Legal case classification involves the categorization of legal documents into predefined categories,which facilitates legal information retrieval and case management.However,real-world legal datasets often suffer from...Legal case classification involves the categorization of legal documents into predefined categories,which facilitates legal information retrieval and case management.However,real-world legal datasets often suffer from class imbalances due to the uneven distribution of case types across legal domains.This leads to biased model performance,in the form of high accuracy for overrepresented categories and underperformance for minority classes.To address this issue,in this study,we propose a data augmentation method that masks unimportant terms within a document selectively while preserving key terms fromthe perspective of the legal domain.This approach enhances data diversity and improves the generalization capability of conventional models.Our experiments demonstrate consistent improvements achieved by the proposed augmentation strategy in terms of accuracy and F1 score across all models,validating the effectiveness of the proposed method in legal case classification.展开更多
针对地图综合中建筑多边形化简方法依赖人工规则、自动化程度低且难以利用已有化简成果的问题,本文提出了一种基于Transformer机制的建筑多边形化简模型。该模型首先把建筑多边形映射至一定范围的网格空间,将建筑多边形的坐标串表达为...针对地图综合中建筑多边形化简方法依赖人工规则、自动化程度低且难以利用已有化简成果的问题,本文提出了一种基于Transformer机制的建筑多边形化简模型。该模型首先把建筑多边形映射至一定范围的网格空间,将建筑多边形的坐标串表达为网格序列,从而获取建筑多边形化简前后的Token序列,构建出建筑多边形化简样本对数据;随后采用Transformer架构建立模型,基于样本数据利用模型的掩码自注意力机制学习点序列之间的依赖关系,最终逐点生成新的简化多边形,从而实现建筑多边形的化简。在训练过程中,模型使用结构化的样本数据,设计了忽略特定索引的交叉熵损失函数以提升化简质量。试验设计包括主试验与泛化验证两部分。主试验基于洛杉矶1∶2000建筑数据集,分别采用0.2、0.3和0.5 mm 3种网格尺寸对多边形进行编码,实现了目标比例尺为1∶5000与1∶10000的化简。试验结果表明,在0.3 mm的网格尺寸下模型性能最优,验证集上的化简结果与人工标注的一致率超过92.0%,且针对北京部分区域的建筑多边形数据的泛化试验验证了模型的迁移能力;与LSTM模型的对比分析显示,在参数规模相近的条件下,LSTM模型无法形成有效收敛,并生成可用结果。本文证实了Transformer在处理空间几何序列任务中的潜力,且能够有效复用已有化简样本,为智能建筑多边形化简提供了具有工程实用价值的途径。展开更多
In the metaverse,digital assets are essential to define identity,shape the virtual environment,and facilitate economic transactions.This study introduces a novel feature to the metaverse by capturing a fundamental asp...In the metaverse,digital assets are essential to define identity,shape the virtual environment,and facilitate economic transactions.This study introduces a novel feature to the metaverse by capturing a fundamental aspect of individuals–their conversations–and transforming them into digital assets.It utilizes natural language processing and machine learning methods to extract key sentences from user conversations and match them with emojis that reflect their sentiments.The selected sentence,which encapsulates the essence of the user’s statements,is then transformed into digital art through a generative visual model.This digital artwork is transformed into a non-fungible token,becoming a valuable digital asset within the blockchain ecosystem that is ideal for integration into metaverse applications.Our aim is to manage personality traits as digital assets to foster individual uniqueness,enrich user experiences,and facilitate more personalized services and interactions with both like-minded users and non-player characters,thereby enhancing the overall user journey.展开更多
The asymmetries of factors influencing the return of cryptocurrencies have already been well documented;however,in the case of NFTs,only information asymmetries and hedging properties related to asymmetries were studi...The asymmetries of factors influencing the return of cryptocurrencies have already been well documented;however,in the case of NFTs,only information asymmetries and hedging properties related to asymmetries were studied.Therefore,the present study examines factors affecting NFT returns,from market-related factors(cryptomarket index return and stock market index return)to the Amihud illiquidity ratio and Google search trends during different market conditions.The wavelet coherences-based methodology was applied separately during the boom,bust,normal,and turbulent periods identified by structural breakpoints.Based on 14 NFT projects between April 2019 and July 2022,results show two fundamental asymmetries influencing these NFT returns.First,there is an asymmetry in the behavior of the factors in different periods;second,there is an asymmetry in how illiquidity manifests itself over NFTs that do or do not possess cash flow-generating potential.展开更多
基金supported by the Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)[RS-2021-II211341,Artificial Intelligence Graduate School Program(Chung-Ang University)],and by the Chung-Ang University Graduate Research Scholarship in 2024.
