Search

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.

The task of predicting the interactions between drugs and targets plays a key role in the process of drug discovery. There is a need to develop novel and efficient prediction approaches in order to avoid costly and laborious yet not-always-deterministic experiments to determine drug–target interactions (DTIs) by experiments alone. These approaches should be capable of identifying the potential DTIs in a timely manner. In this article, we describe the data required for the task of DTI prediction followed by a comprehensive catalog consisting of machine learning methods and databases, which have been proposed and utilized to predict DTIs. The advantages and disadvantages of each set of methods are also briefly discussed. Lastly, the challenges one may face in prediction of DTI using machine learning approaches are highlighted and we conclude by shedding some lights on important future research directions.

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...

Sensitivity analysis (SA) has been used to evaluate the behavior and quality of environmental models by estimating the contributions of potential uncertainty sources to quantities of interest (QoI) in the model output. Although there is an increasing literature on applying SA in environmental modeling, a pragmatic and specific framework for spatially distributed environmental models (SD-EMs) is lacking and remains a challenge. This article reviews the SA literature for the purposes of providing a step-by-step pragmatic framework to guide SA, with an emphasis on addressing potential uncertainty sources related to spatial datasets and the consequent impact on model predictive uncertainty in SD-EMs. The framework includes: identifying potential uncertainty sources; selecting appropriate SA methods and QoI in prediction according to SA purposes and SD-EM properties; propagating perturbations of the selected potential uncertainty sources by considering the spatial structure; and verifying the SA measures based on post-processing. The proposed framework was applied to a SWAT (Soil and Water Assessment Tool) application to demonstrate the sensitivities of the selected QoI to spatial inputs, including both raster and vector datasets - for example, DEM and meteorological information - and SWAT (sub)model parameters. The framework should benefit SA users not only in environmental modeling areas but in other modeling domains such as those embraced by geographical information system communities.

Due to the increasing size of s (s), their design and optimization phases (i.e., ) grow increasingly complex. At design time, a large design space needs to be explored to find an implementation that fulfills all specifications and then optimizes metrics like energy, area, delay, reliability, etc. At run time, a large configuration space needs to be searched to find the best set of parameters (e.g., voltage/frequency) to further optimize the system. Both spaces are infeasible for exhaustive search typically leading to heuristic optimization algorithms that find some trade-off between design quality and computational overhead. ML can build powerful models that have successfully been employed in related domains. In this survey, we categorize how () may be used and is used for design-time and run-time optimization and exploration strategies of s. A meta-study of published techniques unveils areas in that are well-explored and underexplored with, as well as trends in the employed algorithms. We present a comprehensive categorization and summary of the state of the art on for. Finally, we summarize remaining challenges and promising open research directions.

This companion paper supports the replication of the fashion trend forecasting experiments with the KERN (Knowledge Enhanced Recurrent Network) method that we presented in the ICMR 2020. We provide an artifact that allows the replication of the experiments using a Python implementation. The artifact is easy to deploy with simple installation, training and evaluation. We reproduce the experiments conducted in the original paper and obtain similar performance as previously reported. The replication results of the experiments support the main claims in the original paper.

The entire scientific and academic community has been mobilized to gain a better understanding of the COVID-19 disease and its impact on humanity. Most research related to COVID-19 needs to analyze large amounts of data in very little time. This urgency has made Big Data Analysis, and related questions around the privacy and security of the data, an extremely important part of research in the COVID-19 era. The White House OSTP has, for example, released a large dataset of papers related to COVID research from which the research community can extract knowledge and information. We show an example system with a machine learning-based knowledge extractor which draws out key medical information from COVID-19 related academic research papers. We represent this knowledge in a Knowledge Graph that uses the Unified Medical Language System (UMLS). However, publicly available studies rely on dataset that might have sensitive data. Extracting information from academic papers can potentially leak sensitive data, and protecting the security and privacy of this data is equally important. In this paper, we address the key challenges around the privacy and security of such information extraction and analysis systems. Policy regulations like HIPAA have updated the guidelines to access data, specifically, data related to COVID-19, securely. In the US, healthcare providers must also comply with the Office of Civil Rights (OCR) rules to protect data integrity in matters like plasma donation, media access to health care data, telehealth communications, etc. Privacy policies are typically short and unstructured HTML or PDF documents. We have created a framework to extract relevant knowledge from the health centers’ policy documents and also represent these as a knowledge graph. Our framework helps to understand the extent to which individual provider policies comply with regulations and define access control policies that enforce the regulation rules on data in the knowledge graph extracted from COVID-related papers. Along with being compliant, privacy policies must also be transparent and easily understood by the clients. We analyze the relative readability of healthcare privacy policies and discuss the impact. In this paper, we develop a framework for access control decisions that uses policy compliance information to securely retrieve COVID data. We show how policy compliance information can be used to restrict access to COVID-19 data and information extracted from research papers.

