The public has shown great interest in the data factor and data transactions,but the current attention is overly focused on personal behavioral data and transactions happening at Data Exchanges.To deliver a complete p...The public has shown great interest in the data factor and data transactions,but the current attention is overly focused on personal behavioral data and transactions happening at Data Exchanges.To deliver a complete picture of data flaw and transaction,this paper presents a systematic overview of the flow and transaction of personal,corporate and public data on the basis of data factor classification from various perspectives.By utilizing various sources of information,this paper estimates the volume of data generation&storage and the volume&trend of data market transactions for major economies in the world with the following findings:(i)Data classification is diverse due to a broad variety of applying scenarios,and data transaction and profit distribution are complex due to heterogenous entities,ownerships,information density and other attributes of different data types.(ii)Global data transaction has presented with the characteristics of productization,servitization and platform-based mode.(iii)For major economies,there is a commonly observed disequilibrium between data generation scale and storage scale,which is particularly striking for China.(i^v)The global data market is in a nascent stage of rapid development with a transaction volume of about 100 billion US dollars,and China s data market is even more underdeveloped and only accounts for some 10%of the world total.All sectors of the society should be flly aware of the diversity and complexity of data factor classification and data transactions,as well as the arduous and long-term nature of developing and improving relevant institutional systems.Adapting to such features,efforts should be made to improve data classification,enhance computing infrastructure development,foster professional data transaction and development institutions,and perfect the data governance system.展开更多
In 2024,the government of the Hong Kong Special Administrative Region of the People's Republic of China(hereinafter referred to as Hong Kong)introduced a number of policies to support the property market.Its key m...In 2024,the government of the Hong Kong Special Administrative Region of the People's Republic of China(hereinafter referred to as Hong Kong)introduced a number of policies to support the property market.Its key measures are as follows:First,it has removed all property cooling measures,specifically,the Buyer's Stamp Duty(BSD)rate applicable to nonlocal buyers was reduced from a maximum of 30%to approximately 4.25%.展开更多
With the development of sharded blockchains,high cross-shard rates and load imbalance have emerged as major challenges.Account partitioning based on hashing and real-time load faces the issue of high cross-shard rates...With the development of sharded blockchains,high cross-shard rates and load imbalance have emerged as major challenges.Account partitioning based on hashing and real-time load faces the issue of high cross-shard rates.Account partitioning based on historical transaction graphs is effective in reducing cross-shard rates but suffers from load imbalance and limited adaptability to dynamic workloads.Meanwhile,because of the coupling between consensus and execution,a target shard must receive both the partitioned transactions and the partitioned accounts before initiating consensus and execution.However,we observe that transaction partitioning and subsequent consensus do not require actual account data but only need to determine the relative partition order between shards.Therefore,we propose a novel sharded blockchain,called HATLedger,based on Hybrid Account and Transaction partitioning.First,HATLedger proposes building a future transaction graph to detect upcoming hotspot accounts and making more precise account partitioning to reduce transaction cross-shard rates.In the event of an impending overload,the source shard employs simulated partition transactions to specify the partition order across multiple target shards,thereby rapidly partitioning the pending transactions.The target shards can reach consensus on received transactions without waiting for account data.The source shard subsequently sends the account data to the corresponding target shards in the order specified by the previously simulated partition transactions.Based on real transaction history from Ethereum,we conducted extensive sharding scalability experiments.By maintaining low cross-shard rates and a relatively balanced load distribution,HATLedger achieves throughput improvements of 2.2x,1.9x,and 1.8x over SharPer,Shard Scheduler,and TxAllo,respectively,significantly enhancing efficiency and scalability.展开更多
