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Death is an inevitable part of life and highly relevant to information management: its approach often requires preparation, and its occurrence often demands a response. Many works in information science have acknowledged so much, and yet death is rarely a focused topic, appearing instead sporadically and disconnected across research. As a result there is no introduction to, overview of, or synthesis across studies on death and information. We therefore conducted an extensive literature search and reviewed nearly 300 scholarly publications at the intersection of death and information (and data) management. Covering seven topics in total, we review two groups of work directly engaging information management in relation to death (digital possessions, inheritance, and legacy; information behavior, needs, and practices around death), three engaging death and technology that require information and its management (death and the Internet, thanatosensitive design and technology‐augmented death practices, and the digital afterlife and digital immortality), and two reflecting the ethical and legal dimensions unique to death and information. We then integrate the collective findings to summarize the landscape of death‐related information research, outline remaining challenges for individuals, families, institutions, and society, and identify promising directions for future information science research. [ABSTRACT FROM AUTHOR]

Background: The emergence of the artificial intelligence chatbot ChatGPT in November 2022 has garnered substantial attention across diverse disciplines. Despite widespread adoption in various sectors, the exploration of its application in libraries, especially within the medical domain, remains limited. Aims/Objectives: Many areas of interest remain unexplored like ChatGPT in medical libraries and this review aims to synthesise what is currently known about it to identify gaps and stimulate further research. Methods: Employing Cooper's integrative review method, this study involves a comprehensive analysis of existing literature on ChatGPT and its potential implementations within library contexts. Results: A systematic literature search across various databases yielded 166 papers, with 30 excluded for irrelevance. After abstract reviews and methodological assessments, 136 articles were selected. Critical Appraisal Skills Programme qualitative checklist further narrowed down to 29 papers, forming the basis for the present study. The literature analysis reveals diverse applications of ChatGPT in medical libraries, including aiding users in finding relevant medical information, answering queries, providing recommendations and facilitating access to resources. Potential challenges and ethical considerations associated with ChatGPT in this context are also highlighted. Conclusion: Positioned as a review, our study elucidates the applications of ChatGPT in medical libraries and discusses relevant considerations. The integration of ChatGPT into medical library services holds promise for enhancing information retrieval and user experience, benefiting library users and the broader medical community. [ABSTRACT FROM AUTHOR]

Users of research databases, such as CiteSeerX, Google Scholar, and Microsoft Academic, often search for papers using a set of keywords. Unfortunately, many authors avoid listing sufficient keywords for their papers. As such, these applications may need to automatically associate good descriptive keywords with papers. When the full text of the paper is available this problem has been thoroughly studied. In many cases, however, due to copyright limitations, research databases do not have access to the full text. On the other hand, such databases typically maintain metadata, such as the title and abstract and the citation network of each paper. In this paper we study the problem of predicting which keywords are appropriate for a research paper, using different methods based on the citation network and available metadata. Our main goal is in providing search engines with the ability to extract keywords from the available metadata. However, our system can also be used for other applications, such as for recommending keywords for the authors of new papers. We create a data set of research papers, and their citation network, keywords, and other metadata, containing over 470 K papers with and more than 2 million keywords. We compare our methods with predicting keywords using the title and abstract, in offline experiments and in a user study, concluding that the citation network provides much better predictions. [ABSTRACT FROM AUTHOR]

As one of the classic tasks in information retrieval, the core of image retrieval is to identify the images sharing similar features with a query image, aiming to enable users to find the required information from a large number of images conveniently. Street view image retrieval, in particular, finds extensive applications in many fields, such as improvements to navigation and mapping services, formulation of urban development planning scheme, and analysis of historical evolution of buildings. However, the intricate foreground and background details in street view images, coupled with a lack of attribute annotations, render it among the most challenging issues in practical applications. Current image retrieval research mainly uses the visual model that is completely dependent on the image visual features, and the multimodal learning model that necessitates additional data sources (e.g., annotated text). Yet, creating annotated datasets is expensive, and street view images, which contain a large amount of scene texts themselves, are often unannotated. Therefore, this paper proposes a deep unsupervised learning algorithm that combines visual and text features from image data for improving the accuracy of street view image retrieval. Specifically, we employ text detection algorithms to identify scene text, utilize the Pyramidal Histogram of Characters encoding predictor model to extract text information from images, deploy deep convolutional neural networks for visual feature extraction, and incorporate a contrastive learning module for image retrieval. Upon testing across three street view image datasets, the results demonstrate that our model holds certain advantages over the state‐of‐the‐art multimodal models pre‐trained on extensive datasets, characterized by fewer parameters and lower floating point operations. Code and data are available at https://github.com/nwuSY/svtRetrieval. [ABSTRACT FROM AUTHOR]

Summary: Content interest forwarding is a prominent research area in Information Centric Network (ICN). An efficient forwarding strategy can significantly improve data retrieval latency, origin server load, network congestion, and overhead. The state‐of‐the‐art work is either driven by flooding approach trying to minimize the adverse effect of Interest flooding or path‐driven approach trying to minimize the additional cost of maintaining routing information. These approaches are less efficient due to storm issues and excessive overhead. Proposed protocol aims to forward interest to the nearest cache without worrying about FIB construction and with significant improvement in latency and overhead. This paper presents the feasibility of integrating reinforcement learning based Q‐learning strategy for forwarding in ICN. By revising Q‐learning to address the inherent challenges, we introduce Q‐learning based interest packets and data packets forwarding mechanisms, namely, IPQ‐learning and DPQ‐learning. It aims to gain self‐learning through historical events and selects best next node to forward interest. Each node in the network acts as an agent with aim of forwarding packet to best next hop according to the Q value such that content can be fetched within fastest possible route and every action returns to be a learning process, which improves the accuracy of the Q value. The performance investigation of protocol in ndnSIM‐2.0 shows the improvement in a range of 10%–35% for metrics such as data retrieval delay, server hit rate, network overhead, network throughput, and network load. Outcomes are compared by integrating proposed protocol with state‐of‐the‐art caching protocols and also against recent forwarding mechanisms. [ABSTRACT FROM AUTHOR]

The curriculum work of teachers is understood and conceptualised in different ways. A prevalent view is that teachers are an integral part of a system of transmission and their work with curriculum essentially a technical exercise. Some form of this view seems to be assumed by policymakers, parents and at least some teachers. However, when this same work is considered from the perspective of interpretation theory or 'hermeneutics', a contrasting picture emerges. Rather than positioning teachers as relays of explicit information, interpretation theory alerts us to the complexities and uncertainties inherent in the reading, appreciation and application of texts. On this view, meaning is not stable but transformed through interpretation, and the process itself has no definite end point. Interpretation theory thus undermines major assumptions of the technical‐transmission understanding of curriculum work. In this paper, the potential contribution of this body of theory to illuminating curriculum work is explored. Noting that hermeneutics is a vast area of study, a selection of concepts will be made that demonstrates some of these insights. The exploration will yield both critical and generative contributions to curriculum theory. The way hermeneutics undermines the transmission view of teachers' curriculum work will become clear, and at the same time, the inherent creativity of curriculum interpretation becomes an inescapable feature of teacher expertise that could be celebrated rather than neglected, denied or repressed. A hermeneutic analysis of curriculum work thus has implications for what teachers know and do and for their role in contemporary society. [ABSTRACT FROM AUTHOR]

