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In this paper, the implementation of artificial neural networks (multilayer perceptron [MLP] and radial base functions [RBF]) and the upgraded Markov chain model have been studied and performed to identify the human behavior patterns during rock, paper, and scissors game. The main motivation of this research is the design and construction of an intelligent robot with the ability to defeat a human opponent. MATLAB software has been used to implement intelligent algorithms. After implementing the algorithms, their effectiveness in detecting human behavior pattern has been investigated. To ensure the ideal performance of the implemented model, each player played with the desired algorithms in three different stages. The results showed that the percentage of winning computer with MLP and RBF neural networks and upgraded Markov model, on average in men and women is 59%, 76.66%, and 75%, respectively. Obtained results clearly indicate a very good performance of the RBF neural network and the upgraded Markov model in the mental modeling of the human opponent in the game of rock, paper, and scissors. In the end, the designed game has been employed in both hardware and software which include the Zana intelligent robot and a digital version with a graphical user interface design on the stand. To the best knowledge of the authors, the precision of novel presented method for determining human behavior patterns was the highest precision among all of the previous studies.

In this paper, we present a system for automatic evaluation of the quality of a question paper. Question paper plays a major role in educational assessment. The quality of a question paper is crucial to fulfilling the purpose of the assessment. In many education sectors, question papers are prepared manually. A prior analysis of a question paper might help in finding the errors in the question paper, and better achieving the goals of the assessment. In this experiment, we focus on higher education in the technical domain. First, we conducted a student survey to identify the key factors that affect the quality of a question paper. The top factors we identified are question relevance, question difficulty, and time requirement. We explored the strategies to handle these factors and implemented them. We employ various concepts and techniques for the implementation. The system finally assigns a numerical quality score against these factors. The system is evaluated using a set of question papers collected from various sources. The experimental results show that the proposed system is quite promising.

The accuracy of a predictive system is critical for predictive maintenance and to support the right decisions at the right times. Statistical models, such as ARIMA and SARIMA, are unable to describe the stochastic nature of the data. Neural networks, such as long short-term memory (LSTM) and the gated recurrent unit (GRU), are good predictors for univariate and multivariate data. The present paper describes a case study where the performances of long short-term memory and gated recurrent units are compared, based on different hyperparameters. In general, gated recurrent units exhibit better performance, based on a case study on pulp paper presses. The final result demonstrates that, to maximize the equipment availability, gated recurrent units, as demonstrated in the paper, are the best options.

With the increasing advance of computer and information technologies, numerous research papers have been published online as well as offline, and as new research fields have been continuingly created, users have a lot of trouble in finding and categorizing their interesting research papers. In order to overcome the limitations, this paper proposes a research paper classification system that can cluster research papers into the meaningful class in which papers are very likely to have similar subjects. The proposed system extracts representative keywords from the abstracts of each paper and topics by Latent Dirichlet allocation (LDA) scheme. Then, the K-means clustering algorithm is applied to classify the whole papers into research papers with similar subjects, based on the Term frequency-inverse document frequency (TF-IDF) values of each paper.

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.

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.

Background In Software Engineering (SE), conference publications have high importance both in effective communication and in academic careers. Researchers actively discuss how a paper should be organized to be accepted in mainstream conferences. Aiming This work tackles the problem of generalizing and characterizing the type of papers accepted at SE conferences. Method The paper offers a new perspective in the analysis of SE literature: a categorization scheme for SE papers is obtained by merging, extending and revising related proposals from a few existing studies. The categorization scheme is used to classify the papers accepted at three top-tier SE conferences during five years (2012–2016). Results While a broader experience is certainly needed for validation and fine-tuning, preliminary outcomes can be observed relative to what problems and topics are addressed, what types of contributions are presented and how they are validated. Conclusions The results provide insights to paper writers, paper reviewers and conference organizers in focusing their future efforts, without any intent to provide judgments or authoritative guidelines.

In this paper, we analyze the intelligent subdesign of the simulated marking system through an in-depth study of it. This paper proposes a correlation analysis-based quantification of N-element sense values and a rationality enhancement-based scoring fitting algorithm for English essays. This paper also extracts word features, sentence features, and chapter structure features in essays to fit English composition scores. Since not all students can complete the essays according to the topic requirements, a triage scoring model is used to separate the normal essays from the low-scoring essays. Statistically, it was found that the essay scores also showed a certain normal distribution. The standard support vector regression algorithm is prone to data skewing problems, so this paper addresses this problem by using a rationality enhancement method that gives a corresponding penalty factor according to the distribution of the dataset. The results show that the English essay scoring fitting algorithm proposed in this paper can well improve the prediction accuracy of some data and solve the problem of skewed data where the scores show a normal distribution. This paper designs and implements an online mock examination system that incorporates an intelligent scoring system for essays, enabling it to meet the needs of teachers and students for online examinations and intelligent scoring.