Foreign-funded overseas industrial parks(OIPs)are crucial for attracting foreign investment and promoting globalization in developing countries.However,large-scale land acquisition for these parks generates conflicts ...Foreign-funded overseas industrial parks(OIPs)are crucial for attracting foreign investment and promoting globalization in developing countries.However,large-scale land acquisition for these parks generates conflicts between developers and local stakeholders,increasing development costs.A qualitative multicase study was conducted in this study to analyze the land transaction trajectories of China's OIPs.Four OIPs were selected to reveal the underlying mechanisms from the perspectives of institutional arrangements,governance mechanisms,and enterprise heterogeneity.The findings indicate that in host countries with insufficient institutional development,local governments are more inclined to directly engage in OIP land acquisition.High-level intergovernmental mechanisms facilitate land acquisition processes,although their efficacy depends largely on administrative power allocation across parks in host countries.The results also indicate that enterprise characteristics significantly influence land acquisition,where microscale private enterprises lacking political connections often employ low-cost,bottom-up strategies by leveraging international experience.In summary,policy-makers in developing countries should prioritize enhancing OIP governance to mitigate transaction costs,promote diversified land supply,and optimize land allocation.By depicting China's OIP land acquisition processes,this study deepens the academic understanding of OIP governance in developing countries and related international land transactions,offering practical OIP management insights for governments in both host and parent countries.展开更多
The rapid growth of blockchain and Decentralized Finance(DeFi)has introduced new challenges and vulnerabilities that threaten the integrity and efficiency of the ecosystem.This study identifies critical issues such as...The rapid growth of blockchain and Decentralized Finance(DeFi)has introduced new challenges and vulnerabilities that threaten the integrity and efficiency of the ecosystem.This study identifies critical issues such as Transaction Order Dependence(TOD),Blockchain Extractable Value(BEV),and Transaction Importance Diversity(TID),which collectively undermine the fairness and security of DeFi systems.BEV-related activities,including sandwich attacks,liquidations,transaction replay etc.have emerged as significant threats,collectively generating$540.54 million in losses over 32 months across 11,289 addresses,involving 49,691 cryptocurrencies and 60,830 on-chain markets.These attacks exploit transaction mechanics to manipulate asset prices and extract value at the expense of other participants,with sandwich attacks being particularly impactful.Additionally,the growing adoption of blockchain in traditional finance highlights the challenge of TID,wherein high transaction volumes can strain systems and compromise time-sensitive operations.To address these pressing issues,we propose a novel Distributed Transaction Sequencing Strategy(DTSS)that integrates forking mechanisms with an Analytic Hierarchy Process(AHP)to enforce fair and transparent transaction ordering in a decentralized manner.Our approach is further enhanced by an optimization framework and the introduction of a Normalized Allocation Disparity Metric(NADM)that ensures optimal parameter selection for transaction prioritization.Experimental evaluations demonstrated that the DTSS effectively mitigated BEV risks,enhanced transaction fairness,and significantly improved the security and transparency of DeFi ecosystems.展开更多
P2P trading is driving the decentralization of the electricity market,the autonomy and privacy requirements of prosumers may intro-duce safety risks such as voltage violations.Existing security management methods base...P2P trading is driving the decentralization of the electricity market,the autonomy and privacy requirements of prosumers may intro-duce safety risks such as voltage violations.Existing security management methods based on price guidance may face unsolvable situa-tions in trading scenarios and have difficulty assessing the impact of P2P transactions on voltage security.To this end,this paper proposes a novel distribution system operator(DSO)-prosumers bi-level optimization framework incorporating the dynamic operating envelope(DOE)and risk coefficient-based network usage charge(RC-NUC).In the upper-level,the DOE is employed for dynamic voltage man-agement to prevent violations while the RC-NUC further guides prosumers to engage in grid-friendly transactions.The lower-level decen-tralized market enables prosumers to optimize trading decisions autonomously.Only price signals and energy quantities are exchanged between the two levels,ensuring the privacy of both parties.Additionally,an alternating direction method of multipliers(ADMM)with adaptive penalty factor is introduced to improve computational efficiency.Case studies on a modified IEEE 33-bus system demonstrate that the proposed method reduces voltage violation risks by 18.31%and enhances trading efficiency by 32.3%.These results highlight the feasibility and effectiveness of the approach in advancing secure and efficient distributed energy transactions.展开更多
