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With the arrival of the Internet and social media, at the same time as masses of humans have benefitted from the full-size reassets of records available, there was an full-size boom with inside the upward push of cyber-crimes, mainly targeted closer to women. According to a 2019 file with inside the Economics Times, India has witnessed a 457% upward push in cybercrime with inside the 5 years span among 2011 and 2016. Most speculate that that is because of effect of social media inclusive of Facebook, Instagram and Twitter on our day by day lives. While those simply assist in growing a legitimate social network, advent of consumer debts in those websites normally desires simply an email-id. A actual lifestyles man or woman can create more than one fake IDs and for this reason impostors can effortlessly be made. Unlike the actual international state of affairs in which more than one policies and guidelines are imposed to become aware of oneself in a completely unique manner (as an instance at the same time as issuing one’s passport or driver’s license), with inside the digital international of social media, admission does now no longer require this kind of checks. In this paper, we study the one-of-a-kind debts of Instagram, specifically and try and verify an account as fake or actual the use of Machine Learning strategies specifically Logistic Regression and Random Forest Algorithm.

This paper focuses on the opportunities and challenges associated with the use of artificial intelligence (AI) in academic library operations. In the quest to render fast, effective and efficient s...

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.

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.

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.

We envision a richer support from Virtual Reality (VR) development environments than the one available today. We believe this will be part of an Operating System (OS) for VR applications, since those services should be uniform and widespread for all experiences within a VR system, they are generic enough to be part of any application, and possibly they will not have to be developed from scratch. This position paper explores some of the shortcomings in current technology we are identifying while developing an application for the intervention of kids with ADD (Attention Deficit Disorder), and how these OS services might be defined and used.

We report a multiplexed paper-based assay powered by mobile imaging and machine learning for point-of-care diagnosis of Lyme Disease. Blind testing of 96 serum samples showed a sensitivity and specificity of 85.7% and 96.3%, respectively.

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 development of microfluidic devices for point-of-care (POC) testing has been widely applied for food quality control, environmental monitoring, and clinical diagnoses. Regarding the latter, microfluidic paper-based analytical devices (µPADs) have a great potential in developing portable and disposable platforms for fast disease-relevant biomarker detection in remote areas. In this work, k-nearest neighbors (k-NN) classifiers were applied to demonstrate the reliability of using chemical data obtained from µPADs for diagnosis of diseases like diabetes mellitus and hyperuricemia. Predictors based on glucose, lactate, and uric acid biomarkers were evaluated to reach this goal. Healthy adults were differentiated from the diagnosed patients with a perfect success rate. Metabolic disorders were suitably predicted with 95.0% overall accuracy. This model also showed sensitivities and specificities in the ranges 75–100% and 93–100%, respectively, indicating that the classification protocol is robust. The k-NN modeling proved the potential of the µPADs to be applied in telemedicine for reliable off-site diagnoses.

Swarms of autonomous aircraft systems (SAAS) are used to accomplish missions that deal with sensitive and critical data. As such, these systems are subject to attacks among which clone attacks. Several clone attacks detection approaches either in stationary or mobile wireless sensor networks have been proposed. Unfortunately, the existing approaches are not suitable for swarms of autonomous aircraft systems because either they do not guarantee a fast enough detection (while most SAAS missions are real time missions, e.g. surveillance), or they depend on strong assumptions that are hard to achieve in practice (e.g. absence of nodes collusion, impossibility for an attacker to track a genuine node). In this paper, we present a novel clone attack detection approach specific to SAAS that solves several unaddressed issues and that makes it possible to improve the security and the privacy of the assets of the SAAS.

Formulaic expressions, such as 'in this paper we propose', are helpful for authors of scholarly papers because they convey communicative functions; in the above, it is showing the aim of this paper'. Thus, resources of formulaic expressions, such as a dictionary, that could be looked up easily would be useful. However, forms of formulaic expressions can often vary to a great extent. For example, 'in this paper we propose', 'in this study we propose' and 'in this paper we propose a new method to' are all regarded as formulaic expressions. Such a diversity of spans and forms causes problems in both extraction and evaluation of formulaic expressions. In this paper, we propose a new approach that is robust to variation of spans and forms of formulaic expressions. Our approach regards a sentence as consisting of a formulaic part and non-formulaic part. Then, instead of trying to extract formulaic expressions from a whole corpus, by extracting them from each sentence, different forms can be dealt with at once. Based on this formulation, to avoid the diversity problem, we propose evaluating extraction methods by how much they convey specific communicative functions rather than by comparing extracted expressions to an existing lexicon. We also propose a new extraction method that utilises named entities and dependency structures to remove the non-formulaic part from a sentence. Experimental results show that the proposed extraction method achieved the best performance compared to other existing methods., 21 pages, 11 figures

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.

