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Headline generation is a challenging subtask of abstractive text summarization, which its output should be a summary, shorter than one sentence. It would be precious to develop a dataset for the evaluation of abstractive summarization methods on this task in the Persian language. There are several datasets for headline generation in Persian, most of which are not large enough to be used by more sophisticated methods of text summarization, such as deep learning models. Moreover, all of these datasets are focused on daily news and there is no dataset for summarizing scientific Persian papers. In this article, we present “ElmNet,” a headline generation dataset of about 400,000 abstract/headline pairs of scientific papers, gathered from six major publishers for scientific articles in Persian. We, moreover, evaluate the performance of the most important deep learning-based headline generation methods, on the proposed dataset. The results prove the comparability of the performance of the state-of-the-art methods on this task, to their results on the existing English datasets.

This study aimed to evaluate the effect of nanocellulose addition on the physical–mechanical properties of the paper produced from different fiber blends, besides comparing two nanocellulose addition methods. Three different fibers were used for fiber blending (eucalyptus, sisal, and pine). Handsheets were formed based on the mixing of all possible combinations at a 45/55 ratio in 2% consistency and 60 g/m2. Handsheet reinforcements were performed by two methods: The mixture method (MT) was a mixture of nanocellulose along with pulp during paper formation in 3, 5, and 10% addition; the coating method (CT) was the superficial coating of dry formed papers in 10% addition. Nanocellulose was produced by mechanical microfibrillation of sisal pulp. Handsheets were evaluated by physical and strength properties. Nanocellulose addition increased thickness, volume, grammage, apparent density, opacity, roughness, tensile strength, tensile index, stretch, bursting index, tear index, and fold endurance by 8.7, 8.8, 10.4, 2.1, 4.1, 23.2, 45.7, 31.8, 20.1, 14.2, 21.1, and 271.6% but reduced bulk, brightness, and air permeance by 1.9, 3.4, and 71.7%, respectively. The reinforcement methods presented distinct results. In physical properties, an increasing tendency toward nanocellulose (MT) increase was observed in thickness, grammage, and apparent density despite the decreasing trend in air permeance. No tendency was observed in other physical properties. In general, CT presented higher values of thickness, grammage, bulk, and brightness but lower values of apparent density and opacity, compared to MT. The mixture method showed an increasing tendency in strength properties with the increase of nanocellulose content. CT obtained fewer strength properties compared to MT.

Comparative studies on paper and pencil– and computer-based tests principally focus on statistical analysis of students’ performances. In educational assessment, comparing students’ performance (in terms of right or wrong results) does not imply a comparison of problem-solving processes followed by students. In this paper, we present a theoretical tool for task analysis that allows us to highlight how students’ problem-solving processes could change in switching from paper to computer format and how these changes could be affected by the use of one environment rather than another. In particular, the aim of our study lies in identifying a set of indexes to highlight possible consequences that specific changes in task formulation have, in terms of task comparability. Therefore, we propose an example of the use of the tool for comparing paper-based and computer-based tasks.

Scientific research cooperation and co-authored papers are becoming increasingly popular in the era of big science. However, allocating appropriate credit to each co-author of papers remains a challenge. We consider author contribution declarations according to the contributor roles taxonomy (CRediT) scheme (assigning each co-author to 14 contributor roles) and propose a new method of author contribution to allocate co-authors’ credits reasonably by converting the 14 contributor roles in an article into a binary author-role matrix. Based on the data of PLOS ONE, we further explore the new method’s advantages by comparing with other representative methods: It normalizes the total credits of different articles to 1, avoiding the inflationary bias caused by the increasing number of co-authors; awards different credits per co-author based on the participation rate of contributor roles to avoid the equalization bias; reduces the impact of the increasing number of co-authors on the credit of the first co-author.

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.

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.

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.

In heterogeneous networks where macro cells and metro cells use the same frequency band to communicate with their respective users, the major problem limiting performance is the interference caused by macro cells to metro cells. The information theoretic result known as dirty paper coding provides a way to address this problem and significantly improve the performance of heterogeneous networks with co-channel deployment. In this paper, we show how a simple dirty paper coding scheme employing Tomlinson-Harashima pre-coding with partial interference pre-subtraction can be employed by metro cells to mitigate the interference caused by macro cells. A performance study included in this paper shows that the proposed dirty paper coding scheme can lead to significant improvement in user rate statistics.

Zone identification is a topic in the area of text mining which helps researchers be benefited by the content of scientific papers in a satisfactory manner. The major aim of zone identification is to classify the sentences of scientific texts into some predefined zone categories which can be useful for summarization as well as information extraction. In this paper, we propose a two-level approach to zone identification within which the first level is in charge of classifying the sentences in a given paper based on some semantic and lexical features. In this respect, several machine learning algorithms such as Simple Logistics, Logistic Model Trees and Sequential Minimal Optimization are applied. The second level is responsible for applying fusion to the classification results obtained for consecutive sentences of the first level in order to make the final decision. The proposed method is evaluated on ART and DRI corpora as two well-known data sets. Results obtained for the accuracy of zone identification for these corpora are respectively 65.75% and 84.15%, which seem to be quite promising compared to those obtained by previous approaches.

