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Data nowadays is the language of technologies as every process needs a data to be processed the input is data and the output also is data. Analyzing the data is a significant task especially with the increasing production of the data particularly data as a text, it would be difficult to manually analyze the data, extract information and detect the hidden patterns from unstructured text. Data mining is automated technique for gathering or deriving a new high-quality information and uncover the relations among the data. Text mining is one of main branches of the data mining however data mining is more comprehensive this paper, an overview for mining the publication papers via text mining techniques and their results and evaluation would be presented as following: the first approach is keywords extraction using natural language processing (NLP) approach, the second approach named entity recognition and the last approach is document clustering where machine learning techniques are applied to the both of them

Fast and simultaneous identification of multiple viable pathogens on food is critical to public health. Here we report a pathogen identification system using a paper chromogenic array (PCA) enabled by machine learning. The PCA consists of a paper substrate impregnated with 23 chromogenic dyes and dye combinations, which undergo colour changes on exposure to volatile organic compounds emitted by pathogens of interest. These colour changes are digitized and used to train a multi-layer neural network (NN), endowing it with high-accuracy (91–95%) strain-specific pathogen identification and quantification capabilities. The trained PCA–NN system can distinguish between viable Escherichia coli, E. coli O157:H7 and other viable pathogens, and can simultaneously identify both E. coli O157:H7 and Listeria monocytogenes on fresh-cut romaine lettuce, which represents a realistic and complex environment. This approach has the potential to advance non-destructive pathogen detection and identification on food, without enrichment, culturing, incubation or other sample preparation steps. Fast and simultaneous identification of multiple viable pathogens on food is critical to public health. By integrating paper chromogenic arrays (PCAs) and machine learning, a system was developed to automatically recognize PCA patterns on multiplexed viable pathogens with strain-level specificity.

Objective: We propose a MATLAB-based tool to convert electrocardiography (ECG) waveforms from paper-based ECG records into digitized ECG signals that is vendor-agnostic. The tool is packaged as an open source standalone graphical user interface (GUI) based application. Methods and procedures: To reach this objective we: (1) preprocess the ECG records, which includes skew correction, background grid removal and linear filtering; (2) segment ECG signals using Connected Components Analysis (CCA); (3) implement Optical Character Recognition (OCR) for removal of overlapping ECG lead characters and for interfacing of patients’ demographic information with their research records or their electronic medical record (EMR). The ECG digitization results are validated through a reader study where clinically salient features, such as intervals of QRST complex, between the paper ECG records and the digitized ECG records are compared. Results: Comparison of clinically important features between the paper-based ECG records and the digitized ECG signals, reveals intra- and inter-observer correlations of 0.86–0.99 and 0.79–0.94, respectively. The kappa statistic was found to average at 0.86 and 0.72 for intra- and inter-observer correlations, respectively. Conclusion: The clinically salient features of the ECG waveforms such as the intervals of QRST complex, are preserved during the digitization procedure. Clinical and Healthcare Impact: This open-source digitization tool can be used as a research resource to digitize paper ECG records thereby enabling development of new prediction algorithms to risk stratify individuals with cardiovascular disease, and/or allow for development of ECG-based cardiovascular diagnoses relying upon automated digital algorithms.

Objective This study examines the validity of optical mark recognition, a novel user interface, and crowdsourced data validation to rapidly digitize and extract data from paper COVID-19 assessment forms at a large medical center. Methods An optical mark recognition/optical character recognition (OMR/OCR) system was developed to identify fields that were selected on 2,814 paper assessment forms, each with 141 fields which were used to assess potential COVID-19 infections. A novel user interface (UI) displayed mirrored forms showing the scanned assessment forms with OMR results superimposed on the left and an editable web form on the right to improve ease of data validation. Crowdsourced participants validated the results of the OMR system. Overall error rate and time taken to validate were calculated. A subset of forms was validated by multiple participants to calculate agreement between participants. Results The OMR/OCR tools correctly extracted data from scanned forms fields with an average accuracy of 70% and median accuracy of 78% when the OMR/OCR results were compared with the results from crowd validation. Scanned forms were crowd-validated at a mean rate of 157 seconds per document and a volume of approximately 108 documents per day. A randomly selected subset of documents was reviewed by multiple participants, producing an interobserver agreement of 97% for documents when narrative-text fields were included and 98% when only Boolean and multiple-choice fields were considered. Conclusion Due to the COVID-19 pandemic, it may be challenging for health care workers wearing personal protective equipment to interact with electronic health records. The combination of OMR/OCR technology, a novel UI, and crowdsourcing data-validation processes allowed for the efficient extraction of a large volume of paper medical documents produced during the COVID-19 pandemic.