Paper microfluidic or lateral flow devices have found many applications, especially in medical diagnostics. Their low cost and ease of use makes them particularly valuable in resource-limited and point-of-care applications. However, the process of developing new paper microfluidic devices is slowed by the need to find optimal values for their various design parameters, which determine the overall size and fluid volume requirements of the device. Often, researchers must design and test several different versions of a device to find a combination of parameters that functions as intended. To accelerate the development of new paper microfluidics, we developed a software framework that automatically designs custom paper microfluidic devices for a given application. Once the user specifies the desired device parameters, the software generates printable image files of the resulting devices, ready to output to a conventional wax ink color printer and test. As a proof-of-concept, we used our software to automatically design 51 different paper microfluidic devices we needed to create a functional lateral flow assay that detects protein and glucose in urine. These designs took only a few seconds to generate and were used in 120 lab experiments we performed in 16 h in the lab. Thus, with the help of our software framework, we went from an idea to a functional device in just two work days. By accelerating device development and enabling researchers without microfluidics experience to create custom devices, our software can help spread paper microfluidic technology to important new application areas.

Paper-based analytical devices (PADs) employing colorimetric detection and smartphone images have gained wider acceptance in a variety of measurement applications. The PADs are primarily meant to be used in field settings where assay and imaging conditions greatly vary resulting in less accurate results. Recently, machine learning (ML) assisted models have been used in image analysis. We evaluated a combinations of four ML models - logistic regression, support vector machine, random forest, and artificial neural network, and three image color spaces - RGB, HSV, and LAB for their ability to accurately predict analyte concentrations. We used images of PADs taken at varying lighting conditions, with different cameras, and users for food color and enzyme inhibition assays to create training and test datasets. Prediction accuracy was higher for food color than enzyme inhibition assays in most of the ML model and colorspace combinations. All models better predicted coarse level classification than fine grained concentration labels. ML models using sample color along with a reference color increased the models’ ability in predicting the result in which the reference color may have partially factored out the variation in ambient assay and imaging conditions. The best concentration label prediction accuracy obtained for food color was 0.966 when using ANN model and LAB colorspace. The accuracy for enzyme inhibition assay was 0.908 when using SVM model and LAB colorspace. Appropriate model and colorspace combinations can be useful to analyze large numbers of samples on PADs as a powerful low-cost quick field-testing tool.

The uneven utilisation of modes of transport has a big impact on traffic in transport pathway infrastrutures. For motor vehicles for instance, this situation explains rapid road deterioration and the large amounts of money invested in maintenance and development due to overuse. There are many approaches to managing this problem; however, the impact of individual users in infrastructural maintenance is mostly ignored. In this position paper, we hypothesise that important changes torwards a more efficient use of the transport network start with its users and their behavioural changes. To this end, we introduce our vision on how to employ data driven, intelligent agent-based modelling, incorporating human factors aspects, as a toolset to understand travellers and to stimulate behavioural changes. The aim is to achieve better balanced and integrated mobility usage within the transport network. The idea is explored with a few guided questions, and a methodology is proposed. We employ 1) cognitive work analysis to investigate the reasons for travellers' mode choice; 2) computational intelligence to extract and represent knowledge from related datasets; 3) agent-based modelling to represent the real-world and to observe both individual and emergent behaviours. Future directions to adapt our methodology to alternative smart mobility projects are also discussed.

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.

Machine learning (ML) components are increasingly adopted in many automated systems. Their ability to learn and work with novel input/incomplete knowledge and their generalization capabilities make them highly desirable solutions for complex problems. This has motivated the inclusion of ML techniques/components in products for many industrial domains including automotive systems. Such systems are safety-critical systems since their failure may cause death or injury to humans. Therefore, their safety must be ensured before they are used in their operational environment. However, existing safety standards and Verification and Validation (V&V) techniques do not properly address the special characteristics of ML-based components such as non-determinism, non-transparency, instability. This position paper presents the authors' view on the safety of automotive systems incorporating ML-based components, and it is intended to motivate and sketch a research agenda for extending a safety standard, namely ISO 26262, to address challenges posed by incorporating ML-based components in automotive systems.