Objectives The algorithms that define most of the application of oncoplastic breast conserving surgery (OBCS) in breast cancer patients are not clearly defined. Therefore, a consensus survey was conducted between the leading and experienced breast surgeons and oncoplastic breast surgeons in Turkey on the controversial areas of oncoplastic breast surgery. Material and methods This consensus survey was carried out on-line through the Consensus software program (www.consensuss.com) under the roof of Turkish Federation of National Societies for Breast Diseases (TFNSBD). After finalizing each proposition, web-based remote access consensus process was performed on the Likert scale using Delphi method with the Consensus (www.consensuss.com) software program. Through the related software, an invitation was sent to 111 people who had at least 5 years of general surgery expertise in Turkey, and who devoted more than 50% of their daily clinical practice to the treatment and surgery of breast diseases. Results Sixty-two out of 111 people accepted to participate in the panel and made an on-line evaluation. According to the consensus results; Lumpectomy area should be done by placing the clips on at least four walls of the cavity, if the margin of the tumor is clear in central tumors, the distance between the tumor and the nipple is not significantly important, oncoplastic techniques may be used in patients with locally advanced breast cancer after neoadjuvant chemotherapy, in patients who have macromastia with ductal carcinoma in situ or breast cancer, OBCS techniques can be performed, and OBCS should be evaluated in terms of breast aesthetics. After OBCS, re-excision can be performed at a re-do setting in cases with involved surgical margins. Conclusion Our consensus results may provide a basis for the development of some standards in OBCS.

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

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.

Initial Coin Offerings (ICOs) are a new form of venture capital financing that raised more than 30 billion US dollars between 2016 and 2019. ICOs opened up the venture capital market to a more diverse investor base previously limited to “sophisticated” investors. This study analyses 1,258 white papers and finds that the length is positively correlated with the amount raised during the ICO and the likelihood of successfully completing the ICO. In contrast, the readability grade of the white paper is only positively correlated with the amount raised. The study highlights the role of voluntary disclosure in the ICO market.

Contains fulltext : 237435.pdf (Publisher’s version ) (Open Access) Objective: We evaluated reporting completeness and transparency in randomised controlled trials (RCTs) conducted using administrative data based on 2021 CONSORT Extension for Trials Conducted Using Cohorts and Routinely Collected Data (CONSORT-ROUTINE) criteria. Study Design and Setting: MEDLINE and the Cochrane Methodology Register were searched (2011 and 2018). Eligible RCTs used administrative databases for identifying eligible participants or collecting outcomes. We evaluated reporting based on CONSORT-ROUTINE, which modified eight items from CONSORT 2010 and added five new items. Results: Of 33 included trials (76% used administrative databases for outcomes, 3% for identifying participants, 21% both), most were conducted in the United States (55%), Canada (18%), or the United Kingdom (12%). Of eight items modified in the extension; six were adequately reported in a majority (>50%) of trials. For the CONSORT-ROUTINE modification portion of those items, three items were reported adequately in >50% of trials, two in

The integration of telecommunications and information systems in health care first began 4 decades ago with 500 patient consultations performed via interactive television. The use of telemedicine services and technology to deliver health care at a distance is increasing exponentially. Concomitant with this rapid expansion is the exciting ability to provide enhancements in quality and safety of care. Telemedicine enables increased access to care, improvement in health outcomes, reduction in medical costs, better resource use, expanded educational opportunities, and enhanced collaboration between patients and physicians. These potential benefits should be weighed against the risks and challenges of using telemedicine. The American College of Allergy, Asthma, and Immunology advocates for incorporation of meaningful and sustained use of telemedicine in allergy and immunology practice. This article serves to offer policy and position statements of the use of telemedicine pertinent to the allergy and immunology subspecialty.