The existing plethora of document classification techniques exploits different data sources either from the content or metadata of research articles. Various journal publishers like Springer, Elsevier, IEEE, etc., do not provide open access to the content of research articles, whereas metadata is freely available there. Metadata like title, keyword, and abstract can serve as a better alternative to the content in various scenarios. In the current literature, researchers have assessed the role of some of the metadata individually. We believe that the collective contribution of metadata parameters can play a significant role in classifying research papers. This paper presents a comprehensive evaluation of the role of metadata, individually as well as in combinations to achieve the objective of research paper classification. Moreover, we have classified the research articles into ACM hierarchy root categories (e.g. general literature, hardware, software, etc.). In this comprehensive evaluation, we have assessed all the possible combinations of metadata features against different classifiers such as Random Forest, K Nearest Neighbor, and Decision Tree. The results of this research reveal that the title & keywords combination outperforms other combinations with an F-measure score of 0.88.

Performance/security trade-off is widely noticed in CFI research, however, we observe that not every CFI scheme is subject to the trade-off. Motivated by the key observation, we ask three questions: ➊ does trade-off really exist in different CFI schemes? ➋ if trade-off do exist, how do previous works comply with it? ➌ how can it inspire future research? Although the three questions probably cannot be directly answered, they are inspiring. We find that a deeper understanding of the nature of the trade-off will help answer the three questions. Accordingly, we proposed theGPTconjecture to pinpoint the trade-off in designing CFI schemes, which says that at most two out of three properties (fine granularity, acceptable performance, and preventive protection) could be achieved.

Heart disease has been one of the ruling causes for death for quite some time now. About 31% of all deaths every year in the world take place as a result of cardiovascular diseases [1]. A majority of the patients remain uninformed of their symptoms until quite late while others find it difficult to minimise the effects of risk factors that cause heart diseases. Machine Learning Algorithms have been quite efficacious in producing results with a high level of correctness thereby preventing the onset of heart diseases in many patients and reducing the impact in the ones that are already affected by such diseases. It has helped medical researchers and doctors all over the world in recognising patterns in the patients resulting in early detections of heart diseases.

With the advancement of mobile technology, mobile-assisted language learning (MALL) has significant potential regarding its practical applications and benefits in foreign language learning. Neverth...

In March 2021, the UK Government presented its “Integrated Review of Security, Defence, Development and Foreign Policy.” The launch of this 111-page document had been eagerly awaited by the securit...

Thermal papers, commonly used for printed receipts or lottery tickets, are omnipresent in our everyday life. They are regarded as semi-porous substrates, and yet can be critical to analyze when looking for latent fingermarks due to their thermosensibility. The aim of this study was to investigate a development sequence that would better combine the adequate detection techniques in order to maximize the chances to develop latent fingermarks left on these substrates. Different methods of development have been compared on test substrates: black magnetic powder, Lumicyano™, thermal development, ninhydrin and 1,2-indanedione/ZnCl2. Whitening stages and thermal development have been focused on, tested and optimized. The results of these preliminary tests enabled the study of three development sequences. They have subsequently been compared to the one currently used in the Gendarmerie's laboratories and the best results have been provided during pseudo-operational comparative trials by one of these sequences, consisting in 6 stages.

In the general framework of knowledge discovery, different techniques were used for information extraction from multi-label documents. As the world is currently facing COVID-19, it has made it more important than ever to have such knowledge extraction from previous documents. Therefore, Natural Language Processing (NLP) can be an essential model for tackling such an issue. By taking into consideration that having such a model plays an essential role to generate new insights in support of the ongoing fight against this infectious disease. This work introduces a sophisticated model that is able to read data from various articles about COVID-19, and finally give the most appropriate answer to the questions asked in order to gain insight information automatically. The model is applied to COVID-19 open research dataset challenge (CORD-19) that's has caught the attention of many researchers and it contains over 400,000 scholarly articles. The result of the proposed model has shown a good achievement, as it is explained in the result section. It was found that NLP is a good choice for tackling this global pandemic for information extraction and it contribute a new insight in support of the ongoing fight against this infectious disease. ©2021 Seventh Sense Research Group.