With the wide application of keyphrases in many Information Retrieval (IR) and Natural Language Processing (NLP) tasks, automatic keyphrase prediction has been emerging. However, these statistically important phrases are contributing increasingly less to the related tasks because the end‐to‐end learning mechanism enables models to learn the important semantic information of the text directly. Similarly, keyphrases are of little help for readers to quickly grasp the paper's main idea because the relationship between the keyphrase and the paper is not explicit to readers. Therefore, we propose to generate keyphrases with specific functions for readers to bridge the semantic gap between them and the information producers, and verify the effectiveness of the keyphrase function for assisting users' comprehension with a user experiment. A controllable keyphrase generation framework (the CKPG) that uses the keyphrase function as a control code to generate categorized keyphrases is proposed and implemented based on Transformer, BART, and T5, respectively. For the Computer Science domain, the Macro‐avgs of P@5, R@5, and F1@5 on the Paper with Code dataset are up to 0.680, 0.535, and 0.558, respectively. Our experimental results indicate the effectiveness of the CKPG models. [ABSTRACT FROM AUTHOR]

Paper filtering systems that support the effective collection of related journal papers are becoming important as the technological progress has been rapid. However, previous systems achieved insufficient filtering effectiveness because of a lack of available information in the abstract parts of journal papers that are available to the public. In this paper, we propose a paper filtering method using multiple information such as structure, co-authors, subject category, terminology, and terms in patent document. In an evaluation of 15 subjects using 4875 journal papers, it became clear that for all subjects the effectiveness of the proposed method exceeded that of the conventional method when all of the information above was used, thus increasing suitability for practical use. © 2012 Wiley Periodicals, Inc. Electron Comm Jpn, 95(5): 21-32, 2012; Published online in Wiley Online Library (). DOI 10.1002/ecj.10423 [ABSTRACT FROM AUTHOR]

Social tagging and controlled indexing both facilitate access to information resources. Given the increasing popularity of social tagging and the limitations of controlled indexing (primarily cost and scalability), it is reasonable to investigate to what degree social tagging could substitute for controlled indexing. In this study, we compared CiteULike tags to Medical Subject Headings ( MeSH) terms for 231,388 citations indexed in MEDLINE. In addition to descriptive analyses of the data sets, we present a paper-by-paper analysis of tags and MeSH terms: the number of common annotations, Jaccard similarity, and coverage ratio. In the analysis, we apply three increasingly progressive levels of text processing, ranging from normalization to stemming, to reduce the impact of lexical differences. Annotations of our corpus consisted of over 76,968 distinct tags and 21,129 distinct MeSH terms. The top 20 tags/MeSH terms showed little direct overlap. On a paper-by-paper basis, the number of common annotations ranged from 0.29 to 0.5 and the Jaccard similarity from 2.12% to 3.3% using increased levels of text processing. At most, 77,834 citations (33.6%) shared at least one annotation. Our results show that CiteULike tags and MeSH terms are quite distinct lexically, reflecting different viewpoints/processes between social tagging and controlled indexing. [ABSTRACT FROM AUTHOR]

Due to the progress of Internet technology and the increase of distributed information on networks, the present approach to knowledge management has been based more and more on the performance of various experienced users. In addition to the increase in electronic documents, the use of paper documents has not been reduced due to their convenience. This paper describes a method of tracking paper document locations and contents using radio frequency identification (RFID) technology. This research also focuses on the expression of a task process and the seamless structuring of related electronic and paper documents as a result of task knowledge formalization using information organizing. A system is proposed here that implements information organization for both Web documents and paper documents using the task model description and RFID technology. Examples of a prototype system are also presented. © 2012 Wiley Periodicals, Inc. Electron Comm Jpn, 95(8): 52-63, 2012; Published online in Wiley Online Library (). DOI 10.1002/ecj.10370 [ABSTRACT FROM AUTHOR]

This paper deals with the specific features of historical papers relevant for information retrieval and bibliometrics. The analysis is based mainly on the citation indexes accessible under the Web of Science (WoS) but also on field-specific databases: the Chemical Abstracts Service (CAS) literature database and the INSPEC database. First, the journal coverage of the WoS (in particular of the WoS Century of Science archive), the limitations of specific search fields as well as several database errors are discussed. Then, the problem of misspelled citations and their 'mutations' is demonstrated by a few typical examples. Complex author names, complicated journal names, and other sources of errors that result from prior citation practice are further issues. Finally, some basic phenomena limiting the meaning of citation counts of historical papers are presented and explained. [ABSTRACT FROM AUTHOR]

The article presents information on the inclusion of alerting service on health topics for medical journalists. A good journalist has to be able to access the latest information, analyse and filter it, assessing its quality and authoritativeness, while dealing with ever-increasing sources of information. The alerting service was designed using predefined criteria. Medical journals were selected on the basis of their impact factor. The selection of papers was discretional or based on their editorial comments. Various publisher's press alerts where available. Articles were selected when they had received an editorial comment or had been mentioned in the original press alerts from publishers. The service was expected to be objective, authoritative, and to provide items with real news value.

The Systematic Review Toolbox aims provide a web‐based catalogue of tools that support various tasks within the systematic review and wider evidence synthesis process. Identifying publications surrounding specific systematic review tools is currently challenging, leading to a high screening burden for few eligible records. We aimed to develop a search strategy that could be regularly and automatically run to identify eligible records for the SR Toolbox, thus reducing time on task and burden for those involved. We undertook a mapping exercise to identify the PubMed IDs of papers indexed within the SR Toolbox. We then used the Yale MeSH Analyser and Visualisation of Similarities (VOS) Viewer text‐mining software to identify the most commonly used MeSH terms and text words within the eligible records. These MeSH terms and text words were combined using Boolean Operators into a search strategy for Ovid MEDLINE. Prior to the mapping exercise and search strategy development, 81 software tools and 55 'Other' tools were included within the SR Toolbox. Since implementation of the search strategy, 146 tools have been added. There has been an increase in tools added to the toolbox since the search was developed and its corresponding auto‐alert in MEDLINE was originally set up. Developing a search strategy based on a mapping exercise is an effective way of identifying new tools to support the systematic review process. Further research could be conducted to help prioritise records for screening to reduce reviewer burden further and to adapt the strategy for disciplines beyond healthcare. [ABSTRACT FROM AUTHOR]