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...

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.

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.

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.

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.

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.

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.

Spatial visualization—the ability to mentally imagine and manipulate objects—has frequently been measured using the Paper Folding Test (PFT). In this task, participants view diagrams of a paper being folded and a hole being punched. They then identify the resulting punch pattern. Although task instructions promote mentally unfolding the paper, the extent to which people follow this spatial visualization strategy is unknown. The present work assesses hypothesized PFT solution strategies and their relation to problem attributes. Accuracy was impacted by the interaction between fold types, linear mixed models revealed greater use of simple heuristics compared to the suggested unfolding. Furthermore, most participants used a single strategy but simple heuristics were more often used than unfolding. Given this, we recommend redesigning the PFT to utilize the prevalence of strategy use to assess individual differences.

Security inks based on photoluminescent materials are mostly investigated for security applications, such as information encryption and decryption, anti-counterfeiting, and data storage. Although they are invisible to the naked eye under ambient light, they can be detected under ultraviolet or near-infrared light. Herein, a new kind of secret paper made from network-structured ultralong hydroxyapatite nanowires and cellulose fibers has been developed. White vinegar, a common cooking ingredient, is used as an invisible security ink. Covert information on the secret paper written with white vinegar is totally invisible under natural light, but it can be decrypted and clearly read after exposure to fire; the response time to fire is short (

Automatic data processing represents the future for the development of any system, especially in scientific research. In this paper, we describe one of the automatic classification methods applied to scientific research as a supervised learning task. Throughout the process, we identify the main features that are used as keys to play a significant role in terms of predicting the new rank under the supervised learning setup. First, we propose an overview of the work that has been realized in ranking scientific research papers. Second, we evaluate and compare some of state-of-the-art for the classification by supervised learning, semi-supervised learning and non-supervised learning. During the preliminary tests, we have obtained good results for performance on realistic corpus then we have compared performance metrics, such as NDCG, MAP, GMAP, F-Measure, Precision and Recall in order to define the influential features in our work. Keywords: Scientific research, Ranking scientific research papers, Data mining, Supervised learning, Multilayer perceptron algorithm

Images can be helpful for eliciting data in the form of responses from participants. Sometimes photographs can help participants speak about issues, events, thoughts and emotions which they find difficult to talk about. This paper discusses how photos can be used and how the data collected through their use in a discussion can be analysed using Correspondence Analysis (CA). Although CA is considered a quantitative tool for analysing data, this paper describes how it can be used with qualitative data.

Background: People with intellectual disabilities often present with unique challenges that make it more difficult to meet their palliative care needs. Aim: To define consensus norms for palliative care of people with intellectual disabilities in Europe. Design: Delphi study in four rounds: (1) a taskforce of 12 experts from seven European countries drafted the norms, based on available empirical knowledge and regional/national guidelines; (2) using an online survey, 34 experts from 18 European countries evaluated the draft norms, provided feedback and distributed the survey within their professional networks. Criteria for consensus were clearly defined; (3) modifications and recommendations were made by the taskforce; and (4) the European Association for Palliative Care reviewed and approved the final version. Setting and participants: Taskforce members: identified through international networking strategies. Expert panel: a purposive sample identified through taskforce members’ networks. Results: A total of 80 experts from 15 European countries evaluated 52 items within the following 13 norms: equity of access, communication, recognising the need for palliative care, assessment of total needs, symptom management, end-of-life decision making, involving those who matter, collaboration, support for family/carers, preparing for death, bereavement support, education/training and developing/managing services. None of the items scored less than 86% agreement, making a further round unnecessary. In light of respondents’ comments, several items were modified and one item was deleted. Conclusion: This White Paper presents the first guidance for clinical practice, policy and research related to palliative care for people with intellectual disabilities based on evidence and European consensus, setting a benchmark for changes in policy and practice.

The commercialization of electronic paper displays has brought the consumer a truly comfortable and mobile viewing experience, even in bright daylight conditions. We review the display standards activities that support the quality manufacturing of these products and helps drive their market expansion.

How best to estimate the accuracy of a predictive rule has been a longstanding question in statistics. Approaches to this task range from simple methods like Mallow’s Cp to algorithmic techniques l...

