The “Citizen-Centric Complaint Reporting and Analyzing Mechanism” project is designed to create an online complaint system, called “e-Complaint”, to allow citizens to file complaints related to crime and misconduc...The “Citizen-Centric Complaint Reporting and Analyzing Mechanism” project is designed to create an online complaint system, called “e-Complaint”, to allow citizens to file complaints related to crime and misconduct in a secure and user-friendly way. The proposed system aims to address the challenges of the current complaint system, ensuring transparency and accountability in the police force. The “e-Complaint” system aims to increase police accountability and transparency and has significant benefits for both citizens and police departments.展开更多
The healthcare industry deals with highly sensitive data which must be managed in a secure way.Electronic Health Records(EHRs)hold various kinds of personal and sensitive data which contain names,addresses,social secu...The healthcare industry deals with highly sensitive data which must be managed in a secure way.Electronic Health Records(EHRs)hold various kinds of personal and sensitive data which contain names,addresses,social security numbers,insurance numbers,and medical history.Such personal data is valuable to the patients,healthcare service providers,medical insurance companies,and research institutions.However,the public release of this highly sensitive personal data poses serious privacy and security threats to patients and healthcare service providers.Hence,we foresee the requirement of new technologies to address the privacy and security challenges for personal data in healthcare applications.Blockchain is one of the promising solutions,aimed to provide transparency,security,and privacy using consensus-driven decentralised data management on top of peer-to-peer distributed computing systems.Therefore,to solve the mentioned problems in healthcare applications,in this paper,we investigate the use of private blockchain technologies to assess their feasibility for healthcare applications.We create testing scenarios using HyperLedger Fabric to explore different criteria and use-cases for healthcare applications.Additionally,we thoroughly evaluate the representative test case scenarios to assess the blockchain-enabled security criteria in terms of data confidentiality,privacy and access control.The experimental evaluation reveals the promising benefits of private blockchain technologies in terms of security,regulation compliance,compatibility,flexibility,and scalability.展开更多
We introduce a framework for statistical inference of the closure coefficients using machine learning methods.The objective of this framework is to quantify the epistemic uncertainty associated with the closure model ...We introduce a framework for statistical inference of the closure coefficients using machine learning methods.The objective of this framework is to quantify the epistemic uncertainty associated with the closure model by using experimental data via Bayesian statistics.The framework is tailored towards cases for which a limited amount of experimental data is available.It consists of two components.First,by treating all latent variables(non-observed variables)in the model as stochastic variables,all sources of uncertainty of the probabilistic closure model are quantified by a fully Bayesian approach.The probabilistic model is defined to consist of the closure coefficients as parameters and other parameters incorporating noise.Then,the uncertainty associated with the closure coefficients is extracted from the overall uncertainty by considering the noise being zero.The overall uncertainty is rigorously evaluated by using Markov-Chain Monte Carlo sampling assisted by surrogate models.We apply the framework to the Spalart-Allmars one-equation turbulence model.Two test cases are considered,including an industrially relevant full aircraft model at transonic flow conditions,the Airbus XRF1.Eventually,we demonstrate that epistemic uncertainties in the closure coefficients result into uncertainties in flow quantities of interest which are prominent around,and downstream,of the shock occurring over the XRF1 wing.This data-driven approach could help to enhance the predictive capabilities of CFD in terms of reliable turbulence modeling at extremes of the flight envelope if measured data is available,which is important in the context of robust design and towards virtual aircraft certification.The plentiful amount of information about the uncertainties could also assist when it comes to estimating the influence of the measured data on the inferred model coefficients.Finally,the developed framework is flexible and can be applied to different test cases and to various turbulence models.展开更多
文摘The “Citizen-Centric Complaint Reporting and Analyzing Mechanism” project is designed to create an online complaint system, called “e-Complaint”, to allow citizens to file complaints related to crime and misconduct in a secure and user-friendly way. The proposed system aims to address the challenges of the current complaint system, ensuring transparency and accountability in the police force. The “e-Complaint” system aims to increase police accountability and transparency and has significant benefits for both citizens and police departments.
文摘The healthcare industry deals with highly sensitive data which must be managed in a secure way.Electronic Health Records(EHRs)hold various kinds of personal and sensitive data which contain names,addresses,social security numbers,insurance numbers,and medical history.Such personal data is valuable to the patients,healthcare service providers,medical insurance companies,and research institutions.However,the public release of this highly sensitive personal data poses serious privacy and security threats to patients and healthcare service providers.Hence,we foresee the requirement of new technologies to address the privacy and security challenges for personal data in healthcare applications.Blockchain is one of the promising solutions,aimed to provide transparency,security,and privacy using consensus-driven decentralised data management on top of peer-to-peer distributed computing systems.Therefore,to solve the mentioned problems in healthcare applications,in this paper,we investigate the use of private blockchain technologies to assess their feasibility for healthcare applications.We create testing scenarios using HyperLedger Fabric to explore different criteria and use-cases for healthcare applications.Additionally,we thoroughly evaluate the representative test case scenarios to assess the blockchain-enabled security criteria in terms of data confidentiality,privacy and access control.The experimental evaluation reveals the promising benefits of private blockchain technologies in terms of security,regulation compliance,compatibility,flexibility,and scalability.
文摘We introduce a framework for statistical inference of the closure coefficients using machine learning methods.The objective of this framework is to quantify the epistemic uncertainty associated with the closure model by using experimental data via Bayesian statistics.The framework is tailored towards cases for which a limited amount of experimental data is available.It consists of two components.First,by treating all latent variables(non-observed variables)in the model as stochastic variables,all sources of uncertainty of the probabilistic closure model are quantified by a fully Bayesian approach.The probabilistic model is defined to consist of the closure coefficients as parameters and other parameters incorporating noise.Then,the uncertainty associated with the closure coefficients is extracted from the overall uncertainty by considering the noise being zero.The overall uncertainty is rigorously evaluated by using Markov-Chain Monte Carlo sampling assisted by surrogate models.We apply the framework to the Spalart-Allmars one-equation turbulence model.Two test cases are considered,including an industrially relevant full aircraft model at transonic flow conditions,the Airbus XRF1.Eventually,we demonstrate that epistemic uncertainties in the closure coefficients result into uncertainties in flow quantities of interest which are prominent around,and downstream,of the shock occurring over the XRF1 wing.This data-driven approach could help to enhance the predictive capabilities of CFD in terms of reliable turbulence modeling at extremes of the flight envelope if measured data is available,which is important in the context of robust design and towards virtual aircraft certification.The plentiful amount of information about the uncertainties could also assist when it comes to estimating the influence of the measured data on the inferred model coefficients.Finally,the developed framework is flexible and can be applied to different test cases and to various turbulence models.