Paper and ink have been continually evolving since their invention. Conventional printing quality and cost is constantly improving, yet only few examples of commercial embedded electronics on paper exist. This is due to the fact that inks with optical and electronic properties do not suit conventional paper and are still challenging to print reliably. In this project, we aim to develop a new generic mass media "next generation" paper. We present the challenges and progress on book augmentation, using printing technologies such as screen printing and roll-to-roll fabrication. This first step aims to create an augmented book prototype via hybrid integration of optical sensors in its pages and by embedding energyefficient control and communication electronics in the cover. The system senses user interaction and, through wireless connectivity, presents the user with relevant, updated digital content on an adjunct device. The proposed fabrication sequence has been developed with close attention to the requirements of industrial scale-up.

Background Geotagging is the process of attaching geospatial tags to various media data types. In health care, the goal of geotagging is to gain a better understanding of health-related questions applied to populations. Although there has been a prevalence of geographic information in public health, in order to effectively use and expand geotagging across health care there is a requirement to understand other factors such as the disposition, standardization, data sources, technologies, and limitations. Objective The objective of this document is to serve as a resource for new researchers in the field. This report aims to be comprehensive but easy for beginners to understand and adopt in practice. The optimal geocodes, their sources, and a rationale for use are suggested. Geotagging’s issues and limitations are also discussed. Methods A comprehensive review of technical instructions and articles was conducted to evaluate guidelines for geotagging, and online resources were curated to support the implementation of geotagging practices. Summary tables were developed to describe the available geotagging resources (free and for fee) that can be leveraged by researchers and quality improvement personnel to effectively perform geospatial analyses primarily targeting US health care. Results This paper demonstrated steps to develop an initial geotagging and geovisualization project with clear structure and instructions. The geotagging resources were summarized. These resources are essential for geotagging health care projects. The discussion section provides better understanding of geotagging’s limitations and suggests suitable way to approach it. Conclusions We explain how geotagging can be leveraged in health care and offer the necessary initial resources to obtain geocodes, adjustment data, and health-related measures. The resources outlined in this paper can support an individual and/or organization in initiating a geotagging health care project.

Ovarian cancer is the fifth leading cause of female cancer mortality in the U.S. and accounts for five percent of all cancer deaths among women. No environmental risk factors for ovarian cancer have been confirmed. We previously reported that ovarian cancer incidence rates at the state level were significantly correlated with the extent of pulp and paper manufacturing. We evaluated that association using county-level data and advanced geospatial methods. Specifically, we investigated the relationship of spatial patterns of ovarian cancer incidence rates with toxic emissions from pulp and paper facilities using data from the Environmental Protection Agency&rsquo, s Toxic Release Inventory (TRI). Geospatial analysis identified clusters of counties with high ovarian cancer incidence rates in south-central Iowa, Wisconsin, New York, Pennsylvania, Alabama, and Georgia. A bivariate local indicator of spatial autocorrelation (LISA) analysis confirmed that counties with high ovarian cancer rates were associated with counties with large numbers of pulp and paper mills. Regression analysis of state level data indicated a positive correlation between ovarian cancer and water pollutant emissions. A similar relationship was identified from the analysis of county-level data. These data support a possible role of water-borne pollutants from pulp and paper mills in the etiology of ovarian cancer.

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.

The United States Environmental Protection Agency (EPA) developed Toxic Release Inventory (TRI) using information submitted by different industries in the United States. This research examines the contaminants released by paper manufacturing industry in the United States and Ohio over time and what paper manufacturers are doing to diminish the quantities of chemical wastes generated, released to the environment, or managed by other means. It is understood that there is a reduction in the Risk-Screening Environmental Indicators (RSEI) scores from 2002 to 2014 in all the states, which can be related to the adaptation of recycling or reusing of the chemicals in various other manufacturing processes. The other significant observation inferred from the analysis is that the facilities in the states such as Wisconsin, Louisiana, South Carolina, Connecticut, and Pennsylvania based on the economic feasibility or recycling prospects, they are generating higher quantities of the selected chemical resulting in the higher RSEI scores respectively for the above-mentioned states. This article demonstrates the application of the risk-screening indicators model to a large database to analyze pollution data. © 2017 American Institute of Chemical Engineers Environ Prog, 36: 808–814, 2017