Industrial archived process data represent a convenient source of information for data-driven models, such as artificial neural network (ANN), that can be used for safety and efficiency improvement like early or even predictive fault detection and diagnosis (FDD). Nonetheless, most of the data used for model generation are representative of the process nominal states and therefore are not enough for classification problems intended to determine abnormal process conditions. This work proposes the use of techniques to augment the original real data standards, dismissing the need for experiments that could jeopardize process safety. It uses the Monte Carlo technique to artificially increase the number of model inputs coupled to the nearest neighbor search (NNS) by geometric distances to consistently classify the generated patterns in normal or faulty statuses. Finally, a radial basis function neural network is trained with the augmented data. The methodology was validated by a study case in which 3381 pulp and paper industrial data points were expanded to monitor the formation of particles in a recovery boiler. Only 5.8% of the original process data were examples of faulty conditions, but the new expanded and balanced data collection leveraged the classification performance of the neural network, allowing its future use for monitoring purpose.

With paper map use in decline, one of the strategies that libraries and archives can adopt to make the information contained within them more accessible and usable is to extract features of interest from their scanned raster maps and convert those to geospatial vector data. This process adds valuable unique data to library geospatial collections and enables those previously map-bound features to be used separately in geographic information systems (GIS) software for custom mapping and analysis. Advances in partially automating most of the process have made this a much more viable option for libraries and archives. Although there is no one-size-fits-all automated solution for all maps and map features, this paper provides a complete description of the entire process incorporating examples of the various techniques and software used in selected studies that would be applicable in the library and archive environment.

See RECORDING (starts at 00:30:30). In an attempt to increase the reproducibility of contributions to a long-running and established geospatial conference series, the 23rd AGILE Conference on Geographic Information Science 2020 (https://agile-online.org/conference-2020) for the first time provided guidelines on preparing reproducible papers (Nüst etal., 2020) and appointed a reproducibility committee to evaluate computational workflows of accepted papers ( https://www.agile-giscience-series.net/review_process.html). Here, the committee’s members report on the lessons learned from reviewing 23 accepted full papers and outline future plans for the conference series. In summary, six submissions were partially reproduced by reproducibility reviewers, whose reports are published openly on OSF ( https://osf.io/6k5fh/). These papers are promoted with badges on the proceedings’ website (https://agile-giss.copernicus.org/articles/1/index.html). Compared to previous years’ submissions (cf. Nüst etal. 2018), the guidelines and increased community awareness markedly improved reproducibility. However, the reproduction attempts also revealed problems, most importantly insufficient documentation. This was partly mitigated by the non-blind reproducibility review, conducted after paper acceptance, where interaction between reviewers and authors can provide the input and attention needed to increase reproducibility. However, the reviews also showed that anonymisation and public repositories, when properly documented, can enable a successful reproduction without interaction, as was the case with one manuscript. Individual and organisational challenges due to the COVID-19 pandemic and the conference’s eventual cancellation increased the teething problems. Nevertheless, also under normal circumstances, future iterations will have to reduce the reviewer’s efforts to be sustainable, ideally by more readily executable workflows and a larger reproducibility committee. Furthermore, we discuss changes to the reproducibility review process and their challenges. Reproducibility reports could be made available to “regular” reviewers, or the reports could be considered equally for acceptance/rejection decisions. Insufficient information or invalid arguments for not disclosing material could then lead to a submission being rejected or not being sent out to peer review. Further organisational improvements are a publication of reviewers’ activities in public databases, making the guidelines mandatory, and collecting data on used tools/repositories, spent efforts, and communications. Finally, we summarise the revision of the guidelines, including their new section for reproducibility reviewers, and the status of the initiative “Reproducible Publications at AGILE Conferences” (https://reproducible-agile.github.io/initiative/), which we connect to related undertakings such as CODECHECK (Eglen etal., 2019). The AGILE Conference’s experiences may help other communities to transition towards more open and reproducible research publications.

We have developed a simple and direct method to fabricate paper-based microfluidic devices that can be used for a wide range of colorimetric assay applications. With these devices, assays can be performed within minutes to allow for quantitative colorimetric analysis by use of a widely accessible iPhone camera and an RGB color reader application (app) to measure color intensity. In the described laboratory experiment, students design and create their own microfluidic devices with common laboratory supplies such as Kimwipes, Parafilm, and a thermal laminator, and gain hands-on experience in the analysis of Fe2+ and Cu2+ by colorimetric determination.

Identity and access management (IAM) system usually consist of predefined tasks as an information security system. Themain task is the authentication, since it is responsible for user identity proving for service providers that corporate with (IAM).This paper provides a review on intelligent authentication research applicable to IAM systems. These researches areevaluated according to the proposal of intelligent authentication key factors. Depending on this evaluation it could not be foundresearch implement an authentication that satisfies all these key factors.

We give an overview of our critiques of "proofs" of security and a guide to our papers on the subject that have appeared over the past decade and a half. We also provide numerous additional examples and a few updates and errata.

