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
The exponential growth of Internet of Things(IoT)devices has introduced significant security challenges,particularly in securing token-based communication protocols used for authentication and authorization.This surve...The exponential growth of Internet of Things(IoT)devices has introduced significant security challenges,particularly in securing token-based communication protocols used for authentication and authorization.This survey systematically reviews the vulnerabilities in token transmission within IoT environments,focusing on various sophisticated attack vectors such as replay attacks,token hijacking,man-in-the-middle(MITM)attacks,token injection,and eavesdropping among others.These attacks exploit the inherent weaknesses of token-based mechanisms like OAuth,JSON Web Tokens(JWT),and bearer tokens,which are widely used in IoT ecosystems for managing device interactions and access control.The impact of such attacks is profound,leading to unauthorized access,data exfiltration,and control over IoT devices,posing significant threats to privacy,safety,and the operational integrity of critical IoT applications in sectors like healthcare,smart cities,and industrial automation.This paper categorizes these attack vectors,explores real-world case studies,and analyzes their effects on resource-constrained IoT devices that have limited processing power and memory,rendering them more susceptible to such exploits.Furthermore,this survey presents a comprehensive evaluation of existing mitigation techniques,including cryptographic protocols,lightweight secure transmission frameworks,secure token management practices,and network-layer defenses such as Transport Layer Security(TLS)and multi-factor authentication(MFA).The study also highlights the trade-offs between security and performance in IoT systems and identifies key gaps in current research,emphasizing the need for more scalable,energy-efficient,and robust security frameworks to address the evolving landscape of token transmission attacks in IoT devices.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The exponential growth of Internet of Things(IoT)devices has introduced significant security challenges,particularly in securing token-based communication protocols used for authentication and authorization.This survey systematically reviews the vulnerabilities in token transmission within IoT environments,focusing on various sophisticated attack vectors such as replay attacks,token hijacking,man-in-the-middle(MITM)attacks,token injection,and eavesdropping among others.These attacks exploit the inherent weaknesses of token-based mechanisms like OAuth,JSON Web Tokens(JWT),and bearer tokens,which are widely used in IoT ecosystems for managing device interactions and access control.The impact of such attacks is profound,leading to unauthorized access,data exfiltration,and control over IoT devices,posing significant threats to privacy,safety,and the operational integrity of critical IoT applications in sectors like healthcare,smart cities,and industrial automation.This paper categorizes these attack vectors,explores real-world case studies,and analyzes their effects on resource-constrained IoT devices that have limited processing power and memory,rendering them more susceptible to such exploits.Furthermore,this survey presents a comprehensive evaluation of existing mitigation techniques,including cryptographic protocols,lightweight secure transmission frameworks,secure token management practices,and network-layer defenses such as Transport Layer Security(TLS)and multi-factor authentication(MFA).The study also highlights the trade-offs between security and performance in IoT systems and identifies key gaps in current research,emphasizing the need for more scalable,energy-efficient,and robust security frameworks to address the evolving landscape of token transmission attacks in IoT devices.
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.
