Vulnerabilities are a known problem in modern Open Source Software(OSS).Most developers often rely on third-party libraries to accelerate feature implementation.However,these libraries may contain vulnerabilities that...Vulnerabilities are a known problem in modern Open Source Software(OSS).Most developers often rely on third-party libraries to accelerate feature implementation.However,these libraries may contain vulnerabilities that attackers can exploit to propagate malicious code,posing security risks to dependent projects.Existing research addresses these challenges through Software Composition Analysis(SCA)for vulnerability detection and remediation.Nevertheless,current solutions may introduce additional issues,such as incompatibilities,dependency conflicts,and additional vulnerabilities.To address this,we propose Vulnerability Scan and Protection(VulnScanPro),a robust solution for detection and remediation vulnerabilities in Java projects.Specifically,VulnScanPro builds a finegrained method graph to identify unreachable methods.The method graph is mapped to the project’s dependency tree,constructing a comprehensive vulnerability propagation graph that identifies unreachable vulnerable APIs and dependencies.Based on this analysis,we propose three solutions for vulnerability remediation:(1)Removing unreachable vulnerable dependencies,thereby resolving security risks and reducing maintenance overhead.(2)Upgrading vulnerable dependencies to the closest non-vulnerable versions,while pinning the versions of transitive dependencies introduced by the vulnerable dependency,in order to mitigate compatibility issues and prevent the introduction of new vulnerabilities.(3)Eliminating unreachable vulnerable APIs,particularly when security patches are either incompatible or absent.Experimental results show that these solutions effectively mitigate vulnerabilities and enhance the overall security of the project.展开更多
The next-generation RAN,known as Open Radio Access Network(ORAN),allows for several advantages,including cost-effectiveness,network flexibility,and interoperability.Now ORAN applications,utilising machine learning(ML)...The next-generation RAN,known as Open Radio Access Network(ORAN),allows for several advantages,including cost-effectiveness,network flexibility,and interoperability.Now ORAN applications,utilising machine learning(ML)and artificial intelligence(AI)techniques,have become standard practice.The need for Federated Learning(FL)for ML model training in ORAN environments is heightened by the modularised structure of the ORAN architecture and the shortcomings of conventional ML techniques.However,the traditional plaintext model update sharing of FL in multi-BS contexts is susceptible to privacy violations such as deep-leakage gradient assaults and inference.Therefore,this research presents a novel blockchain-assisted improved cryptographic privacy-preserving federated learning(BICPPFL)model,with the help of ORAN,to safely carry out federated learning and protect privacy.This model improves on the conventional masking technique for sharing model parameters by adding new characteristics.These features include the choice of distributed aggregators,validation for final model aggregation,and individual validation for BSs.To manage the security and privacy of FL processes,a combined homomorphic proxy-reencryption(HPReE)and lattice-cryptographic method(HPReEL)has been used.The upgraded delegated proof of stake(Up-DPoS)consensus protocol,which will provide quick validation of model exchanges and protect against malicious attacks,is employed for effective consensus across blockchain nodes.Without sacrificing performance metrics,the BICPPFL model strengthens privacy and adds security layers while facilitating the transfer of sensitive data across several BSs.The framework is deployed on top of a Hyperledger Fabric blockchain to evaluate its effectiveness.The experimental findings prove the reliability and privacy-preserving capability of the BICPPFL model.展开更多
For granular materials,discrete element modeling is one of the best computer tools to simulate their behavior and interactions.A field experiment was carried out to evaluate the performance of disc furrow openers in p...For granular materials,discrete element modeling is one of the best computer tools to simulate their behavior and interactions.A field experiment was carried out to evaluate the performance of disc furrow openers in paddy soil.Discrete element simulation was done to develop a 3D DEM model for notched,toothed and double disc furrow openers using EDEM software.Hertz Mindlin contact model with bonding was applied for simulation to fulfill the obligations of the soil moisture and bonding between the cohesive particles.Simulated and field experimental data were compared to determine the applicability in the different working conditions.The results of the simulation validated the applicability of the Hertz-Mindlin contact model with bonding to simulate the no till paddy soil using an extremely narrow tillage tool.The calibrated value of normal and shear stiffness was 5×10^(7)N/m,and the calibrated value of bond normal and shear strength was 3×10^(7)Pa.The relative error(-1.7%to 20.6%)for the double disc furrow opener was lower as compared with that notch typed(29.2%to 44.4%)and toothed type(31.5%to 45.9%)furrow openers.展开更多