Blockchain platform swith the unique characteristics of anonymity,decentralization,and transparency of their transactions,which are faced with abnormal activities such as money laundering,phishing scams,and fraudulent...Blockchain platform swith the unique characteristics of anonymity,decentralization,and transparency of their transactions,which are faced with abnormal activities such as money laundering,phishing scams,and fraudulent behavior,posing a serious threat to account asset security.For these potential security risks,this paper proposes a hybrid neural network detection method(HNND)that learns multiple types of account features and enhances fusion information among them to effectively detect abnormal transaction behaviors in the blockchain.In HNND,the Temporal Transaction Graph Attention Network(T2GAT)is first designed to learn biased aggregation representation of multi-attribute transactions among nodes,which can capture key temporal information from node neighborhood transactions.Then,the Graph Convolutional Network(GCN)is adopted which captures abstract structural features of the transaction network.Further,the Stacked Denoising Autoencode(SDA)is developed to achieve adaptive fusion of thses features from different modules.Moreover,the SDA enhances robustness and generalization ability of node representation,leading to higher binary classification accuracy in detecting abnormal behaviors of blockchain accounts.Evaluations on a real-world abnormal transaction dataset demonstrate great advantages of the proposed HNND method over other compared methods.展开更多
This paper proposes a novel hybrid fraud detection framework that integrates multi-stage feature selection,unsupervised clustering,and ensemble learning to improve classification performance in financial transaction m...This paper proposes a novel hybrid fraud detection framework that integrates multi-stage feature selection,unsupervised clustering,and ensemble learning to improve classification performance in financial transaction monitoring systems.The framework is structured into three core layers:(1)feature selection using Recursive Feature Elimination(RFE),Principal Component Analysis(PCA),and Mutual Information(MI)to reduce dimensionality and enhance input relevance;(2)anomaly detection through unsupervised clustering using K-Means,Density-Based Spatial Clustering(DBSCAN),and Hierarchical Clustering to flag suspicious patterns in unlabeled data;and(3)final classification using a voting-based hybrid ensemble of Support Vector Machine(SVM),Random Forest(RF),and Gradient Boosting Classifier(GBC).The experimental evaluation is conducted on a synthetically generated dataset comprising one million financial transactions,with 5% labelled as fraudulent,simulating realistic fraud rates and behavioural features,including transaction time,origin,amount,and geo-location.The proposed model demonstrated a significant improvement over baseline classifiers,achieving an accuracy of 99%,a precision of 99%,a recall of 97%,and an F1-score of 99%.Compared to individual models,it yielded a 9% gain in overall detection accuracy.It reduced the false positive rate to below 3.5%,thereby minimising the operational costs associated with manually reviewing false alerts.The model’s interpretability is enhanced by the integration of Shapley Additive Explanations(SHAP)values for feature importance,supporting transparency and regulatory auditability.These results affirm the practical relevance of the proposed system for deployment in real-time fraud detection scenarios such as credit card transactions,mobile banking,and cross-border payments.The study also highlights future directions,including the deployment of lightweight models and the integration of multimodal data for scalable fraud analytics.展开更多
The global wave of digitalization has accelerated the process of corporate digital transformation,placing higher demands on financial and accounting management.Since then,accounting work has shifted from the tradition...The global wave of digitalization has accelerated the process of corporate digital transformation,placing higher demands on financial and accounting management.Since then,accounting work has shifted from the traditional transactional model to a modern value management-oriented model,leading to a transformation of accounting functions.As corporate managers,they must advance their work proactively,empower the modernization and innovation of financial accounting with digital technology,and transition to management accounting to ensure enterprises keep pace with the times.Therefore,this paper explores the current status of corporate financial and accounting work amid the digital wave,identifies the challenges in the transformation of accounting from transactional to value management-oriented,and finally proposes several feasible and effective improvement strategies,aiming to provide more references for relevant practitioners.展开更多