One of the benefits of Internet of Vehicles (IoV) is improved traffic safety and efficiency, for example due to the capability to share vehicular messages in real-time. While most of the vehicular messages only need to be shared by nearby vehicles, some messages (e.g., announcement messages) may need to be more broadly distributed, for example to vehicles in a wider region. Finding a single trusted entity to store and distribute such messages can be challenging, and vehicles may not be inclined to participate (e.g., generation and distribution of announcement messages) unless they can benefit from such participation. In addition, achieving both security and privacy can be challenging. In this paper, we propose a blockchain based secure data sharing system to address the above challenges in an IoV setting. Specifically, in our system, announcement messages are stored using blockchain. To encourage/incentivize participation, vehicles that faithfully broadcast the announcement messages and/or contribute to the block generation will be rewarded by some cryptocurrency. Our system is also designed to be privacy-preserving and realizes both priori and posteriori countermeasures.

In 2011, the Taiwanese Ministry of Education issued a policy on the internationalisation of schooling. The policy assumes that being ‘international’ has become necessary in order to maintain economic competitiveness in a globalising world. It was published in two languages: Mandarin, the official language in Taiwan, and English, presupposed as a lingua franca. Treating ‘policy as discourse’, the article analyses internationalism and its performativity through the publication and specific textual construction of this policy. The analysis considers the layout, target readerships, conceptual translation, and spatial representation and construction of ‘Chineseness’ in the two policy texts. This attempt at internationalisation is related to the very specific historical and geopolitical conditions of Taiwan and its liminal position in the globe. The significance of the article rests in expanding ideas of ‘translation’ in education policy studies and in the analysis of the specific geopolitics of East Asia. Refereed/Peer-reviewed

School psychologists are increasingly tasked with assessing, supporting, and intervening with dual language learner (DLL) students, their teachers, and their families. Understanding the assets of bilingualism along with multiple issues associated with comprehensive practice with DLL has become paramount. Currently, practitioners often lack the depth in knowledge of cultural variables, dual language acquisition, knowledge of programs to effectively serve DLL students, bilingual assessment, and research and evidence-based practice to serve DLLs competently, as well as depth in second language competence. This white paper, endorsed by the School Psychology Educators of California (SPEC) and the California Association of School Psychologists (CASP), outlines areas of competence deemed to be essential to all psychologists, as well as additional areas of competence for practitioners identifying themselves bilingual school psychologists.

We present a facile paper-based microfluidic device fabrication technique leveraging off-the-shelf carbon paper for the deposition of hydrophobic barriers using a novel "stencil scratching" method. This exceedingly frugal approach (0.05$) requires practically no technical training to employ. Hydrophobic barriers fabricated using this approach offer a width of 3 mm and a hydrophilic channel width of 849 μm, with an ability to confine major aqueous solvents without leakage. The utility of the device is demonstrated by porting a cell viability assay showing a limit-of-detection (LOD) of 0.6 × 108 CFU mL-1 and bilirubin assay with human serum showing a detection range of 1.76-6.9 mg dL-1 and a limit-of-detection (LOD) of 1.76 mg dL-1. The intuitiveness and economic viability of the fabrication method afford it great potential in the field of point-of-care diagnostics geared towards providing testing infrastructure in resource-scarce regions globally.

International audience; This paper focuses on the problem of the hierarchical multi‐label classification of research papers, which is the task of assigning the set of relevant labels for a paper from a hierarchy, using reduced amounts of labelled training data. Specifically, we study leveraging unlabelled data, which are usually plentiful and easy to collect, in addition to the few available labelled ones in a semi‐supervised learning framework for achieving better performance results. Thus, in this paper, we propose a semi‐supervised approach for the hierarchical multi‐label classification task of research papers based on the well‐known Co‐training algorithm, which exploit content and bibliographic coupling information as two distinct papers' views. In our approach, two hierarchical multi‐label classifiers, are learnt on different views of the labelled data, and iteratively select their most confident unlabelled samples, which are further added to the labelled set. The success of our suggested Co‐training‐based approach lies in two main components. The first is the use of two suggested selection criteria (i.e., Maximum Agreement and Labels Cardinality Consistency) that enforce selecting confident unlabelled samples. The second is the appliance of an oversampling method that rebalances the labels distribution of the initial labelled set, which reduces the reinforcement of the label imbalance issue during the Co‐training learning. The proposed approach is evaluated using a collection of scientific papers extracted from the ACM digital library. Performed experiments show the effectiveness of our approach with regards to several baseline methods.

