of applying AI in libraries. We find that there is a broad prospect of AI applications in libraries, but the current application is scattered and lacks a comprehensive view. There are different attitudes towards the application of AI in libraries and it is important to learn about different views.
Linking Allusion Words: A Method of Combining <scp>Fine‐Grained</scp> Co‐citation Relationship and Semantic Features

ABSTRACT It is a common phenomenon for Tang poems to cite the allusions, which can generate a rich relationship network. However, insufficient attention has been paid to investigating the relationship network. To address the research gap, by employing theories and methods of information science, this study presents a method of combining fine‐grained co‐citation relationship and semantic features to link allusion words. We constructed a fine‐grained co‐citation network between allusion words by adding cited positions and sentiments. We then transformed the fine‐grained weights into relational similarities. Moreover, we also leveraged the explanatory text as semantic information for each allusion word, mapping the semantic embedding vectors and calculating the similarities as the semantic similarities. Finally, we applied the link prediction algorithm to implement the allusion word linking. Our experimental results reveal that adding the cited positions and sentiments as well as semantic similarities can improve the performance of allusion word linking, achieving 0.869 on score. Additionally, we explore the linking results from the perspective of the shortest path and find some regular knowledge. Overall, our study extends the application of information science and promotes the development of Chinese traditional cultural resources.
A <scp>FAIR</scp> Data Ecosystem for Science of Science

ABSTRACT This poster discusses Automated Research Workflows (ARWs) in the context of a FAIR data ecosystem for the science of science research. We offer a conceptual discussion from the point of view of information science and technology using several cases of “data problems” in the science of science research to illustrate the characteristics and expectations for designers and developers of a FAIR data ecosystem. Drawing from a 10‐year data science project developing GenBank metadata workflows, we incorporate the ideas of ARWs into the FAIR data ecosystem discussion to set a broader context and increase generalizability. Researchers can use these as a guide for their data science projects to automate research workflows in the science of science domain and beyond.
Evaluating the Role of Machine Learning in Defense Applications and Industry

Machine learning (ML) has become a critical technology in the defense sector, enabling the development of advanced systems for threat detection, decision making, and autonomous operations. However, the increasing ML use in defense systems has raised ethical concerns related to accountability, transparency, and bias. In this paper, we provide a comprehensive analysis of the impact of ML on the defense sector, including the benefits and drawbacks of using ML in various applications such as surveillance, target identification, and autonomous weapons systems. We also discuss the ethical implications of using ML in defense, focusing on privacy, accountability, and bias issues. Finally, we present recommendations for mitigating these ethical concerns, including increased transparency, accountability, and stakeholder involvement in designing and deploying ML systems in the defense sector.
Normed Space of Fuzzy Intervals and Its Topological Structure

The space, Ƒcc(R), of all fuzzy intervals in R cannot form a vector space. However, the space Ƒcc(R) maintains a vector structure by treating the addition of fuzzy intervals as a vector addition and treating the scalar multiplication of fuzzy intervals as a scalar multiplication of vectors. The only difficulty in taking care of Ƒcc(R) is missing the additive inverse element. This means that each fuzzy interval that is subtracted from itself cannot be a zero element in Ƒcc(R). Although Ƒcc(R) cannot form a vector space, we still can endow a norm on the space Ƒcc(R) by following its vector structure. Under this setting, many different types of open sets can be proposed by using the different types of open balls. The purpose of this paper is to study the topologies generated by these different types of open sets.
Evaluating the Effectiveness of <scp>VR</scp> Training for Crisis Communication Skills Development Among <scp>LIS</scp> Graduate Students

ABSTRACT This research analyzes the results of a study that is part of a larger, interdisciplinary, and multi‐institutional project that examines the usability and effectiveness of virtual reality (VR) training for library and information science (LIS) graduate students and professionals in gaining skills for interacting effectively with patrons in crisis. This paper reports on key findings related to the effectiveness of VR training for teaching empathy, confidence, and de‐escalation skills for LIS graduate students. The findings illustrate that VR has the potential to impact LIS graduate education by reaching a wider audience that introduces training in low‐stakes, immersive environments and that does not pose harm to patrons in crisis. This study also contributes innovative approaches that support training in skills including empathy, confidence, and de‐escalation.
Theorising Information Literacy: Opportunities and Constraints

ABSTRACT Information literacy research is growing in importance but has been critiqued for remaining focused on practical topics of interest and attainment approaches to practice. Prior attempts to conceptualise information literacy have also often taken place without a comprehensive understanding of the ontological or epistemological foundations of theoretical work. The aim of this panel is to critically examine theory development and use within information literacy research through discussing the ways in which understandings of information literacy, including how it happens and how it shapes social life, are both enabled and constrained through critical, sociomaterial and discursive theoretical approaches. Providing a space to discuss and reflect on the impact of theory on information literacy scholarship, this panel creates a focal point for researchers, practitioners and students interested in the construction and advancement of conceptually rich information literacy research and practice.
Data Paper's Functions in Scholarly Communication Ecosystem as Perceived by Natural Scientists

ABSTRACT Data papers, a new class of scholarly publication emerging from the open‐science movement, foster data discovery and reuse by offering comprehensive descriptions of research data. Yet, despite their promising growth, the role of data papers in scholarly communication remains underexplored. This work therefore investigates the perceived contributions and functions of data papers to scholarly communication by interviewing 14 data‐paper authors operating in the field of natural science. Using conceptual frameworks adopted from Borgman (2007) and Van de Sompel et al. (2004), we identify four general functions of scholarly communication (i.e., legitimization; dissemination; access, preservation, and curation; and rewarding). Additionally, our data lead us to propose that verification is a distinct scholarly communication, underscoring the importance of data papers in validating research findings in the context of ensuring research transparency. By elucidating the crucial role that data papers now play within the scholarly communication ecosystem, this study seeks to raise the academic community's awareness of their fundamental position, as well as their co‐existence with other forms of data publication, in advancing scientific research.