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.

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 introduce a comprehensive benchmark for local features and robust estimation algorithms, focusing on the downstream task -- the accuracy of the reconstructed camera pose -- as our primary metric. Our pipeline's modular structure allows easy integration, configuration, and combination of different methods and heuristics. This is demonstrated by embedding dozens of popular algorithms and evaluating them, from seminal works to the cutting edge of machine learning research. We show that with proper settings, classical solutions may still outperform the perceived state of the art. Besides establishing the actual state of the art, the conducted experiments reveal unexpected properties of Structure from Motion (SfM) pipelines that can help improve their performance, for both algorithmic and learned methods. Data and code are online https://github.com/vcg-uvic/image-matching-benchmark, providing an easy-to-use and flexible framework for the benchmarking of local features and robust estimation methods, both alongside and against top-performing methods. This work provides a basis for the Image Matching Challenge https://vision.uvic.ca/image-matching-challenge., Comment: Added: KeyNet-SOSNet, AffNet-HardNet, TFeat, MKD from kornia

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 ).

Web of Science and Scopus are two world-leading and competing citation databases. By using the Science Citation Index Expanded and Social Sciences Citation Index, this paper conducts a comparative, dynamic, and empirical study focusing on the use of Web of Science (WoS) and Scopus in academic papers published during 2004 and 2018. This brief communication reveals that although both Web of Science and Scopus are increasingly used in academic papers, Scopus as a new-comer is really challenging the dominating role of WoS. Researchers from more and more countries/regions and knowledge domains are involved in the use of these two databases. Even though the main producers of related papers are developed economies, some developing economies such as China, Brazil and Iran also act important roles but with different patterns in the use of these two databases. Both two databases are widely used in meta-analysis related studies especially for researchers in China. Health/medical science related domains and the traditional Information Science & Library Science field stand out in the use of citation databases., Forthcoming in Scientometrics

A paper’s novelty enhances its impact and citation. In this paper, we examine two dimensions of a paper’s novelty: new combinations and new components. We define new combinations as new pairs of knowledge elements in a related research area, and new components as new knowledge elements that have never appeared in a related research area previously. The importance of both dimensions is stressed, and we analyze the mechanisms that affect the frequency of a paper’s citation; we believe that a paper’s new combinations and new components both have an inverted U-shaped effect on its citation count. Utilizing a text-mining approach, we develop a novel method for constructing new combinations and new components using a paper’s keywords. Using keywords from papers published in the wind energy field between 2002 and 2015 as our sample, we conduct an empirical analysis on the above-mentioned relationships. To do so, we use the negative binomial regression method and several robustness tests. The results provide support for our hypotheses that propose a paper’s new combinations and new components significantly affect its impact. Specifically, new combinations and new components lead to more citation counts up to a specific threshold. When the number of new combinations and new components exceed the threshold, the paper is likely to be cited less frequently. Finally, we discuss the theoretical contributions, methodological contributions, and practical implications of these findings.

The JEL classification system is a standard way of assigning key topics to economic articles to make them more easily retrievable in the bulk of nowadays massive literature. Usually the JEL (Journal of Economic Literature) is picked by the author(s) bearing the risk of suboptimal assignment. Using the database of the Collaborative Research Center from Humboldt-Universität zu Berlin we employ a new adaptive clustering technique to identify interpretable JEL (sub)clusters. The proposed Adaptive Weights Clustering (AWC) is available on http://www.quantlet.de/ and is based on the idea of locally weighting each point (document, abstract) in terms of cluster membership. Comparison with $$k$$ k -means or CLUTO reveals excellent performance of AWC.

Since English is probably the most popular second language, helping students learn English through technology is a critical issue in non-English-speaking countries. With the growth of digital pen technologies, developing an interactive learning environment that combines printed textbooks and a digital pen to support English-language classroom learning has become feasible. This work presents an attention-based diagnosing and review mechanism (ADRM) based on brainwave detection to help learners identify the passages with low attention level in a lesson as review targets in order to perform efficiently and accurately review processes while reading paper-based English texts with digital pen support in autonomous learning environments. Based on the true experimental design, this work aims to confirm whether the ADRM improves the review performance and sustained attention of learners while reading paper-based English texts with digital pen support. The research participants were a total of 108 students at an industrial vocational high school in Taipei City, Taiwan. All research participants were male and aged from 17 to 18 years. The experimental group used the ADRM while reading paper-based English texts with digital pen support, whereas the control group used the autonomous review while reading paper-based English texts with digital pen support. Experimental results reveal that the review performance of the experimental group was significantly better than that of the control group, proving that the ADRM improved review performance. The results also show that the field-dependent learners in the experimental group exhibited a great improvement in review performance in comparison with the field-independent learners. Additionally, the low-ability learners in the experimental group exhibited better review performance compared to those in the control group. Furthermore, learners with high attention level in the experimental group have exhibited better review performance and sustained attention than the learners in the control group. This work confirms that developing an ADRM based on brainwave detection to assist learners’ review processes is practicable. However, the usability and acceptability of using ADRM instead of human autonomous review should be further considered in the information society.