Cancer is characterized as a heterogeneous disease of various types. The early detection and prognosis of a cancer type have turned into a major requirement, as it facilitates successive medical treatment of patients. The research team has classified the cancer patients into high or low-risk groups. This makes it a significant task for the medical teams to study the application of deep learning and machine learning models. As a result, such techniques have been employed for modeling the development and treatment of cancer conditions. Additionally, the machine learning tools can have the ability the significant detection features from complex datasets. Numerous techniques like Support Vector Machines (SVM), Bayesian Networks (BN), Decision Trees (DT), Artificial Neural Networks (ANN), Recurrent Neural Network (RNN), and Deep Neural Network (DNN) has been broadly utilized in cancer research. As per the current survey, the detection rate is about 99.89%, which shows the prediction models' efficiency and precise decision making. However, it is proven that deep learning and machine learning approaches can enhance cancer progression. An adequate level of estimation is required for such approaches for considering the daily medical practice. This survey analyzes and learns the diverse contributions of cancer prediction models using intelligent approaches. Further, the paper tries to categorize the different algorithms, the utilized datasets, and utilized environments. Along with this, various performance measures evaluated in each contribution is sorted out. An extensive search is conducted relevant to machine learning and deep learning methods in cancer susceptibility, recurrence, and survivability prediction, and the existing challenges in this area are clearly described. However, ML models are still in the testing as well as the experimentation phase for cancer prognoses. As the datasets are getting larger with higher quality, researchers are building increasingly accurate models. Moreover, ML models have a long way to go, and most of the models still lack sufficient data and suffer from bias.

Machine learning, a branch of artificial intelligence, is increasingly used in health research, including nursing and maternal outcomes research. Machine learning algorithms are complex and involve statistics and terminology that are not common in health research. The purpose of this methods paper is to describe three machine learning algorithms in detail and provide an example of their use in maternal outcomes research. The three algorithms, classification and regression trees, least absolute shrinkage and selection operator, and random forest, may be used to understand risk groups, select variables for a model, and rank variables’ contribution to an outcome, respectively. While machine learning has plenty to contribute to health research, it also has some drawbacks, and these are discussed as well. In order to provide an example of the different algorithms’ function, they were used on a completed cross-sectional study examining the association of oxytocin total dose exposure with primary cesarean section. The results of the algorithms are compared to what was done or found using more traditional methods.

Recent advances in telecommunication technology and video conferencing systems have opened a new avenue for surgical instruction called “surgical telementoring.” This report from the Technology Working Group of the SAGES Project 6 Summit reviews the telementoring technology that currently exists and proposes recommendations for minimum technology requirements and future technology development. While also providing insight in regulatory considerations, this review offers what prospective surgical telementoring participants need to know about the underlying technology with a specific focus on safety, reliability, transmission quality, ease of use, and cost. Content experts from around the world, in minimally invasive surgery, surgical mentoring and telementoring, surgical education, business development, healthcare innovation, and regulation were invited to attend a 2-day summit in Los Angeles, USA to outline the current state of surgical telementoring and chart the challenges and opportunities going forward. This article summarizes the discussion, conclusions, and recommendation of the technology group with regard to telementoring technology. This article reviews the technical requirements which can be divided into the following categories: (1) safety, (2) reliability, (3) transmission quality, (4) ease of use, and (5) cost. Telementoring applications are technology driven. Given the pace of change of technology, guiding principles in technology design and selection are warranted (Table 4). Telementoring technologies require two basic components, video capturing and display devices at the transmitting and receiving end, and a telecommunication link between them. Many additional features can be added to this basic setup including multiple cameras or video sources, remote camera zoom and pan, recording and storage of videos and images, and telestration capabilities to mention just a few. In general, the cost of these technologies is feature driven. The education framework for each specific application should determine the need for these features (Schlachta in Surg Endosc https://doi.org/10.1007/s00464-016-4988-5 ).

