The process of ranking scientific publications in dynamic citation networks plays a crucial rule in a variety of applications. Despite the availability of a number of ranking algorithms, most of them use common popula...The process of ranking scientific publications in dynamic citation networks plays a crucial rule in a variety of applications. Despite the availability of a number of ranking algorithms, most of them use common popularity metrics such as the citation count, h-index, and Impact Factor (IF). These adopted metrics cause a problem of bias in favor of older publications that took enough time to collect as many citations as possible. This paper focuses on solving the problem of bias by proposing a new ranking algorithm based on the PageRank (PR) algorithm;it is one of the main page ranking algorithms being widely used. The developed algorithm considers a newly suggested metric called the Citation Average rate of Change (CAC). Time information such as publication date and the citation occurrence’s time are used along with citation data to calculate the new metric. The proposed ranking algorithm was tested on a dataset of scientific papers in the field of medical physics published in the Dimensions database from years 2005 to 2017. The experimental results have shown that the proposed ranking algorithm outperforms the PageRank algorithm in ranking scientific publications where 26 papers instead of only 14 were ranked among the top 100 papers of this dataset. In addition, there were no radical changes or unreasonable jump in the ranking process, i.e., the correlation rate between the results of the proposed ranking method and the original PageRank algorithm was 92% based on the Spearman correlation coefficient.展开更多
A knowledge graph is a structured graph in which data obtained from multiple sources are standardized to acquire and integrate human knowledge.Research is being actively conducted to cover a wide variety of knowledge,...A knowledge graph is a structured graph in which data obtained from multiple sources are standardized to acquire and integrate human knowledge.Research is being actively conducted to cover a wide variety of knowledge,as it can be applied to applications that help humans.However,existing researches are constructing knowledge graphs without the time information that knowledge implies.Knowledge stored without time information becomes outdated over time,and in the future,the possibility of knowledge being false or meaningful changes is excluded.As a result,they can’t reect information that changes dynamically,and they can’t accept information that has newly emerged.To solve this problem,this paper proposes Time-Aware PolarisX,an automatically extended knowledge graph including time information.TimeAware PolarisX constructed a BERT model with a relation extractor and an ensemble NER model including a time tag with an entity extractor to extract knowledge consisting of subject,relation,and object from unstructured text.Through two application experiments,it shows that the proposed system overcomes the limitations of existing systems that do not consider time information when applied to an application such as a chatbot.Also,we verify that the accuracy of the extraction model is improved through a comparative experiment with the existing model.展开更多
With the widespread adoption of blockchain applications, the imperative for seamless data migration among decentralized applications has intensified. This necessity arises from various factors, including the depletion...With the widespread adoption of blockchain applications, the imperative for seamless data migration among decentralized applications has intensified. This necessity arises from various factors, including the depletion of blockchain disk space, transitions between blockchain systems, and specific requirements such as temporal data analysis. To meet these challenges and ensure the sustained functionality of applications, it is imperative to conduct time-aware cross-blockchain data migration. This process is designed to facilitate the smooth iteration of decentralized applications and the construction of a temporal index for historical data, all while preserving the integrity of the original data. In various application scenarios, this migration task may encompass the transfer of data between multiple blockchains, involving movements from one chain to another, from one chain to several chains, or from multiple chains to a single chain. However, the success of data migration hinges on the careful consideration of factors such as the reliability of the data source, data consistency, and migration efficiency. This paper introduces a time-aware cross-blockchain data migration approach tailored to accommodate diverse application scenarios, including migration between multiple chains. The proposed solution integrates a collective mechanism for controlling, executing, and storing procedures to address the complexities of data migration, incorporating elements such as transaction classification and matching. Extensive experiments have been conducted to validate the efficacy of the proposed approach.展开更多
Dynamic Quality of Service(QoS)prediction for services is currently a hot topic and a challenge for research in the fields of service recommendation and composition.Our paper addresses the problem with a Time-aWare se...Dynamic Quality of Service(QoS)prediction for services is currently a hot topic and a challenge for research in the fields of service recommendation and composition.Our paper addresses the problem with a Time-aWare service Quality Prediction method(named TWQP),a two-phase approach with one phase based on historical time slices and one on the current time slice.In the first phase,if the user had invoked the service in a previous time slice,the QoS value for the user calling the service on the next time slice is predicted on the basis of the historical QoS data;if the user had not invoked the service in a previous time slice,then the Covering Algorithm(CA)is applied to predict the missing values.In the second phase,we predict the missing values for the current time slice according to the results of the previous phase.A large number of experiments on a real-world dataset,WS-Dream,show that,when compared with the classical QoS prediction algorithms,our proposed method greatly improves the prediction accuracy.展开更多
文摘The process of ranking scientific publications in dynamic citation networks plays a crucial rule in a variety of applications. Despite the availability of a number of ranking algorithms, most of them use common popularity metrics such as the citation count, h-index, and Impact Factor (IF). These adopted metrics cause a problem of bias in favor of older publications that took enough time to collect as many citations as possible. This paper focuses on solving the problem of bias by proposing a new ranking algorithm based on the PageRank (PR) algorithm;it is one of the main page ranking algorithms being widely used. The developed algorithm considers a newly suggested metric called the Citation Average rate of Change (CAC). Time information such as publication date and the citation occurrence’s time are used along with citation data to calculate the new metric. The proposed ranking algorithm was tested on a dataset of scientific papers in the field of medical physics published in the Dimensions database from years 2005 to 2017. The experimental results have shown that the proposed ranking algorithm outperforms the PageRank algorithm in ranking scientific publications where 26 papers instead of only 14 were ranked among the top 100 papers of this dataset. In addition, there were no radical changes or unreasonable jump in the ranking process, i.e., the correlation rate between the results of the proposed ranking method and the original PageRank algorithm was 92% based on the Spearman correlation coefficient.
