An Importance Assessment Model of Open-Source Community Java Projects Based on Domain Knowledge Graph

With the rise of open-source software,the social development paradigm occupies an indispensable position in the current software development process.This paper puts forward a variant of the PageRank algorithm to build the importance assessment model,which provides quantifiable importance assessment metrics for new Java projects based on Java open-source projects or components.The critical point of the model is to use crawlers to obtain relevant information about Java open-source projects in the GitHub open-source community to build a domain knowledge graph.According to the three dimensions of the Java open-source project’s project influence,project activity and project popularity,the project is measured.A modified PageRank algorithm is proposed to construct the importance evaluation model.Thereby providing quantifiable importance evaluation indicators for new Java projects based on or components of Java open-source projects.This article evaluates the importance of 4512 Java open-source projects obtained on GitHub and has a good effect.

Domain knowledge discovery from abstracts of scientific literature on Nickel-based single crystal superalloys

Despite the huge accumulation of scientific literature,it is inefficient and laborious to manually search it for useful information to investigate structure-activity relationships.Here,we propose an efficient text-mining framework for the discovery of credible and valuable domain knowledge from abstracts of scientific literature focusing on Nickel-based single crystal superalloys.Firstly,the credibility of abstracts is quantified in terms of source timeliness,publication authority and author’s academic standing.Next,eight entity types and domain dictionaries describing Nickel-based single crystal superalloys are predefined to realize the named entity recognition from the abstracts,achieving an accuracy of 85.10%.Thirdly,by formulating 12 naming rules for the alloy brands derived from the recognized entities,we extract the target entities and refine them as domain knowledge through the credibility analysis.Following this,we also map out the academic cooperative“Author-Literature-Institute”network,characterize the generations of Nickel-based single crystal superalloys,as well as obtain the fractions of the most important chemical elements in superalloys.The extracted rich and diverse knowledge of Nickel-based single crystal superalloys provides important insights toward understanding the structure-activity relationships for Nickel-based single crystal superalloys and is expected to accelerate the design and discovery of novel superalloys.

Discovering a formula for the high temperature oxidation behavior of FeCrAlCoNi based high entropy alloys by domain knowledge-guided machine learning

A mathematical formula of high physical interpretation,and accurate prediction and large generaliza-tion power is highly desirable for science,technology and engineering.In this study,we performed a domain knowledge-guided machine learning to discover high interpretive formula describing the high-temperature oxidation behavior of FeCrAlCoNi-based high entropy alloys(HEAs).The domain knowledge suggests that the exposure time dependent and thermally activated oxidation behavior can be described by the synergy formula of power law multiplying Arrhenius equation.The pre-factor,time exponent(m),and activation energy(Q)are dependent on the chemical compositions of eight elements in the FeCrAlCoNi-based HEAs.The Tree-Classifier for Linear Regression(TCLR)algorithm utilizes the two exper-imental features of exposure time(t)and temperature(T)to extract the spectrums of activation energy(Q)and time exponent(m)from the complex and high dimensional feature space,which automatically gives the spectrum of pre-factor.The three spectrums are assembled by using the element features,which leads to a general and interpretive formula with high prediction accuracy of the determination coefficient R^(2)=0.971.The role of each chemical element in the high-temperature oxidation behavior is analytically illustrated in the three spectrums,thereby the discovered interpretative formula provides a guidance to the inverse design of HEAs against high-temperature oxidation.The present work demonstrates the sig-nificance of domain knowledge in the development of materials informatics.

Domain knowledge enhanced deep learning for electrocardiogram arrhythmia classification

Deep learning provides an effective way for automatic classification of cardiac arrhythmias,but in clinical decisionmaking,pure data-driven methods working as black-boxes may lead to unsatisfactory results.A promising solution is combining domain knowledge with deep learning.This paper develops a flexible and extensible framework for integrating domain knowledge with a deep neural network.The model consists of a deep neural network to capture the statistical pattern between input data and the ground-truth label,and a knowledge module to guarantee consistency with the domain knowledge.These two components are trained interactively to bring the best of both worlds.The experiments show that the domain knowledge is valuable in refining the neural network prediction and thus improves accuracy.

A Citespace Mapping-knowledge-domain Analysis of SLA from the Lin⁃guistic Perspective in China

Research papers in the field of SLA published between 2009 and 2019 are analyzed in terms of research status of domes⁃tic SLA researchers,research institutions,research frontiers and hotspots in the paper,and maps the knowledge domains of SLA re⁃searches.The data are retrieved from 10 core journals of linguistics via the CNKI journal database.By means of CiteSpace 5.3,an analysis of the overall trend of studies on SLA in China is made.