In the era of emerging Smart Built Environments (SBEs), a smart house, unlike regular houses with static "components", consists of numerous interconnected and often actuated devices, capable of executing tasks independent of user supervision. Living in such a SBE, where for example, the furniture can rearrange itself, and the doors open and close of their own volition, may be difficult and unpredictable. Furthermore, cyber-security attacks and intrusion could allow attackers to assume control of the SBE, damage its components and to potentially harm its inhabitants. Such novel characteristics of SBEs present developers with several unique challenges with regards to implementing the needed safety and security measures and protocols that go along with them. With such environments, therefore, there is a need for a system that is capable of monitoring user activities in real-time, identifying the safety and security hazards to users in their immediate local context, warning users of these hazards, and perhaps even taking preventative and mitigative action against the hazards that it identified. In this paper, we survey some of these challenges and explore the design and implementation of a system designed around the safety and security of SBE inhabitants. We propose an approach to modeling SBE safety that combines the three laws of robotics and the swarm behavior model. We also present a preliminary prototype and discuss a case study.

Blockchain technology enables a transaction to be handled in a decentralized fashion. In this position paper, we aim to introduce the background of blockchain technology, discuss one of its important component — smart contract, and present its recent applications in many fields such as cryptocurrency, financial services, risk management, and Internet of Things.

To support nanocrystal device development, we have been working on a computational framework to utilize information in research papers on nanocrystal devices. We developed an annotated corpus called “ NaDev” (Nanocrystal Device Development) for this purpose. We also proposed an automatic information extraction system called “NaDevEx” (Nanocrystal Device Automatic Information Extraction Framework). NaDevEx aims at extracting information from research papers on nanocrystal devices using the NaDev corpus and machine-learning techniques. However, the characteristics of NaDevEx were not examined in detail. In this paper, we conduct system evaluation experiments for NaDevEx using the NaDev corpus. We discuss three main issues: system performance, compared with human annotators; the effect of paper type (synthesis or characterization) on system performance; and the effects of domain knowledge features (e.g., a chemical named entity recognition system and list of names of physical quantities) on system performance. We found that overall system performance was 89% in precision and 69% in recall. If we consider identification of terms that intersect with correct terms for the same information category as the correct identification, i.e., loose agreement (in many cases, we can find that appropriate head nouns such as temperature or pressure loosely match between two terms), the overall performance is 95% in precision and 74% in recall. The system performance is almost comparable with results of human annotators for information categories with rich domain knowledge information (source material). However, for other information categories, given the relatively large number of terms that exist only in one paper, recall of individual information categories is not high (39–73%); however, precision is better (75–97%). The average performance for synthesis papers is better than that for characterization papers because of the lack of training examples for characterization papers. Based on these results, we discuss future research plans for improving the performance of the system.

Paper currency recognition (PCR) is an important area of pattern recognition. A system for the recognition of paper currency is one kind of intelligent system which is a very important need of the current automation systems in the modern world of today. It has various potential applications including electronic banking, currency monitoring systems, money exchange machines, etc. This paper proposes an automatic paper currency recognition system for paper currency. A method of recognizing paper currencies has been introduced. This is based on interesting features and correlation between images. It uses Radial Basis Function Network for classification. The method uses the case of Saudi Arabian paper currency as a model. The method is quite reasonable in terms of accuracy. The system deals with 110 images, 10 of which are tilted with an angle less than 15o. The rest of the currency images consist of mixed including noisy and normal images 50 each. It uses fourth series (1984–2007) of currency issued by Saudi Arabian Monetary Agency (SAMA) as a model currency under consideration. The system produces accuracy of recognition as 95.37%, 91.65%, and 87.5%, for the Normal Non-Tilted Images, Noisy Non-Tilted Images, and Tilted Images respectively. The overall Average Recognition Rate for the data of 110 images is computed as 91.51%. The proposed algorithm is fully automatic and requires no human intervention. The proposed technique produces quite satisfactory results in terms of recognition and efficiency.

The Affect Misattribution Procedure (AMP) (Payne et al., 2005) can measure implicit attitudes based on a conceptually different procedure from the Implicit Association Test (IAT) (Greenwald et al., 1998). We aim to improve the administrative advantage of the AMP by converting it into a paper-based performance test so that it can collect massive data easily without using a computer for each examinee. We conduct three Paper-based AMP experiments to achieve the typical AMP results with large effect sizes (Study 1), obtain sufficient reliability by the test-retest method (Study 2), and find a similar phenomenon of the disappearance of misattribution when participants evaluate the primes consciously prior to the target evaluation (Study 3).