Paper-based devices are an excellent match for low-cost point-of-care testing (POCT) tools. Their user-friendliness, portability, and short time of analysis, coupled with ease of local manufacture make these devices the best option for inexpensive diagnostic testing tools. However, despite all their positive features, these low-cost diagnostic devices must present good performance indicators, such as sensitivity, specificity, and accuracy. We developed and validated a paper-based ELISA for toxoplasmosis diagnosis through the detection of Toxoplasma gondii immunoglobulin G (IgG) antibodies in 100 human serum samples. From among the different ways to define the cut-off value, we chose Youden's J index (cut-off = 21.73 A.U.), which presented a higher sensitivity value. Our paper-based assay presented a sensitivity of 0.96, a specificity of 0.87, and a gray zone comprising 16 samples (±15% of the cut-off value, with 3 false positive outputs). The accuracy of the test was estimated by using ROC curves (AUC = 0.97). We also created a macro in Microsoft Excel® to estimate the accuracy of the test (m-Accuracy) based on a non-parametric method, which evidenced a value = 0.88, which classifies our test as moderately to highly accurate. We also provide the m-Accuracy macro for download and the paper-based microplate designs for printing, in order to collaborate with the scientific community and facilitate further studies using this platform. The improvement of these diagnostic tools can bring this technology for those who need it, contributing to population health and well-being.

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.

This companion paper is to support the replication of paper "Campus3D: A Photogrammetry Point Cloud Benchmark for Outdoor Scene Hierarchical Understanding", which was presented at ACM Multimedia 2020. The supported paper's main purpose was to provide a photogrammetry point cloud-based dataset with hierarchical multilabels to facilitate the area of 3D deep learning. Based on this provided dataset and source code, in this work, we build a complete package to reimplement the proposed methods and experiments (i.e., the hierarchical learning framework and the benchmarks of the hierarchical semantic segmentation task). Specifically, this paper contains the technical details of the package, including file structure, dataset preparation, installation package, and the conduction of the experiment. We also present the replicated experiment results and indicate our contributions to the original implementation.

In this companion paper, we provide the details of the reproducibility artifacts of the paper "Visual Relation of Interest Detection" presented at MM'20. Visual Relation of Interest Detection (VROID) aims to detect visual relations that are important for conveying the main content of an image. In this paper, we explain the file structure of the source code and publish the details of our ViROI dataset, which can be used to retrain the model with custom parameters. We also detail the scripts for component analysis and comparison with other methods and list the parameters that can be modified for custom training and inference.

In this paper we reproduce experimental results presented in our earlier work titled "Describing Subjective Experiment Consistency by $p$-Value P-P Plot" that was presented in the course of the 28th ACM International Conference on Multimedia. The paper aims at verifying the soundness of our prior results and helping others understand our software framework. We present artifacts that help reproduce tables, figures and all the data derived from raw subjective responses that were included in our earlier work. Using the artifacts we show that our results are reproducible. We invite everyone to use our software framework for subjective responses analyses going beyond reproducibility efforts., Comment: Please refer to the original publication: https://dl.acm.org/doi/10.1145/3474085.3477935 Related paper: https://dl.acm.org/doi/abs/10.1145/3394171.3413749 or arXiv:2009.13372

This chapter focuses on cost control by analyzing the intelligent control system of waste paper deinking production line, and finally designs a solution. In the aspect of control index, machine learning has designed a solution that pays less attention to the benefit of a single control loop and focuses on maximizing the overall benefit of the production line. In the aspect of control demand, according to the complexity of waste paper deinking production line, a distributed control solution is designed Finally, according to the dynamic fuzziness in machine learning system, the basic concept of dynamic fuzzy machine learning is constructed by using dynamic fuzzy set The dynamic fuzzy machine learning model is described and the related learning algorithm is given. Finally, a simulation example is given, and satisfactory results are obtained by comparing with the elastic BP algorithm RPROP of neural network.