To fully leverage the advantages of mechanization and informatization in tunnel boring machine(TBM)operations,the authors aim to promote the advancement of tunnel construction technology toward intelligent development...To fully leverage the advantages of mechanization and informatization in tunnel boring machine(TBM)operations,the authors aim to promote the advancement of tunnel construction technology toward intelligent development.This involved exploring the deep integration of next-generation artificial intelligence technologies,such as sensing technology,automatic control technology,big data technology,deep learning,and machine vision,with key operational processes,including TBM excavation,direction adjustment,step changes,inverted arch block assembly,material transportation,and operation status assurance.The results of this integration are summarized as follows.(1)TBM key excavation parameter prediction algorithm was developed with an accuracy rate exceeding 90%.The TBM intelligent step-change control algorithm,based on machine vision,achieved an image segmentation accuracy rate of 95%and gripper shoe positioning error of±5 mm.(2)An automatic positioning system for inverted arch blocks was developed,enabling real-time perception of the spatial position and deviation during the assembly process.The system maintains an elevation positioning deviation within±3 mm and a horizontal positioning deviation within±10 mm,reducing the number of surveyors in each work team.(3)A TBM intelligent rail transportation system that achieves real-time human-machine positioning,automatic switch opening and closing,automatic obstacle avoidance,intelligent transportation planning,and integrated scheduling and command was designed.Each locomotive formation reduces one shunter and improves comprehensive transportation efficiency by more than 20%.(4)Intelligent analysis and prediction algorithms were developed to monitor and predict the trends of the hydraulic and gear oil parameters in real time,enhancing the proactive maintenance and system reliability.展开更多
Purpose:For a set of 1,561 Open Access(OA)and non-OA journals in business and economics,this study evaluates the relationships between four citation metrics-five-year Impact Factor(5IF),CiteScore,Article Influence(AI)...Purpose:For a set of 1,561 Open Access(OA)and non-OA journals in business and economics,this study evaluates the relationships between four citation metrics-five-year Impact Factor(5IF),CiteScore,Article Influence(AI)score,and SCImago Journal Rank(SJR)-and the journal ratings assigned by expert reviewers.We expect that the OA journals will have especially high citation impact relative to their perceived quality(reputation).Design/methodology/approach:Regression is used to estimate the ratings assigned by expert reviewers for the 2021 CABS(Chartered Association of Business Schools)journal assessment exercise.The independent variables are the four citation metrics,evaluated separately,and a dummy variable representing the OA/non-OA status of each journal.Findings:Regardless of the citation metric used,OA journals in business and economics have especially high citation impact relative to their perceived quality(reputation).That is,they have especially low perceived quality(reputation)relative to their citation impact.Research limitations:These results are specific to the CABS journal ratings and the four citation metrics.However,there is strong evidence that CABS is closely related to several other expert ratings,and that 5IF,CiteScore,AI,and SJR are representative of the other citation metrics that might have been chosen.Practical implications:There are at least two possible explanations for these results:(1)expert evaluators are biased against OA journals,and(2)OA journals have especially high citation impact due to their increased accessibility.Although this study does not allow us to determine which of these explanations are supported,the results suggest that authors should consider publishing in OA journals whenever overall readership and citation impact are more important than journal reputation within a particular field.Moreover,the OA coefficients provide a useful indicator of the extent to which anti-OA bias(or the citation advantage of OA journals)is diminishing over time.Originality/value:This is apparently the first study to investigate the impact of OA status on the relationships between expert journal ratings and journal citation metrics.展开更多