The intelligent task of semantically assigning a paper to a reviewer with respect to his knowledge domain remains a challenging task in academic conferences. From literature, a number of automated reviewer assignment systems have been presented which are based on distributional semantic models such as Term Frequency-Inverse Document Frequency (TF-IDF), Latent Semantic Indexing (LSI) and Latent Dirichlet Allocation (LDA) have been used to capture semantics. Thus, this study presents the comparative study of the three models based on their derived suitability scores between a paper meant for review and a reviewer’s representation papers. From the experimental results obtained, it shows that TF-IDF outperformed the accuracy level of the other two models by a substantial margin.

The use of reliable, valid measures in implementation practice will remain limited without pragmatic measures. Previous research identified the need for pragmatic measures, though the characteristic identification used only expert opinion and literature review. Our team completed four studies to develop a stakeholder-driven pragmatic rating criteria for implementation measures. We published Studies 1 (identifying dimensions of the pragmatic construct) and 2 (clarifying the internal structure) that engaged stakeholders—participants in mental health provider and implementation settings—to identify 17 terms/phrases across four categories: Useful, Compatible, Acceptable, and Easy. This paper presents Studies 3 and 4: a Delphi to ascertain stakeholder-prioritized dimensions within a mental health context, and a pilot study applying the rating criteria. Stakeholders (N = 26) participated in a Delphi and rated the relevance of 17 terms/phrases to the pragmatic construct. The investigator team further defined and shortened the list, which were piloted with 60 implementation measures. The Delphi confirmed the importance of all pragmatic criteria, but provided little guidance on relative importance. The investigators removed or combined terms/phrases to obtain 11 criteria. The 6-point rating system assigned to each criterion demonstrated sufficient variability across items. The grey literature did not add critical information. This work produced the first stakeholder-driven rating criteria to assess whether measures are pragmatic. The Psychometric and Pragmatic Evidence Rating Scale (PAPERS) combines the pragmatic criteria with psychometric rating criteria, from previous work. Use of PAPERS can inform development of implementation measures and to assess the quality of existing measures.

Introduction: Assessment is a very important component of medical education and written examination is widely used tool of assessment. The instrument used in the written examination is the “Question Paper” (QP). The QP often lacks the validity, reliability, relevance and objectivity. 1,2 The present study is undertaken to assess the characteristics in construction of University question papers. Aim and Objectives: To analyze the theory question papers of Microbiology subject of Saurashtra University, with respect to; question form, learning objectives, relevance to core syllabus, relevance to teaching hours, and language and grammatical errors. Materials and Methods: Total 28 question papers of Saurashtra University of Microbiology subject (Mar-05 to Mar-15) were analyzed. The questions were analyzed with respect to types of question form and categorized in long answer type, short note type and short answer type. All questions were analyzed with respect to learning objectives and were classified in to three cognitive domains of learning knowledge, understanding and synthesis. All questions were analyzed to determine the focus of questions with respect to the syllabus and classified into must know, desirable to know and nice to know areas. Questions were analyzed to determine the weather questions cover the area of syllabus with regards to teaching hours dedicated. Questions were analyzed to identify the clarity and ambiguity in language, spelling and grammatical errors and scientific and taxonomic errors. Results: There were two paper styles noted. Question papers have long answer; short note and short answer questions. 95% questions were framed to test basic knowledge level of cognitive domain, and 5% question were framed to test the understanding level, while none of the questions were framed to test the synthesis level of cognitive domain. The majority of question were framed from must know area (97%) of syllabus, while less number of questions were from desirable to know (2%) and nice to know (1%) area. 30% of teaching hours were dedicated to bacteriology section and carried 22% weightage in examination. 6% of teaching hours were dedicated to mycology section which carried 11% weightage in examination. A balance was observed for General microbiology, Immunology and Virology sections with regards to teaching hours and weightage in examination. On an average of 11.1 errors per paper was noted. Spelling errors were common (8.8) followed by taxonomic errors (2.2) and grammatical errors (0.1). Conclusions: Major numbers of questions were subjective type than objective type. Majority of questions were framed to test basic knowledge level of cognitive domain. The majority of question were framed from must know area of syllabus which may have poor discriminatory ability between low and high achievers. A balance was observed for General microbiology, Immunology and Virology sections for dedicated teaching hours and its weightage in examination while Bacteriology has less and Parasitology and Mycology has more weightage in comparison to teaching hours. Many different types of errors were noted in question papers which include spelling errors, taxonomic errors and grammatical errors. A standard “Blue Print” is essential to bring the uniform standard in theory examination. Keywords: Question paper analysis, Blue-printing.