Deep learning technologies have brought us many models that outperform human beings on a few benchmarks. An interesting question is: can these models well solve real‐world problems with similar settings (e.g., identical input/output) to the benchmark datasets? We argue that a model is trained to answer the same information need in a similar context (e.g., the information available), for which the training dataset is created. The trained model may be used to solve real‐world problems for a similar information need in a similar context. However, information need is independent of the format of dataset input/output. Although some datasets may share high structural similarities, they may represent different research tasks aiming for answering different information needs. Examples are question–answer pairs for the question answering (QA) task, and image‐caption pairs for the image captioning (IC) task. In this paper, we use the QA task and IC task as two case studies and compare their widely used benchmark datasets. From the perspective of information need in the context of information retrieval, we show the differences in the dataset creation processes and the differences in morphosyntactic properties between datasets. The differences in these datasets can be attributed to the different information needs and contexts of the specific research tasks. We encourage all researchers to consider the information need perspective of a research task when selecting the appropriate datasets to train a model. Likewise, while creating a dataset, researchers may also incorporate the information need perspective as a factor to determine the degree to which the dataset accurately reflects the real‐world problem or the research task they intend to tackle. [ABSTRACT FROM AUTHOR]

Modern information retrieval systems use keywords within documents as indexing terms for search of relevant documents. As Chinese is an ideographic character-based language, the words in the texts are not delimited by white spaces. Indexing of Chinese documents is impossible without a proper segmentation algorithm. Many Chinese segmentation algorithms have been proposed in the past. Traditional segmentation algorithms cannot operate without a large dictionary or a large corpus of training data. Nowadays, the Web has become the largest corpus that is ideal for Chinese segmentation. Although most search engines have problems in segmenting texts into proper words, they maintain huge databases of documents and frequencies of character sequences in the documents. Their databases are important potential resources for segmentation. In this paper, we propose a segmentation algorithm by mining Web data with the help of search engines. On the other hand, the Romanized pinyin of Chinese language indicates boundaries of words in the text. Our algorithm is the first to utilize the Romanized pinyin to segmentation. It is the first unified segmentation algorithm for the Chinese language from different geographical areas, and it is also domain independent because of the nature of the Web. Experiments have been conducted on the datasets of a recent Chinese segmentation competition. The results show that our algorithm outperforms the traditional algorithms in terms of precision and recall. Moreover, our algorithm can effectively deal with the problems of segmentation ambiguity, new word (unknown word) detection, and stop words. [ABSTRACT FROM AUTHOR]

Proofs of Data Reliability are cryptographic protocols that provide assurance to a user that a cloud storage system correctly stores her data and has provisioned sufficient redundancy to be able to guarantee reliable storage service. In this paper, we consider distributed cloud storage systems that make use of erasure codes to guarantee data reliability. We propose two novel Proof of Data Reliability schemes, namely POROS and PORTOS, that on the one hand guarantees the retrieval of the outsourced data in their entirety through the use of proofs of data possession and on the other hand ensures the actual storage of redundancy. As a result, POROS and PORTOS are compatible with the current cloud computing model where the cloud autonomously performs all maintenance operations without any interaction with the user. Moreover, POROS and PORTOS delegate the burden of generating the redundancy to the cloud as well. The security of both solutions is proved in the face of a rational adversary whereby the cheating cloud provider tries to gain storage savings without increasing its total operational cost. [ABSTRACT FROM AUTHOR]

The importance of involving humans in the data science process has been widely discussed in the literature. However, studies lack details on how to involve humans in the process. Using a design science approach, this paper proposes and evaluates a human‐supervised data science framework in the context of local governments. Our findings suggest that the involvement of a stakeholder group, public managers in this case, in the process of data science project enhanced quality of data science outcomes. Public managers' detailed knowledge on both the data and context was beneficial for improving future data science infrastructure. In addition, the study suggests that local governments can harness the value of data‐driven approaches to policy and decision making through focalized investments in improving data and data science infrastructure, which includes culture and processes necessary to incorporate data science and analytics into the decision‐making process. [ABSTRACT FROM AUTHOR]

The paper reports a longitudinal analysis of the topical and methodological development of Library and Information Science (LIS). Its focus is on the effects of researchers' disciplines on these developments. The study extends an earlier cross‐sectional study (Vakkari et al., Journal of the Association for Information Science and Technology, 2022a, 73, 1706–1722) by a coordinated dataset representing a content analysis of articles published in 31 scholarly LIS journals in 1995, 2005, and 2015. It is novel in its coverage of authors' disciplines, topical and methodological aspects in a coordinated dataset spanning two decades thus allowing trend analysis. The findings include a shrinking trend in the share of LIS from 67 to 36% while Computer Science, and Business and Economics increase their share from 9 and 6% to 21 and 16%, respectively. The earlier cross‐sectional study (Vakkari et al., Journal of the Association for Information Science and Technology, 2022a, 73, 1706–1722) for the year 2015 identified three topical clusters of LIS research, focusing on topical subfields, methodologies, and contributing disciplines. Correspondence analysis confirms their existence already in 1995 and traces their development through the decades. The contributing disciplines infuse their concepts, research questions, and approaches to LIS and may also subsume vital parts of LIS in their own structures of knowledge production. [ABSTRACT FROM AUTHOR]

Main path analysis is a popular method for extracting the scientific backbone from the citation network of a research domain. Existing approaches ignored the semantic relationships between the citing and cited publications, resulting in several adverse issues, in terms of coherence of main paths and coverage of significant studies. This paper advocated the semantic main path network analysis approach to alleviate these issues based on citation function analysis. A wide variety of SciBERT‐based deep learning models were designed for identifying citation functions. Semantic citation networks were built by either including important citations, for example, extension, motivation, usage and similarity, or excluding incidental citations like background and future work. Semantic main path network was built by merging the top‐K main paths extracted from various time slices of semantic citation network. In addition, a three‐way framework was proposed for the quantitative evaluation of main path analysis results. Both qualitative and quantitative analysis on three research areas of computational linguistics demonstrated that, compared to semantics‐agnostic counterparts, different types of semantic main path networks provide complementary views of scientific knowledge flows. Combining them together, we obtained a more precise and comprehensive picture of domain evolution and uncover more coherent development pathways between scientific ideas. [ABSTRACT FROM AUTHOR]

Cloud computing has emerged as a promising platform that grants users direct yet shared access to computing resources and services without worrying about the internal complex infrastructure. However, secure data storage and data retrieval is the basic characteristics. Therefore, this paper proposes a novel double keys adapted elliptic curve cryptography (DKECC) and log normalized Gaussian sigmoid adaptive neuro‐fuzzy interference system (LGS‐ANFIS) based secure resource allocation system in decentralized cloud storage. Initially, the input data undergoes data fragmentation. After that, encryption is carried out using DKECC algorithm. Next, hash code generation using Pearson hash function is done. Then, resource availability is estimated via logistic sine chaotic mapping indulged rock hyraxes swarm optimization technique. Afterwards, data is checked for deduplication. Finally, resource allocation through LGS‐ANFIS takes place. The experimental outcomes demonstrated better results compared to baseline techniques. The modelling process showed that the suggested safe resource allocation model achieves the total security level of 96.27% while also having shorter reaction times (3018 ms), greater throughputs (1258), reduced load balancing (0.355874), and reduced latency (4372 ms). [ABSTRACT FROM AUTHOR]