本文根据企业公开信息,收集并整理了国内部分AI Agent应用相关企业。从Chatbots到AI Agents根据OpenAI的定义,通用人工智能(artificial general intelligence,AGI)的终极目标是创造一种能够像人类一样完成各种复杂任务,同时还能自然交...本文根据企业公开信息,收集并整理了国内部分AI Agent应用相关企业。从Chatbots到AI Agents根据OpenAI的定义,通用人工智能(artificial general intelligence,AGI)的终极目标是创造一种能够像人类一样完成各种复杂任务,同时还能自然交流的人工智能(AI)。OpenAI将AGI的发展分成了五个等级:第一级是“聊天机器人”(chatbots),具备语言对话能力的人工智能;第二级是“推理者”(reasoners),具备人类的推理水平,能解决多种复杂难题的人工智能;第三级是“代理人”(agents)。展开更多
Open caissons are widely used in foundation engineering because of their load-bearing efficiency and adaptability in diverse soil conditions.However,accurately predicting their undrained bearing capacity in layered so...Open caissons are widely used in foundation engineering because of their load-bearing efficiency and adaptability in diverse soil conditions.However,accurately predicting their undrained bearing capacity in layered soils remains a complex challenge.This study presents a novel application of five ensemble machine(ML)algorithms-random forest(RF),gradient boosting machine(GBM),extreme gradient boosting(XGBoost),adaptive boosting(AdaBoost),and categorical boosting(CatBoost)-to predict the undrained bearing capacity factor(Nc)of circular open caissons embedded in two-layered clay on the basis of results from finite element limit analysis(FELA).The input dataset consists of 1188 numerical simulations using the Tresca failure criterion,varying in geometrical and soil parameters.The FELA was performed via OptumG2 software with adaptive meshing techniques and verified against existing benchmark studies.The ML models were trained on 70% of the dataset and tested on the remaining 30%.Their performance was evaluated using six statistical metrics:coefficient of determination(R²),mean absolute error(MAE),root mean squared error(RMSE),index of scatter(IOS),RMSE-to-standard deviation ratio(RSR),and variance explained factor(VAF).The results indicate that all the models achieved high accuracy,with R²values exceeding 97.6%and RMSE values below 0.02.Among them,AdaBoost and CatBoost consistently outperformed the other methods across both the training and testing datasets,demonstrating superior generalizability and robustness.The proposed ML framework offers an efficient,accurate,and data-driven alternative to traditional methods for estimating caisson capacity in stratified soils.This approach can aid in reducing computational costs while improving reliability in the early stages of foundation design.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.62141210)the Fundamental Research Funds for the Central Universities(Grant No.N2217005)+1 种基金Open Fund of State Key Lab.for Novel Software Technology,Nanjing University(KFKT2021B01)111 Project(B16009).
文摘Vulnerabilities are a known problem in modern Open Source Software(OSS).Most developers often rely on third-party libraries to accelerate feature implementation.However,these libraries may contain vulnerabilities that attackers can exploit to propagate malicious code,posing security risks to dependent projects.Existing research addresses these challenges through Software Composition Analysis(SCA)for vulnerability detection and remediation.Nevertheless,current solutions may introduce additional issues,such as incompatibilities,dependency conflicts,and additional vulnerabilities.To address this,we propose Vulnerability Scan and Protection(VulnScanPro),a robust solution for detection and remediation vulnerabilities in Java projects.Specifically,VulnScanPro builds a finegrained method graph to identify unreachable methods.The method graph is mapped to the project’s dependency tree,constructing a comprehensive vulnerability propagation graph that identifies unreachable vulnerable APIs and dependencies.Based on this analysis,we propose three solutions for vulnerability remediation:(1)Removing unreachable vulnerable dependencies,thereby resolving security risks and reducing maintenance overhead.(2)Upgrading vulnerable dependencies to the closest non-vulnerable versions,while pinning the versions of transitive dependencies introduced by the vulnerable dependency,in order to mitigate compatibility issues and prevent the introduction of new vulnerabilities.(3)Eliminating unreachable vulnerable APIs,particularly when security patches are either incompatible or absent.Experimental results show that these solutions effectively mitigate vulnerabilities and enhance the overall security of the project.