In this work, a kind of nanocomposite paper was obtained by evaporation-induced self-assembly of a mixture of sisal cellulose nanofibers (CNF) and polyethylene glycol (PEG) as the matrix and citric acid (CA) as a cross-linking agent. The CNF/PEG/CA paper exhibited good water swelling resistance which could be controlled by changing the concentration of CA. In addition, this nanocomposite paper exhibited good mechanical properties and water-induced shape memory performance. In particular, when the dosage of CA was 30 wt.%, the tensile strength and the tensile modulus of the CNF/PEG/CA paper after swelling were 25.2 MPa and 813.0 MPa, respectively. Further, this nanocomposite showed great potential for water-induced shape memory materials with fast response speed. The shape recovery rate (Rr) of the CNF/PEG/CA paper reached 90.2% with 30 wt.% CA after being immersed in water for 11 s. It is anticipated that our current work can be used to exploit more efficient methods to overcome the poor water swelling resistance of the cellulose-based shape memory materials.

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

We present a computational framework for stability analysis of systems of coupled linear Partial-Differential Equations (PDEs). The class of PDE systems considered in this paper includes parabolic, elliptic and hyperbolic systems with Dirichlet, Neuman and mixed boundary conditions. The results in this paper apply to systems with a single spatial variable. We exploit a new concept of state for PDE systems which allows us to include the boundary conditions directly in the dynamics of the PDE. The resulting algorithms are implemented in Matlab, tested on several motivating and illustrative examples, and the codes have been posted online. Numerical testing indicates the approach has little or no conservatism for a large class of systems and can analyze systems of up to 20 coupled PDEs.

The notion of attribute-based encryption (ABE)was proposed as an economical alternative to public-keyinfrastructures. It is the set of descriptive attributes, used as anidentity to generate a secret key, as well as serving as theaccess structure that performs access control. ABE is also auseful building block in various cryptographic primitives suchas searchable encryption. . It successfully integratesEncryption and Access Control and is ideal for sharing secretsamong groups, especially in a Cloud environment. Mostdeveloped ABE schemes support key-policy or ciphertextpolicyaccess control in addition to other features such asdecentralized authority, efficient revocation and keydelegation. This paper surveys mainstream papers, analyzesmain features for desired ABE systems, and classifies theminto different categories. With this high-level guidance, futureresearchers can treat these features as individual modules andselect related ones to build their ABE systems on demand.For ABE, it is not realistic to trust a single authority tomonitor all attributes and hence distributing control over manyattribute-authorities is desirable.A multi-authority ABE scheme can be realized with a trustedcentral authority (CA) which issues part of the decryption keyaccording to a user's global identifier (GID). However, this CAmay have the power to decrypt every cipher text, and the useof a consistent GID allowed the attribute-authorities tocollectively build user's attributes. Decentralized ABE schemecan eliminate the burden of heavy communication andcollaborative computation. It is observed that privacypreservingdecentralized key-policy ABE scheme has claimedto achieve better privacy for users and is provably secure inthe standard model

Communicability functions quantify the flow of information between two nodes of a network. In this work, we use them to explore the concept of the influence of a paper in a citation network. These functions depend on a parameter. By varying the parameter in a continuous way we explore different definitions of influence. We study six citation networks, three from physics and three from computer science. As a benchmark, we compare our results against two frequently used measures: the number of citations of a paper and the PageRank algorithm. We show that the ranking of the articles in a network can be varied from being equivalent to the ranking obtained from the number of citations to a behaviour tending to the eigenvector centrality, these limits correspond to small and large values of the communicability-function parameter, respectively. At an intermediate value of the parameter a PageRank-like behaviour is recovered. As a test case, we apply communicability functions to two sets of articles, where at least one author of each paper was awarded a Nobel Prize for the research presented in the corresponding article.

