The language barrier is the biggest obstacle for users watching foreign-language videos.Because of this,videos cannot be famous across borders,and their viewership is limited to a single language and culture.The easie...The language barrier is the biggest obstacle for users watching foreign-language videos.Because of this,videos cannot be famous across borders,and their viewership is limited to a single language and culture.The easiest way to solve this problem is to add subtitles in the language of the viewer.However,the current subtitling system lacks incentives,the ability to build a secure transaction environment,and a trusting relationship between video creators and subtitling makers.In response to the above situation,a tokenized subtitling crowdsourcing system(TSCS)based on blockchain and smart contract technologies is proposed.The source files for the subtitles are stored on the inter-planetary file system(IPFS)in the proposed system.Based on the ERC-721 standard,the returned corresponding address and subtitling-related information are made into a non-fungible token(NFT).At the same time,depending on the expected revenue from video view counts,the video token(VT),based on the ERC-777 standard and endorsed by the video platform,will be used as the payment token.The TSCS has two payment strategies:one-time and dividend.Through such a settlement mechanism,the subtitling maker’s revenue is also guaranteed by the code invariance and rule certainty of smart contract deployment.On the other hand,introducing an incentive mechanism for viewers to audit subtitles enables community autonomy,thus increasing the applicability of subtitles and the activity of users.展开更多
Nonfungible tokens(NFTs)have become highly sought-after assets in recent years,exhibiting potential for profitability and hedging.The large and lucrative NFT market has attracted both practitioners and researchers to ...Nonfungible tokens(NFTs)have become highly sought-after assets in recent years,exhibiting potential for profitability and hedging.The large and lucrative NFT market has attracted both practitioners and researchers to develop NFT price-prediction models.However,the extant models have some weaknesses in terms of model comprehensiveness and operational convenience.To address these research gaps,we propose a multimodal end-to-end interpretable deep learning(MEID)framework for NFT investment.Our model integrates visual features,textual descriptions,transaction indicators,and historical price time series by leveraging the advantages of convolutional neural networks(CNNs),adopts integrated gradient(IG)to improve interpretability,and designs a built-in financial evaluation mechanism to generate not only the predicted price category but also the recommended purchase level.The experimental results demonstrate that the proposed MEID framework has excellent properties in terms of the evaluation metrics.The proposed MEID framework could help investors identify market opportunities and help NFT transaction platforms design smart investment tools and improve transaction volume.展开更多
Data trading is a crucial means of unlocking the value of Internet of Things(IoT)data.However,IoT data differs from traditional material goods due to its intangible and replicable nature.This difference leads to ambig...Data trading is a crucial means of unlocking the value of Internet of Things(IoT)data.However,IoT data differs from traditional material goods due to its intangible and replicable nature.This difference leads to ambiguous data rights,confusing pricing,and challenges in matching.Additionally,centralized IoT data trading platforms pose risks such as privacy leakage.To address these issues,we propose a profit-driven distributed trading mechanism for IoT data.First,a blockchain-based trading architecture for IoT data,leveraging the transparent and tamper-proof features of blockchain technology,is proposed to establish trust between data owners and data requesters.Second,an IoT data registration method that encompasses both rights confirmation and pricing is designed.The data right confirmation method uses non-fungible token to record ownership and authenticate IoT data.For pricing,we develop an IoT data value assessment index system and introduce a pricing model based on a combination of the sparrow search algorithm and the back propagation neural network.Finally,an IoT data matching method is designed based on the Stackelberg game.This establishes a Stackelberg game model involving multiple data owners and requesters,employing a hierarchical optimization method to determine the optimal purchase strategy.The security of the mechanism is analyzed and the performance of both the pricing method and matching method is evaluated.Experiments demonstrate that both methods outperform traditional approaches in terms of error rates and profit maximization.展开更多
Drone photography is an essential building block of intelligent transportation,enabling wide-ranging monitoring,precise positioning,and rapid transmission.However,the high computational cost of transformer-based metho...Drone photography is an essential building block of intelligent transportation,enabling wide-ranging monitoring,precise positioning,and rapid transmission.However,the high computational cost of transformer-based methods in object detection tasks hinders real-time result transmission in drone target detection applications.Therefore,we propose mask adaptive transformer (MAT) tailored for such scenarios.Specifically,we introduce a structure that supports collaborative token sparsification in support windows,enhancing fault tolerance and reducing computational overhead.This structure comprises two modules:a binary mask strategy and adaptive window self-attention (A-WSA).The binary mask strategy focuses on significant objects in various complex scenes.The A-WSA mechanism is employed to self-attend for balance perfomance and computational cost to select objects and isolate all contextual leakage.Extensive experiments on the challenging CarPK and VisDrone datasets demonstrate the effectiveness and superiority of the proposed method.Specifically,it achieves a mean average precision (mAP@0.5) improvement of 1.25%over car detector based on you only look once version 5 (CD-YOLOv5) on the CarPK dataset and a 3.75%average precision(AP@0.5) improvement over cascaded zoom-in detector (CZ Det) on the VisDrone dataset.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