The amount of scientific papers is growing together with the development of science itself; but, although there is an unprecedented availability of large citation indexes, some daily activities of researchers remain time‐consuming and poorly supported. In this paper, we present Visual Bibliographies (VisualBib), a real‐time visual platform, designed using a zz‐structure‐based model for linking metadata and a narrative, visual approach for showing bibliographies. VisualBib represents a usable, advanced, and visual tool, which simplifies the management of bibliographies, supports a core set of bibliographic tasks, and helps researchers during complex analyses on scientific bibliographies. We present the variety of metadata formats and visualization methods, proposing two use case scenarios. The maturity of the system implementation allowed us two studies, for evaluating both the effectiveness of VisualBib in providing answers to specific data analysis tasks and to support experienced users during real‐life uses. The results of the evaluation are positive and describe an effective and usable platform. [ABSTRACT FROM AUTHOR]

Controllable synthesis of high‐performance materials with low‐cost is critical for the development of functional devices. Herein, a series of sequence‐controlled low‐dispersity oligomers with acid–base chromotropic capability via simple copolymerization between N‐substituted maleimides (NMI) and methyldiallylamine (MDAA) are synthesized. The structure and molecular weight of the oligomers are characterized. Owing to the presence of strong electrophilic carbonyl oxygen and imine nitrogen, a rapid‐responsive color switching system is achieved. This change is attributed to the acid/base‐triggered isomerization between enolate state and the enol or keto tautomer via intramolecular proton transfer. More importantly, the color of P(MDAA/NMI) is regulated by the variation of the substituent group of the N‐substituted maleimide. A new type of rewritable paper based on the designed N‐substituted maleimide oligomers is fabricated by a simple spin‐coating process, on which images with high resolution can be acid‐printed and base‐erased for over ten cycles. The writing and erasure times can be as short as 10 s and the legible time can be more than 90 days under ambient conditions. The as‐formed rewritable paper with excellent rewriting performances is low‐cost and easy for large‐scale production and may find more advanced potential applications in memory devices, rewritable labels, an sensors. [ABSTRACT FROM AUTHOR]

As part of the effort to digitally transform, organizations are seeking more and better solutions to long‐standing enterprise content management challenges. Such solutions rarely investigate the relationship between knowledge workers' daily work to capture information and the perceived or actual value of that information to the enterprise per established content management strategy. The study described in this paper seeks to identify gaps in content management practices versus policy by modeling the conventions by which one organization's knowledge workers typically generate, store, and later recover their daily work products. Thirty‐five interviews with knowledge workers at the Jet Propulsion Laboratory were conducted on this subject. The results of these interviews provide an insight as to how knowledge workers interact with enterprise content in their dual roles as both the primary creators and primary consumers of enterprise content. This paper, which outlines various permutations of the digital object creation, description, and storage (CDS) model, provides basic strategies for bringing the value perceptions of knowledge workers into alignment with institutional directives related to improving content findability and reuse in the enterprise. [ABSTRACT FROM AUTHOR]

Systematic reviews are vital instruments for researchers to understand broad trends in a field and synthesize evidence on the effectiveness of interventions in addressing specific issues. The quality of a systematic review depends critically on having comprehensively surveyed all relevant literature on the review topic. In addition to database searching, handsearching is an important supplementary technique that helps increase the likelihood of identifying all relevant studies in a literature search. Traditional handsearching requires reviewers to manually browse through a curated list of field‐specific journals and conference proceedings to find articles relevant to the review topic. This manual process is not only time‐consuming, laborious, costly, and error‐prone due to human fatigue, but it also lacks replicability due to its cumbersome manual nature. To address these issues, this paper presents a free and open‐source Python package and an accompanying web‐app, Paperfetcher, to automate the retrieval of article metadata for handsearching. With Paperfetcher's assistance, researchers can retrieve article metadata from designated journals within a specified time frame in just a few clicks. In addition to handsearching, it also incorporates a beta version of citation searching in both forward and backward directions. Paperfetcher has an easy‐to‐use interface, which allows researchers to download the metadata of retrieved studies as a list of DOIs or as an RIS file to facilitate seamless import into systematic review screening software. To the best of our knowledge, Paperfetcher is the first tool to automate handsearching with high usability and a multi‐disciplinary focus. [ABSTRACT FROM AUTHOR]

Text‐based question answering (QA) is a challenging task which aims at finding short concrete answers for users' questions. This line of research has been widely studied with information retrieval (IR) techniques and has received increasing attention in recent years by considering deep neural network approaches. Deep learning (DL) approaches, which are the main focus of this paper, provide a powerful technique to learn multiple layers of representations and interaction between the questions and the answer sentences. In this paper, we provide a comprehensive overview of different models proposed for the QA task, including both a traditional IR perspective and a more recent deep neural network environment. We also introduce well‐known datasets for the task and present available results from the literature to have a comparison between different techniques. This article is categorized under:Algorithmic Development > Text MiningTechnologies > Machine Learning [ABSTRACT FROM AUTHOR]

Geological named entity recognition (NER) is an essential prerequisite to realizing geological information extraction and information retrieval and is an actual means for accomplishing structured reconstruction of unstructured geological data. Existing geological NER methods mainly focus on coarse-grained geological entity recognition, but geological entities are fine-grained. To solve this problem, a Chinese fine-grained geological entity corpus encompassing 21 types of fine-grained labels is constructed. In addition, in this article, a fine-grained geological entity recognition model based on Bidirectional Encoder Representations from Transformer (BERT)-Flat-Lattice Transformer is designed. This paper names this method FGNER (Fine-grained Geological Named Entity Recognition) which adds geological naming rules to revise the model results to improve the recognition of complex geological entities. The fine-grained geological entity recognition method is evaluated using regional geological literature reports as experimental data. The experimental results show that the precision, recall, and F1-score of the FGNER model are 95.73%, 89.26%, and 92.05%, respectively, thus achieving better performance than baseline models, such as BERT-Conditional Random Field. [ABSTRACT FROM AUTHOR]

This minireview summarizes the information on the development and application of ion‐sensitive field‐effect transistors (ISFETs) with UV‐cured polyurethane ion‐selective membranes. Among the advantages of photopolymerizable polymer membranes in comparison with traditional poly(vinyl chloride) are excellent adhesion to a solid sensor surface and very fast curing time. Moreover, processes of membrane deposition and curing are compatible with ISFETs fabrication technology which is important for sensors mass production. The paper presents specific features of photopolymerizable membranes and discusses required precautions in their preparation and treatment. Examples of various analytical applications of this kind of sensors are provided. Finally, the outlook and perspectives of further development in the field of miniature solid‐state ion sensors are given. [ABSTRACT FROM AUTHOR]