Objective Our aim was to develop an efficient search strategy for prognostic studies and clinical prediction guides (CPGs), optimally balancing sensitivity and precision while independent of MeSH terms, as relying on them may miss the most current literature. Materials and Methods We combined 2 Hedges-based search strategies, modified to remove MeSH terms for overall prognostic studies and CPGs, and ran the search on 269 journals. We read abstracts from a random subset of retrieved references until ≥ 20 per journal were reviewed and classified them as positive when fulfilling standardized quality criteria, thereby assembling a standard dataset used to calibrate the search strategy. We determined performance characteristics of our new search strategy against the Hedges standard and performance characteristics of published search strategies against the standard dataset. Results Our search strategy retrieved 16 089 references from 269 journals during our study period. One hundred fifty-four journals yielded ≥ 20 references and ≥ 1 prognostic study or CPG. Against the Hedges standard, the new search strategy had sensitivity/specificity/precision/accuracy of 84%/80%/2%/80%, respectively. Existing published strategies tested against our standard dataset had sensitivities of 36%–94% and precision of 5%–10%. Discussion We developed a new search strategy to identify overall prognosis studies and CPGs independent of MeSH terms. These studies are important for medical decision-making, as they identify specific populations and individuals who may benefit from interventions. Conclusion Our results may benefit literature surveillance and clinical guideline efforts, as our search strategy performs as well as published search strategies while capturing literature at the time of publication.

Background Data papers have emerged as a powerful instrument for open data publishing, obtaining credit, and establishing priority for datasets generated in scientific experiments. Academic publishing improves data and metadata quality through peer review and increases the impact of datasets by enhancing their visibility, accessibility, and reusability. Objective We aimed to establish a new type of article structure and template for omics studies: the omics data paper. To improve data interoperability and further incentivize researchers to publish well-described datasets, we created a prototype workflow for streamlined import of genomics metadata from the European Nucleotide Archive directly into a data paper manuscript. Methods An omics data paper template was designed by defining key article sections that encourage the description of omics datasets and methodologies. A metadata import workflow, based on REpresentational State Transfer services and Xpath, was prototyped to extract information from the European Nucleotide Archive, ArrayExpress, and BioSamples databases. Findings The template and workflow for automatic import of standard-compliant metadata into an omics data paper manuscript provide a mechanism for enhancing existing metadata through publishing. Conclusion The omics data paper structure and workflow for import of genomics metadata will help to bring genomic and other omics datasets into the spotlight. Promoting enhanced metadata descriptions and enforcing manuscript peer review and data auditing of the underlying datasets brings additional quality to datasets. We hope that streamlined metadata reuse for scholarly publishing encourages authors to create enhanced metadata descriptions in the form of data papers to improve both the quality of their metadata and its findability and accessibility.

This position paper focuses on the problem of building dialogue systems for people who have lost the ability to communicate via speech, e.g., patients of locked-in syndrome or severely disabled people. In order for such people to communicate to other people and computers, dialogue systems that are based on brain responses to (imagined) speech are needed. A speech-based dialogue system typically consists of an automatic speech recognition module and a speech synthesis module. In order to build a dialogue system that is able to work on the basis of brain signals, a system needs to be developed that is able to recognize speech imagined by a person and can synthesize speech from imagined speech. This paper proposes combining new and emerging technology on neural speech recognition and auditory stimulus construction from brain signals to build brain signal-based dialogue systems. Such systems have a potentially large impact on society.

IoT has turned into an amazing system of worldwide and worldwide changes, including numerous internet advisors, interchanges, and information trade. This paper presents state-of-the-art background, introduction, characteristics, challenges, threats, attacks, related work, and security architecture and research limitation of IoT technology. This paper discusses key security issues and attacks over different layers of IOT along with its existing prevention techniques. This paper deals with the security of services used in IoT. In detail, IoT devices are mentioned in this paper, which are the premise of IoT and their significance. In addition, IoT Security Architecture highlights some recent studies. This paper is far from comprehensive and mainly focuses on attacks and IoT devices prevention mechanism and covers related discussion and arguments.