Change detection(CD)plays a crucial role in numerous fields,where both convolutional neural networks(CNNs)and Transformers have demonstrated exceptional performance in CD tasks.However,CNNs suffer from limited recepti...Change detection(CD)plays a crucial role in numerous fields,where both convolutional neural networks(CNNs)and Transformers have demonstrated exceptional performance in CD tasks.However,CNNs suffer from limited receptive fields,hindering their ability to capture global features,while Transformers are constrained by high computational complexity.Recently,Mamba architecture,which is based on state space models(SSMs),has shown powerful global modeling capabilities while achieving linear computational complexity.Although some researchers have incorporated Mamba into CD tasks,the existing Mamba⁃based remote sensing CD methods struggle to effectively perceive the inherent locality of changed regions when flattening and scanning remote sensing images,leading to limitations in extracting change features.To address these issues,we propose a novel Mamba⁃based CD method termed difference feature fusion Mamba model(DFFMamba)by mitigating the loss of feature locality caused by traditional Mamba⁃style scanning.Specifically,two distinct difference feature extraction modules are designed:Difference Mamba(DMamba)and local difference Mamba(LDMamba),where DMamba extracts difference features by calculating the difference in coefficient matrices between the state⁃space equations of the bi⁃temporal features.Building upon DMamba,LDMamba combines a locally adaptive state⁃space scanning(LASS)strategy to enhance feature locality so as to accurately extract difference features.Additionally,a fusion Mamba(FMamba)module is proposed,which employs a spatial⁃channel token modeling SSM(SCTMS)unit to integrate multi⁃dimensional spatio⁃temporal interactions of change features,thereby capturing their dependencies across both spatial and channel dimensions.To verify the effectiveness of the proposed DFFMamba,extensive experiments are conducted on three datasets of WHU⁃CD,LEVIR⁃CD,and CLCD.The results demonstrate that DFFMamba significantly outperforms state⁃of⁃the⁃art CD methods,achieving intersection over union(IoU)scores of 90.67%,85.04%,and 66.56%on the three datasets,respectively.展开更多
文摘The language barrier is the biggest obstacle for users watching foreign-language videos.Because of this,videos cannot be famous across borders,and their viewership is limited to a single language and culture.The easiest way to solve this problem is to add subtitles in the language of the viewer.However,the current subtitling system lacks incentives,the ability to build a secure transaction environment,and a trusting relationship between video creators and subtitling makers.In response to the above situation,a tokenized subtitling crowdsourcing system(TSCS)based on blockchain and smart contract technologies is proposed.The source files for the subtitles are stored on the inter-planetary file system(IPFS)in the proposed system.Based on the ERC-721 standard,the returned corresponding address and subtitling-related information are made into a non-fungible token(NFT).At the same time,depending on the expected revenue from video view counts,the video token(VT),based on the ERC-777 standard and endorsed by the video platform,will be used as the payment token.The TSCS has two payment strategies:one-time and dividend.Through such a settlement mechanism,the subtitling maker’s revenue is also guaranteed by the code invariance and rule certainty of smart contract deployment.On the other hand,introducing an incentive mechanism for viewers to audit subtitles enables community autonomy,thus increasing the applicability of subtitles and the activity of users.
基金supported by the National Key Research and Development Program of China(Project No.2022YFC3320800)the National Natural Science Foundation of China(Project No.72571210).
文摘Nonfungible tokens(NFTs)have become highly sought-after assets in recent years,exhibiting potential for profitability and hedging.The large and lucrative NFT market has attracted both practitioners and researchers to develop NFT price-prediction models.However,the extant models have some weaknesses in terms of model comprehensiveness and operational convenience.To address these research gaps,we propose a multimodal end-to-end interpretable deep learning(MEID)framework for NFT investment.Our model integrates visual features,textual descriptions,transaction indicators,and historical price time series by leveraging the advantages of convolutional neural networks(CNNs),adopts integrated gradient(IG)to improve interpretability,and designs a built-in financial evaluation mechanism to generate not only the predicted price category but also the recommended purchase level.The experimental results demonstrate that the proposed MEID framework has excellent properties in terms of the evaluation metrics.The proposed MEID framework could help investors identify market opportunities and help NFT transaction platforms design smart investment tools and improve transaction volume.