The task of detecting fraud in credit card transactions is crucial to ensure the security and stability of a financial system,as well as to enforce customer confidence in digital payment systems.Historically,credit ca...The task of detecting fraud in credit card transactions is crucial to ensure the security and stability of a financial system,as well as to enforce customer confidence in digital payment systems.Historically,credit card companies have used rulebased approaches to detect fraudulent transactions,but these have proven inadequate due to the complexity of fraud strategies and have been replaced by much more powerful solutions based on machine learning or deep learning algorithms.Despite significant progress,the current approaches to fraud detection suffer from a number of limitations:for example,it is unclear whether some transaction features are more effective than others in discriminating fraudulent transactions,and they often neglect possible correlations among transactions,even though they could reveal illicit behaviour.In this paper,we propose a novel credit card fraud detection(CCFD)method based on a transaction behaviour-based hierarchical gated network.First,we introduce a feature-oriented extraction module capable of identifying key features from original transactions,and such analysis is effective in revealing the behavioural characteristics of fraudsters.Second,we design a transaction-oriented extraction module capable of capturing the correlation between users’historical and current transactional behaviour.Such information is crucial for revealing users’sequential behaviour patterns.Our approach,called transactional-behaviour-based hierarchical gated network model(TbHGN),extracts two types of new transactional features,which are then combined in a feature interaction module to learn the final transactional representations used for CCFD.We have conducted extensive experiments on a real-world credit card transaction dataset with an increase in average F1 between 1.42%and 6.53%and an improvement in average AUC between 0.63%and 2.78%over the state of the art.展开更多
Low data encryption efficiency and inadequate security are two issues with the current blockchain cross-chain transaction protection schemes.To address these issues,a blockchain cross-chain transaction protection sche...Low data encryption efficiency and inadequate security are two issues with the current blockchain cross-chain transaction protection schemes.To address these issues,a blockchain cross-chain transaction protection scheme based on Fully Homomorphic Encryption(FHE)is proposed.In the proposed scheme,the functional relationship is established by Box-Muller,Discrete Gaussian Distribution Function(DGDF)and Uniform Random Distribution Func-tion(URDF)are used to improve the security and efficiency of key generation.Subsequently,the data preprocessing function is introduced to perform cleaning,deduplication,and normalization operations on the transaction data of multi-key signature,and it is classified into interactive data and asset data,so as to perform different homomorphic operations in the FHE encryption stage.Ultimately,in the FHE encryption stage,homomorphic multiplication and homomorphic addition are used targeted for the interactive data and asset data,thereby reducing the computational complexity and enhancing the FHE encryption efficiency.The significance of the proposed scheme is proved by experimental results:Firstly,the multi-key generation function and its specific sampling method and transformation ensure the security and efficiency of key generation.Data preprocessing can also accelerate the FHE encryption process by eliminating invalid data and redundancy,so the FHE encryption efficiency is significantly improved.Secondly,the FHE encryption method based on discrete logarithm problem enhances the security of transaction data and can effectively resist multiple attacks.In addition,the preprocessed data also has good performance in capacity storage.The proposed scheme has significant impacts on key indicators such as encryption efficiency and security,it provides a new reference for blockchain cross-chain transaction protection technology and has an important impact on the security improvement of various cross-chain transaction data.展开更多
In CSCW system, there are many long-time, cooperative, interactive transactions. Traditional transaction model and advanced transaction model do not effectively support transaction processing in CSCW system. In this p...In CSCW system, there are many long-time, cooperative, interactive transactions. Traditional transaction model and advanced transaction model do not effectively support transaction processing in CSCW system. In this paper, a semantics-based cooperative transaction model(SCTM) is put forward. This model is based on the semantics information of cooperative process and data objects, and can satisfy the demands of transaction processing in CSCW system.展开更多
