Editors Yang Wang,Xi'an Jiaotong University Dongbo Shi,Shanghai Jiaotong University Ye Sun,University College London Zhesi Shen,National Science Library,CAS Topic of the Special Issue What are the top questions to...Editors Yang Wang,Xi'an Jiaotong University Dongbo Shi,Shanghai Jiaotong University Ye Sun,University College London Zhesi Shen,National Science Library,CAS Topic of the Special Issue What are the top questions towards better science and innovation and the required data to answer these questions?展开更多
Cyberattacks are difficult to prevent because the targeted companies and organizations are often relying on new and fundamentally insecure cloudbased technologies,such as the Internet of Things.With increasing industr...Cyberattacks are difficult to prevent because the targeted companies and organizations are often relying on new and fundamentally insecure cloudbased technologies,such as the Internet of Things.With increasing industry adoption and migration of traditional computing services to the cloud,one of the main challenges in cybersecurity is to provide mechanisms to secure these technologies.This work proposes a Data Security Framework for cloud computing services(CCS)that evaluates and improves CCS data security from a software engineering perspective by evaluating the levels of security within the cloud computing paradigm using engineering methods and techniques applied to CCS.This framework is developed by means of a methodology based on a heuristic theory that incorporates knowledge generated by existing works as well as the experience of their implementation.The paper presents the design details of the framework,which consists of three stages:identification of data security requirements,management of data security risks and evaluation of data security performance in CCS.展开更多
Calibrating the building energy simulation(BES)models is a typical way to bridge energy performance gaps for existing buildings.Currently,evidence-based and data-driven calibration are both widely used and have their ...Calibrating the building energy simulation(BES)models is a typical way to bridge energy performance gaps for existing buildings.Currently,evidence-based and data-driven calibration are both widely used and have their advantages and limitations.However,a systematic approach to combining the advantages of these two approaches has not been established.This study performed evidence-based and data-driven calibration consecutively using a real-world data-rich building,assuming different scenarios of data availability.24 intermediate and ultimate models were obtained and comprehensively evaluated,by inspecting the calibrated parameters,calculating the predictive errors,and evaluating the energy conservation measures.It is shown that a satisfactory CV(RMSE)could be achieved even without any detailed evidence about the building,which was misleading due to the drifted parameter values.Accordingly,data acquisition recommendations were made considering the importance and acquisition costs.Moreover,despite similarly low errors,different models can estimate monthly energy savings and percentages with discrepancies exceeding 1000 kWh and 10%.Using a“calibrated”model without knowing the potential risk,the cascading performance gaps can lead to wrong decisions.展开更多
文摘Editors Yang Wang,Xi'an Jiaotong University Dongbo Shi,Shanghai Jiaotong University Ye Sun,University College London Zhesi Shen,National Science Library,CAS Topic of the Special Issue What are the top questions towards better science and innovation and the required data to answer these questions?
文摘Cyberattacks are difficult to prevent because the targeted companies and organizations are often relying on new and fundamentally insecure cloudbased technologies,such as the Internet of Things.With increasing industry adoption and migration of traditional computing services to the cloud,one of the main challenges in cybersecurity is to provide mechanisms to secure these technologies.This work proposes a Data Security Framework for cloud computing services(CCS)that evaluates and improves CCS data security from a software engineering perspective by evaluating the levels of security within the cloud computing paradigm using engineering methods and techniques applied to CCS.This framework is developed by means of a methodology based on a heuristic theory that incorporates knowledge generated by existing works as well as the experience of their implementation.The paper presents the design details of the framework,which consists of three stages:identification of data security requirements,management of data security risks and evaluation of data security performance in CCS.
基金supported by the Singapore Ministry of Education Academic Research Fund(MOE ARF)Tier 1(Grant Number A-8002103-00-00).
文摘Calibrating the building energy simulation(BES)models is a typical way to bridge energy performance gaps for existing buildings.Currently,evidence-based and data-driven calibration are both widely used and have their advantages and limitations.However,a systematic approach to combining the advantages of these two approaches has not been established.This study performed evidence-based and data-driven calibration consecutively using a real-world data-rich building,assuming different scenarios of data availability.24 intermediate and ultimate models were obtained and comprehensively evaluated,by inspecting the calibrated parameters,calculating the predictive errors,and evaluating the energy conservation measures.It is shown that a satisfactory CV(RMSE)could be achieved even without any detailed evidence about the building,which was misleading due to the drifted parameter values.Accordingly,data acquisition recommendations were made considering the importance and acquisition costs.Moreover,despite similarly low errors,different models can estimate monthly energy savings and percentages with discrepancies exceeding 1000 kWh and 10%.Using a“calibrated”model without knowing the potential risk,the cascading performance gaps can lead to wrong decisions.