With the speedy progress of network technology and internet applications, protection of digitized data against unauthorized access and modification has become a paramount issue. In order to face this challenge, numerous secret image sharing schemes have been introduced. Secret image sharing scheme is a method used for safeguarding the sensitive digitized image against illegal copying and tempering. The secret image is divided into many random shares in such a way that each share does not reveal any information to the intruders. In this paper, we present a comprehensive survey from over 100 papers which explains the new approaches and challenges. This paper also provides a comparative analysis of different methods based on different properties.

The continuing growth of scientific publications has posed a double-challenge to researchers, to not only grasp the overall research trends in a scientific domain, but also get down to research details embedded in a collection of core papers. Existing work on science mapping provides multiple tools to visualize research trends in domain on macro-level, and work from the digital humanities have proposed text visualization of documents, topics, sentences, and words on micro-level. However, existing micro-level text visualizations are not tailored for scientific paper corpus, and cannot support meso-level scientific reading, which aligns a set of core papers based on their research progress, before drilling down to individual papers. To bridge this gap, the present paper proposes LitStoryTeller+, an interactive system under a unified framework that can support both meso-level and micro-level scientific paper visual storytelling. More specifically, we use entities (concepts and terminologies) as basic visual elements, and visualize entity storylines across papers and within a paper borrowing metaphors from screen play. To identify entities and entity communities, named entity recognition and community detection are performed. We also employ a variety of text mining methods such as extractive text summarization and comparative sentence classification to provide rich textual information supplementary to our visualizations. We also propose a top-down story-reading strategy that best takes advantage of our system. Two comprehensive hypothetical walkthroughs to explore documents from the computer science domain and history domain with our system demonstrate the effectiveness of our story-reading strategy and the usefulness of LitStoryTeller+.

Looking at the past, among the main modalities of transmission of knowledge (alongside with manuscripts and printed treatises and with respect to the various ‘precepts’) we would have found also various professional figures. This is even truer with regard to the training and dissemination of knowledge among military engineers, who used to be real ‘living vehicles’ in national and international cultural systems. This is also true for what concern that circumscribed set of knowledge of geometrical-mathematical theories, including their application to the codes of architectural Representation and Design. This research aims to investigate the example of the military engineer Gaspare Beretta (Milan 1620–1703), whose theoretical production both on architectural graphic and on various applications of Geometry is still unpublished. With the aim of developing the relationship between geometry, architecture and the transmission of knowledge, we will go through his practices of the regular fortress tracing on ‘paper and ground’, in order to verify the resulting connections between geometric reconstruction and Author’s intentions.

This white paper explores the technical challenges and solutions for acquiring (capturing) and managing enterprise images, particularly those involving visible light applications. The types of acquisition devices used for various general-purpose photography and specialized applications including dermatology, endoscopy, and anatomic pathology are reviewed. The formats and standards used, and the associated metadata requirements and communication protocols for transfer and workflow are considered. Particular emphasis is placed on the importance of metadata capture in both order- and encounter-based workflow. The benefits of using DICOM to provide a standard means of recording and accessing both metadata and image and video data are considered, as is the role of IHE and FHIR.

With the advent of digital cameras, there has been an explosion in the number of medical specialties using images to diagnose or document disease and guide interventions. In many specialties, these images are not added to the patient’s electronic medical record and are not distributed so that other providers caring for the patient can view them. As hospitals begin to develop enterprise imaging strategies, they have found that there are multiple challenges preventing the implementation of systems to manage image capture, image upload, and image management. This HIMSS-SIIM white paper will describe the key workflow challenges related to enterprise imaging and offer suggestions for potential solutions to these challenges.

The confluence of significant computational power and inexpensive sensors provides the opportunity to reliably collect large volumes of information from the world and extract humanly useful information resources. This paper reviews a coherent body of work over the last 20+ years focused on development of advanced bio-inspired computing techniques, and their applications primarily for human related data in behaviour and human centered computing. We close with a synthesis proposing an experiment analysis methodology combining these tools.

Executable papers take transparency and openness in research communication one step further. In this comment, an early career researcher reports her experience of creating an executable paper as a journey through Open Science.