Technological advances led to the generation of large scale complex data. Thus, extraction and retrieval of information to automatically discover latent pattern have been largely studied in the various domains of science and technology. Consequently, machine learning experienced tremendous development and various statistical approaches have been suggested. In particular, data clustering has received a lot of attention. Finite mixture models have been revealed to be one of the flexible and popular approaches in data clustering. Considering mixture models, three crucial aspects should be addressed. The first issue is choosing a distribution which is flexible enough to fit the data. In this paper, a model based on multivariate Beta distributions is proposed. The two other challenges in mixture models are estimation of model's parameters and model complexity. To tackle these challenges, variational inference techniques demonstrated considerable robustness. In this paper, two methods are studied, namely, batch and online variational inferences and the models are evaluated on four medical applications including image segmentation of colorectal cancer, multi‐class colon tissue analysis, digital imaging in skin lesion diagnosis and computer aid detection of Malaria. [ABSTRACT FROM AUTHOR]

This paper addresses the importance of improving the effectiveness of voice search systems through partial query modification. A user‐centered experiment was designed to compare the effectiveness of an experimental system using partial query modification feature to a baseline system in which users could issue complete queries only, with 32 participants each searching on eight different tasks. The results indicate that the participants spent significantly more time preparing the modification but significantly less time speaking the modification by using the experimental system than by using the baseline system. The participants found that the experimental system (a) was more effective, (b) gave them more control, (c) was easier for the search tasks, and (d) saved them time than the baseline system. The results contribute to improving future voice search system design and benefiting the research community in general. System implications and future work were discussed. [ABSTRACT FROM AUTHOR]

Bug localization is an important field in software engineering research. The traditional bug localization approaches based on information retrieval separate words through lexical analysis. In this way, the comments of the source code are ignored or treated as plain text, which will lose some semantic information. In this paper, MBL_SHL, an automatic Method‐level Bug Localization approach, which utilises code Summarization, Historical fixed bugs and code Length, is presented. Based on the code summarization technology, this approach first supplements the comment for uncommented code, and then calculates the Word2vec vector and Term Frequency–Inverse Document Frequency vector for the bug report, methods and comments, respectively. After that the authors calculate separately the similarity between the bug report and each method, the bug report and each comment. The code length information and historical fix information are also considered as a weight and a part of the score, respectively, to calculate the final score of each method. Finally, the scores are sorted to determine the list of methods that may need to be modified when fixing the software bugs. We built a method‐granular bug localization dataset, which contains five open‐source projects. The experimental results show that the proposed approach significantly outperforms the existing approaches on the method level. [ABSTRACT FROM AUTHOR]

Background: Processing and comprehending information from multiple sources have been a primary means of learning and are essential 21st‐century skills to construct knowledge for a deeper understanding. Objectives: This study examined students' individual differences in search strategies and internet epistemic beliefs as well as the effect of distinct note‐taking formats on students' online multiple‐document reading about measurement scales and statistical graphs, considering students' gender and prior knowledge. Methods: Participants were 124 Taiwanese university students (79% female) taking the Educational Statistics course in three different classes. I used a quasi‐experimental design to test the effect of different note‐taking formats on online multiple document reading. Three classes of students were randomly assigned to one of the experimental conditions with sourcing cues: the individual note taking with multiple tabs, the collaborative note taking with multiple tabs, or the individual note taking in a single tab. Results and conclusion: The study showed that the uncertainty belief about Internet‐based information and note‐taking formats predicted students' online multiple document reading. A stronger belief that the internet information is uncertain and may change with time positively predict online multiple document reading comprehension. Moreover, individuals taking notes in a single tab outperformed those taking notes individually in separate tabs. Major takeaways from the study: Implications for instruction were made to foster students' uncertainty belief and teach them how to construct pre‐outlined tables with sourcing cues to collect the contextual information and reflect on the source information to streamline learners' online multiple document reading comprehension. Lay Description: What is already known about this topic?: Online multiple document reading comprehension (OMDRC) is a complex mental process, which involves locating relevant information from the Internet, evaluating the quality and credibility of the online information, and constructing and reconciling conflicting views across multiple sources.Better Internet search skills predicted higher online reading outcomes.Internet epistemic beliefs or one's epistemic processing to acquire, justify, and use knowledge during the online learning task is associated with more experienced online search behaviour and quality of argumentation. What this paper adds?: This study adds to the literature in OMDRC to investigate how Internet epistemic beliefs and formats of note taking with sourcing cues will affect OMDRC, beyond learners' online search strategies, gender, and prior knowledge.This study shows that more adaptive Internet epistemic beliefs (particularly the uncertainty beliefs) and a coherent presentation in note taking will contribute to better OMDRC. Implications for practice and/or policy: Interventions (such as modelling and collaborative discussion) targeting students' awareness of the uncertainty about the Internet information can be applied to encourage them to examine the information from different perspectives.Effective pedagogical support, such as pre‐outline note‐taking devices with sourcing cues can help learners' multiple text integration from the Internet and benefit their OMDRC. [ABSTRACT FROM AUTHOR]

Summary: Representing semantic web information in the form of existing formats like extensible mark‐up language (XML), resource description framework (RDF) or web ontology language (OWL) is limited by the inability to maintain intradocument relationships between different semantic entities. Moreover, these standards are built for generic data representation and thus do not have inherent support for the semantic description of the document and, consequently, do not support features like multidocument linkages and context‐sensitivity. In order to remove these drawbacks, a semantic web‐specific document representation approach, namely, deep semantic XML (DS‐XML) using deep linking and machine learning, is presented in this paper. The DS‐XML approach facilitates the use of semantics with the help of a novel document structure that focuses on defining any web document in a semantic form. It includes ontologies and linkage between different vocabularies, which assists in achieving semantic interoperability. In order to make this approach future proof, a novel deep linking approach is integrated at the document level, which allows DS‐XML to understand more about the contents of the document and represent information in an aggregated manner. The deep linking layer is combined with the machine learning‐based classification layer that allows the document information to be represented in an interactive manner. The proposed DS‐XML approach is tested on different document retrieval applications, including a few Internet of Things (IoT) applications, and the representation performance is found to be superior to the existing semantic approach (XML, RDF and OWL). [ABSTRACT FROM AUTHOR]

The approach to identifying evidence for inclusion in realist reviews differs from that used in 'traditional' systematic reviews. Guidance suggests that realist reviews should be inclusive of diverse data from a range of sources, gathered in iterative searching cycles. Saturation is prioritised over exhaustiveness. Supplementary techniques such as citation snowballing are emphasised as potentially important sources of evidence. This paper describes the processes used to identify evidence in a selection of realist reviews focused on primary health care settings and examines the origin and type of evidence selected for inclusion. Data from five realist reviews were extracted from (a) reviewers' reference management libraries and (b) records kept by review teams. Although all reviews focused on primary health care, they used data from a wide range of document types and research designs, drawing on learning from multiple perspectives and settings, and sourced the documents containing this data in a variety of ways. Systematic searching of academic databases played an important role, supplementary search techniques such as snowballing were used to identify a significant proportion of documents included in the reviews. Our analysis demonstrates the diverse data sources used within realist reviews and the need for flexible, responsive efforts to identify relevant documents. Reviewers and information specialists should devise approaches to data gathering that reflect the individual needs of realist review projects and report these transparently. [ABSTRACT FROM AUTHOR]

