Software vulnerabilities pose significant risks to computer systems,impacting our daily lives,productivity,and even our health.Identifying and addressing security vulnerabilities in a timely manner is crucial to preve...Software vulnerabilities pose significant risks to computer systems,impacting our daily lives,productivity,and even our health.Identifying and addressing security vulnerabilities in a timely manner is crucial to prevent hacking and data breaches.Unfortunately,current vulnerability identification methods,including classical and deep learning-based approaches,exhibit critical drawbacks that prevent them from meeting the demands of the contemporary software industry.To tackle these issues,we present JFinder,a novel architecture for Java vulnerability identification that leverages quad self-attention and pre-training mechanisms to combine structural information and semantic representations.Experimental results demonstrate that JFinder outperforms all baseline methods,achieving an accuracy of 0.97 on the CWE dataset and an F1 score of 0.84 on the PROMISE dataset.Furthermore,a case study reveals that JFinder can accurately identify four cases of vulnerabilities after patching.展开更多
Resilient enhancement measures are crucial for increasing systems’capacities to deal with extreme natural disasters.However,in the pre-disaster prevention stage of hurricanes,research that simultaneously consid-ers l...Resilient enhancement measures are crucial for increasing systems’capacities to deal with extreme natural disasters.However,in the pre-disaster prevention stage of hurricanes,research that simultaneously consid-ers load importance,vulnerable lines,and multiple resil-ience enhancement measures is lacking.To address this issue,a novel resilience-oriented transmission expansion planning(ROTEP)model is proposed that incorporates two resilience assessment indices:the combined loss of loads(CLL)and the vulnerable line survival proportion(VLSP).In addition,the novel function of the proposed model meets the requirements of normal and hurricane damage scenarios based on the collaborative implemen-tation of three resilience enhancement measures(expan-sion planning,hardening,and unit commitment).The proposed ROTEP model is structured in two stages.The first-stage model aims to meet the load growth demand while minimizing the total planning cost of transmission lines,the operating cost of generators,and the penalty cost of wind power and load shedding across several normal scenarios.Based on the scheme obtained from the first-stage model,damage scenarios are constructed,and a fault chain set is formulated using a hurricane simula-tion model.Then,a cascading fault graph is constructed to identify vulnerable lines.The second-stage model fur-ther enhances the CLL and VLSP(if necessary)under several damage scenarios by hardening the high-est-contributing or most vulnerable line.Finally,the ef-ficacy of the proposed ROTEP model for enhancing re-silience is validated with a modified IEEE RTS-24 system and a two-area IEEE RTS-1996 system.展开更多
Identifying ecologically vulnerable areas is critical for constructing ecological barriers and precisely controlling ecological risks.With the rapid development of big data and Artificial Intelligence(AI)technologies,...Identifying ecologically vulnerable areas is critical for constructing ecological barriers and precisely controlling ecological risks.With the rapid development of big data and Artificial Intelligence(AI)technologies,many intelligent methods have been developed to support the identification of vulnerable ecological areas.This paper reviews the methodological advancements in identifying ecologically vulnerable areas,including geographic zoning,expert integration,mathematical statistics,geographic information visualization,artificial neural networks,and unsupervised deep learning clustering methods.Additionally,we assessed several classic software tools used in ecology and natural resource management.Based on the review,several urgent research challenges for ecological function zoning research are proposed,such as the application of ecological vulnerability assessment intelligent algorithms,big data collaborative analysis,and the development of automated identification software.Considering the requirements in the Mongolian Plateau,this study proposes future development prospects of methods for identifying ecologically vulnerable area zoning,combined with the new AI research paradigm.They include enhancing the comprehensive analysis of multimodal data,increasing ecological barrier big data collaborative processing,advancing the interpretability of ecological function partitioning algorithms,developing automatic zoning software tools,and pushing the collaborative analysis of geographic big data and citizen science data.展开更多
Oral squamous cell carcinoma(OSCC),a major subgroup of head and neck squamous cell carcinoma(HNSCC),is an aggressive disease that preferentially spreads to cervical lymph nodes.Positive lymph node status is an importa...Oral squamous cell carcinoma(OSCC),a major subgroup of head and neck squamous cell carcinoma(HNSCC),is an aggressive disease that preferentially spreads to cervical lymph nodes.Positive lymph node status is an important predictor of survival in OSCC[1-3].Hence,a better understanding of the molecular mechanisms underlying oral cancer metastasis and the identification of therapeutic vulnerabilities are needed to prevent and treat metastatic disease.展开更多
In order to reduce the occurrence or expansion of accidents and maintain safety in distribution networks,it is essential to find out the vulnerable points for the power system in time.In this paper,a vulnerable point ...In order to reduce the occurrence or expansion of accidents and maintain safety in distribution networks,it is essential to find out the vulnerable points for the power system in time.In this paper,a vulnerable point identification method based on heterogeneous interdependent(HI)node theory and risk theory is proposed.Compared with the methods based on betweenness theory,the method based on HI nodes theory can deal with the shortcomings of the power flow shortest path,and consider the direct and indirect relationship of nodes.It is more suitable for identifying vulnerable points in a realistic power system.First,according to the analysis of heterogenous interdependent networks,the HI nodes are defined and used to evaluate the utility coupling value of each node.Then an identification indicator,which combines the utility coupling value and the risk indicators,is utilized to evaluate the vulnerability of each node.Results show that the proposed method is a suitable one to find the vulnerable points and better than betweennessbased methods for a distribution network.展开更多
基金supported by the National Key R&D Program of China(2019YFB2102600)the National Natural Science Foundation of China(62002067)+1 种基金the Guangzhou Youth Talent of Science(QT20220101174)the Project of Philosophy and Social Science Planning of GuangDong(GD21YGL16).
