Languages–independent text tokenization can aid in classification of languages with few sources.There is a global research effort to generate text classification for any language.Human text classification is a slow p...Languages–independent text tokenization can aid in classification of languages with few sources.There is a global research effort to generate text classification for any language.Human text classification is a slow procedure.Conse-quently,the text summary generation of different languages,using machine text classification,has been considered in recent years.There is no research on the machine text classification for many languages such as Czech,Rome,Urdu.This research proposes a cross-language text tokenization model using a Transformer technique.The proposed Transformer employs an encoder that has ten layers with self-attention encoding and a feedforward sublayer.This model improves the efficiency of text classification by providing a draft text classification for a number of documents.We also propose a novel Sub-Word tokenization model with frequent vocabulary usage in the documents.The Sub-Word Byte-Pair Tokenization technique(SBPT)utilizes the sharing of the vocabulary of one sentence with other sentences.The Sub-Word tokenization model enhances the performance of other Sub-Word tokenization models such pair encoding model by+10%using precision metric.展开更多
Digital assets have been introduced to the global market as one of the innovations with the potential.Even though their impact on the traditional economy is impossible to measure.Security tokens(ST)are the ones that s...Digital assets have been introduced to the global market as one of the innovations with the potential.Even though their impact on the traditional economy is impossible to measure.Security tokens(ST)are the ones that stand out due to the preference they have from producers and consumers.The former obtains financial resources efficiently for their specific projects.While the latter look for STs in global digital platforms of trust and security.Which are regulated by public securities sales offices.The research proposes a method under the fuzzy logic theory and its applied models.It highlights the use of the triangular fuzzy numbers,the Fuzzy Delfi,Expertons,Hamming Distance,and the fuzzy inference system(FIS).The benefits and limitations of the proposal were highlighted when the proposal was used in an agro-export company.The route or algorithm of the value system to be followed in the execution of the investments stands out.Therefore,the research fulfills its objective and is very useful for small and medium export 4.0 companies.Since they are eager to obtain cash flow to improve their technical efficiency and to be able to export their artifacts to global markets.That is to say,the producer of goods can obtain an unprecedented benefit in an agile and efficient way in the context of Industry 4.0.展开更多
With the increasing growth of online news,fake electronic news detection has become one of the most important paradigms of modern research.Traditional electronic news detection techniques are generally based on contex...With the increasing growth of online news,fake electronic news detection has become one of the most important paradigms of modern research.Traditional electronic news detection techniques are generally based on contextual understanding,sequential dependencies,and/or data imbalance.This makes distinction between genuine and fabricated news a challenging task.To address this problem,we propose a novel hybrid architecture,T5-SA-LSTM,which synergistically integrates the T5 Transformer for semantically rich contextual embedding with the Self-Attentionenhanced(SA)Long Short-Term Memory(LSTM).The LSTM is trained using the Adam optimizer,which provides faster and more stable convergence compared to the Stochastic Gradient Descend(SGD)and Root Mean Square Propagation(RMSProp).The WELFake and FakeNewsPrediction datasets are used,which consist of labeled news articles having fake and real news samples.Tokenization and Synthetic Minority Over-sampling Technique(SMOTE)methods are used for data preprocessing to ensure linguistic normalization and class imbalance.The incorporation of the Self-Attention(SA)mechanism enables the model to highlight critical words and phrases,thereby enhancing predictive accuracy.The proposed model is evaluated using accuracy,precision,recall(sensitivity),and F1-score as performance metrics.The model achieved 99%accuracy on the WELFake dataset and 96.5%accuracy on the FakeNewsPrediction dataset.It outperformed the competitive schemes such as T5-SA-LSTM(RMSProp),T5-SA-LSTM(SGD)and some other models.展开更多
The infrastructure finance gap has long-standing implications for economic and social development.Owing to low efficiency,high transaction costs,and long transaction time,conventional infrastructure financing instrume...The infrastructure finance gap has long-standing implications for economic and social development.Owing to low efficiency,high transaction costs,and long transaction time,conventional infrastructure financing instruments are considered to be major contributors to the increasing mismatch between the need for infrastructure development and available financing.Implemented through smart contracts,blockchain tokenization has shown characteristics that are poised to change the capital stack of infrastructure investment.This study analyzed the first SEC-compliant energy asset security token,Ziyen-Coin,from the perspective of the key participants,relevant regulations,and token offering procedures.Results show that tokenization can improve infrastructure assets liquidity,transaction efficiency,and transparency across intermediaries.Conventional infrastructure financing instruments were compared with blockchain tokenization by reviewing the literature on infrastructure finance.The benefits and barriers of tokenizing infrastructure assets were thoroughly discussed to devise ways of improving infrastructure financing.The study also found that the potential of tokenization has not yet been fully realized because of the limited technical infrastructures,regulation uncertainties,volatilities in the token market,and absence of the public sector.This study contributes to the present understanding of how blockchain technology can be implemented in infrastructure finance and the role of tokenization in the structure of public-private partnership and project finance.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
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R113),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Languages–independent text tokenization can aid in classification of languages with few sources.There is a global research effort to generate text classification for any language.Human text classification is a slow procedure.Conse-quently,the text summary generation of different languages,using machine text classification,has been considered in recent years.There is no research on the machine text classification for many languages such as Czech,Rome,Urdu.This research proposes a cross-language text tokenization model using a Transformer technique.The proposed Transformer employs an encoder that has ten layers with self-attention encoding and a feedforward sublayer.This model improves the efficiency of text classification by providing a draft text classification for a number of documents.We also propose a novel Sub-Word tokenization model with frequent vocabulary usage in the documents.The Sub-Word Byte-Pair Tokenization technique(SBPT)utilizes the sharing of the vocabulary of one sentence with other sentences.The Sub-Word tokenization model enhances the performance of other Sub-Word tokenization models such pair encoding model by+10%using precision metric.