Based on the Essential Science Indicators database, this study analyzed 2140 highly cited papers (HCPs) in the field of Economics and Business from 4499 authors, 914 universities, and 64 countries/territories. From this data, three lists were created: the top 76 scientists, 50 most influential universities, and 33 most influential countries/territories. The results show that the USA is the global leader in Business and Economics with 1517 HCPs, ranking number 1. Also, 46 of the top scientists (60.5%), and 37 of the most influential universities (74%) are from the USA. This study also found: (1) the collaboration network among the top 76 scientists is not very close but a relatively tight sub-network with 13 top scientists has formed; (2) all 50 most influential universities are interconnected, and the cooperation between Harvard University and MIT was the strongest, producing 23 HCPs together; (3) the collaboration network among the most influential countries is quite close with a large network of 60 nodes and only four isolated nodes. In addition, this study demonstrates that significant positive correlations exist between authors’ HCP and h-index, between universities’ HCP and h-index, and between countries’ HCP and h-index. Since h-index is known to be a reliable indicator, these correlations indicate that when evaluating the academic impact of scholars, universities, and countries, the HCP approach is also considerably useful.

The article by Muller, Quintana, and Page reviews a variety of Bayesian nonparametric models and demonstrates them in a few applications. They emphasize applications in spatial data on which our discussion focuses as well. In particular, we consider two types of mixture models based on species sampling models (SSM) for spatial clustering and apply them to the Chilean mathematics testing score data analyzed by the authors. We conclude that only the mixture model of SSM with spatial locations as part of observations renders spatially non-overlapping clusters.

In this paper, we aim to implement augmented reality (AR) on distant text documents or books. For this purpose, we propose a new paper-based AR system that can detect text documents in real scenes, markerize and identify them, estimate their relative 3D poses to the camera, and augment them with virtual contents. Unlike the previous paper-based AR systems (applicable to only close documents), the proposed system not only requires no detection of words or characters, but allows partial occlusions like the previous systems. In our experiments, the proposed system worked at 24 fps and could consistently achieve high identification rates for both occluded and unoccluded pages.

As mobile devices, such as e-book readers and tablet computers, have emerged as alternatives to traditional printed media, they are also being increasingly employed as survey administration equipment. However, the question arises as to whether mobile devices are actually more effective than paper for survey administration. To address this question, the current study conducted a between-subjects experiment (N = 60) in which participants were asked to complete a survey questionnaire either on paper or an iPad. The results showed that the mobile device-based survey was more effective than the paper-based survey in encouraging participants to disclose information. In addition, self-disclosure was found to mediate the effects of the survey medium type on perceived security of information, usefulness of the medium, and intention to use the medium in the future.

In this paper, a new programmable structure of paper-based analytical devices and its programming method are represented to be used for the purpose of rapid prototyping without involving the fabrication process. The proposed ready-to-program structure is prefabricated by the screen-printing method before rendering it to the end user. The users can prototype the device rapidly for their specific applications simply by programming the prefabricated device. The proposed programmable device is composed of three different layers patterned on the filter paper substrate including a hydrophobic barrier layer, a path-blocking layer and a heater network layer. The device programming procedure is totally electrical and it is implemented by applying proper DC voltages to several predetermined heater lines. This procedure is applicable manually or automatically using a programmer setup connected to a personal computer (PC). Therefore, the user can configure the device by selecting appropriate heater lines in a simple firmware through the PC. The proposed programmable structure is implemented in four different µPAD patterns for different applications. Biochemical assays of glucose, total protein and albumin are performed on the programmable devices to verify their applicability for bioassays.

In the paper we present an approach to the automatic detection and identification of important elements in paper documents. This includes stamps, logos, printed text blocks, signatures and tables. Presented approach consists of two stages. The first one includes object detection by means of AdaBoost cascade of weak classifiers and Haar-like features. Resulting image blocks are, at the second stage, subjected to verification based on selected features calculated from recently proposed low-level descriptors combined with certain classifiers representing current machine-learning approaches. The training phase, for both stages, uses bootstrapping, i.e., integrative process, aiming at increasing the accuracy. Experiments performed on large set of digitized paper documents showed that adopted strategy is useful and efficient.