Most existing studies and public datasets for handwritten Chinese text recognition are based on the regular documents with clean and blank background, lacking research reports for handwritten text recognition on challenging areas such as educational documents and financial bills. In this paper, we focus on examination paper text recognition and construct a challenging dataset named examination paper text (SCUT-EPT) dataset, which contains 50 000 text line images (40 000 for training and 10 000 for testing) selected from the examination papers of 2 986 volunteers. The proposed SCUT-EPT dataset presents numerous novel challenges, including character erasure, text line supplement, character/phrase switching, noised background, nonuniform word size, and unbalanced text length. In our experiments, the current advanced text recognition methods, such as convolutional recurrent neural network (CRNN) exhibits poor performance on the proposed SCUT-EPT dataset, proving the challenge and significance of the dataset. Nevertheless, through visualizing and error analysis, we observe that humans can avoid vast majority of the error predictions, which reveal the limitations and drawbacks of the current methods for handwritten Chinese text recognition (HCTR). Finally, three popular sequence transcription methods, connectionist temporal classification (CTC), attention mechanism, and cascaded attention-CTC are investigated for HCTR problem. It is interesting to observe that although the attention mechanism has been proved to be very effective in English scene text recognition, its performance is far inferior to the CTC method in the case of HCTR with large-scale character set.

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.

New discoveries in science are often built upon previous knowledge. Ideally, such dependency information should be made explicit in a scientific knowledge graph. The Keystone Framework was proposed for tracking the validity dependency among papers. A keystone citation indicates that the validity of a given paper depends on a previously published paper it cites. In this paper, we propose and evaluate a strategy that repurposes rhetorical category classifiers for the novel application of extracting keystone citations that relate to research methods. Five binary rhetorical category classifiers were constructed to identify Background, Objective, Methods, Results, and Conclusions sentences in biomedical papers. The resulting classifiers were used to test the strategy against two datasets. The initial strategy assumed that only citations contained in Methods sentences were methods keystone citations, but our analysis revealed that citations contained in sentences classified as either Methods or Results had a high likelihood to be methods keystone citations. Future work will focus on fine tuning the rhetorical category classifiers, experimenting with multiclass classifiers, evaluating the revised strategy with more data, and constructing a larger gold standard citation context sentence dataset for model training.

Complex target SELEX always have been an intriguing approach to the scientific community, as it offers the potential discovery of novel biomarkers. We herein successfully performed SELEX on Bungarus caeruleus venom to develop a panel of highly affine aptamers that specifically recognizes the B. caeruleus (common krait) venom and was able to discriminate the B. caeruleus venom from Cobra, Russell's, and Saw-scaled viper's venom. The aptamers generated against the crude venom also lead to the identification of the specific component of the venom, which is β-Bungarotoxin, a toxin uniquely present in the B. caeruleus venom. The best performing aptamer candidates were used as a molecular recognition element in a paper-based device and were able to detect as low as 2 ng krait venom in human serum background. The developed aptamer-based paper device can be used for potential point-of-care venom detection applications due to its simplicity and affordability.

Background and ObjectiveQuestionnaires are essential tools in many scientific fields, including health and medicine. However, the analysis of paper-and-pencil questionnaires is time consuming, source of errors and expensive, limiting its use in large cohort studies. Computer-based questionnaires might be a valuable alternative but they may introduce bias, especially for sensitive questions, and they require programming skills. The aim of this study is to develop a reliable and adaptable open-source technique (i.e. LightQuest) to automatically analyse various types of scanned paper-and-pencil questionnaires with closed questions, including those with inverted scale.MethodsTo evaluate the usefulness of LightQuest, the time needed for 7 experimenters for manually code 10 sets of 4 frequently used questionnaires and the number of errors (i.e. reliability) were compared with the time and errors their made using LightQuest.ResultsLightQuest was twice as fast as the manual analysis, even though the time to create the reference model was taken into account (933s vs. 1935s, t(2)=8.81, p-1 for the manual technique versus 0.55s.question-1 for LightQuest (t(2)=22.5, pConclusionLightQuest demonstrated clear superiority both in terms of time and reliability. The script of this first open-source technique, which does not require programming skills, is downloadable in supplemental data and may become an asset for all studies using questionnaires.

Effectively reading and interpreting scientific literature are fundamental skills for students pursuing degrees in STEM fields. In-person classes allow for real-time discussion and dissection of scientific literature; however, with increasing focus on virtual learning environments, alternative approaches are needed to promote student development of these skills. This manuscript presents suggestions for conducting paper discussions in asynchronous environments via learning management system (LMS) message boards. Modifications of this paper discussion activity for different class sizes, educational levels, and assignment goals are included.

This paper introduces a novel software framework to support automated development of paper-based microfluidic devices. Compared to existing lab-on-a-chip technologies, paper-based microfluidics differs in terms of substrate technologies and point-of-care usage across a wide variety environmental conditions. This paper addresses the contexts in which the software can address these challenges and presents several initial case studies that demonstrate the capabilities of the framework to produce workable and usable paper microfluidic devices.