文摘The next-generation RAN,known as Open Radio Access Network(ORAN),allows for several advantages,including cost-effectiveness,network flexibility,and interoperability.Now ORAN applications,utilising machine learning(ML)and artificial intelligence(AI)techniques,have become standard practice.The need for Federated Learning(FL)for ML model training in ORAN environments is heightened by the modularised structure of the ORAN architecture and the shortcomings of conventional ML techniques.However,the traditional plaintext model update sharing of FL in multi-BS contexts is susceptible to privacy violations such as deep-leakage gradient assaults and inference.Therefore,this research presents a novel blockchain-assisted improved cryptographic privacy-preserving federated learning(BICPPFL)model,with the help of ORAN,to safely carry out federated learning and protect privacy.This model improves on the conventional masking technique for sharing model parameters by adding new characteristics.These features include the choice of distributed aggregators,validation for final model aggregation,and individual validation for BSs.To manage the security and privacy of FL processes,a combined homomorphic proxy-reencryption(HPReE)and lattice-cryptographic method(HPReEL)has been used.The upgraded delegated proof of stake(Up-DPoS)consensus protocol,which will provide quick validation of model exchanges and protect against malicious attacks,is employed for effective consensus across blockchain nodes.Without sacrificing performance metrics,the BICPPFL model strengthens privacy and adds security layers while facilitating the transfer of sensitive data across several BSs.The framework is deployed on top of a Hyperledger Fabric blockchain to evaluate its effectiveness.The experimental findings prove the reliability and privacy-preserving capability of the BICPPFL model.
基金The authors are grateful for the support of the Key Research and Development Project(2016YFD0200708)the Key Research and Development Project(Modern Agriculture)of Jiangsu Province,China(BE2017354)National Science and Technology Support Program(2018BAD08B04).
文摘For granular materials,discrete element modeling is one of the best computer tools to simulate their behavior and interactions.A field experiment was carried out to evaluate the performance of disc furrow openers in paddy soil.Discrete element simulation was done to develop a 3D DEM model for notched,toothed and double disc furrow openers using EDEM software.Hertz Mindlin contact model with bonding was applied for simulation to fulfill the obligations of the soil moisture and bonding between the cohesive particles.Simulated and field experimental data were compared to determine the applicability in the different working conditions.The results of the simulation validated the applicability of the Hertz-Mindlin contact model with bonding to simulate the no till paddy soil using an extremely narrow tillage tool.The calibrated value of normal and shear stiffness was 5×10^(7)N/m,and the calibrated value of bond normal and shear strength was 3×10^(7)Pa.The relative error(-1.7%to 20.6%)for the double disc furrow opener was lower as compared with that notch typed(29.2%to 44.4%)and toothed type(31.5%to 45.9%)furrow openers.
文摘To fully leverage the advantages of mechanization and informatization in tunnel boring machine(TBM)operations,the authors aim to promote the advancement of tunnel construction technology toward intelligent development.This involved exploring the deep integration of next-generation artificial intelligence technologies,such as sensing technology,automatic control technology,big data technology,deep learning,and machine vision,with key operational processes,including TBM excavation,direction adjustment,step changes,inverted arch block assembly,material transportation,and operation status assurance.The results of this integration are summarized as follows.(1)TBM key excavation parameter prediction algorithm was developed with an accuracy rate exceeding 90%.The TBM intelligent step-change control algorithm,based on machine vision,achieved an image segmentation accuracy rate of 95%and gripper shoe positioning error of±5 mm.(2)An automatic positioning system for inverted arch blocks was developed,enabling real-time perception of the spatial position and deviation during the assembly process.The system maintains an elevation positioning deviation within±3 mm and a horizontal positioning deviation within±10 mm,reducing the number of surveyors in each work team.(3)A TBM intelligent rail transportation system that achieves real-time human-machine positioning,automatic switch opening and closing,automatic obstacle avoidance,intelligent transportation planning,and integrated scheduling and command was designed.Each locomotive formation reduces one shunter and improves comprehensive transportation efficiency by more than 20%.(4)Intelligent analysis and prediction algorithms were developed to monitor and predict the trends of the hydraulic and gear oil parameters in real time,enhancing the proactive maintenance and system reliability.