The usage of psychological networks that conceptualize psychological behavior as a complex interplay of psychological and other components has gained increasing popularity in various fields of psychology. While prior publications have tackled the topics of estimating and interpreting such networks, little work has been conducted to check how accurate (i.e., prone to sampling variation) networks are estimated, and how stable (i.e., interpretation remains similar with less observations) inferences from the network structure (such as centrality indices) are. In this tutorial paper, we aim to introduce the reader to this field and tackle the problem of accuracy under sampling variation. We first introduce the current state-of-the-art of network estimation. Second, we provide a rationale why researchers should investigate the accuracy of psychological networks. Third, we describe how bootstrap routines can be used to (A) assess the accuracy of estimated network connections, (B) investigate the stability of centrality indices, and (C) test whether network connections and centrality estimates for different variables differ from each other. We introduce two novel statistical methods: for (B) the correlation stability coefficient, and for (C) the bootstrapped difference test for edge-weights and centrality indices. We conducted and present simulation studies to assess the performance of both methods. Finally, we developed the free R-package bootnet that allows for estimating psychological networks in a generalized framework in addition to the proposed bootstrap methods. We showcase bootnet in a tutorial, accompanied by R syntax, in which we analyze a dataset of 359 women with posttraumatic stress disorder available online., Comment: Accepted for publication in Behavior Research Methods

We present a method of setting up a website using the Drupal CMS to publish CRM data. Our setup requires basic technical expertise by researchers who are then able to publish their records in both a human accessible way through HTML and a machine friendly format through RDFa. We begin by examining previous work on Drupal and the CRM and identifying useful patterns. We present the Drupal modules that are required by our setup and we explain why these are sustainable. We continue by giving guidelines for setting up Drupal to serve CRM data easily and we describe a specific installation for our case study which is related to decorated papers alongside our CRM mapping. We finish with highlighting the benefits of our method (i.e. speed and user-friendliness) and we refer to a number of issues which require further work (i.e. automatic validation, UI improvements and the provision for SPARQL endpoints).

Scientific Reports 6: Article number: 38171; published online: 02 December 2016; updated: 16 March 2017 The original version of this Article contained errors in the spelling of the authors Chung-Yuh Tzeng and Hsueh-Yao Chu, which were incorrectly given as Chung-Yuh Tseng and Shueh-Yao Chu respectively.

© 2015, Springer International Publishing Switzerland.Purpose: Patient-reported outcome (PRO) measures are essential for assessing subjective patient experiences. Interactive voice response (IVR) data collection provides advantages for clinical trial design by standardizing and centralizing the assessment. Prior to adoption of IVR as a mode of PRO administration in the Testosterone Trials (TTrials), we compared IVR to paper versions of the instruments to be used. Methods: IVR versions of the FACIT-Fatigue scale and Psychosexual Daily Questionnaire, Question 4, were developed. In one pilot study, IVR versions of these scales were compared to paper versions in 25 men ≥ 65 years at each of two clinical sites. In another study, IVR versions of the SF-36 Vitality Scale (SF-36), Positive and Negative Affect Scale, and Patient Health Questionnaire were evaluated in comparison with previously validated paper versions in 25 men at two clinical sites. Both paper and IVR versions of each instrument were administered in counterbalanced order, and test–retest reliability was evaluated by repeated administration of the test. Bland–Altman plots were used to assess the degree of agreement. Test–retest correlations for each measure were also determined. Results: Satisfactory agreement was observed between IVR and paper versions of each study measure. Specifically, linear and highly positive associations were observed consistently across the study for IVR and paper versions of all study measures. These ranged from r = 0.91–0.99. Test–retest reliability for all measures was acceptable or better (r = 0.70–0.90). Conclusions: The IVR versions of TTrials endpoints in these two studies performed consistently well in comparison with paper versions.

A new indirect indicator is introduced for the assessment of scientific publications. The proposed indicator ($$fp^{k}$$fpk-index) takes into account both the direct and indirect impact of scientific publications and their age. The indicator builds on the concept of generations of citations and acts as a measure of the accumulated impact of each scientific publication. A number of cases are examined that demonstrate the way the indicator behaves under well defined conditions in a Paper-Citation graph, like when a paper is cited by a highly cited paper, when cycles exist and when self-citations and chords are examined. Two new indicators for the assessment of authors are also proposed (fa-index and fas-index) that utilize the $$fp^{k}$$fpk-index values of the scientific publications included in the Publication Record of an author. Finally, a comparative study of the $$fp^{k}$$fpk and $$fa^{k}$$fak indices and a list of well known direct (Number of Citations, Mean number of citations, Contemporary h-index) and indirect (PageRank, SCEAS) indicators is presented.

