Currently, it has been noticed a strong use of ICTs in several companies. The objective of this study was to analyze and measure the impact of the implementation of ICTs on the strategy and structure of management of ...Currently, it has been noticed a strong use of ICTs in several companies. The objective of this study was to analyze and measure the impact of the implementation of ICTs on the strategy and structure of management of core competence in SMEs in Bukavu, DRC. The scale used shows the good psychometric properties (χ<sup>2</sup> = 75.045;P = 0.16;χ<sup>2</sup>/ddl = 1.471;CFI = 0.92;IFI = 0.92;RMSEA = 0.049;NFI = 0.79;GFI = 0.94;AGFI = 0.91. This study analyzed primary data collected from 195 SMEs in the city of Bukavu. Data processing was achieved using exploratory and confirmatory factor analysis and flat sorting. The results indicate that the use of ICTs by SME managers in the city of Bukavu has a positive impact on all dimensions of core competence selected in this study. They also reveal that the views of these leaders on the importance attached to the adoption of ICTs were divided and the level of ICT use was above average.展开更多
The National Institute of Standards and Technology(NIST)has identified natural language policies as the preferred expression of policy and implicitly called for an automated translation of ABAC natural language access...The National Institute of Standards and Technology(NIST)has identified natural language policies as the preferred expression of policy and implicitly called for an automated translation of ABAC natural language access control policy(NLACP)to a machine-readable form.To study the automation process,we consider the hierarchical ABAC model as our reference model since it better reflects the requirements of real-world organizations.Therefore,this paper focuses on the questions of:how can we automatically infer the hierarchical structure of an ABAC model given NLACPs;and,how can we extract and define the set of authorization attributes based on the resulting structure.To address these questions,we propose an approach built upon recent advancements in natural language processing and machine learning techniques.For such a solution,the lack of appropriate data often poses a bottleneck.Therefore,we decouple the primary contributions of this work into:(1)developing a practical framework to extract authorization attributes of hierarchical ABAC system from natural language artifacts,and(2)generating a set of realistic synthetic natural language access control policies(NLACPs)to evaluate the proposed framework.Our experimental results are promising as we achieved-in average-an F1-score of 0.96 when extracting attributes values of subjects,and 0.91 when extracting the values of objects’attributes from natural language access control policies.展开更多
The National Institute of Standards and Technology(NIST)has identified natural language policies as the preferred expression of policy and implicitly called for an automated translation of ABAC natural language access...The National Institute of Standards and Technology(NIST)has identified natural language policies as the preferred expression of policy and implicitly called for an automated translation of ABAC natural language access control policy(NLACP)to a machine-readable form.To study the automation process,we consider the hierarchical ABAC model as our reference model since it better reflects the requirements of real-world organizations.Therefore,this paper focuses on the questions of:how can we automatically infer the hierarchical structure of an ABAC model given NLACPs;and,how can we extract and define the set of authorization attributes based on the resulting structure.To address these questions,we propose an approach built upon recent advancements in natural language processing and machine learning techniques.For such a solution,the lack of appropriate data often poses a bottleneck.Therefore,we decouple the primary contributions of this work into:(1)developing a practical framework to extract authorization attributes of hierarchical ABAC system from natural language artifacts,and(2)generating a set of realistic synthetic natural language access control policies(NLACPs)to evaluate the proposed framework.Our experimental results are promising as we achieved-in average-an F1-score of 0.96 when extracting attributes values of subjects,and 0.91 when extracting the values of objects’attributes from natural language access control policies.展开更多
文摘Currently, it has been noticed a strong use of ICTs in several companies. The objective of this study was to analyze and measure the impact of the implementation of ICTs on the strategy and structure of management of core competence in SMEs in Bukavu, DRC. The scale used shows the good psychometric properties (χ<sup>2</sup> = 75.045;P = 0.16;χ<sup>2</sup>/ddl = 1.471;CFI = 0.92;IFI = 0.92;RMSEA = 0.049;NFI = 0.79;GFI = 0.94;AGFI = 0.91. This study analyzed primary data collected from 195 SMEs in the city of Bukavu. Data processing was achieved using exploratory and confirmatory factor analysis and flat sorting. The results indicate that the use of ICTs by SME managers in the city of Bukavu has a positive impact on all dimensions of core competence selected in this study. They also reveal that the views of these leaders on the importance attached to the adoption of ICTs were divided and the level of ICT use was above average.
文摘The National Institute of Standards and Technology(NIST)has identified natural language policies as the preferred expression of policy and implicitly called for an automated translation of ABAC natural language access control policy(NLACP)to a machine-readable form.To study the automation process,we consider the hierarchical ABAC model as our reference model since it better reflects the requirements of real-world organizations.Therefore,this paper focuses on the questions of:how can we automatically infer the hierarchical structure of an ABAC model given NLACPs;and,how can we extract and define the set of authorization attributes based on the resulting structure.To address these questions,we propose an approach built upon recent advancements in natural language processing and machine learning techniques.For such a solution,the lack of appropriate data often poses a bottleneck.Therefore,we decouple the primary contributions of this work into:(1)developing a practical framework to extract authorization attributes of hierarchical ABAC system from natural language artifacts,and(2)generating a set of realistic synthetic natural language access control policies(NLACPs)to evaluate the proposed framework.Our experimental results are promising as we achieved-in average-an F1-score of 0.96 when extracting attributes values of subjects,and 0.91 when extracting the values of objects’attributes from natural language access control policies.
基金supported by Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘The National Institute of Standards and Technology(NIST)has identified natural language policies as the preferred expression of policy and implicitly called for an automated translation of ABAC natural language access control policy(NLACP)to a machine-readable form.To study the automation process,we consider the hierarchical ABAC model as our reference model since it better reflects the requirements of real-world organizations.Therefore,this paper focuses on the questions of:how can we automatically infer the hierarchical structure of an ABAC model given NLACPs;and,how can we extract and define the set of authorization attributes based on the resulting structure.To address these questions,we propose an approach built upon recent advancements in natural language processing and machine learning techniques.For such a solution,the lack of appropriate data often poses a bottleneck.Therefore,we decouple the primary contributions of this work into:(1)developing a practical framework to extract authorization attributes of hierarchical ABAC system from natural language artifacts,and(2)generating a set of realistic synthetic natural language access control policies(NLACPs)to evaluate the proposed framework.Our experimental results are promising as we achieved-in average-an F1-score of 0.96 when extracting attributes values of subjects,and 0.91 when extracting the values of objects’attributes from natural language access control policies.