Comparative analysis of packaging print quality was carried out on the printed output with reference to using Computer to Film (CtF) and Computer to Conventional Plate (CtCP). The comparison of the print results from the two systems is analyzed from cost, durability, and dot gain. In this analysis, 400 gram duplex paper was used and two types of print references were used, namely CtF and CtCP. The pre-printing equipment used is the manufacture of conventional printing plates and plates. What is achieved when observing is comparing two print references with the help of a quality control (QC) tool. Before comparing the two print references, direct printing is done with the two print references, using an offset machine. The differences that exist in CtF and CtCP are caused by different irradiation processes. CtF goes through two irradiation processes while CtCP is in the irradiation process using only lasers. The irradiation process using a laser is uneven and makes the plate not get a raster (neat) result. This process is very influential with the result of the trigger. CtF goes through an iterative process but the results appear better than CtCP.Keywords— Print packaging, Duplex, CtF, CtCp

Automatically detecting the positions of key-points (e.g., facial key-points or finger key-points) in an image is an essential problem in many applications, such as driver's gaze detection and drowsiness detection in automated driving systems. With the recent advances of Deep Neural Networks (DNNs), Key-Points detection DNNs (KP-DNNs) have been increasingly employed for that purpose. Nevertheless, KP-DNN testing and validation have remained a challenging problem because KP-DNNs predict many independent key-points at the same time -- where each individual key-point may be critical in the targeted application -- and images can vary a great deal according to many factors. In this paper, we present an approach to automatically generate test data for KP-DNNs using many-objective search. In our experiments, focused on facial key-points detection DNNs developed for an industrial automotive application, we show that our approach can generate test suites to severely mispredict, on average, more than 93% of all key-points. In comparison, random search-based test data generation can only severely mispredict 41% of them. Many of these mispredictions, however, are not avoidable and should not therefore be considered failures. We also empirically compare state-of-the-art, many-objective search algorithms and their variants, tailored for test suite generation. Furthermore, we investigate and demonstrate how to learn specific conditions, based on image characteristics (e.g., head posture and skin color), that lead to severe mispredictions. Such conditions serve as a basis for risk analysis or DNN retraining., to appear in ISSTA 2021

Attention to the environment and its problems has undergone unprecedented growth in recent years [...]

Most visual code authentication schemes in the literature have been shown to be vulnerable to relay attacks: the attacker logs into the victim’s “account A” using credentials that the victim provides with the intent of logging into “account B”. Visual codes are not human-readable and therefore the victim cannot distinguish between the codes for A and B; on the other hand, codes must be machine-readable in order to automate the login process. We introduce a new type of visual code, the SAVVIcode, that contains an integrity-validated human-readable bitmap. With SAVVIcode, attackers have a harder time swapping visual codes surreptitiously because the integrity check prevents them from modifying or hiding the human-readable distinguisher.

Machine learning has been applied to several problems in particle physics research, beginning with applications to high-level physics analysis in the 1990s and 2000s, followed by an explosion of applications in particle and event identification and reconstruction in the 2010s. In this document we discuss promising future research and development areas for machine learning in particle physics. We detail a roadmap for their implementation, software and hardware resource requirements, collaborative initiatives with the data science community, academia and industry, and training the particle physics community in data science. The main objective of the document is to connect and motivate these areas of research and development with the physics drivers of the High-Luminosity Large Hadron Collider and future neutrino experiments and identify the resource needs for their implementation. Additionally we identify areas where collaboration with external communities will be of great benefit., Comment: Editors: Sergei Gleyzer, Paul Seyfert and Steven Schramm

Nowadays, forecasting applications are receiving unprecedent attention thanks to their capability to improve the decision-making processes by providing useful indications [...]

Automation is at the core of modern industry. It aims to increase production rates, decrease production costs, and reduce human intervention in order to avoid human mistakes and time delays during manufacturing. On the other hand, human assistance is usually required to customize products and reconfigure control systems through a special process interface called Human Machine Interface (HMI). Machine Learning (ML) algorithms can effectively be used to resolve this tradeoff between full automation and human assistance.This paper provides an example of the industrial application of ML algorithms to help human operators save their mental effort and avoid time delays and unintended mistakes for the sake of high production rates. Based on real-time sensor measurements, several ML algorithms have been tried to classify paper rolls according to paper grammage in a white paper mill. The performance evaluation shows that the AdaBoost algorithm is the best ML algorithm for this application with classification accuracy (CA), precision, and recall of 97.1%. The generalization of the proposed approach for achieving cost-effective mills construction will be the subject of our future research., 9 pages