A Domain Question Answering Algorithm Based on the Contrastive Language-Image Pretraining Mechanism

Research on specific domain question-answering technology has become important with the increasing demand for intelligent question-answering systems. This paper proposes a domain question-answering algorithm based on the CLIP mechanism to improve the accuracy and efficiency of interaction. First, this paper reviewed relevant technologies involved in the question-answering field. Then, the question-answering model based on the CLIP mechanism was produced, including its design, implementation, and optimization. It also described the construction process of the specific domain knowledge graph, including graph design, data collection and processing, and graph construction methods. The paper compared the performance of the proposed algorithm with classic question-answering algorithms BiDAF, R-Net, and XLNet models, using a military domain dataset. The experimental results show that the proposed algorithm has advanced performance, with an F1 score of 84.6% on the constructed military knowledge graph test set, which is at least 1.5% higher than other models. We conduct a detailed analysis of the experimental results, which illustrates the algorithm’s advantages in accuracy and efficiency, as well as its potential for further improvement. These findings demonstrate the practical application potential of the proposed algorithm in the military domain.

Bibliometric analysis and mapping knowledge domain of pterygium:2000-2019

AIM:To track the knowledge structure,topics in focus,and trends in emerging research in pterygium in the past 20 y.METHODS:Base on the Web of Science Core Collection(Wo SCC),studies related to pterygium in the past 20 y from 2000-2019 have been included.With the help of VOSviewer software,a knowledge map was constructed and the distribution of countries,institutions,journals,and authors in the field of pterygium noted.Meanwhile,using cocitation analysis of references and co-occurrence analysis of keywords,we identified basis and hotspots,thereby obtaining an overview of this field.RESULTS:The search retrieved 1516 publications from Wo SCC on pterygium published between 2000 and 2019.In the past two decades,the annual number of publications is on the rise and fluctuated a little.Most productive institutions are from Singapore but the most prolific and active country is the United States.Journal Cornea published the most articles and Coroneo MT contributed the most publications on pterygium.From cooccurrence analysis,the keywords formed 3 clusters:1)surgical therapeutic techniques and adjuvant of pterygium,2)occurrence process and pathogenesis of pterygium,and 3)epidemiology,and etiology of pterygium formation.These three clusters were consistent with the clustering in co-citation analysis,in which Cluster 1 contained the most references(74 publications,47.74%),Cluster 2 contained 53 publications,accounting for 34.19%,and Cluster 3 focused on epidemiology with 18.06%of total 155 cocitation publications.CONCLUSION:This study demonstrates that the research of pterygium is gradually attracting the attention of scholars and researchers.The interaction between authors,institutions,and countries is lack of.Even though,the research hotspot,distribution,and research status in pterygium in this study could provide valuable information for scholars and researchers.

Intelligent Multilevel Knowledge Acquisition System for Product Design and Its Implementation

The characteristics of design process, design object and domain knowledge of complex product are analyzed. A kind of knowledge representation schema based on integrated generalized rule is stated. An AND-OR tree based model of concept for domain knowledge is set up. The strategy of multilevel domain knowledge acquisition based on the model is presented. The intelligent multilevel knowledge acquisition system (IMKAS) for product design is developed, and it is applied in the intelligent decision support system of concept design of complex product.

Knowledge Graph Extension Based on Crowdsourcing in Textile and Clothing Field

Generally,knowledge extraction technology is used to obtain nodes and relationships of unstructured data and structured data,and then the data fuse with the original knowledge graph to achieve the extension of the knowledge graph.Because the concepts and knowledge structures expressed on the Internet have problems of multi-source heterogeneity and low accuracy,it is usually difficult to achieve a good effect simply by using knowledge extraction technology.Considering that domain knowledge is highly dependent on the relevant expert knowledge,the method of this paper try to expand the domain knowledge through the crowdsourcing method.The method split the domain knowledge system into subgraph of knowledge according to corresponding concept,form subtasks with moderate granularity,and use the crowdsourcing technology for the acquisition and integration of knowledge subgraph to improve the knowledge system.