This article considers the interdisciplinary opportunities and challenges of working with digital cultural heritage, such as digitized historical newspapers, and proposes an integrated digital hermeneutics workflow to combine purely disciplinary research approaches from computer science, humanities, and library work. Common interests and motivations of the above‐mentioned disciplines have resulted in interdisciplinary projects and collaborations such as the NewsEye project, which is working on novel solutions on how digital heritage data is (re)searched, accessed, used, and analyzed. We argue that collaborations of different disciplines can benefit from a good understanding of the workflows and traditions of each of the disciplines involved but must find integrated approaches to successfully exploit the full potential of digitized sources. The paper is furthermore providing an insight into digital tools, methods, and hermeneutics in action, showing that integrated interdisciplinary research needs to build something in between the disciplines while respecting and understanding each other's expertise and expectations. [ABSTRACT FROM AUTHOR]

Background: Norwegian public health nurses prevent diseases and promote health in children and young people aged 0–20 and their families. Public health nursing programs prepare students for their practical role and provide relevant theoretical knowledge. Objectives: To gain knowledge of the literature in the Norwegian public health nursing curricula, and to examine further the nursing base in these curricula. Design: An explorative and descriptive design was chosen. Sample: Reading lists based on syllabus documents from the 10 higher educational institutions in Norway offering programs in public health nursing. Measurements: A summative content analysis and a categorization of content from reading lists were performed. Results: Numerical information on the content and categorization of reading lists shows that social science and humanities literature dominates, followed by psychology and medicine. Nursing texts, theories and philosophy comprise only a minor part of the curricula. Conclusion: The paper provides valuable data on the theoretical focus in Norwegian public health nursing and raises important questions about the paucity of nursing texts in the curriculum. The imbalance in reading lists in Norway should be studied further and similar studies conducted in other countries to encourage reflection on the theoretical content of public health nursing education globally. [ABSTRACT FROM AUTHOR]

Cross modal retrieval can retrieve images through a text query and vice versa. In recent years, cross modal retrieval has attracted extensive attention. The purpose of most now available cross modal retrieval methods is to find a common subspace and maximize the different modal correlation. To generate specific representations consistent with cross modal tasks, this paper proposes a novel cross modal retrieval framework, which integrates feature learning and latent space embedding. In detail, we proposed a deep CNN and a shallow CNN to extract the feature of the samples. The deep CNN is used to extract the representation of images, and the shallow CNN uses a multi‐dimensional kernel to extract multi‐level semantic representation of text. Meanwhile, we enhance the semantic manifold by constructing cross modal ranking and within‐modal discriminant loss to improve the division of semantic representation. Moreover, the most representative samples are selected by using online sampling strategy, so that the approach can be implemented on a large‐scale data. This approach not only increases the discriminative ability among different categories, but also maximizes the relativity between different modalities. Experiments on three real word datasets show that the proposed method is superior to the popular methods. [ABSTRACT FROM AUTHOR]

Summary: In big cities, the drivers face a major problem in identifying an optimal vacant parking space for their vehicles as the number of vehicles has rapidly grown over the recent years. This objective of determining the available parking space results in wastage of time, air pollution, and traffic congestion. Smart parking systems facilitate the driver to book parking slots and attain real‐time parking information. However, the existing smart parking solutions necessitate the disclosure of sensitive information like desired destination from the vehicle drivers. Moreover, the current smart parking solutions are highly centralized, making them vulnerable for launching privacy breaches by service providers and resulting in the issues of single‐point failure. In this paper, ring signature and improved multi‐transaction mode consortium blockchain‐based private information retrieval (RS‐IMTMCB‐PIR) scheme is proposed for privacy preserving in smart parking system. This RS‐IMTMCB‐PIR scheme initially utilizes an improved multi‐transaction mode consortium blockchain constructed by different number of parking lot owners in order to achieve maximized parking offers based on the factor of availability, transparency, and security. It incorporates an improved private information retrieval approach for protecting the location privacy of drivers and secretly retrieving the offers of parking from the improved multi‐transaction mode consortium blockchain. It further utilizes the benefits of ring signature for permitting the drivers in a potential anonymous authentication process that aids in available parking slot reservation. The simulation results of this proposed RS‐IMTMCB‐PIR scheme portrayed a predominant performance in ensuring maximized degree of drivers' privacy with reduced computation and communication overheads. [ABSTRACT FROM AUTHOR]

This paper investigates error correction in two‐dimensional (2‐D) intersymbol interference (ISI) channels using polar codes. 2‐D channels offer high data storage capacity compared to traditional one‐dimensional (1‐D) channels but suffer from greater ISI effects. Error correction codes can be used to mitigate the effects of ISI in 2‐D data storage systems and improve their performance and reliability. Polar codes achieve the symmetric capacity for the class of binary‐input discrete memoryless channels (B‐DMCs). A post‐processing technique is proposed to improve the performance of polar codes in the 2‐D ISI channel. The simplified successive cancellation (SSC) polar decoder is useful for data storage systems owing to its good waterfall region performance, low error floors and good throughput rates and is therefore adopted for the study. Simulation results indicate that the proposed technique has an error correction performance comparable to some existing decoders and has better performance than other decoding techniques. [ABSTRACT FROM AUTHOR]

This is the first study that compares types of orientation tactics that blind and sighted users applied in their initial interactions with a digital library (DL) and the associated factors. Multiple methods were employed for data collection: questionnaires, think-aloud protocols, and transaction logs. The paper identifies seven types of orientation tactics applied by the two groups of users. While sighted users focused on skimming DL content, blind users concentrated on exploring DL structure. Moreover, the authors discovered 13 types of system, user, and interaction factors that led to the use of orientation tactics. More system factors than user factors affect blind users' tactics in browsing DL structures. The findings of this study support the social model that the sightcentered design of DLs, rather than blind users' disability, prohibits them from effectively interacting with a DL. Simultaneously, the results reveal the limitation of existing interactive information retrieval models that do not take people with disabilities into consideration. DL design implications are discussed based on the identified factors. [ABSTRACT FROM AUTHOR]