Traditional anomaly detection methods often assume that data points are independent or exhibit regularly structured relationships,as in Euclidean data such as time series or image grids.However,real-world data frequen...Traditional anomaly detection methods often assume that data points are independent or exhibit regularly structured relationships,as in Euclidean data such as time series or image grids.However,real-world data frequently involve irregular,interconnected structures,requiring a shift toward non-Euclidean approaches.This study introduces a novel anomaly detection framework designed to handle non-Euclidean data by modeling transactions as graph signals.By leveraging graph convolution filters,we extract meaningful connection strengths that capture relational dependencies often overlooked in traditional methods.Utilizing the Graph Convolutional Networks(GCN)framework,we integrate graph-based embeddings with conventional anomaly detection models,enhancing performance through relational insights.Ourmethod is validated on European credit card transaction data,demonstrating its effectiveness in detecting fraudulent transactions,particularly thosewith subtle patterns that evade traditional,amountbased detection techniques.The results highlight the advantages of incorporating temporal and structural dependencies into fraud detection,showcasing the robustness and applicability of our approach in complex,real-world scenarios.展开更多
Cloud computing is the highly demanded technology nowadays.Due to the service oriented architecture,seamless accessibility and other advantages of this advent technology,many transaction rich applications are making u...Cloud computing is the highly demanded technology nowadays.Due to the service oriented architecture,seamless accessibility and other advantages of this advent technology,many transaction rich applications are making use of it.At the same time,it is vulnerable to hacks and threats.Hence securing this environment is of at most important and many research works are being reported focusing on it.This paper proposes a safe storage mechanism using Elliptic curve cryptography(ECC)for the Transaction Rich Applications(TRA).With ECC based security scheme,the security level of the protected system will be increased and it is more suitable to secure the delivered data in the portable devices.The proposed scheme shields the aligning of different kind of data elements to each provider using an ECC algorithm.Analysis,comparison and simulation prove that the proposed system is more effective and secure for the Transaction rich applications in Cloud.展开更多
An integrated method for concurrency control in parallel real-time databases has been proposed in this paper. The nested transaction model has been investigated to offer more atomic execution units and finer grained c...An integrated method for concurrency control in parallel real-time databases has been proposed in this paper. The nested transaction model has been investigated to offer more atomic execution units and finer grained control within in a transaction. Based on the classical nested locking protocol and the speculative concurrency control approach, a two-shadow adaptive concurrency control protocol, which combines the Sacrifice based Optimistic Concurrency Control (OPT-Sacrifice) and High Priority two-phase locking (HP2PL) algorithms together to support both optimistic and pessimistic shadow of each sub-transaction, has been proposed to increase the likelihood of successful timely commitment and to avoid unnecessary replication overload.展开更多
With the introduction of the“dual carbon”goal and the continuous promotion of low-carbon development,the integrated energy system(IES)has gradually become an effective way to save energy and reduce emissions.This st...With the introduction of the“dual carbon”goal and the continuous promotion of low-carbon development,the integrated energy system(IES)has gradually become an effective way to save energy and reduce emissions.This study proposes a low-carbon economic optimization scheduling model for an IES that considers carbon trading costs.With the goal of minimizing the total operating cost of the IES and considering the transferable and curtailable characteristics of the electric and thermal flexible loads,an optimal scheduling model of the IES that considers the cost of carbon trading and flexible loads on the user side was established.The role of flexible loads in improving the economy of an energy system was investigated using examples,and the rationality and effectiveness of the study were verified through a comparative analysis of different scenarios.The results showed that the total cost of the system in different scenarios was reduced by 18.04%,9.1%,3.35%,and 7.03%,respectively,whereas the total carbon emissions of the system were reduced by 65.28%,20.63%,3.85%,and 18.03%,respectively,when the carbon trading cost and demand-side flexible electric and thermal load responses were considered simultaneously.Flexible electrical and thermal loads did not have the same impact on the system performance.In the analyzed case,the total cost and carbon emissions of the system when only the flexible electrical load response was considered were lower than those when only the flexible thermal load response was taken into account.Photovoltaics have an excess of carbon trading credits and can profit from selling them,whereas other devices have an excess of carbon trading and need to buy carbon credits.展开更多