Necessary and Sufficient Conditions for Feasible Neighbourhood Solutions in the Local Search of the Job-Shop Scheduling Problem

The meta-heuristic algorithm with local search is an excellent choice for the job-shop scheduling problem(JSP).However,due to the unique nature of the JSP,local search may generate infeasible neighbourhood solutions.In the existing literature,although some domain knowledge of the JSP can be used to avoid infeasible solutions,the constraint conditions in this domain knowledge are sufficient but not necessary.It may lose many feasible solutions and make the local search inadequate.By analysing the causes of infeasible neighbourhood solutions,this paper further explores the domain knowledge contained in the JSP and proposes the sufficient and necessary constraint conditions to find all feasible neighbourhood solutions,allowing the local search to be carried out thoroughly.With the proposed conditions,a new neighbourhood structure is designed in this paper.Then,a fast calculation method for all feasible neighbourhood solutions is provided,significantly reducing the calculation time compared with ordinary methods.A set of standard benchmark instances is used to evaluate the performance of the proposed neighbourhood structure and calculation method.The experimental results show that the calculation method is effective,and the new neighbourhood structure has more reliability and superiority than the other famous and influential neighbourhood structures,where 90%of the results are the best compared with three other well-known neighbourhood structures.Finally,the result from a tabu search algorithm with the new neighbourhood structure is compared with the current best results,demonstrating the superiority of the proposed neighbourhood structure.

Prediction of lattice thermal conductivity with two-stage interpretable machine learning

Thermoelectric and thermal materials are essential in achieving carbon neutrality. However, the high cost of lattice thermal conductivity calculations and the limited applicability of classical physical models have led to the inefficient development of thermoelectric materials. In this study, we proposed a two-stage machine learning framework with physical interpretability incorporating domain knowledge to calculate high/low thermal conductivity rapidly. Specifically, crystal graph convolutional neural network(CGCNN) is constructed to predict the fundamental physical parameters related to lattice thermal conductivity. Based on the above physical parameters, an interpretable machine learning model–sure independence screening and sparsifying operator(SISSO), is trained to predict the lattice thermal conductivity. We have predicted the lattice thermal conductivity of all available materials in the open quantum materials database(OQMD)(https://www.oqmd.org/). The proposed approach guides the next step of searching for materials with ultra-high or ultralow lattice thermal conductivity and promotes the development of new thermal insulation materials and thermoelectric materials.

An Ontology-Based Question Answering System for University Admissions Advising

Question-Answer systems are now very popular and crucial to support human in automatically responding frequent questions in manyfields.However,these systems depend on learning methods and training data.Therefore,it is necessary to prepare such a good dataset,but it is not an easy job.An ontol-ogy-based domain knowledge base is able to help to reason semantic information and make effective answers given user questions.This study proposes a novel chatbot model involving ontology to generate efficient responses automatically.A case study of admissions advising at the International University–VNU HCMC is taken into account in the proposed chatbot.A domain ontology is designed and built based on the domain knowledge of university admissions using Protégé.The Web user interface of the proposed chatbot system is developed as a prototype using NetBeans.It includes a search engine reasoning the ontology and generat-ing answers to users’questions.Two experiments are carried out to test how the system reacts to different questions.Thefirst experiment examines questions made from some templates,and the second one examines normal questions taken from frequent questions.Experimental results have shown that the ontology-based chatbot can release meaningful and long answers.The results are analysed to prove the proposed chatbot is usable and promising.

RepDNet:A re-parameterization despeckling network for autonomous underwater side-scan sonar imaging with prior-knowledge customized convolution

Side-scan sonar(SSS)is now a prevalent instrument for large-scale seafloor topography measurements,deployable on an autonomous underwater vehicle(AUV)to execute fully automated underwater acoustic scanning imaging along a predetermined trajectory.However,SSS images often suffer from speckle noise caused by mutual interference between echoes,and limited AUV computational resources further hinder noise suppression.Existing approaches for SSS image processing and speckle noise reduction rely heavily on complex network structures and fail to combine the benefits of deep learning and domain knowledge.To address the problem,Rep DNet,a novel and effective despeckling convolutional neural network is proposed.Rep DNet introduces two re-parameterized blocks:the Pixel Smoothing Block(PSB)and Edge Enhancement Block(EEB),preserving edge information while attenuating speckle noise.During training,PSB and EEB manifest as double-layered multi-branch structures,integrating first-order and secondorder derivatives and smoothing functions.During inference,the branches are re-parameterized into a 3×3 convolution,enabling efficient inference without sacrificing accuracy.Rep DNet comprises three computational operations:3×3 convolution,element-wise summation and Rectified Linear Unit activation.Evaluations on benchmark datasets,a real SSS dataset and Data collected at Lake Mulan aestablish Rep DNet as a well-balanced network,meeting the AUV computational constraints in terms of performance and latency.