NASA's Ice, Cloud, and land Elevation Satellite‐2 (ICESat‐2) laser altimeter launched in Fall 2018, providing an invaluable addition to the polar altimetry record generated by ESA's CryoSat‐2 radar altimeter. The simultaneous operation of these two satellite altimeters enables unique comparison studies of sea ice altimetry, utilizing the different frequencies and profiling strategies of the two instruments. Here, we use freeboard data from ICESat‐2 to assess Antarctic snow freeboard retrievals from CryoSat‐2. We first discuss updates made to a previously published CryoSat‐2 retrieval process and show how this Version 2 algorithm improves upon the original method by comparing the new retrievals to ICESat‐2 in specific along‐track profiles as well as on the basin‐scale. In two near‐coincident along‐track profiles, we find mean snow freeboard differences (standard deviations of differences) of 0.3 (9.3) and 7.6 cm (9.6 cm) with 25 km binned correlation coefficients of 0.77 and 0.89. Monthly mean freeboard differences range between −2.9 (10.8) and 6.6 cm (16.8 cm) basin wide, with the largest differences typically occurring in Austral fall months that is hypothesized to be related to new ice growth and the use of static snow backscatter coefficients in the retrieval. Monthly mean correlation coefficients range between 0.57 and 0.80. While coincident data show good agreement between the two sensors, they highlight issues related to geometric and frequency sampling differences that can impact the freeboard distributions. Plain Language Summary: Measuring sea ice freeboard from space is an important first step in estimating its thickness. A previous study had developed a new method of measuring freeboard over Antarctic sea ice using ESA's CryoSat‐2 altimeter, however, few validation data existed at the time to determine how well it performed. In this paper, we improve the CryoSat‐2 processing and make use of data from NASA's ICESat‐2 altimeter for comparisons with the CryoSat‐2 data. While agreement is strong overall, there are still differences between the measurements that we hypothesize come from the different footprint sizes and wavelengths of the two instruments. Key Points: We present an updated CryoSat‐2 Antarctic snow freeboard retrieval methodThese improved CryoSat‐2 snow freeboard retrievals show strong agreement with ICESat‐2 data both along‐track and basin‐wideThis assessment highlights difficulties in laser‐radar comparisons brought on by frequency and geometric sampling discrepancies [ABSTRACT FROM AUTHOR]

There is limited guidance on how to web‐search in systematic reviews and concern relates to the reproducibility of searches using search engines such as Google. The aim of this paper is to address one potential source of variation in Google searches: does the geographical location of a researcher affect Google search returns? Using a virtual private network, we ran the same web‐search for the medical technology Dasatinib in 12 different countries. Two researchers independently extracted the search returns by country organised by page rank. We compared: C1. any difference in the items returned by Google searches between countries and C2. any difference in the page rank of items returned between countries. Searches were undertaken on Monday September 28th 2020. From 12 countries, 43 items were identified. For C1: 19 items were common to all 12 countries. Twenty‐four items were missed by searches in some countries. This means that there were differences in search returns between countries. For C2: a randomised trial reported by Raddich et al was the first search return for all countries. All other items, common to all countries, varied in their page‐rank. We find that geographic location would appear to influence Google search returns based on the findings of this case study. The findings suggest that recording the location of the researcher undertaking web‐searching may now be an important factor to report alongside detail on steps taken to minimise personalisation of web‐searches covered by recent guidance. This finding also has implications for stopping‐rules. [ABSTRACT FROM AUTHOR]

Term position feature is widely and successfully used in IR and Web search engines, to enhance the retrieval effectiveness. This feature is essentially used for two purposes: to capture query terms proximity or to boost the weight of terms appearing in some parts of a document. In this paper, we are interested in this second category. We propose two novel query‐independent techniques based on absolute term positions in a document, whose goal is to boost the weight of terms appearing in the beginning of a document. The first one considers only the earliest occurrence of a term in a document. The second one takes into account all term positions in a document. We formalize each of these two techniques as a document model based on term position, and then we incorporate it into a basic language model (LM). Two smoothing techniques, Dirichlet and Jelinek‐Mercer, are considered in the basic LM. Experiments conducted on three TREC test collections show that our model, especially the version based on all term positions, achieves significant improvements over the baseline LMs, and it also often performs better than two state‐of‐the‐art baseline models, the chronological term rank model and the Markov random field model. [ABSTRACT FROM AUTHOR]

Rationale, aims, and objectives: Assessing the performance of diagnostic tests requires evaluation of the amount of diagnostic uncertainty a test reduces. Statistical measures, such as sensitivity and specificity, currently dominating the evidence‐based medicine (EBM) and related fields, cannot explicitly measure this reduction in diagnostic uncertainty. Mutual information (MI), an information theory statistic, explicitly quantifies diagnostic uncertainty by measuring information gain before vs after diagnostic testing. In this paper, we propose the use of MI as a single measure to express diagnostic test performance and demonstrate how it can be used in the meta‐analysis of diagnostic test studies. Methods: We use two case studies from the literature to demonstrate the applicability of MI meta‐analysis in assessing diagnostic performance. Meta‐analysis of studies evaluating (a) ultrasonography (US) to detect endometrial cancer and (b) magnetic resonance angiography to detect arterial stenosis. Results: The results of MI meta‐analyses are comparable to those of traditional statistical measures' meta‐analyses. However, the results of MI are easier to understand as it relates directly to the extent of uncertainty a diagnostic test can reduce. For example, the US test, diagnosing endometrial cancer, is 40% specific and 94% sensitive. The combination of these values is difficult to interpret and may lead to inappropriate assessment (eg, one could favour the test due to its high sensitivity, ignoring its low specificity). In terms of MI, however, a single metric shows that the test reduces diagnostic uncertainty by 10%, which many users may consider small under most circumstances. Conclusions: We have demonstrated the suitability of MI in assessing the performance of diagnostic tests, which can facilitate easier interpretation of the true utility of diagnostic tests. Similarly, to the guidance for interpretation of effect size of treatment interventions, we also propose the guidelines for interpretation of the utility of diagnostic tests based on the magnitude of reduction in diagnostic uncertainty. [ABSTRACT FROM AUTHOR]

Recently, resistance random access memories (RRAMs) have been studied extensively, because the demand for information storage is increasing. However, it remains challenging to obtain a flexible device because the active materials involved need to be nontoxic, nonpolluting, distortion‐tolerable, and biodegradable as well adhesive to diverse flexible substrates. In this paper, tannic acid (TA) and an iron ion (FeIII) coordination complex were employed as the active layer in a sandwich‐like (Al/active layer/substrate) device to achieve memory performance. A nontoxic, biocompatible TA‐FeIII coordination complex was synthesized by a one‐step self‐assembly solution method. The retention time of the TA‐FeIII memory performance was up to 15 000 s, the yield up to 53 %. Furthermore, the TA‐FeIII coordination complex can form a high‐quality film and shows stable ternary memory behavior on various flexible substrates, such as polyethylene terephthalate (PET), polyimide (PI), printer paper, and leaf. The device can be degraded by immersing it in vinegar solution. Our work will broaden the application of organic coordination complexes in flexible memory devices with diverse substrates. Flexible memory: Tannic acid (TA) and an iron (FeIII) coordination complex were employed as an active layer in a sandwich‐like (Al/active layer/substrate) device to achieve memory performance. A nontoxic, biocompatible TA‐FeIII coordination complex was synthesized by a one‐step self‐assembly solution method. This complex can form a film on various flexible substrates (PET, PI, paper, and leaf) and all display ternary memory performance (see graphic). [ABSTRACT FROM AUTHOR]