© 2018 Association for Computing Machinery. Recursive programs can now be expressed as normal forms within some rewriting systems, including traditional combinatory logic, a new variant of lambda-calculus called closure calculus, and recent variants of combinatory logic that support queries of internal program structure. In all these settings, partial evaluation of primitive recursive functions, such as addition, can reduce open terms to normal form without fear of non-termination. In those calculi where queries of program structure are supported, program optimisations that are expressed as non-standard rewriting rules can be represented as functions in the calculus, without any need for quotation or other meta-theory.

The focus of this study is to analyze whether there are differences in the difficulty index of items if the same items are displayed with the Paper Based Test (PBT) model and the Computer-Based Testing (CBT) model. The PBT and CBT models have the same paradigm of measuring estimated abilities but have differences in the context and feel aspects. These differences include the number of items in the range of eyesight, use of tools, how to work on items, basic knowledge needs about computer operations, and habit factors. These differences can affect the results of estimating the ability of test participants. This study uses development methods and quantitative methods. Development methods are used to develop package items and CBT software. Two groups of respondents were used with equal ability to work on the same question package. One group uses the PBT model, and the other group uses CBT so that the response choices are obtained in each test model. The results of the respondent’s answer choices were then analyzed by the ITEMAN software to get the item difficulty index. The item difficulty index of each model was tested statistically using SPSS software, whether there was a significant average difference between the two test models. From the results of the study and analysis in classical theory, it can be concluded that statistically there are differences in the average difficulty index of the items if the same item is displayed with the PBT and CBT models. It was found that the items displayed with the PBT model had a more difficult tendency than when displayed with the CBT model.

This article reviews research on informal digital English (IDLE) learning that has increased in the field of English language teaching to other language speakers and computer-assisted language learning written by Ju Seong Lee (2019), entitled Quantity and Diversity of informal digital learning of English published by Language Learning & Technology. This present paper uses descriptive qualitative analysis in an attempt to understand how the quantity and diversity of IDLE can make a unique contribution to the English language outcomes of EFL learners from the researcher's perspective. Lee uses hierarchical linear regression analysis to show that IDLE Quantity, Age, and Major are significant predictors of two affective variables (Confidence and Pleasure), while IDLE Diversity and Major significantly predict productive language outcomes (Speaking and Productive Vocabulary Knowledge), score in the standard English Test (TOEIC), and one affective variable (Lack of Anxiety). This present article aims to review and discuss the findings on the strengths and the weaknesses found in Lee’s 2019 article. The article Lee made seems to possess a clear flow on how to explain these two types of education and make this article easy to understand. Therefore, the replication of Lee’s research is easy enough for similar research purposes.

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.

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.

Introduction: The electrocardiogram (ECG) plays an important role in the diagnosis of heart diseases. However, most patterns of diseases are based on old datasets and stepwise algorithms that provide limited accuracy. Improving diagnostic accuracy of the ECG can be done by applying machine learning algorithms. This requires taking existing scanned or printed ECGs of old cohorts and transforming the ECG signal to the raw digital (time (milliseconds), voltage (millivolts)) form. Objectives: We present a MATLAB-based tool and algorithm that converts a printed or scanned format of the ECG into a digitized ECG signal. Methods: 30 ECG scanned curves are utilized in our study. An image processing method is first implemented for detecting the ECG regions of interest and extracting the ECG signals. It is followed by serial steps that digitize and validate the results. Results: The validation demonstrates very high correlation values of several standard ECG parameters: PR interval 0.984 +/−0.021 (p-value < 0.001), QRS interval 1+/− SD (p-value < 0.001), QT interval 0.981 +/− 0.023 p-value < 0.001, and RR interval 1 +/− 0.001 p-value < 0.001. Conclusion: Digitized ECG signals from existing paper or scanned ECGs can be obtained with more than 95% of precision. This makes it possible to utilize historic ECG signals in machine learning algorithms to identify patterns of heart diseases and aid in the diagnostic and prognostic evaluation of patients with cardiovascular disease., We present a MATLAB-based tool and algorithm that converts a printed or scanned ECG into a digitized ECG signal. Validation on 30 ECG scanned curves more than 95% precision. This makes it possible to utilize historic ECG signals in machine learning algorithms to identify patterns of heart diseases and aid in the diagnosis and evaluation of patients with cardiovascular disease.