This paper deals with the security of stock market transactions within financial markets, particularly that of the West African Economic and Monetary Union (UEMOA). The confidentiality and integrity of sensitive data ...This paper deals with the security of stock market transactions within financial markets, particularly that of the West African Economic and Monetary Union (UEMOA). The confidentiality and integrity of sensitive data in the stock market being crucial, the implementation of robust systems which guarantee trust between the different actors is essential. We therefore proposed, after analyzing the limits of several security approaches in the literature, an architecture based on blockchain technology making it possible to both identify and reduce the vulnerabilities linked to the design, implementation work or the use of web applications used for transactions. Our proposal makes it possible, thanks to two-factor authentication via the Blockchain, to strengthen the security of investors’ accounts and the automated recording of transactions in the Blockchain while guaranteeing the integrity of stock market operations. It also provides an application vulnerability report. To validate our approach, we compared our results to those of three other security tools, at the level of different metrics. Our approach achieved the best performance in each case.展开更多
Switzerland is one of the most desirable European destinations for Chinese tourists;therefore, a better understanding of Chinese tourists is essential for successful business practices. In China, the largest and leadi...Switzerland is one of the most desirable European destinations for Chinese tourists;therefore, a better understanding of Chinese tourists is essential for successful business practices. In China, the largest and leading social media platform—Sina Weibo, a hybrid of Twitter and Facebook—has more than 600 million users. Weibo’s great market penetration suggests that tourism operators and markets need to understand how to build effective and sustainable communications on Chinese social media platforms. In order to offer a better decision support platform to tourism destination managers as well as Chinese tourists, we proposed a framework using linked data on Sina Weibo. Linked Data is a term referring to using the Internet to connect related data. We will show how it can be used and how ontology can be designed to include the users’ context (e.g., GPS locations). Our framework will provide a good theoretical foundation for further understand Chinese tourists’ expectation, experiences, behaviors and new trends in Switzerland.展开更多
Abundant sensor data are now available online from a wealth of sources,which greatly enhance research efforts on the Digital Earth.The combination of distributed sensor networks and expanding citizen-sensing capabilit...Abundant sensor data are now available online from a wealth of sources,which greatly enhance research efforts on the Digital Earth.The combination of distributed sensor networks and expanding citizen-sensing capabilities provides a more synchronized image of earth’s social and physical landscapes.However,it remains difficult for researchers to use such heterogeneous Sensor Webs for scientific applications since data are published by following different standards and protocols and are in arbitrary formats.In this paper,we investigate the core challenges faced when consuming multiple sources for environmental applications using the Linked Data approach.We design and implement a system to achieve better data interoperability and integration by republishing real-world data into linked geo-sensor data.Our contributions include presenting:(1)best practices of re-using and matching the W3C Semantic Sensor Network(SSN)ontology and other popular ontologies for heterogeneous data modeling in the water resources application domain,(2)a newly developed spatial analysis tool for creating links,and(3)a set of RESTful OGC Sensor Observation Service(SOS)like Linked Data APIs.Our results show how a Linked Sensor Web can be built and used within the integrated water resource decision support application domain.展开更多
This work leveraged predictive modeling techniques in machine learning (ML) to predict heart disease using a dataset sourced from the Center for Disease Control and Prevention in the US. The dataset was preprocessed a...This work leveraged predictive modeling techniques in machine learning (ML) to predict heart disease using a dataset sourced from the Center for Disease Control and Prevention in the US. The dataset was preprocessed and used to train five machine learning models: random forest, support vector machine, logistic regression, extreme gradient boosting and light gradient boosting. The goal was to use the best performing model to develop a web application capable of reliably predicting heart disease based on user-provided data. The extreme gradient boosting classifier provided the most reliable results with precision, recall and F1-score of 97%, 72%, and 83% respectively for Class 0 (no heart disease) and 21% (precision), 81% (recall) and 34% (F1-score) for Class 1 (heart disease). The model was further deployed as a web application.展开更多
Treatment plan selection is a complex process because it sometimes needs sufficient experience and clinical information.Nowadays it is even harder for doctors to select an appropriate treatment plan for certain patien...Treatment plan selection is a complex process because it sometimes needs sufficient experience and clinical information.Nowadays it is even harder for doctors to select an appropriate treatment plan for certain patients since doctors might encounter difficulties in obtaining the right information and analyzing the diverse clinical data.In order to improve the effectiveness of clinical decision making in complicated information system environments,we first propose a linked data-based approach for treatment plan selection.The approach integrates the patients’clinical records in hospitals with open linked data sources out of hospitals.Then,based on the linked data net,treatment plan selection is carried on aided by similar historical therapy cases.Finally,we reorganize the electronic medical records of 97 colon cancer patients using the linked data model and count the similarity of these records to help treatment selecting.The experiment shows the usability of our method in supporting clinical decisions.展开更多