As the typical peer-to-peer distributed networks, blockchain systemsrequire each node to copy a complete transaction database, so as to ensure newtransactions can by verified independently. In a blockchain system (e.g...As the typical peer-to-peer distributed networks, blockchain systemsrequire each node to copy a complete transaction database, so as to ensure newtransactions can by verified independently. In a blockchain system (e.g., bitcoinsystem), the node does not rely on any central organization, and every node keepsan entire copy of the transaction database. However, this feature determines thatthe size of blockchain transaction database is growing rapidly. Therefore, with thecontinuous system operations, the node memory also needs to be expanded tosupport the system running. Especially in the big data era, the increasing networktraffic will lead to faster transaction growth rate. This paper analyzes blockchaintransaction databases and proposes a storage optimization scheme. The proposedscheme divides blockchain transaction database into cold zone and hot zone usingexpiration recognition method based on Least Recently Used (LRU) algorithm. Itcan achieve storage optimization by moving unspent transaction outputs outsidethe in-memory transaction databases. We present the theoretical analysis on theoptimization method to validate the effectiveness. Extensive experiments showour proposed method outperforms the current mechanism for the blockchaintransaction databases.展开更多
Cryptocurrency, as a typical application scene of blockchain, has attracted broad interests from both industrial and academic communities. With its rapid development, the cryptocurrency transaction network embedding(C...Cryptocurrency, as a typical application scene of blockchain, has attracted broad interests from both industrial and academic communities. With its rapid development, the cryptocurrency transaction network embedding(CTNE) has become a hot topic. It embeds transaction nodes into low-dimensional feature space while effectively maintaining a network structure,thereby discovering desired patterns demonstrating involved users' normal and abnormal behaviors. Based on a wide investigation into the state-of-the-art CTNE, this survey has made the following efforts: 1) categorizing recent progress of CTNE methods, 2) summarizing the publicly available cryptocurrency transaction network datasets, 3) evaluating several widely-adopted methods to show their performance in several typical evaluation protocols, and 4) discussing the future trends of CTNE. By doing so, it strives to provide a systematic and comprehensive overview of existing CTNE methods from static to dynamic perspectives,thereby promoting further research into this emerging and important field.展开更多
文摘The public has shown great interest in the data factor and data transactions,but the current attention is overly focused on personal behavioral data and transactions happening at Data Exchanges.To deliver a complete picture of data flaw and transaction,this paper presents a systematic overview of the flow and transaction of personal,corporate and public data on the basis of data factor classification from various perspectives.By utilizing various sources of information,this paper estimates the volume of data generation&storage and the volume&trend of data market transactions for major economies in the world with the following findings:(i)Data classification is diverse due to a broad variety of applying scenarios,and data transaction and profit distribution are complex due to heterogenous entities,ownerships,information density and other attributes of different data types.(ii)Global data transaction has presented with the characteristics of productization,servitization and platform-based mode.(iii)For major economies,there is a commonly observed disequilibrium between data generation scale and storage scale,which is particularly striking for China.(i^v)The global data market is in a nascent stage of rapid development with a transaction volume of about 100 billion US dollars,and China s data market is even more underdeveloped and only accounts for some 10%of the world total.All sectors of the society should be flly aware of the diversity and complexity of data factor classification and data transactions,as well as the arduous and long-term nature of developing and improving relevant institutional systems.Adapting to such features,efforts should be made to improve data classification,enhance computing infrastructure development,foster professional data transaction and development institutions,and perfect the data governance system.