文摘Purpose:For a set of 1,561 Open Access(OA)and non-OA journals in business and economics,this study evaluates the relationships between four citation metrics-five-year Impact Factor(5IF),CiteScore,Article Influence(AI)score,and SCImago Journal Rank(SJR)-and the journal ratings assigned by expert reviewers.We expect that the OA journals will have especially high citation impact relative to their perceived quality(reputation).Design/methodology/approach:Regression is used to estimate the ratings assigned by expert reviewers for the 2021 CABS(Chartered Association of Business Schools)journal assessment exercise.The independent variables are the four citation metrics,evaluated separately,and a dummy variable representing the OA/non-OA status of each journal.Findings:Regardless of the citation metric used,OA journals in business and economics have especially high citation impact relative to their perceived quality(reputation).That is,they have especially low perceived quality(reputation)relative to their citation impact.Research limitations:These results are specific to the CABS journal ratings and the four citation metrics.However,there is strong evidence that CABS is closely related to several other expert ratings,and that 5IF,CiteScore,AI,and SJR are representative of the other citation metrics that might have been chosen.Practical implications:There are at least two possible explanations for these results:(1)expert evaluators are biased against OA journals,and(2)OA journals have especially high citation impact due to their increased accessibility.Although this study does not allow us to determine which of these explanations are supported,the results suggest that authors should consider publishing in OA journals whenever overall readership and citation impact are more important than journal reputation within a particular field.Moreover,the OA coefficients provide a useful indicator of the extent to which anti-OA bias(or the citation advantage of OA journals)is diminishing over time.Originality/value:This is apparently the first study to investigate the impact of OA status on the relationships between expert journal ratings and journal citation metrics.
文摘本文根据企业公开信息,收集并整理了国内部分AI Agent应用相关企业。从Chatbots到AI Agents根据OpenAI的定义,通用人工智能(artificial general intelligence,AGI)的终极目标是创造一种能够像人类一样完成各种复杂任务,同时还能自然交流的人工智能(AI)。OpenAI将AGI的发展分成了五个等级:第一级是“聊天机器人”(chatbots),具备语言对话能力的人工智能;第二级是“推理者”(reasoners),具备人类的推理水平,能解决多种复杂难题的人工智能;第三级是“代理人”(agents)。
文摘Open caissons are widely used in foundation engineering because of their load-bearing efficiency and adaptability in diverse soil conditions.However,accurately predicting their undrained bearing capacity in layered soils remains a complex challenge.This study presents a novel application of five ensemble machine(ML)algorithms-random forest(RF),gradient boosting machine(GBM),extreme gradient boosting(XGBoost),adaptive boosting(AdaBoost),and categorical boosting(CatBoost)-to predict the undrained bearing capacity factor(Nc)of circular open caissons embedded in two-layered clay on the basis of results from finite element limit analysis(FELA).The input dataset consists of 1188 numerical simulations using the Tresca failure criterion,varying in geometrical and soil parameters.The FELA was performed via OptumG2 software with adaptive meshing techniques and verified against existing benchmark studies.The ML models were trained on 70% of the dataset and tested on the remaining 30%.Their performance was evaluated using six statistical metrics:coefficient of determination(R²),mean absolute error(MAE),root mean squared error(RMSE),index of scatter(IOS),RMSE-to-standard deviation ratio(RSR),and variance explained factor(VAF).The results indicate that all the models achieved high accuracy,with R²values exceeding 97.6%and RMSE values below 0.02.Among them,AdaBoost and CatBoost consistently outperformed the other methods across both the training and testing datasets,demonstrating superior generalizability and robustness.The proposed ML framework offers an efficient,accurate,and data-driven alternative to traditional methods for estimating caisson capacity in stratified soils.This approach can aid in reducing computational costs while improving reliability in the early stages of foundation design.
