Software projects are becoming larger and more complicated. Managing those projects is based on several software development methodologies. One of those methodologies is software version control, which is used in the ...Software projects are becoming larger and more complicated. Managing those projects is based on several software development methodologies. One of those methodologies is software version control, which is used in the majority of worldwide software projects. Although existing version control systems provide sufficient functionality in many situations, they are lacking in terms of semantics and structure for source code. It is commonly believed that improving software version control can contribute substantially to the development of software. We present a solution that considers a structural model for matching source code that can be used in version control.展开更多
In the context of modern software development characterized by increasing complexity and compressed development cycles,traditional static vulnerability detection methods face prominent challenges including high false ...In the context of modern software development characterized by increasing complexity and compressed development cycles,traditional static vulnerability detection methods face prominent challenges including high false positive rates and missed detections of complex logic due to their over-reliance on rule templates.This paper proposes a Syntax-Aware Hierarchical Attention Network(SAHAN)model,which achieves high-precision vulnerability detection through grammar-rule-driven multi-granularity code slicing and hierarchical semantic fusion mechanisms.The SAHAN model first generates Syntax Independent Units(SIUs),which slices the code based on Abstract Syntax Tree(AST)and predefined grammar rules,retaining vulnerability-sensitive contexts.Following this,through a hierarchical attention mechanism,the local syntax-aware layer encodes fine-grained patterns within SIUs,while the global semantic correlation layer captures vulnerability chains across SIUs,achieving synergistic modeling of syntax and semantics.Experiments show that on benchmark datasets like QEMU,SAHAN significantly improves detection performance by 4.8%to 13.1%on average compared to baseline models such as Devign and VulDeePecker.展开更多
在食品感官研究领域,消费者品评文本承载了丰富的感官评价信息,分析这些文本有助于更好分析食品感官、挖掘消费者偏好和体验。目前人工分析通常需要花费大量时间和精力,同时分析人员的主观倾向也影响最终感官分析结果。为了解决此类问题...在食品感官研究领域,消费者品评文本承载了丰富的感官评价信息,分析这些文本有助于更好分析食品感官、挖掘消费者偏好和体验。目前人工分析通常需要花费大量时间和精力,同时分析人员的主观倾向也影响最终感官分析结果。为了解决此类问题,基于方面级意见提取提出一种细粒度感官分析模型FGSAM-OI(Fine-grained sensory analysis model with opinion intensity)。该模型旨在基于深度学习有效提取品评文本中针对食品某方面的感官词及相应感官强度,以准确获取消费者对食品的感官体验。首先,在FGSAM-OI中设计了一种强度注意力机制,以增强对输入序列中感官强度词的表示能力。其次,为了进一步将强度词关联到相应感官词,设计了一种强度句法树学习品评文本中的句法关系,以更准确获取感官词与强度词间的联系,进而从整体上提升对食品各个方面的感官分析效果。实验结果表明,增加强度注意力机制和强度句法树分别使感官词和感官强度的提取精确率提高3.73、5.1个百分点,有效提升了对食品品评文本的细粒度感官分析能力。展开更多
文摘Software projects are becoming larger and more complicated. Managing those projects is based on several software development methodologies. One of those methodologies is software version control, which is used in the majority of worldwide software projects. Although existing version control systems provide sufficient functionality in many situations, they are lacking in terms of semantics and structure for source code. It is commonly believed that improving software version control can contribute substantially to the development of software. We present a solution that considers a structural model for matching source code that can be used in version control.
基金supported by the research start-up funds for invited doctor of Lanzhou University of Technology under Grant 14/062402。
文摘In the context of modern software development characterized by increasing complexity and compressed development cycles,traditional static vulnerability detection methods face prominent challenges including high false positive rates and missed detections of complex logic due to their over-reliance on rule templates.This paper proposes a Syntax-Aware Hierarchical Attention Network(SAHAN)model,which achieves high-precision vulnerability detection through grammar-rule-driven multi-granularity code slicing and hierarchical semantic fusion mechanisms.The SAHAN model first generates Syntax Independent Units(SIUs),which slices the code based on Abstract Syntax Tree(AST)and predefined grammar rules,retaining vulnerability-sensitive contexts.Following this,through a hierarchical attention mechanism,the local syntax-aware layer encodes fine-grained patterns within SIUs,while the global semantic correlation layer captures vulnerability chains across SIUs,achieving synergistic modeling of syntax and semantics.Experiments show that on benchmark datasets like QEMU,SAHAN significantly improves detection performance by 4.8%to 13.1%on average compared to baseline models such as Devign and VulDeePecker.
文摘在食品感官研究领域,消费者品评文本承载了丰富的感官评价信息,分析这些文本有助于更好分析食品感官、挖掘消费者偏好和体验。目前人工分析通常需要花费大量时间和精力,同时分析人员的主观倾向也影响最终感官分析结果。为了解决此类问题,基于方面级意见提取提出一种细粒度感官分析模型FGSAM-OI(Fine-grained sensory analysis model with opinion intensity)。该模型旨在基于深度学习有效提取品评文本中针对食品某方面的感官词及相应感官强度,以准确获取消费者对食品的感官体验。首先,在FGSAM-OI中设计了一种强度注意力机制,以增强对输入序列中感官强度词的表示能力。其次,为了进一步将强度词关联到相应感官词,设计了一种强度句法树学习品评文本中的句法关系,以更准确获取感官词与强度词间的联系,进而从整体上提升对食品各个方面的感官分析效果。实验结果表明,增加强度注意力机制和强度句法树分别使感官词和感官强度的提取精确率提高3.73、5.1个百分点,有效提升了对食品品评文本的细粒度感官分析能力。