文摘In 2024,the government of the Hong Kong Special Administrative Region of the People's Republic of China(hereinafter referred to as Hong Kong)introduced a number of policies to support the property market.Its key measures are as follows:First,it has removed all property cooling measures,specifically,the Buyer's Stamp Duty(BSD)rate applicable to nonlocal buyers was reduced from a maximum of 30%to approximately 4.25%.
基金funded by the National Key Research and Development Program of China(Grant No.2024YFE0209000)the NSFC(Grant No.U23B2019)。
文摘With the development of sharded blockchains,high cross-shard rates and load imbalance have emerged as major challenges.Account partitioning based on hashing and real-time load faces the issue of high cross-shard rates.Account partitioning based on historical transaction graphs is effective in reducing cross-shard rates but suffers from load imbalance and limited adaptability to dynamic workloads.Meanwhile,because of the coupling between consensus and execution,a target shard must receive both the partitioned transactions and the partitioned accounts before initiating consensus and execution.However,we observe that transaction partitioning and subsequent consensus do not require actual account data but only need to determine the relative partition order between shards.Therefore,we propose a novel sharded blockchain,called HATLedger,based on Hybrid Account and Transaction partitioning.First,HATLedger proposes building a future transaction graph to detect upcoming hotspot accounts and making more precise account partitioning to reduce transaction cross-shard rates.In the event of an impending overload,the source shard employs simulated partition transactions to specify the partition order across multiple target shards,thereby rapidly partitioning the pending transactions.The target shards can reach consensus on received transactions without waiting for account data.The source shard subsequently sends the account data to the corresponding target shards in the order specified by the previously simulated partition transactions.Based on real transaction history from Ethereum,we conducted extensive sharding scalability experiments.By maintaining low cross-shard rates and a relatively balanced load distribution,HATLedger achieves throughput improvements of 2.2x,1.9x,and 1.8x over SharPer,Shard Scheduler,and TxAllo,respectively,significantly enhancing efficiency and scalability.
基金Philosophy and Social Science Planning Projects in Yunnan Province,No.QN202428China Postdoctoral Science Foundation,No.2024M752918。
文摘Foreign-funded overseas industrial parks(OIPs)are crucial for attracting foreign investment and promoting globalization in developing countries.However,large-scale land acquisition for these parks generates conflicts between developers and local stakeholders,increasing development costs.A qualitative multicase study was conducted in this study to analyze the land transaction trajectories of China's OIPs.Four OIPs were selected to reveal the underlying mechanisms from the perspectives of institutional arrangements,governance mechanisms,and enterprise heterogeneity.The findings indicate that in host countries with insufficient institutional development,local governments are more inclined to directly engage in OIP land acquisition.High-level intergovernmental mechanisms facilitate land acquisition processes,although their efficacy depends largely on administrative power allocation across parks in host countries.The results also indicate that enterprise characteristics significantly influence land acquisition,where microscale private enterprises lacking political connections often employ low-cost,bottom-up strategies by leveraging international experience.In summary,policy-makers in developing countries should prioritize enhancing OIP governance to mitigate transaction costs,promote diversified land supply,and optimize land allocation.By depicting China's OIP land acquisition processes,this study deepens the academic understanding of OIP governance in developing countries and related international land transactions,offering practical OIP management insights for governments in both host and parent countries.
基金funded by the University of Macao(file no.MYRG2022-00162-FST and MYRG2019-00136-FST).