文摘Software vulnerabilities pose significant risks to computer systems,impacting our daily lives,productivity,and even our health.Identifying and addressing security vulnerabilities in a timely manner is crucial to prevent hacking and data breaches.Unfortunately,current vulnerability identification methods,including classical and deep learning-based approaches,exhibit critical drawbacks that prevent them from meeting the demands of the contemporary software industry.To tackle these issues,we present JFinder,a novel architecture for Java vulnerability identification that leverages quad self-attention and pre-training mechanisms to combine structural information and semantic representations.Experimental results demonstrate that JFinder outperforms all baseline methods,achieving an accuracy of 0.97 on the CWE dataset and an F1 score of 0.84 on the PROMISE dataset.Furthermore,a case study reveals that JFinder can accurately identify four cases of vulnerabilities after patching.
基金supported by the National Natural Sci-ence Foundation of China(No.U23B6006and No.52307120).
文摘Resilient enhancement measures are crucial for increasing systems’capacities to deal with extreme natural disasters.However,in the pre-disaster prevention stage of hurricanes,research that simultaneously consid-ers load importance,vulnerable lines,and multiple resil-ience enhancement measures is lacking.To address this issue,a novel resilience-oriented transmission expansion planning(ROTEP)model is proposed that incorporates two resilience assessment indices:the combined loss of loads(CLL)and the vulnerable line survival proportion(VLSP).In addition,the novel function of the proposed model meets the requirements of normal and hurricane damage scenarios based on the collaborative implemen-tation of three resilience enhancement measures(expan-sion planning,hardening,and unit commitment).The proposed ROTEP model is structured in two stages.The first-stage model aims to meet the load growth demand while minimizing the total planning cost of transmission lines,the operating cost of generators,and the penalty cost of wind power and load shedding across several normal scenarios.Based on the scheme obtained from the first-stage model,damage scenarios are constructed,and a fault chain set is formulated using a hurricane simula-tion model.Then,a cascading fault graph is constructed to identify vulnerable lines.The second-stage model fur-ther enhances the CLL and VLSP(if necessary)under several damage scenarios by hardening the high-est-contributing or most vulnerable line.Finally,the ef-ficacy of the proposed ROTEP model for enhancing re-silience is validated with a modified IEEE RTS-24 system and a two-area IEEE RTS-1996 system.
基金The National Key Research and Development Program(2022YFE0119200)The Key Research and Development and Achievement Transformation Plan Project of Inner Mongolia Autonomous Region(2023KJHZ0027)+1 种基金The Key Project of Innovation LREIS(KPI006)The Construction Project of China Knowledge Center for Engineering Sciences and Technology(CKCEST-2023-1-5)。
文摘Identifying ecologically vulnerable areas is critical for constructing ecological barriers and precisely controlling ecological risks.With the rapid development of big data and Artificial Intelligence(AI)technologies,many intelligent methods have been developed to support the identification of vulnerable ecological areas.This paper reviews the methodological advancements in identifying ecologically vulnerable areas,including geographic zoning,expert integration,mathematical statistics,geographic information visualization,artificial neural networks,and unsupervised deep learning clustering methods.Additionally,we assessed several classic software tools used in ecology and natural resource management.Based on the review,several urgent research challenges for ecological function zoning research are proposed,such as the application of ecological vulnerability assessment intelligent algorithms,big data collaborative analysis,and the development of automated identification software.Considering the requirements in the Mongolian Plateau,this study proposes future development prospects of methods for identifying ecologically vulnerable area zoning,combined with the new AI research paradigm.They include enhancing the comprehensive analysis of multimodal data,increasing ecological barrier big data collaborative processing,advancing the interpretability of ecological function partitioning algorithms,developing automatic zoning software tools,and pushing the collaborative analysis of geographic big data and citizen science data.
基金supported by intramural funding from the Medical Faculty(Wilhelm-Roux program,FKZ32/19).
文摘Oral squamous cell carcinoma(OSCC),a major subgroup of head and neck squamous cell carcinoma(HNSCC),is an aggressive disease that preferentially spreads to cervical lymph nodes.Positive lymph node status is an important predictor of survival in OSCC[1-3].Hence,a better understanding of the molecular mechanisms underlying oral cancer metastasis and the identification of therapeutic vulnerabilities are needed to prevent and treat metastatic disease.
基金This work was supported in part by the Science and Technology Project of SGCC“Research on Key Technology of High Reliability Distribution Network in Xiongan New Area”(PDB17201800056)。
文摘In order to reduce the occurrence or expansion of accidents and maintain safety in distribution networks,it is essential to find out the vulnerable points for the power system in time.In this paper,a vulnerable point identification method based on heterogeneous interdependent(HI)node theory and risk theory is proposed.Compared with the methods based on betweenness theory,the method based on HI nodes theory can deal with the shortcomings of the power flow shortest path,and consider the direct and indirect relationship of nodes.It is more suitable for identifying vulnerable points in a realistic power system.First,according to the analysis of heterogenous interdependent networks,the HI nodes are defined and used to evaluate the utility coupling value of each node.Then an identification indicator,which combines the utility coupling value and the risk indicators,is utilized to evaluate the vulnerability of each node.Results show that the proposed method is a suitable one to find the vulnerable points and better than betweennessbased methods for a distribution network.