Paper-based digital microfluidic biochips (or P-DMFBs) are becoming highly impelling due to its low-cost and in-place fabrication of electrodes and control wiring on a single piece of paper having an inkjet printer and conductive ink. Despite enormous advantages, several complex design rules also subsist, such as avoidance of induced control interference, minimum separation among the control lines, and congestion-free wiring on a single layer, which is to be correlated leading toward overall feasibility of the design. Several cost raising issues, such as schedule length, control pin count, and wire length, must be considered for attaining a successful fluid-control codesign. Moreover, design gaps exist among the subtasks of the fluid level, control level, and fluid-control design as a whole, which undeniably impose expensive design cycles increasing overall cost. This article builds a machine learning-based model for the pin-constrained P-DMFBs to predict violation in control design beforehand and accordingly guides the fluid-control codesign to tackle important cost-driving issues while attaining congestion- and conflict-free wiring. This model effectively eliminates the design cycles producing a low-cost platform. The predictive model has been evaluated over a balanced data set. Several benchmarks for assessing the performance are studied.

The twentieth century has witnessed a tremendous shift in the practice of art, relying on experimentation with rawness, where the material has shifted from being a physical medium to being a concept in itself, thus giving modern art innovative lessons in freedom from traditional materials and utilizing new materials that the artist can formulate in an art form of creativity and modernity to free from all that is traditional and look for unfamiliar alternatives to open new horizons in the field of artistic creativity and innovation. Paper is a natural raw material that has special qualities that open for free and wide for expression and may allow the artist when trying to form new artistic dimensions contribute to making the artistic activity more innovative occurs in the detection of the formulations and technological processors contribute in the field of formation in general and formation on themannequin in a special capacity for this the researcher used paper dough and employed them because of its values The composition, aesthetics and rhythms of different to obtain designs that support the environment and fall under the term sustainable environmental development and thus can convert waste that poses a danger to the industrial society from visual pollution and health degradation to artistic works contribute to the development of the sense of aesthetic and behavioral values in society, in addition to the researcher's aspiration to get out of the ordinary and provoke the innovative creative addition in the field of formation on the mannequin hence the problem of research in answering the following questions: What is the possibility of preparing clothing designs that carryvalues using paper dough? What techniques are required to form and design paper dough on themannequin? The success of the proposed designs from the point of view of specialists? Objectives: find modern experimental approaches through paper dough and the possibility of using them in the field of design onthe mannequin through: Producing designs of a special nature using paper dough that is unique and unique. Research methodology: descriptive, and experimental methods. The results have shown: It is possible to use paper dough in the formation on the Manican from the point of view of specialists, where it was found that all the designs have obtained a proper level with averages ranging from (3:2.70), and all designs and supplements are considered executable and the order of designs

Background: Multimedia creation and curation for assignments have the potential to engage students in deeper learning. Objective: This study explored whether the same homework assignments delivered in paper versus multimedia format resulted in different student experiences. Method: Introductory psychology students submitted either traditional papers or multimedia projects to address the same assignment prompt, a process that was repeated seven times throughout four courses and was either instructor-determined or student-selected. Students were surveyed about the assignment formats, and assignment grades were analyzed. Results: Overall, students found the multimedia assignments more engaging, more enjoyable, more interesting, and more difficult than paper assignments. When assignments were instructor-determined, students reported higher perceived learning for the multimedia assignments. Assignment grades were not significantly different between assignment formats. Conclusion and teaching implications: These results suggest that moving traditional paper assignments into a multimedia format is a no-cost way to improve the student experience.

This study analyzed top papers published in the field of Library and Information Science (LIS) published between 2009 and 2019 and included in the Web of Science (WoS) subject category “Information Science & Library Science”. Data of the 501 top papers were extracted from the Essential Science Indicators (ESI) database comprising 499 highly cited papers and 16 hot papers in the field. The distributions of document type, language of publication, scientific output, and publication of journals are reported in this paper. The co-authorship network visualization of authors, organizations and countries, co-occurrence network visualization of all keywords are visualized using VOSviewer software. The 501 papers, all written in English language, were from 1,579 authors employed at 680 organizations based in 59 countries/territories. The papers were published in 40 journals in the field. The top 5 core journals ranked based on the impact factor (IF) were MIS Quarterly, Journal of the American Medical Informatics Association, International Journal of Information Management, Journal of the Association for Information Science and Technology, and Information Management. The top 5 organizations were University of Maryland (USA), University of Wolverhampton (UK), Vanderbilt University (USA), Indiana University (USA), and Wuhan University (China). Authors from the following countries contributed the most - USA, People’s Republic of China, England, Canada and Netherlands. Based on network map using VOSviewer, there were micro, meso and macro level collaborations based on common interests in a specific topics. Analysis of all keywords showed that the research were distributed into 6 clusters. This study concludes that one important characteristic of top papers is the journal reputation, therefore authors can choose their ideal journal with a high JIF and quartile to publish papers in the English language related to this research field.