BackgroundPeriodic demographic health surveillance and surveys are the main sources of health information in developing countries. Conducting a survey requires extensive use of paper-pen and manual work and lengthy processes to generate the required information. Despite the rise of popularity in using electronic data collection systems to alleviate the problems, sufficient evidence is not available to support the use of electronic data capture (EDC) tools in interviewer-administered data collection processes. ObjectiveThis study aimed to compare data quality parameters in the data collected using mobile electronic and standard paper-based data capture tools in one of the health and demographic surveillance sites in northwest Ethiopia. MethodsA randomized controlled crossover health care information technology evaluation was conducted from May 10, 2016, to June 3, 2016, in a demographic and surveillance site. A total of 12 interviewers, as 2 individuals (one of them with a tablet computer and the other with a paper-based questionnaire) in 6 groups were assigned in the 6 towns of the surveillance premises. Data collectors switched the data collection method based on computer-generated random order. Data were cleaned using a MySQL program and transferred to SPSS (IBM SPSS Statistics for Windows, Version 24.0) and R statistical software (R version 3.4.3, the R Foundation for Statistical Computing Platform) for analysis. Descriptive and mixed ordinal logistic analyses were employed. The qualitative interview audio record from the system users was transcribed, coded, categorized, and linked to the International Organization for Standardization 9241-part 10 dialogue principles for system usability. The usability of this open data kit–based system was assessed using quantitative System Usability Scale (SUS) and matching of qualitative data with the isometric dialogue principles. ResultsFrom the submitted 1246 complete records of questionnaires in each tool, 41.89% (522/1246) of the paper and pen data capture (PPDC) and 30.89% (385/1246) of the EDC tool questionnaires had one or more types of data quality errors. The overall error rates were 1.67% and 0.60% for PPDC and EDC, respectively. The chances of more errors on the PPDC tool were multiplied by 1.015 for each additional question in the interview compared with EDC. The SUS score of the data collectors was 85.6. In the qualitative data response mapping, EDC had more positive suitability of task responses with few error tolerance characteristics. ConclusionsEDC possessed significantly better data quality and efficiency compared with PPDC, explained with fewer errors, instant data submission, and easy handling. The EDC proved to be a usable data collection tool in the rural study setting. Implementation organization needs to consider consistent power source, decent internet connection, standby technical support, and security assurance for the mobile device users for planning full-fledged implementation and integration of the system in the surveillance site.

Game-based learning represents a promising alternative to teach computing in higher education. This paper presents “BDD Assemble!”, a paper-based game proposal for teaching Behavior Driven Development (BDD) competences. For this, the proposed game and the evaluation approach with software engineering students are described. As a result, a simple, interactive and colaborative game was provided, able to teach BDD concepts in a practical, competitive and fun way.

Paper-based analytical devices (PADs) are simple, convenient, and portable devices that can detect selected substances and quickly display results. PADs are being adopted at increasing rates for use in an expanding array of fields including biochemistry, drug test assaying, and environmental analysis. Compared with traditional analytical methodology, PADs provide several advantages including the following: (1) the capacity for easy mass production; (2) portability; (3) low cost; and (4) small sample volume requirements. These features position PADs as significantly useful tools in rural areas and developing countries, where sophisticated laboratories are limited. In addition to these advantages, PADs offer great potential for integration with the Internet and new artificial intelligence technology. Integrating what may be called “lab-on-a-paper” technology with the Internet of Things offers new opportunities for individuals, in the lab, in the field, and even at home, to carry out advanced examinations and analyses of a variety of things including point-of-care health diagnostics, health status, and environmental quality. In this way, PADs can significantly improve quality of life for a great number of people.

We examine voluntary disclosures made by issuers of initial coin offerings (ICOs) in their “white papers” and use them to rate the blockchain technology being developed by the ICO issuers. First, we find that most ICOs receive a low rating. In fact, over 80% of the ICOs do not need to use a blockchain. Second, ICO projects with a higher rating raised more funds than their counterparts, after controlling for other project-, issuer-, and token-related features. Third, ICOs with a high rating are more likely to have their digital coins/tokens listed on a cryptocurrency exchange within 180 days after their ICO end dates. Finally, results indicate that providing technical details in the white paper can be an effective way to signal the quality of an ICO project. Overall, these results are consistent with ICO token buyers/investors taking into consideration the underlying blockchain technology of the ICO projects when making their purchasing/investing decisions. Since the white paper is the main source of ICO information, our findings imply that the credibility of this information is important to protect the interests of ICO buyers/investors and to ensure the long-term viability of the ICO as a capital-raising tool for blockchain-based ventures.