文摘Legal case classification involves the categorization of legal documents into predefined categories,which facilitates legal information retrieval and case management.However,real-world legal datasets often suffer from class imbalances due to the uneven distribution of case types across legal domains.This leads to biased model performance,in the form of high accuracy for overrepresented categories and underperformance for minority classes.To address this issue,in this study,we propose a data augmentation method that masks unimportant terms within a document selectively while preserving key terms fromthe perspective of the legal domain.This approach enhances data diversity and improves the generalization capability of conventional models.Our experiments demonstrate consistent improvements achieved by the proposed augmentation strategy in terms of accuracy and F1 score across all models,validating the effectiveness of the proposed method in legal case classification.
文摘针对地图综合中建筑多边形化简方法依赖人工规则、自动化程度低且难以利用已有化简成果的问题,本文提出了一种基于Transformer机制的建筑多边形化简模型。该模型首先把建筑多边形映射至一定范围的网格空间,将建筑多边形的坐标串表达为网格序列,从而获取建筑多边形化简前后的Token序列,构建出建筑多边形化简样本对数据;随后采用Transformer架构建立模型,基于样本数据利用模型的掩码自注意力机制学习点序列之间的依赖关系,最终逐点生成新的简化多边形,从而实现建筑多边形的化简。在训练过程中,模型使用结构化的样本数据,设计了忽略特定索引的交叉熵损失函数以提升化简质量。试验设计包括主试验与泛化验证两部分。主试验基于洛杉矶1∶2000建筑数据集,分别采用0.2、0.3和0.5 mm 3种网格尺寸对多边形进行编码,实现了目标比例尺为1∶5000与1∶10000的化简。试验结果表明,在0.3 mm的网格尺寸下模型性能最优,验证集上的化简结果与人工标注的一致率超过92.0%,且针对北京部分区域的建筑多边形数据的泛化试验验证了模型的迁移能力;与LSTM模型的对比分析显示,在参数规模相近的条件下,LSTM模型无法形成有效收敛,并生成可用结果。本文证实了Transformer在处理空间几何序列任务中的潜力,且能够有效复用已有化简样本,为智能建筑多边形化简提供了具有工程实用价值的途径。
文摘In the metaverse,digital assets are essential to define identity,shape the virtual environment,and facilitate economic transactions.This study introduces a novel feature to the metaverse by capturing a fundamental aspect of individuals–their conversations–and transforming them into digital assets.It utilizes natural language processing and machine learning methods to extract key sentences from user conversations and match them with emojis that reflect their sentiments.The selected sentence,which encapsulates the essence of the user’s statements,is then transformed into digital art through a generative visual model.This digital artwork is transformed into a non-fungible token,becoming a valuable digital asset within the blockchain ecosystem that is ideal for integration into metaverse applications.Our aim is to manage personality traits as digital assets to foster individual uniqueness,enrich user experiences,and facilitate more personalized services and interactions with both like-minded users and non-player characters,thereby enhancing the overall user journey.
文摘The asymmetries of factors influencing the return of cryptocurrencies have already been well documented;however,in the case of NFTs,only information asymmetries and hedging properties related to asymmetries were studied.Therefore,the present study examines factors affecting NFT returns,from market-related factors(cryptomarket index return and stock market index return)to the Amihud illiquidity ratio and Google search trends during different market conditions.The wavelet coherences-based methodology was applied separately during the boom,bust,normal,and turbulent periods identified by structural breakpoints.Based on 14 NFT projects between April 2019 and July 2022,results show two fundamental asymmetries influencing these NFT returns.First,there is an asymmetry in the behavior of the factors in different periods;second,there is an asymmetry in how illiquidity manifests itself over NFTs that do or do not possess cash flow-generating potential.