To assess the equivalence of patient-reported outcome (PRO) survey responses across Web, interactive voice response system (IVRS), and paper modes of administration. Postoperative colorectal cancer patients with home Web/e-mail and phone were randomly assigned to one of the eight study groups: Groups 1–6 completed the survey via Web, IVRS, and paper, in one of the six possible orders; Groups 7–8 completed the survey twice, either by Web or by IVRS. The 20-item survey, including the MSKCC Bowel Function Instrument (BFI), the LASA Quality of Life (QOL) scale, and the Subjective Significance Questionnaire (SSQ) adapted to bowel function, was completed from home on consecutive days. Mode equivalence was assessed by comparison of mean scores across modes and intraclass correlation coefficients (ICCs) and was compared to the test–retest reliability of Web and IVRS. Of 170 patients, 157 completed at least one survey and were included in analysis. Patients had mean age 56 (SD = 11), 53 % were male, 81 % white, 53 % colon, and 47 % rectal cancer; 78 % completed all assigned surveys. Mean scores for BFI total score, BFI subscale scores, LASA QOL, and adapted SSQ varied by mode by less than one-third of a score point. ICCs across mode were: BFI total score (Web–paper = 0.96, Web–IVRS = 0.97, paper–IVRS = 0.97); BFI subscales (range = 0.88–0.98); LASA QOL (Web–paper = 0.98, Web–IVRS = 0.78, paper–IVRS = 0.80); and SSQ (Web–paper = 0.92, Web–IVRS = 0.86, paper–IVRS = 0.79). Mode equivalence was demonstrated for the BFI total score, BFI subscales, LASA QOL, and adapted SSQ, supporting the use of multiple modes of PRO data capture in clinical trials.

OBJECTIVES: Medical records are legal documentation of patients’ care hence must be accurate and complete for both medical and legal purposes. Electronic patient record (EPR) systems aim to improve the accuracy of documentation, provide better organisation and access of data. This study compares the completeness of traditional note records and EPR in glaucoma patients. METHODS: Using criteria from the April 2009 National Institute for Health and Care Excellence (NICE) guidelines completeness of data entry was compared between EPR and paper notes in three units. Moorfields Eye Hospital (City Road) uses the Openeyes EPR. Bedford Hospital (Moorfields Eye Centre) and Western Eye Hospital use the Medisoft EPR. The standard was set at 100% compliance for predetermined parameters. RESULTS: One hundred seventy paper notes and 270 electronic records were analysed. With the exception of central corneal thickness (p = 0.31), all other key parameters were more consistently recorded in the paper records than in the EPR. Intraocular pressure (p = 0.004), anterior chamber configuration and depth assessments using gonioscopy (p

In this work is presented and described an optical system specifically designed and implemented to be applied for the study of the ink---paper interaction at the paper surface, considering the two main directions of the paper sheet (machine and cross directions). This work is particularly concerned with the system description, complete characterization, test, and its usage under real conditions. Concerning the system characterization, the main parameters were addressed and set having in mind its optimization. Relatively to the test of the system, several measurements using specific object samples with known dimensions were carried out to evaluate the accuracy of the obtained results. In terms of its usage under real conditions, an experiment was carried out using a commercial ink and common copy paper to dynamically collect data of its interaction. The collected data, after processing, enabled the determination of the main results of the analyzed ink---paper interaction, which allowed to reach the following conclusions; (1) the implemented system offers two different analysis approaches (qualitative and quantitative), both showing to be in agreement; and (2) the system also shows to be important to identify variations of the ink---paper interaction, manifested in the machine and cross directions of the paper.

Exploring massive mobile data for location-based services becomes one of the key challenges in mobile data mining. In this paper, we investigate a problem of finding a correlation between the collective behavior of mobile users and the distribution of points of interest (POIs) in a city. Specifically, we use large-scale cell tower data dumps collected from cell towers and POIs extracted from a popular social network service, Weibo. Our objective is to make use of the data from these two different types of sources to build a model for predicting the POI densities of different regions in the covered area. An application domain that may benefit from our research is a business recommendation application, where a prediction result can be used as a recommendation for opening a new store/branch. The crux of our contribution is the method of representing the collective behavior of mobile users as a histogram of connection counts over a period of time in each region. This representation ultimately enables us to apply a supervised learning algorithm to our problem in order to train a POI prediction model using the POI data set as the ground truth. We studied 12 state-of-the-art classification and regression algorithms; experimental results demonstrate the feasibility and effectiveness of the proposed method.

A number of the people who have recovered from the acute effects of COVID-19 are facing long term sequelae from the infection. As the COVID-19 pandemic is still evolving, so is knowledge of the long-term effects of the virus on patients who still experience symptoms. Clearly, news media play a crucial role in distributing information and this distribution of information can, in turn, influence the actions of the public. The purpose of this study was to describe the content of news coverage of COVID-19 long haul symptoms currently posted on the internet. This study utilized Google News, a news aggregator service, and included the first 100 English language pieces of news. Video content and news article content were coded in depth for information on COVID-19 long haul symptoms. A total of 41% of news reports mentioned the length of time that the COVID-19 related symptoms persist. The length of time was reported to range from 1 month to more than 1 year. The symptom most commonly mentioned was tiredness or fatigue (74%), followed by difficulty breathing or shortness of breath (62 cases; 62%), and difficulty thinking or concentrating (50 cases; 50%). Other symptoms were mentioned less frequently. There were no statistically significant differences in any of the content including having video, written news reports, or both video and written news reports by source of the news reports based on consumer, professional, or television or internet-based news (p = .14). More complete coverage by online news media of the long-term effects of COVID-19 enhances public awareness of the post-acute syndromes, augments health providers' awareness of the range of chronic COVID-19 effects and the possibility of a second infection, increases the probability of patients' seeking and obtaining the proper care for their symptoms, and contributes to preventive actions for enhancing public health.