Outlining the keyword co-occurrence trends in Shuanghuanglian injection research: A bibliometric study using CiteSpace Ⅲ

Objective:To explore the evolvement and new trends in the use of Shuanghuanglian injection(SHLI).Methods:China National Knowledge Infrastructure(CNKI),PubMed,and Embase were extensively searched using the search terms“Shuanghuanglian injection”and“Shuanghuanglian fenzhen”to retrieve articles relevant to SHLI(1992e2020).Retrieved articles were further investigated by two authors to exclude those unrelated to SHLI.The bibliographical references of the included articles were exported as raw data and then treated using the CiteSpace tool to visualize the mapping of the SHLI research domain.Essential clusters and highly frequent keywords were quantified for further analysis.The clusters were automatically labeled by the algorithm of tf*idf for objective analysis.Basic bibliometric features,including article types and yearly trend in article numbers were also determined and discussed.Results:The modules of the keywords of interest presented clear boundaries with a high modularity score(Q=0.73).High-confidence clusters were identified,including bioactivity fingerprint(S=0.99),equal pupils(S=0.91),drug preparation department(S=0.87),difficulty in respiration(S=0.85),peristalsis(0.88),and Danshen powder injection(S=0.94).The characteristic keywords in terms of frequency and burstiness were Shuanghuanglian powder for injection(F=235,B=5.22),SHLI(F=112,B=11.39),and adverse drug reactions(ADRs;F=104,B=7.35).Conclusion:In the field of SHLI study,there are five major topic categories:bioactivity fingerprint;ADR mechanism and cause detection;proper preparation;clinical evidence accumulation;and efficacy in diseases with no effective treatment and combination usage.The trend for using modern methodologies from a science-based perspective to study SHLI will continue to exist.The causes of multi-factorial ADRs may be an important topic for future studies.

Integrating Variable Reduction Strategy With Evolutionary Algorithms for Solving Nonlinear Equations Systems

Nonlinear equations systems(NESs)are widely used in real-world problems and they are difficult to solve due to their nonlinearity and multiple roots.Evolutionary algorithms(EAs)are one of the methods for solving NESs,given their global search capabilities and ability to locate multiple roots of a NES simultaneously within one run.Currently,the majority of research on using EAs to solve NESs focuses on transformation techniques and improving the performance of the used EAs.By contrast,problem domain knowledge of NESs is investigated in this study,where we propose the incorporation of a variable reduction strategy(VRS)into EAs to solve NESs.The VRS makes full use of the systems of expressing a NES and uses some variables(i.e.,core variable)to represent other variables(i.e.,reduced variables)through variable relationships that exist in the equation systems.It enables the reduction of partial variables and equations and shrinks the decision space,thereby reducing the complexity of the problem and improving the search efficiency of the EAs.To test the effectiveness of VRS in dealing with NESs,this paper mainly integrates the VRS into two existing state-of-the-art EA methods(i.e.,MONES and DR-JADE)according to the integration framework of the VRS and EA,respectively.Experimental results show that,with the assistance of the VRS,the EA methods can produce better results than the original methods and other compared methods.Furthermore,extensive experiments regarding the influence of different reduction schemes and EAs substantiate that a better EA for solving a NES with more reduced variables tends to provide better performance.

Science Mapping:A Systematic Review of the Literature

Purpose: We present a systematic review of the literature concerning major aspects of science mapping to serve two primary purposes: First, to demonstrate the use of a science mapping approach to perform the review so that researchers may apply the procedure to the review of a scientific domain of their own interest, and second, to identify major areas of research activities concerning science mapping, intellectual milestones in the development of key specialties, evolutionary stages of major specialties involved, and the dynamics of transitions from one specialty to another.Design/methodology/approach: We first introduce a theoretical framework of the evolution of a scientific specialty. Then we demonstrate a generic search strategy that can be used to construct a representative dataset of bibliographic records of a domain of research. Next, progressively synthesized co-citation networks are constructed and visualized to aid visual analytic studies of the domain's structural and dynamic patterns and trends. Finally, trajectories of citations made by particular types of authors and articles are presented to illustrate the predictive potential of the analytic approach.Findings: The evolution of the science mapping research involves the development of a number of interrelated specialties. Four major specialties are discussed in detail in terms of four evolutionary stages: conceptualization, tool construction, application, and codification. Underlying connections between major specialties are also explored. The predictive analysis demonstrates citations trajectories of potentially transformative contributions.Research limitations: The systematic review is primarily guided by citation patterns in the dataset retrieved from the literature. The scope of the data is limited by the source of the retrieval, i.e. the Web of Science, and the composite query used. An iterative query refinement is possible if one would like to improve the data quality, although the current approach serves our purpose adequately. More in-depth analyses of each specialty would be more revealing by incorporating additional methods such as citation context analysis and studies of other aspects of scholarly publications.Practical implications: The underlying analytic process of science mapping serves many practical needs, notably bibliometric mapping, knowledge domain visualization, and visualization of scientific literature. In order to master such a complex process of science mapping, researchers often need to develop a diverse set of skills and knowledge that may span multiple disciplines. The approach demonstrated in this article provides a generic method for conducting a systematic review.Originality/value: Incorporating the evolutionary stages of a specialty into the visual analytic study of a research domain is innovative. It provides a systematic methodology for researchers to achieve a good understanding of how scientific fields evolve, to recognize potentially insightful patterns from visually encoded signs, and to synthesize various information so as to capture the state of the art of the domain.