Technology is shaping our lives in a multitude of ways. This is fuelled by a technology infrastructure, both legacy and state of the art, composed of a heterogeneous group of hardware, software, services, and organisations. Such infrastructure faces a diverse range of challenges to its operations that include security, privacy, resilience, and quality of services. Among these, cybersecurity and privacy are taking the centre-stage, especially since the General Data Protection Regulation (GDPR) came into effect. Traditional security and privacy techniques are overstretched and adversarial actors have evolved to design exploitation techniques that circumvent protection. With the ever-increasing complexity of technology infrastructure, security and privacy-preservation specialists have started to look for adaptable and flexible protection methods that can evolve (potentially autonomously) as the adversarial actor changes its techniques. For this, Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) were put forward as saviours. In this paper, we look at the promises of AI, ML, and DL stated in academic and industrial literature and evaluate how realistic they are. We also put forward potential challenges a DL based security and privacy protection system has to overcome. Finally, we conclude the paper with a discussion on what steps the DL and the security and privacy-preservation community have to take to ensure that DL is not just going to be hype, but an opportunity to build a secure, reliable, and trusted technology infrastructure on which we can rely on for so much in our lives.

In Japan, dental charting following a disaster is performed by handwriting the information on a paper-based dental chart before manual input into a computer database. Even if available, radiographic facial or intraoral images, regardless of whether they are analogue or digital, must be stored separately from the dental chart. To create a comprehensive and more permanent database accessible worldwide, we developed a system employing digital pens with image microprocessors. This system digitizes and records characters and drawings as they are being handwritten on a microdotted-paper-based post-mortem dental chart. Data stored in the built-in memory of the digital pen are transferred to a computer or tablet via Bluetooth. Using a cognitive wireless router, the information is relayed to a remote data centre. Radiographic and other images are also transferred into the same folder under a unique ID number. After several practical field tests, the system was employed at a landslide disaster in Hiroshima ...

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.

Paper-based digital microfluidic biochips (PB-DMFBs) provide a promising solution for microfluidic bioassays. Due to the low-cost substrate material and low demand for complicated manufacturing equipment, PB-DMFBs can be fabricated without foundry. On the flip side, convenience of fabrication allows PB-DMFBs to be fabricated everywhere, which makes it is difficult to manage production and distribution of IP (bioassays). As a result, PB-DMFBs are vulnerable to security threats. IP and its creator, the biocoders, may suffer from infringement. To ensure IP protection, in this paper, we proposed the first Digital Rights Management (DRM) scheme to protect IPs of PB-DMFBs from security threats. A chip-level synthesis algorithm is also presented to realize not only complex biochemical operations but also the demand of DRM.

Background Acquired brain injury (ABI) is damage to the brain that occurs after birth caused either by a traumatic or by a nontraumatic injury. The rehabilitation process following ABI should be performed by a multi-professional team, working in an interdisciplinary way, with the aim of organizing a comprehensive and holistic approach to persons with every severity of ABI. This Evidence Based Position Paper represents the official position of the European Union through the UEMS Physical and Rehabilitation Medicine (PRM) Section and designates the professional role of PRM physicians for people with ABI. The aim was to formulate recommendations on the PRM physician's professional practice for persons with ABI in order to promote their functioning and enhance quality of life. Methods This paper has been developed according to the methodology defined by the Professional Practice Committee of the UEMS-PRM Section: a systematic literature search has been performed in PubMed and Core Clinical Journals. On the basis of the selected papers, recommendations have been made as a result of five Delphi rounds. Results The literature review as well as thirty-one recommendations are presented. Conclusions The expert consensus is that structured, comprehensive and holistic rehabilitation program delivered by the multi-professional team, working in an interdisciplinary way, with the leadership and coordination of the PRM physician, is likely to be effective, especially for those with severe disability after brain injury.