Efficient real time data exchange over the Internet plays a crucial role in the successful application of web-based systems. In this paper, a data transfer mechanism over the Internet is proposed for real time web bas...Efficient real time data exchange over the Internet plays a crucial role in the successful application of web-based systems. In this paper, a data transfer mechanism over the Internet is proposed for real time web based applications. The mechanism incorporates the eXtensible Markup Language （XML） and Hierarchical Data Format （HDF） to provide a flexible and efficient data format. Heterogeneous transfer data is classified into light and heavy data, which are stored using XML and HDF respectively; the HDF data format is then mapped to Java Document Object Model （JDOM） objects in XML in the Java environment. These JDOM data objects are sent across computer networks with the support of the Java Remote Method Invocation （RMI） data transfer infrastructure. Client＇s defined data priority levels are implemented in RMI, which guides a server to transfer data objects at different priorities. A remote monitoring system for an industrial reactor process simulator is used as a case study to illustrate the proposed data transfer mechanism.展开更多
Purpose:To develop a set of metrics and identify criteria for assessing the functionality of LOD KOS products while providing common guiding principles that can be used by LOD KOS producers and users to maximize the f...Purpose:To develop a set of metrics and identify criteria for assessing the functionality of LOD KOS products while providing common guiding principles that can be used by LOD KOS producers and users to maximize the functions and usages of LOD KOS products.Design/methodology/approach:Data collection and analysis were conducted at three time periods in 2015–16,2017 and 2019.The sample data used in the comprehensive data analysis comprises all datasets tagged as types of KOS in the Datahub and extracted through their respective SPARQL endpoints.A comparative study of the LOD KOS collected from terminology services Linked Open Vocabularies(LOV)and BioPortal was also performed.Findings:The study proposes a set of Functional,Impactful and Transformable(FIT)metrics for LOD KOS as value vocabularies.The FAIR principles,with additional recommendations,are presented for LOD KOS as open data.Research limitations:The metrics need to be further tested and aligned with the best practices and international standards of both open data and various types of KOS.Practical implications:Assessment performed with FAIR and FIT metrics support the creation and delivery of user-friendly,discoverable and interoperable LOD KOS datasets which can be used for innovative applications,act as a knowledge base,become a foundation of semantic analysis and entity extractions and enhance research in science and the humanities.Originality/value:Our research provides best practice guidelines for LOD KOS as value vocabularies.展开更多
Linked data is a decentralized space of interlinked Resource Description Framework(RDF) graphs that are published,accessed,and manipulated by a multitude of Web agents.Here,we present a multi-agent framework for minin...Linked data is a decentralized space of interlinked Resource Description Framework(RDF) graphs that are published,accessed,and manipulated by a multitude of Web agents.Here,we present a multi-agent framework for mining hypothetical semantic relations from linked data,in which the discovery,management,and validation of relations can be carried out independently by different agents.These agents collaborate in relation mining by publishing and exchanging inter-dependent knowledge elements,e.g.,hypotheses,evidence,and proofs,giving rise to an evidentiary network that connects and ranks diverse knowledge elements.Simulation results show that the framework is scalable in a multi-agent environment.Real-world applications show that the framework is suitable for interdisciplinary and collaborative relation discovery tasks in social domains.展开更多
文摘This paper deals with the security of stock market transactions within financial markets, particularly that of the West African Economic and Monetary Union (UEMOA). The confidentiality and integrity of sensitive data in the stock market being crucial, the implementation of robust systems which guarantee trust between the different actors is essential. We therefore proposed, after analyzing the limits of several security approaches in the literature, an architecture based on blockchain technology making it possible to both identify and reduce the vulnerabilities linked to the design, implementation work or the use of web applications used for transactions. Our proposal makes it possible, thanks to two-factor authentication via the Blockchain, to strengthen the security of investors’ accounts and the automated recording of transactions in the Blockchain while guaranteeing the integrity of stock market operations. It also provides an application vulnerability report. To validate our approach, we compared our results to those of three other security tools, at the level of different metrics. Our approach achieved the best performance in each case.
文摘Switzerland is one of the most desirable European destinations for Chinese tourists;therefore, a better understanding of Chinese tourists is essential for successful business practices. In China, the largest and leading social media platform—Sina Weibo, a hybrid of Twitter and Facebook—has more than 600 million users. Weibo’s great market penetration suggests that tourism operators and markets need to understand how to build effective and sustainable communications on Chinese social media platforms. In order to offer a better decision support platform to tourism destination managers as well as Chinese tourists, we proposed a framework using linked data on Sina Weibo. Linked Data is a term referring to using the Internet to connect related data. We will show how it can be used and how ontology can be designed to include the users’ context (e.g., GPS locations). Our framework will provide a good theoretical foundation for further understand Chinese tourists’ expectation, experiences, behaviors and new trends in Switzerland.