Pattern mining from graph transactional data (GTD) is an active area of research with applications in the domains of bioinformatics, chemical informatics and social networks. Existing works address the problem of mining frequent subgraphs from GTD. However, the knowledge concerning the coverage aspect of a set of subgraphs is also valuable for improving the performance of several applications. In this regard, we introduce the notion of subgraph coverage patterns (SCPs). Given a GTD, a subgraph coverage pattern is a set of subgraphs subject to relative frequency, coverage and overlap constraints provided by the user. We propose the Subgraph ID-based Flat Transactional (SIFT) framework for the efficient extraction of SCPs from a given GTD. Our performance evaluation using three real datasets demonstrates that our proposed SIFT framework is indeed capable of efficiently extracting SCPs from GTD. Furthermore, we demonstrate the effectiveness of SIFT through a case study in computer-aided drug design.

We report the results of a novel protocol for running online experiments using a combination of an online experimental platform in parallel with web-conferencing software in two formats—with and without subject webcams—to improve subjects’ attention and engagement. We compare the results between our online sessions with the offline (lab) sessions of the same experiment. We find that both online formats lead to comparable subject characteristics and performance as the offline (lab) experiment. However, the webcam-on protocol has less noisy data, and hence better statistical power, than the protocol without a webcam. The webcam-on protocol can detect reasonable effect sizes with a comparable sample size as in the offline (lab) protocol. Supplementary Information The online version contains supplementary material available at 10.1007/s40881-021-00112-w.

Automatic authentication of paper money is becoming an increasingly urgent problem because of new and improved uses of counterfeits. In this paper, we describe a system developed for discriminating fake notes from genuine ones and apply it to Indian banknotes. Image processing and pattern recognition techniques are used to design the overall approach. The ability of the embedded security aspects is thoroughly analysed for detecting fake currencies. Real samples are used in the experiments that show a high-precision machine can be developed for authentication of paper money. The system performance is reported for both accuracy and processing speed. The analysis of security features to prevent counterfeiting highlights some of the issues that should be considered in designing of currency notes in the future.

Peer reviewing is the key ingredient of evaluating the quality of scientific work. Based on the review scores assigned by individual reviewers to papers, program committees of conferences and journal editors decide which papers to accept for publication and which to reject. A similar procedure is part of the selection process of grant applications and, among other fields, in sports. It is well known that the reviewing process suffers from measurement errors due to a lack of agreement among multiple reviewers of the same paper. And if not all papers are reviewed by all reviewers, the naive approach of averaging the scores is biased. Several statistical methods are proposed for aggregating review scores, which all can be realized by standard statistical software. The simplest method uses the well-known fixed-effects two-way classification with identical variances, while a more advanced method assumes different variances. As alternatives a mixed linear model and a generalized linear model are employed. The application of these methods implies an evaluation of the reviewers, which may help to improve reviewing processes. An application example with real conference data shows the potential of these statistical methods.

Nowadays, reading on screens is an essential part of our daily life. Tablet computers, with great portability, usability, and interactivity, have become a popular mobile device for learning and reading. However, there were few studies to explore the effects on reading using tablet computers, and the difference between computer-based reading and paper-based reading is still not clear. Therefore, this study focused on to investigate the effects of reading comprehension across paper, tablets, and computers. Moreover, the user familiarity was regarded as a potential issue to affect the reading on digital devices. Accordingly, this study took into account to figure out the effects and impacts of the user familiarity on reading comprehension while reading on tablets. Two question types of reading comprehension, multiple choice questions as the shallow level comprehension and summarization as deep level comprehension, were considered to test the reading comprehension. Data from 90 college students were used in the data analysis. It was indicated that the paper group performed significantly better than the computer-based reading on the shallow level comprehension. This finding confirmed and eliminated the factors of reading in paper and computer for the previous studies. For example, the way of navigation was still the main reason to affect the process of reading. However, the switch between the reading and test media was not the reason to affect the reading comprehension. In order to figure out why there was no difference between tablets and the other two media platforms, the tablet familiarity was evaluated to find out whether the familiarity affect reading comprehension or not. The result showed that the high level of tablet familiarity group performed significantly better than low level group on deep level comprehension. For this result, it could conclude that if providing students enough and appropriate training, they could have higher familiarity with tablets and then gain better reading comprehension using tablets.

