The increasing data pool in finance sectors forces machine learning(ML)to step into new complications.Banking data has significant financial implications and is confidential.Combining users data from several organizat...The increasing data pool in finance sectors forces machine learning(ML)to step into new complications.Banking data has significant financial implications and is confidential.Combining users data from several organizations for various banking services may result in various intrusions and privacy leakages.As a result,this study employs federated learning(FL)using a flower paradigm to preserve each organization’s privacy while collaborating to build a robust shared global model.However,diverse data distributions in the collaborative training process might result in inadequate model learning and a lack of privacy.To address this issue,the present paper proposes the imple-mentation of Federated Averaging(FedAvg)and Federated Proximal(FedProx)methods in the flower framework,which take advantage of the data locality while training and guaranteeing global convergence.Resultantly improves the privacy of the local models.This analysis used the credit card and Canadian Institute for Cybersecurity Intrusion Detection Evaluation(CICIDS)datasets.Precision,recall,and accuracy as performance indicators to show the efficacy of the proposed strategy using FedAvg and FedProx.The experimental findings suggest that the proposed approach helps to safely use banking data from diverse sources to enhance customer banking services by obtaining accuracy of 99.55%and 83.72%for FedAvg and 99.57%,and 84.63%for FedProx.展开更多
This paper uses an SBM-GML index model to assess Green Total Factor Productivity(GTFP)in China's carbon-intensive sectors and conducts an empirical investigation into which factors influence GTFP in these sectors....This paper uses an SBM-GML index model to assess Green Total Factor Productivity(GTFP)in China's carbon-intensive sectors and conducts an empirical investigation into which factors influence GTFP in these sectors.The GTFP in the carbon-intensive sectors experienced a decline between 2006 and 2011,followed by an upward trend beginning in 2012.Technological progress was the primary driver of GTFP growth,while business size was also a notable contributor.Irrational energy structures negatively influenced the high-quality development of the carbon-intensive sectors,and environmental regulation and foreign direct investment(FDI)have not yet significantly impacted GTFP.Based on these findings,this paper suggests that the carbon-intensive sectors should expedite their green transitions by focusing on system improvement,technological innovations,energy revolutions,and high-level opening up.展开更多
近日,武汉大学国家网络安全学院2022级硕士生方正撰写的论文被第31届国际计算机与通信安全会议(ACM Conference on Computer and Communications Security,ACM CCS2024)录用。论文题目为“Zero-Query Adversarial Attack on Black-box A...近日,武汉大学国家网络安全学院2022级硕士生方正撰写的论文被第31届国际计算机与通信安全会议(ACM Conference on Computer and Communications Security,ACM CCS2024)录用。论文题目为“Zero-Query Adversarial Attack on Black-box Automatic Speech Recognition Systems”(《针对黑盒智能语音识别系统的零查询对抗攻击》),指导老师为武汉大学国家网络安全学院王骞教授和赵令辰副教授(通讯作者),与清华大学李琦副教授、西安交通大学沈超教授合作完成。国家网络安全学院2022级硕士生王涛、2022级博士生张神轶、2022级硕士生李博文和2021级博士生葛云洁参与了该成果的研究工作。展开更多
Bangladeshi textile and garment sectors defied the global challenges over the last five years and continued to grow although the growth momentum was not so strong before the COVID-19 pandemic.Before the start of the p...Bangladeshi textile and garment sectors defied the global challenges over the last five years and continued to grow although the growth momentum was not so strong before the COVID-19 pandemic.Before the start of the pandemic,both the textile and garment sectors were growing at a higher rate because of rising demand from the international clothing retailers and brands because of two main reasons including the trade war between USA and China and competitive prices of locally made garment.展开更多