Exam paper generation is an indispensable part of teaching. Existing methods focus on the use of question extraction algorithms with labels for each question provided. Obviously, manual labeling is inefficient and cannot avoid label bias. Furthermore, the quality of the exam papers generated by the existing methods is not guaranteed. To address these problems, we propose a novel approach to generating exam papers based on prediction of exam performance. As such, we update the quality of the initially generated questions one by using dynamic programming, as well as in batches by using genetic algorithms. We performed the prediction task by using Deep Knowledge Tracing. Our approach considered the skill weight, difficulty, and distribution of exam scores. By comparisons, experimental results indicate that our approach performed better than the two baselines. Furthermore, it can generate exam papers with adaptive difficulties closely to the expected levels, and the related student exam scores will be guaranteed to be relatively reasonable distribution. In addition, our approach was evaluated in a real learning scenarios and shows advantages.

Students frequently utilize digital devices and applications to assist studying. Past research has yielded mixed results on their effectiveness, and scant research has compared portable technologies with each other or focused on smartphones specifically. We compared college students’ learning of vocabulary on paper, laptop, and smartphone. Students were randomly assigned to one platform for reviewing vocabulary flashcards, and then they completed a memory quiz and answered questions regarding perceptions of these technologies. Results showed equivalency in students’ learning across platforms, with the only exception being that students spent longer reviewing paper flashcards. Self-reported cognitive load and satisfaction were equal across platforms. Quantitative and qualitative data also suggested that, though students quickly label both laptops and smartphones as helpful for education, they see more educational value in laptops. This research suggests that smartphones present new ways of learning, but care must be taken with how smartphones are utilized in educational settings.

A database of historic paper properties was developed using 729 samples of European origin (1350–1990), analysed for acidity, degree or polymerisation (DP), molecular weight of cellulose, grammage, tensile strength, as well as contents of ash, aluminium, carbonyl groups, rosin, protein, lignin and fibre furnish. Using Spearman’s rank correlation coefficient and principal component analysis, the data were examined with respect to methods of manufacture, as well as chemical stability of paper. Novel patterns emerged related to loss of DP and accumulation of carbonyl groups and acidity with time and the role of lignin and rosin, as well as rate of degradation (k = 10−5 year−1) at room conditions. In-depth understanding of long-term degradation of lignin and rosin is needed to better understand the relationships between composition and degradation of historic paper. This study highlights the importance of mining significant volumes of analytical data, and its variability, obtained from real historic objects.

Purpose This paper aims to provide a snapshot of key learnings about paid online peer-to-peer accommodation trading, as it relates to tourism and hospitality, and to identify future research questions. Design/methodology/approach The paper paints a picture and discusses research conducted in the past, which relates to paid online peer-to-peer accommodation, in brief. It also lists a number of specific research questions which should be investigated in future. Findings Some of the key topics, such as the business model of facilitators of peer-to-peer trading and the necessary regularly responses, have been extensively studied. The focus should now turn on how peer-to-peer trading of travel-related services can best be leveraged to the benefit of economies, communities and people. Originality/value The main value of this perspective paper lies in offering a succinct overview of research into paid online peer-to-peer accommodation and pointing to key questions for future research.

Authentication devices transform cheap paper into legitimate documents. They are the sensory, informational, and computational features that make up valuable papers like banknotes and passports, and they provide the confidence required in moments of exchange and passage. These devices – which include techniques like watermarks and specialized threads, proprietary substrates and inks, or RFID chips – are the product of security printing, an industry that continuously reinvents the possibilities of paper. Importantly, these components protect paper things from counterfeiting, allowing it to function as an original and authentic copy and to do the logistical work of connecting quotidian materials to global networks. The value of valuable papers is therefore not purely extrinsic, socially or discursively established, but is also performed through its intrinsic material qualities. These are the authentication devices that are read, assessed, and trusted as paper things are circulated, and they are what securely connects paper to infrastructures of mobility.