Open-source model management frameworks such as OCL and ATL tend to focus on manipulating models built atop the Eclipse Modelling Framework (EMF), a de facto standard for domain specific modelling. MATLAB Simulink is a widely used proprietary modelling framework for dynamic systems that is built atop an entirely different technical stack to EMF. To leverage the facilities of open-source model management frameworks with Simulink models, these can be transformed into an EMF-compatible representation. Downsides of this approach include the synchronisation of the native Simulink model and its EMF representation as they evolve; the completeness of the EMF representation, and the transformation cost which can be crippling for large Simulink models. We propose an alternative approach to bridge Simulink models with open-source model management frameworks that uses an “on-the-fly” translation of model management constructs into MATLAB statements. Our approach does not require an EMF representation and can mitigate the cost of the upfront transformation on large models. To evaluate both approaches we measure the performance of a model validation process with Epsilon (a model management framework) on a sample of large Simulink models available on GitHub. Our previous results suggest that, with our approach, the total validation time can be reduced by up to 80%. In this paper, we expand our approach to support the management of Simulink requirements and dictionaries, and we improve the approach to perform queries on collections of model elements more efficiently. We demonstrate the use of the Simulink requirements and dictionaries with a case study and we evaluate the optimisations on collection queries with an experiment that compares the performance of a set of queries on models with different sizes. Our results suggest an improvement by up to 99% on some queries.

Enterprise architecture has become an important driver to facilitate digital transformation in companies, since it allows to manage IT and business in a holistic and integrated manner by establishing connections among technology concerns and strategical/motivational ones. Enterprise architecture modelling is critical to accurately represent business and their IT assets in combination. This modelling is important when companies start to manage their enterprise architecture, but also when it is remodelled so that the enterprise architecture is realigned in a changing world. Enterprise architecture is commonly modelled by few experts in a manual way, which is error-prone and time-consuming and makes continuous realignment difficult. In contrast, other enterprise architecture modelling proposal automatically analyses some artefacts like source code, databases, services, etc. Previous automated modelling proposals focus on the analysis of individual artefacts with isolated transformations toward ArchiMate or other enterprise architecture notations and/or frameworks. We propose the usage of Knowledge Discovery Metamodel (KDM) to represent all the intermediate information retrieved from information systems’ artefacts, which is then transformed into ArchiMate models. Thus, the core contribution of this paper is the model transformation between KDM and ArchiMate metamodels. The main implication of this proposal is that ArchiMate models are automatically generated from a common knowledge repository. Thereby, the relationships between different-nature artefacts can be exploited to get more complete and accurate enterprise architecture representations.

Real-time execution of machine learning (ML) pipelines on radiology images is difficult due to limited computing resources in clinical environments, whereas running them in research clusters requires efficient data transfer capabilities. We developed Niffler, an open-source Digital Imaging and Communications in Medicine (DICOM) framework that enables ML and processing pipelines in research clusters by efficiently retrieving images from the hospitals’ PACS and extracting the metadata from the images. We deployed Niffler at our institution (Emory Healthcare, the largest healthcare network in the state of Georgia) and retrieved data from 715 scanners spanning 12 sites, up to 350 GB/day continuously in real-time as a DICOM data stream over the past 2 years. We also used Niffler to retrieve images bulk on-demand based on user-provided filters to facilitate several research projects. This paper presents the architecture and three such use cases of Niffler. First, we executed an IVC filter detection and segmentation pipeline on abdominal radiographs in real-time, which was able to classify 989 test images with an accuracy of 96.0%. Second, we applied the Niffler Metadata Extractor to understand the operational efficiency of individual MRI systems based on calculated metrics. We benchmarked the accuracy of the calculated exam time windows by comparing Niffler against the Clinical Data Warehouse (CDW). Niffler accurately identified the scanners’ examination timeframes and idling times, whereas CDW falsely depicted several exam overlaps due to human errors. Third, with metadata extracted from the images by Niffler, we identified scanners with misconfigured time and reconfigured five scanners. Our evaluations highlight how Niffler enables real-time ML and processing pipelines in a research cluster.

Unmanned Air Vehicles (UAVs) are becoming popular in real-world scenarios due to current advances in sensor technology and hardware platform development. The applications of UAVs in the medical field are broad and may be shared worldwide. With the recent outbreak of COVID-19, fast diagnostic testing has become one of the challenges due to the lack of test kits. UAVs can help in tackling the COVID-19 by delivering medication to the hospital on time. In this paper, to detect the number of COVID-19 cases in a hospital, we propose a deep convolution neural architecture using transfer learning, classifying the patient into three categories as COVID-19 (positive) and normal (negative), and pneumonia based on given X-ray images. The proposed deep-learning architecture is compared with state-of-the-art models. The results show that the proposed model provides an accuracy of 94.92%. Further to offer time-bounded services to COVID-19 patients, we have proposed a scheme for delivering emergency kits to the hospitals in need using an optimal path planning approach for UAVs in the network.