近日,西安电子科技大学网络与信息安全学院李兴华教授团队的研究成果“PIC-BI:Practical and Intelligent Combinatorial Batch Identificationfor UAV Assisted Io T Networks”以及李金库教授团队的研究成果“Boosting Practical Cont...近日,西安电子科技大学网络与信息安全学院李兴华教授团队的研究成果“PIC-BI:Practical and Intelligent Combinatorial Batch Identificationfor UAV Assisted Io T Networks”以及李金库教授团队的研究成果“Boosting Practical Control-Flow Integrity with Complete Field Sensitivity and Origin Awareness”同时被ACM CCS2024国际学术会议全文收录,并将作大会报告。ACM CCS的全称是ACM Conference on Computer and Communications Security,已有30多年的历史,与IEEE S&P、USENIX Security、NDSS并列称为网络安全领域的四大国际学术会议,被中国计算机学会(CCF)列为A类会议。该会议收录的论文代表着相关领域的前沿学术研究成果,在业界具有广泛而深远的影响。展开更多
文摘The increasing data pool in finance sectors forces machine learning(ML)to step into new complications.Banking data has significant financial implications and is confidential.Combining users data from several organizations for various banking services may result in various intrusions and privacy leakages.As a result,this study employs federated learning(FL)using a flower paradigm to preserve each organization’s privacy while collaborating to build a robust shared global model.However,diverse data distributions in the collaborative training process might result in inadequate model learning and a lack of privacy.To address this issue,the present paper proposes the imple-mentation of Federated Averaging(FedAvg)and Federated Proximal(FedProx)methods in the flower framework,which take advantage of the data locality while training and guaranteeing global convergence.Resultantly improves the privacy of the local models.This analysis used the credit card and Canadian Institute for Cybersecurity Intrusion Detection Evaluation(CICIDS)datasets.Precision,recall,and accuracy as performance indicators to show the efficacy of the proposed strategy using FedAvg and FedProx.The experimental findings suggest that the proposed approach helps to safely use banking data from diverse sources to enhance customer banking services by obtaining accuracy of 99.55%and 83.72%for FedAvg and 99.57%,and 84.63%for FedProx.
基金part of the project“Research on the Investment Game and Market Improvement of Social Capital in Supporting Poverty Reduction and Development in Ethnic Regions in Western China” (16XMZ094)funded by the National Social Science Fund of China。
文摘This paper uses an SBM-GML index model to assess Green Total Factor Productivity(GTFP)in China's carbon-intensive sectors and conducts an empirical investigation into which factors influence GTFP in these sectors.The GTFP in the carbon-intensive sectors experienced a decline between 2006 and 2011,followed by an upward trend beginning in 2012.Technological progress was the primary driver of GTFP growth,while business size was also a notable contributor.Irrational energy structures negatively influenced the high-quality development of the carbon-intensive sectors,and environmental regulation and foreign direct investment(FDI)have not yet significantly impacted GTFP.Based on these findings,this paper suggests that the carbon-intensive sectors should expedite their green transitions by focusing on system improvement,technological innovations,energy revolutions,and high-level opening up.
文摘Bangladeshi textile and garment sectors defied the global challenges over the last five years and continued to grow although the growth momentum was not so strong before the COVID-19 pandemic.Before the start of the pandemic,both the textile and garment sectors were growing at a higher rate because of rising demand from the international clothing retailers and brands because of two main reasons including the trade war between USA and China and competitive prices of locally made garment.
文摘近日,西安电子科技大学网络与信息安全学院李兴华教授团队的研究成果“PIC-BI:Practical and Intelligent Combinatorial Batch Identificationfor UAV Assisted Io T Networks”以及李金库教授团队的研究成果“Boosting Practical Control-Flow Integrity with Complete Field Sensitivity and Origin Awareness”同时被ACM CCS2024国际学术会议全文收录,并将作大会报告。ACM CCS的全称是ACM Conference on Computer and Communications Security,已有30多年的历史,与IEEE S&P、USENIX Security、NDSS并列称为网络安全领域的四大国际学术会议,被中国计算机学会(CCF)列为A类会议。该会议收录的论文代表着相关领域的前沿学术研究成果,在业界具有广泛而深远的影响。