文摘Abundant sensor data are now available online from a wealth of sources,which greatly enhance research efforts on the Digital Earth.The combination of distributed sensor networks and expanding citizen-sensing capabilities provides a more synchronized image of earth’s social and physical landscapes.However,it remains difficult for researchers to use such heterogeneous Sensor Webs for scientific applications since data are published by following different standards and protocols and are in arbitrary formats.In this paper,we investigate the core challenges faced when consuming multiple sources for environmental applications using the Linked Data approach.We design and implement a system to achieve better data interoperability and integration by republishing real-world data into linked geo-sensor data.Our contributions include presenting:(1)best practices of re-using and matching the W3C Semantic Sensor Network(SSN)ontology and other popular ontologies for heterogeneous data modeling in the water resources application domain,(2)a newly developed spatial analysis tool for creating links,and(3)a set of RESTful OGC Sensor Observation Service(SOS)like Linked Data APIs.Our results show how a Linked Sensor Web can be built and used within the integrated water resource decision support application domain.
文摘This work leveraged predictive modeling techniques in machine learning (ML) to predict heart disease using a dataset sourced from the Center for Disease Control and Prevention in the US. The dataset was preprocessed and used to train five machine learning models: random forest, support vector machine, logistic regression, extreme gradient boosting and light gradient boosting. The goal was to use the best performing model to develop a web application capable of reliably predicting heart disease based on user-provided data. The extreme gradient boosting classifier provided the most reliable results with precision, recall and F1-score of 97%, 72%, and 83% respectively for Class 0 (no heart disease) and 21% (precision), 81% (recall) and 34% (F1-score) for Class 1 (heart disease). The model was further deployed as a web application.
基金This work was supported by the National Natural Science Foundation of China,[grant number 71171132,61373030].
文摘Treatment plan selection is a complex process because it sometimes needs sufficient experience and clinical information.Nowadays it is even harder for doctors to select an appropriate treatment plan for certain patients since doctors might encounter difficulties in obtaining the right information and analyzing the diverse clinical data.In order to improve the effectiveness of clinical decision making in complicated information system environments,we first propose a linked data-based approach for treatment plan selection.The approach integrates the patients’clinical records in hospitals with open linked data sources out of hospitals.Then,based on the linked data net,treatment plan selection is carried on aided by similar historical therapy cases.Finally,we reorganize the electronic medical records of 97 colon cancer patients using the linked data model and count the similarity of these records to help treatment selecting.The experiment shows the usability of our method in supporting clinical decisions.
文摘Efficient real time data exchange over the Internet plays a crucial role in the successful application of web-based systems. In this paper, a data transfer mechanism over the Internet is proposed for real time web based applications. The mechanism incorporates the eXtensible Markup Language （XML） and Hierarchical Data Format （HDF） to provide a flexible and efficient data format. Heterogeneous transfer data is classified into light and heavy data, which are stored using XML and HDF respectively; the HDF data format is then mapped to Java Document Object Model （JDOM） objects in XML in the Java environment. These JDOM data objects are sent across computer networks with the support of the Java Remote Method Invocation （RMI） data transfer infrastructure. Client＇s defined data priority levels are implemented in RMI, which guides a server to transfer data objects at different priorities. A remote monitoring system for an industrial reactor process simulator is used as a case study to illustrate the proposed data transfer mechanism.
基金College of Communication and Information(CCI)Research and Creative Activity Fund,Kent State University
文摘Purpose:To develop a set of metrics and identify criteria for assessing the functionality of LOD KOS products while providing common guiding principles that can be used by LOD KOS producers and users to maximize the functions and usages of LOD KOS products.Design/methodology/approach:Data collection and analysis were conducted at three time periods in 2015–16,2017 and 2019.The sample data used in the comprehensive data analysis comprises all datasets tagged as types of KOS in the Datahub and extracted through their respective SPARQL endpoints.A comparative study of the LOD KOS collected from terminology services Linked Open Vocabularies(LOV)and BioPortal was also performed.Findings:The study proposes a set of Functional,Impactful and Transformable(FIT)metrics for LOD KOS as value vocabularies.The FAIR principles,with additional recommendations,are presented for LOD KOS as open data.Research limitations:The metrics need to be further tested and aligned with the best practices and international standards of both open data and various types of KOS.Practical implications:Assessment performed with FAIR and FIT metrics support the creation and delivery of user-friendly,discoverable and interoperable LOD KOS datasets which can be used for innovative applications,act as a knowledge base,become a foundation of semantic analysis and entity extractions and enhance research in science and the humanities.Originality/value:Our research provides best practice guidelines for LOD KOS as value vocabularies.
基金supported by the National Natural Science Foundation of China (Nos.61070156 and 61100183)the Natural Science Foundation of Zhejiang Province,China (No.Y1110477)
文摘Linked data is a decentralized space of interlinked Resource Description Framework(RDF) graphs that are published,accessed,and manipulated by a multitude of Web agents.Here,we present a multi-agent framework for mining hypothetical semantic relations from linked data,in which the discovery,management,and validation of relations can be carried out independently by different agents.These agents collaborate in relation mining by publishing and exchanging inter-dependent knowledge elements,e.g.,hypotheses,evidence,and proofs,giving rise to an evidentiary network that connects and ranks diverse knowledge elements.Simulation results show that the framework is scalable in a multi-agent environment.Real-world applications show that the framework is suitable for interdisciplinary and collaborative relation discovery tasks in social domains.