基金supported by Basic Science Research Program through the NRF(National Research Foundation of Korea)the MSIT(Ministry of Science and ICT),Korea,under the National Program for Excellence in SW supervised by the IITP(Institute for Information&communications Technology Promotion)the Gachon University research fund of 2019(Nos.NRF2019R1A2C1008412,2015-0-00932,GCU-2019-0773)。
文摘A knowledge graph is a structured graph in which data obtained from multiple sources are standardized to acquire and integrate human knowledge.Research is being actively conducted to cover a wide variety of knowledge,as it can be applied to applications that help humans.However,existing researches are constructing knowledge graphs without the time information that knowledge implies.Knowledge stored without time information becomes outdated over time,and in the future,the possibility of knowledge being false or meaningful changes is excluded.As a result,they can’t reect information that changes dynamically,and they can’t accept information that has newly emerged.To solve this problem,this paper proposes Time-Aware PolarisX,an automatically extended knowledge graph including time information.TimeAware PolarisX constructed a BERT model with a relation extractor and an ensemble NER model including a time tag with an entity extractor to extract knowledge consisting of subject,relation,and object from unstructured text.Through two application experiments,it shows that the proposed system overcomes the limitations of existing systems that do not consider time information when applied to an application such as a chatbot.Also,we verify that the accuracy of the extraction model is improved through a comparative experiment with the existing model.
基金supported by the National Key Research and Development Program of China(No.2020YFA0909100)the Shenzhen Key Basic Research Project(No.JCYJ20200109115422828)the Huawei Cloud Research Project(No.YBN2020085125).
文摘With the widespread adoption of blockchain applications, the imperative for seamless data migration among decentralized applications has intensified. This necessity arises from various factors, including the depletion of blockchain disk space, transitions between blockchain systems, and specific requirements such as temporal data analysis. To meet these challenges and ensure the sustained functionality of applications, it is imperative to conduct time-aware cross-blockchain data migration. This process is designed to facilitate the smooth iteration of decentralized applications and the construction of a temporal index for historical data, all while preserving the integrity of the original data. In various application scenarios, this migration task may encompass the transfer of data between multiple blockchains, involving movements from one chain to another, from one chain to several chains, or from multiple chains to a single chain. However, the success of data migration hinges on the careful consideration of factors such as the reliability of the data source, data consistency, and migration efficiency. This paper introduces a time-aware cross-blockchain data migration approach tailored to accommodate diverse application scenarios, including migration between multiple chains. The proposed solution integrates a collective mechanism for controlling, executing, and storing procedures to address the complexities of data migration, incorporating elements such as transaction classification and matching. Extensive experiments have been conducted to validate the efficacy of the proposed approach.
基金supported by the National Natural Science Foundation of China (No.61872002)the National Natural Science Foundation of Anhui Province of China (No.1808085MF197)the Philosophy and Social Science Planned Project of Anhui Province (No. AHSKY2015D67)
文摘Dynamic Quality of Service(QoS)prediction for services is currently a hot topic and a challenge for research in the fields of service recommendation and composition.Our paper addresses the problem with a Time-aWare service Quality Prediction method(named TWQP),a two-phase approach with one phase based on historical time slices and one on the current time slice.In the first phase,if the user had invoked the service in a previous time slice,the QoS value for the user calling the service on the next time slice is predicted on the basis of the historical QoS data;if the user had not invoked the service in a previous time slice,then the Covering Algorithm(CA)is applied to predict the missing values.In the second phase,we predict the missing values for the current time slice according to the results of the previous phase.A large number of experiments on a real-world dataset,WS-Dream,show that,when compared with the classical QoS prediction algorithms,our proposed method greatly improves the prediction accuracy.