文摘Digital assets have been introduced to the global market as one of the innovations with the potential.Even though their impact on the traditional economy is impossible to measure.Security tokens(ST)are the ones that stand out due to the preference they have from producers and consumers.The former obtains financial resources efficiently for their specific projects.While the latter look for STs in global digital platforms of trust and security.Which are regulated by public securities sales offices.The research proposes a method under the fuzzy logic theory and its applied models.It highlights the use of the triangular fuzzy numbers,the Fuzzy Delfi,Expertons,Hamming Distance,and the fuzzy inference system(FIS).The benefits and limitations of the proposal were highlighted when the proposal was used in an agro-export company.The route or algorithm of the value system to be followed in the execution of the investments stands out.Therefore,the research fulfills its objective and is very useful for small and medium export 4.0 companies.Since they are eager to obtain cash flow to improve their technical efficiency and to be able to export their artifacts to global markets.That is to say,the producer of goods can obtain an unprecedented benefit in an agile and efficient way in the context of Industry 4.0.
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R195)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘With the increasing growth of online news,fake electronic news detection has become one of the most important paradigms of modern research.Traditional electronic news detection techniques are generally based on contextual understanding,sequential dependencies,and/or data imbalance.This makes distinction between genuine and fabricated news a challenging task.To address this problem,we propose a novel hybrid architecture,T5-SA-LSTM,which synergistically integrates the T5 Transformer for semantically rich contextual embedding with the Self-Attentionenhanced(SA)Long Short-Term Memory(LSTM).The LSTM is trained using the Adam optimizer,which provides faster and more stable convergence compared to the Stochastic Gradient Descend(SGD)and Root Mean Square Propagation(RMSProp).The WELFake and FakeNewsPrediction datasets are used,which consist of labeled news articles having fake and real news samples.Tokenization and Synthetic Minority Over-sampling Technique(SMOTE)methods are used for data preprocessing to ensure linguistic normalization and class imbalance.The incorporation of the Self-Attention(SA)mechanism enables the model to highlight critical words and phrases,thereby enhancing predictive accuracy.The proposed model is evaluated using accuracy,precision,recall(sensitivity),and F1-score as performance metrics.The model achieved 99%accuracy on the WELFake dataset and 96.5%accuracy on the FakeNewsPrediction dataset.It outperformed the competitive schemes such as T5-SA-LSTM(RMSProp),T5-SA-LSTM(SGD)and some other models.
文摘The infrastructure finance gap has long-standing implications for economic and social development.Owing to low efficiency,high transaction costs,and long transaction time,conventional infrastructure financing instruments are considered to be major contributors to the increasing mismatch between the need for infrastructure development and available financing.Implemented through smart contracts,blockchain tokenization has shown characteristics that are poised to change the capital stack of infrastructure investment.This study analyzed the first SEC-compliant energy asset security token,Ziyen-Coin,from the perspective of the key participants,relevant regulations,and token offering procedures.Results show that tokenization can improve infrastructure assets liquidity,transaction efficiency,and transparency across intermediaries.Conventional infrastructure financing instruments were compared with blockchain tokenization by reviewing the literature on infrastructure finance.The benefits and barriers of tokenizing infrastructure assets were thoroughly discussed to devise ways of improving infrastructure financing.The study also found that the potential of tokenization has not yet been fully realized because of the limited technical infrastructures,regulation uncertainties,volatilities in the token market,and absence of the public sector.This study contributes to the present understanding of how blockchain technology can be implemented in infrastructure finance and the role of tokenization in the structure of public-private partnership and project finance.
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.