Designing clean, reusable, and repeatable experiments for a research paper does not have to be difficult. We report on our efforts to create an integrated toolchain for running, processing, and including the results of computer experiments in scientific publications.

In order to satisfy the demands for both safety and global warming reduction, a high-strength seismic reinforced steel bar is required in the structural steel market. Recent developments in computational thermodynamics and related application software have made it possible to design a suitable material as well as support engineers of steel manufacturing companies in the production of the designed material with minimum benchmarks in practical operations. This paper reports our recent success in developing grade 600 MPa reinforced steel bars for seismic safety in South Korea. First, conventional alloy design based on CALPHAD-type computational thermodynamics was carried out. For this purpose, a typical alloy system of Fe-0.30C-0.23Si-1.37Mn-0.14V-0.22Cu (in wt%) was selected, and thermodynamic and kinetic calculations were carried out using MatCalc and JMatPro software. Second, in order to reduce V content in the steel for economic reasons, a cooling process designed using finite element (FE) simulation based on the thermodynamic database was performed. For this application, Fe-0.34C-0.22Si-1.34Mn-0.04 V (in wt%) alloy was chosen, and the FE software ABAQUS was applied for modeling the TempCore process. The mechanical properties of the steel products with a diameter of 32 mm produced based on the simulated results satisfy the required properties for grade 600 MPa seismic reinforced steel bars.

JavaScript has seen meteoric growth in popularity as it has in- creasingly become the language of choice for developers, both for front-end web development and server code development through various JavaScript frameworks and Node.js. Part of the reason for its wide use is that it is a prototype based language with dynamic types, making it easy to learn and program in. This flexibility and ease of programming comes at the cost of performance. There are two sources of significant slowdown. First, since the number and type of properties of prototypes is dynamic, accessing a property involves a slow dictionary lookup, as opposed to it being present at a fixed offset from the base address. Second, the dynamism in type of values necessitates wrapping and unwrapping of values into objects with a variety of checks including for type of the value. To mitigate these performance problems, this paper proposes JSCore, a core specialized for JavaScript execution, that vastly reduces the performance degradation due to the above two causes. It uses a hardware lookup table to accelerate property access, and extends the data path to store data types with the data, nearly eliminating the second source of slowdown. Combining the two, JSCore accelerates real world JavaScript applications by 23%.

Deep Neural Networks (DNNs) are the algorithm of choice for image processing applications. DNNs present highly parallel workloads that lead to the emergence of custom hardware accelerators. Deep Learning (DL) models specialized in different tasks require a programmable custom hardware and a compiler/mapper to efficiently translate different DNNs into an efficient dataflow in the accelerator. The goal of this paper is to present a compiler for running DNNs on Snowflake, which is a programmable hardware accelerator that targets DNNs. The compiler correctly generates instructions for various DL models: AlexNet, VGG, ResNet and LightCNN9. Snowflake, with a varying number of processing units, was implemented on FPGA to measure the compiler and Snowflake performance properties upon scaling up. The system achieves 70 frames/s and 4.5 GB/s of off-chip memory bandwidth for AlexNet without linear layers on Xilinx’s Zynq-SoC XC7Z045 FPGA.

Dear Prospective Authors: I am delighted to inform you that this game-changing publishing initiative for bringing the best actionable insights on cybersecurity is entering its third year. The revie...

Whenever personal data is processed, privacy is a serious issue. Especially in the document-centric e-health area, the patients' privacy must be preserved in order to prevent any negative repercussions for the patient. Clinical research, for example, demands structured health records to carry out efficient clinical trials, whereas legislation (e.g. HIPAA) regulates that only de-identified health records may be used for research. However, unstructured and often paper-based data dominates information technology, especially in the healthcare sector. Existing approaches are geared towards data in English-language documents only and have not been designed to handle the recognition of erroneous personal data which is the result of the OCR-based digitization of paper-based health records.

In the view of Social Exchange Theory (SET), this article provides an insight into the knowledge sharing behavior of an employee in the organization. Trust is a core factor affecting knowledge shar...

Nature-Inspired Metaheuristics (NIM) have emerged as a potent tool for solving complex and difficult optimization problems, arising in various industries, which otherwise become quite difficult (if not impossible) to solve by the classical methods based on gradient search. Further, NIM techniques are more likely to obtain a global optimal solution, often desired and sometimes a necessity in several real life situations. In this study we employ SABC, a variant of Artificial Bee Colony (ABC), a relatively newer NIM algorithm, for solving four typical processes of a paper mill where optimization can be applied. We have also considered two chemical process problems that can be related to paper industry. Numerical results show that the proposed SABC scheme is efficient in dealing with these problems.