The matter of boat migrants crossing the Mediterranean Sea, seeking to reach Europe, is interchangeably defined as a ‘security issue', requiring stricter border controls, and as a humanitarian issue with corresponding rescue and protection requirements. This paper seeks to understand what role various surveillance technologies, such as radar, satellites, and unmanned aerial vehicles, can play in this respect (legally and technically), in comparison to the role that they are assigned (that is, political expectations). To unravel what surveillance technologies can and cannot do vis‐à‐vis the aims of control and rescue, there is a need to comprehend what information can be collected and what information is needed to fulfil these objectives. The paper contends that there is a mismatch between the information sought to ‘control’ borders, but which cannot be gathered effectively by or processed using surveillance technologies, and the valuable information needed to perform rescue operations, which these surveillance technologies can supply. [ABSTRACT FROM AUTHOR]

Retrieval practice promotes retention more than restudying (i.e., the testing effect) and is applied to many educational settings. However, little research has investigated means to enhance this effect in educational settings. Theoretical accounts assume retrieval practice to be the most effective whenever retrieval is difficult but successful. Therefore, we developed a novel retrieval practice procedure, which adapts to learners' abilities and can be applied irrespective of learning content. This adaptive procedure aims to make retrieval gradually easier whenever students provide an incorrect answer. In a field experiment, students read book chapters as part of a weekly university course. In three consecutive weeks, they then practiced reading assignments by (a) adaptive testing, (b) non‐adaptive testing and (c) restudy. In Week 4, a surprise criterial test took place. Restudy outperformed both testing conditions, whereas adaptive testing performed equally well as non‐adaptive testing. However, exploratory analyses revealed that with increasing retention intervals, the superiority of restudy disappeared. Furthermore, whenever participants fully read the assignments and retention intervals increased, adaptive testing outperformed non‐adaptive testing. In sum, adaptive retrieval practice did not prove to be generally superior, but retention interval and students' preparation for class might be conditions rendering adaptive retrieval useful in educational settings. Lay Description: What is already known about this topic: Testing is a commonly used technique to assess progress in computer‐assisted learning environmentsStudents often benefit from the testing effect, that is practicing retrieval of information (testing) from memory benefits long‐term retention compared to restudyingCurrently, empirical evidence how to enhance the benefits of this technique in educational contexts is scarce What this paper adds: We propose a new approach to increase the benefit of the testing effect: For each question in retrieval practice the individual ability to retrieve this information from memory is consideredTo do so, cue informativeness of retrieval cues adapts to the individual ability level and thus makes the tests cognitively effortful but solvableThis adaptive approach is put to test in a higher education context and by using multiple‐choice questionsIn sum, adaptive retrieval practice did not prove to be generally superior to non‐adaptive retrieval practice or restudy However, retention interval and students' preparation for class might be conditions rendering adaptive retrieval useful in educational settings Implications for practice and/or policy: Findings can be implemented in educational contexts when multiple‐choice questions are used to enhance learningLearning can be optimized in computer‐assisted learning environments that allow adaptive testingWe advise adaptive retrieval only when students are well prepared for practice and when time between practice and final assessment exceeds two weeks [ABSTRACT FROM AUTHOR]

Aim: To link data from a large maternal perinatal trial with the Australian Cerebral Palsy Register (ACPR) to identify children with cerebral palsy (CP).Method: Deidentified data from the Australasian Collaborative Trial of Magnesium Sulphate (ACTOMgSO4 ) and the ACPR were linked. Children born from 1996 to 2000 at Australian hospitals who survived and had 2-year paediatric assessments were included. Children identified with CP in: (1) both the ACTOMgSO4 (2y) and the ACPR (5y), (2) the ACTOMgSO4 only, and (3) the ACPR only were compared.Results: We included 913 children (492 males, 421 females; mean gestational age at birth 27.8wks [standard deviation 2.1wks]; range 23.0-40.0wks). Eighty-four children received a CP diagnosis: 35 by the ACTOMgSO4 and the ACPR, 29 by the ACTOMgSO4 only, and 20 by the ACPR only. The ACTOMgSO4 diagnosed 76.2% (95% confidence interval [CI] 65.9-84.1) and the ACPR identified 65.5% (95% CI 54.7-74.9). Children born in states/territories with long-standing versus more recently established registers were more likely to be included on the ACPR (p<0.05).Interpretation: Linking deidentified perinatal trial data with the ACPR was achieved. Limitations of both strategies for identifying children with CP in this era (late 1990s and early 2000s) probably explain many of the differences observed, and inform future linkage studies and evaluations of CP-preventive interventions.What This Paper Adds: Randomized trial data were linked with the Australian Cerebral Palsy Register. Trial (2y) and register (up to 5y) diagnoses of cerebral palsy (CP) differed. States with long-standing registers were more likely to include children with CP. [ABSTRACT FROM AUTHOR]

Cybersecurity is one of the main concerns of many organizations today, and accessibility to cybersecurity information in a timely manner is crucial to maintaining cybersecurity. Various repositories of cybersecurity-related information are publicly available on the Internet. However, users are unaware of many of them, and it is impractical for them to keep track of all of them. Cybersecurity information stored in these repositories must be able to be located and accessed by the parties who need it. To address this issue, this paper proposes a mechanism of linking, locating, and discovering various cybersecurity information to improve its accessibility in a timely manner. This mechanism allows us to locate various cybersecurity information having different schemata by generating metadata with which a list of cybersecurity information is managed. The information structure incorporated in this mechanism is unique, and it makes our mechanism flexible and extensible. The structure consists of categories and formats that are linked to each other. The mechanism can propagate information updates to minimize the risk of obsolete information. This paper also introduces a prototype of the mechanism to demonstrate its feasibility, and it analyzes the mechanism's extensibility, scalability, and information credibility. Through this study, we aim to improve the accessibility of cybersecurity information on the Internet and facilitate information sharing beyond organizational borders, with the eventual goal of creating a web of cybersecurity. [ABSTRACT FROM AUTHOR]

Abstract: Cognitive architectures have often been applied to data from individual experiments. In this paper, I develop an ACT‐R reader that can model a much larger set of data, eye‐tracking corpus data. It is shown that the resulting model has a good fit to the data for the considered low‐level processes. Unlike previous related works (most prominently, Engelmann, Vasishth, Engbert & Kliegl, ), the model achieves the fit by estimating free parameters of ACT‐R using Bayesian estimation and Markov‐Chain Monte Carlo (MCMC) techniques, rather than by relying on the mix of manual selection + default values. The method used in the paper is generalizable beyond this particular model and data set and could be used on other ACT‐R models. [ABSTRACT FROM AUTHOR]