Bibliometric analysis of UAV swarms

Projects on unmanned aerial vehicle(UAV) swarms have been initiated in a big way in the last few years, especially from 2015 to 2016. As a result, the number of related works on UAV swarms has been on the rise, with the rate of growth dramatically accelerating since 2017. This research conducts a bibliometric analysis of robotics swarms and UAV swarms to answer the following questions:(i) Disciplines mentioned in the UAV swarms research.(ii) The future development trends and hotspots in the UAV swarms research.(iii) Tracking related outcomes in the UAV swarms research.

Describing Citations as a Function of Time

Purpose:Providing an overview of types of citation curves.Design/methodology/approach:The terms citation curves or citation graphs are made explicit.Findings:A framework for the study of diachronous(and synchronous)citation curves is proposed.Research limitations:No new practical applications are given.Practical implications:This short note about citation curves will help readers to make the optimal choice for their applications.Originality/value:A new scheme for the meaning of the term"citation curve"is designed.

Tendency and foci of nurse practitioners:bibliometric analysis based on the CiteSpaceV

Objective:Nurse practitioners(NPs)have drawn significant attention recently and played a major role in healthcare.We aim to find the evaluation of NPs through published studies and then visualize the research status and hotspots in this field.Methods:All data came from the Web of Science Core Collection,and the data were counted and entered into Excel 2016.The key documents nodes were excavated by analyzing the knowledge network map using Cite Space V software.In this study,these nodes of“author,country,institution,keyword,co-citation(referencecited-authorcited-journal),and grant”were harvested for analysis and comparison.Results:A total of 4912 records,which were published between 2007 and 2018 and pertained to NPs,were retrieved from the Web of Science Core Collection database(Wo SCC)from a diversity of languages.The total and the annual number of publications and citations of these continually increased over time.Most publications were in 2018(618 records).This study involved 8241 authors located in 98 countries and 4557 institutions totally.The United States(2737 records)and the University of Michigan(90 records)dominated in publication frequency.There are 902 journals and 2449 articles with funding support that have been analyzed.Most articles were from JAMA:The Journal of the American Medical Association(1386,IF=47.661),followed by the Journal of Advanced Nursing(1359,IF=2.267),and The New England Journal of Medicine(1109,IF=79.258).The reference“The Role of Nurse Practitioners in Reinventing Primary Care”was co-cited most frequently,which revealed it as the highest landmark article in NP.The top-ranked keyword was“Care,”other than“Nurse practitioner,”which has an ultra-high frequency.Some of the high-frequency keywords represent the significant direction of NPs.Conclusions:NPs are at the crux of health-care delivery and play an important role in providing high-quality nursing.Publications on NPs in Wo SCC have increased notably during the recent years,and have appeared in some journals that have a high impact factor.Research frontiers and developmental trends were revealed by the analysis in this study,which can be used to forecast future research developments in NPs and taken as a reference to choose the right directions by subsequent researchers who wish to use these results.However,the grant support from administration or research institutions is still in need of improvement and the scope of research in NPs should be broadened in the future.

Trends and patterns in agriculture tourism research:A 30-year bibliometric analysis based on Web of Science

The article uses the information visualization software Citespace5.5 to study the data related to agriculture tourism in the Web of Science database,from macro to micro to the representative countries of the literature,keywords and co-cited literatures.Through visualization analysis,the article clarifies the key research directions,key literatures and hot spot frontiers in the field of agriculture tourism research,forecasts the future development trends in this field.The results show that the study of agriculture tourism research has become one of the hot topics in academia.The research results of international agriculture tourism are rich.Scholars have many research perspectives.International agriculture tourism research has formed a mature and stable academic community.Mainstream journals in related international fields play an important role in disseminating agriculture tourism research content,methods,and results.Research stage is progressive.In the future,we can focus on the basic theoretical construction of agriculture tourism research,the deep integration of agriculture and tourism,the regional effects of agriculture tourism,agriculture tourism research methods,and multidisciplinary and multi-regional comparative research,etc.This paper can help readers to better understand the status quo and development trend of agriculture tourism and to easier recognize the shortcomings in the development of agriculture tourism,so as to provide a promising direction for future research.