基金supported by the National Key Research and Development Program of China(No.2022YFF0610003)the BUPT Excellent Ph.D.Students Foundation(No.CX2022218)the Fund of Central University Basic Research Projects(No.2023ZCTH11).
文摘Data trading is a crucial means of unlocking the value of Internet of Things(IoT)data.However,IoT data differs from traditional material goods due to its intangible and replicable nature.This difference leads to ambiguous data rights,confusing pricing,and challenges in matching.Additionally,centralized IoT data trading platforms pose risks such as privacy leakage.To address these issues,we propose a profit-driven distributed trading mechanism for IoT data.First,a blockchain-based trading architecture for IoT data,leveraging the transparent and tamper-proof features of blockchain technology,is proposed to establish trust between data owners and data requesters.Second,an IoT data registration method that encompasses both rights confirmation and pricing is designed.The data right confirmation method uses non-fungible token to record ownership and authenticate IoT data.For pricing,we develop an IoT data value assessment index system and introduce a pricing model based on a combination of the sparrow search algorithm and the back propagation neural network.Finally,an IoT data matching method is designed based on the Stackelberg game.This establishes a Stackelberg game model involving multiple data owners and requesters,employing a hierarchical optimization method to determine the optimal purchase strategy.The security of the mechanism is analyzed and the performance of both the pricing method and matching method is evaluated.Experiments demonstrate that both methods outperform traditional approaches in terms of error rates and profit maximization.
文摘Drone photography is an essential building block of intelligent transportation,enabling wide-ranging monitoring,precise positioning,and rapid transmission.However,the high computational cost of transformer-based methods in object detection tasks hinders real-time result transmission in drone target detection applications.Therefore,we propose mask adaptive transformer (MAT) tailored for such scenarios.Specifically,we introduce a structure that supports collaborative token sparsification in support windows,enhancing fault tolerance and reducing computational overhead.This structure comprises two modules:a binary mask strategy and adaptive window self-attention (A-WSA).The binary mask strategy focuses on significant objects in various complex scenes.The A-WSA mechanism is employed to self-attend for balance perfomance and computational cost to select objects and isolate all contextual leakage.Extensive experiments on the challenging CarPK and VisDrone datasets demonstrate the effectiveness and superiority of the proposed method.Specifically,it achieves a mean average precision (mAP@0.5) improvement of 1.25%over car detector based on you only look once version 5 (CD-YOLOv5) on the CarPK dataset and a 3.75%average precision(AP@0.5) improvement over cascaded zoom-in detector (CZ Det) on the VisDrone dataset.
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.
基金supported by the National Natural Science Foundation of China(Nos.42371449,41801386).
文摘Change detection(CD)plays a crucial role in numerous fields,where both convolutional neural networks(CNNs)and Transformers have demonstrated exceptional performance in CD tasks.However,CNNs suffer from limited receptive fields,hindering their ability to capture global features,while Transformers are constrained by high computational complexity.Recently,Mamba architecture,which is based on state space models(SSMs),has shown powerful global modeling capabilities while achieving linear computational complexity.Although some researchers have incorporated Mamba into CD tasks,the existing Mamba⁃based remote sensing CD methods struggle to effectively perceive the inherent locality of changed regions when flattening and scanning remote sensing images,leading to limitations in extracting change features.To address these issues,we propose a novel Mamba⁃based CD method termed difference feature fusion Mamba model(DFFMamba)by mitigating the loss of feature locality caused by traditional Mamba⁃style scanning.Specifically,two distinct difference feature extraction modules are designed:Difference Mamba(DMamba)and local difference Mamba(LDMamba),where DMamba extracts difference features by calculating the difference in coefficient matrices between the state⁃space equations of the bi⁃temporal features.Building upon DMamba,LDMamba combines a locally adaptive state⁃space scanning(LASS)strategy to enhance feature locality so as to accurately extract difference features.Additionally,a fusion Mamba(FMamba)module is proposed,which employs a spatial⁃channel token modeling SSM(SCTMS)unit to integrate multi⁃dimensional spatio⁃temporal interactions of change features,thereby capturing their dependencies across both spatial and channel dimensions.To verify the effectiveness of the proposed DFFMamba,extensive experiments are conducted on three datasets of WHU⁃CD,LEVIR⁃CD,and CLCD.The results demonstrate that DFFMamba significantly outperforms state⁃of⁃the⁃art CD methods,achieving intersection over union(IoU)scores of 90.67%,85.04%,and 66.56%on the three datasets,respectively.