文摘The rapid growth of blockchain and Decentralized Finance(DeFi)has introduced new challenges and vulnerabilities that threaten the integrity and efficiency of the ecosystem.This study identifies critical issues such as Transaction Order Dependence(TOD),Blockchain Extractable Value(BEV),and Transaction Importance Diversity(TID),which collectively undermine the fairness and security of DeFi systems.BEV-related activities,including sandwich attacks,liquidations,transaction replay etc.have emerged as significant threats,collectively generating$540.54 million in losses over 32 months across 11,289 addresses,involving 49,691 cryptocurrencies and 60,830 on-chain markets.These attacks exploit transaction mechanics to manipulate asset prices and extract value at the expense of other participants,with sandwich attacks being particularly impactful.Additionally,the growing adoption of blockchain in traditional finance highlights the challenge of TID,wherein high transaction volumes can strain systems and compromise time-sensitive operations.To address these pressing issues,we propose a novel Distributed Transaction Sequencing Strategy(DTSS)that integrates forking mechanisms with an Analytic Hierarchy Process(AHP)to enforce fair and transparent transaction ordering in a decentralized manner.Our approach is further enhanced by an optimization framework and the introduction of a Normalized Allocation Disparity Metric(NADM)that ensures optimal parameter selection for transaction prioritization.Experimental evaluations demonstrated that the DTSS effectively mitigated BEV risks,enhanced transaction fairness,and significantly improved the security and transparency of DeFi ecosystems.
文摘P2P trading is driving the decentralization of the electricity market,the autonomy and privacy requirements of prosumers may intro-duce safety risks such as voltage violations.Existing security management methods based on price guidance may face unsolvable situa-tions in trading scenarios and have difficulty assessing the impact of P2P transactions on voltage security.To this end,this paper proposes a novel distribution system operator(DSO)-prosumers bi-level optimization framework incorporating the dynamic operating envelope(DOE)and risk coefficient-based network usage charge(RC-NUC).In the upper-level,the DOE is employed for dynamic voltage man-agement to prevent violations while the RC-NUC further guides prosumers to engage in grid-friendly transactions.The lower-level decen-tralized market enables prosumers to optimize trading decisions autonomously.Only price signals and energy quantities are exchanged between the two levels,ensuring the privacy of both parties.Additionally,an alternating direction method of multipliers(ADMM)with adaptive penalty factor is introduced to improve computational efficiency.Case studies on a modified IEEE 33-bus system demonstrate that the proposed method reduces voltage violation risks by 18.31%and enhances trading efficiency by 32.3%.These results highlight the feasibility and effectiveness of the approach in advancing secure and efficient distributed energy transactions.
文摘Blockchain platform swith the unique characteristics of anonymity,decentralization,and transparency of their transactions,which are faced with abnormal activities such as money laundering,phishing scams,and fraudulent behavior,posing a serious threat to account asset security.For these potential security risks,this paper proposes a hybrid neural network detection method(HNND)that learns multiple types of account features and enhances fusion information among them to effectively detect abnormal transaction behaviors in the blockchain.In HNND,the Temporal Transaction Graph Attention Network(T2GAT)is first designed to learn biased aggregation representation of multi-attribute transactions among nodes,which can capture key temporal information from node neighborhood transactions.Then,the Graph Convolutional Network(GCN)is adopted which captures abstract structural features of the transaction network.Further,the Stacked Denoising Autoencode(SDA)is developed to achieve adaptive fusion of thses features from different modules.Moreover,the SDA enhances robustness and generalization ability of node representation,leading to higher binary classification accuracy in detecting abnormal behaviors of blockchain accounts.Evaluations on a real-world abnormal transaction dataset demonstrate great advantages of the proposed HNND method over other compared methods.
基金funded by the Deanship of Scientific Research,Vice Presidency for Graduate Studies and Scientific Research,King Faisal University,Saudi Arabia[Grant No.KFU241683].
文摘This paper proposes a novel hybrid fraud detection framework that integrates multi-stage feature selection,unsupervised clustering,and ensemble learning to improve classification performance in financial transaction monitoring systems.The framework is structured into three core layers:(1)feature selection using Recursive Feature Elimination(RFE),Principal Component Analysis(PCA),and Mutual Information(MI)to reduce dimensionality and enhance input relevance;(2)anomaly detection through unsupervised clustering using K-Means,Density-Based Spatial Clustering(DBSCAN),and Hierarchical Clustering to flag suspicious patterns in unlabeled data;and(3)final classification using a voting-based hybrid ensemble of Support Vector Machine(SVM),Random Forest(RF),and Gradient Boosting Classifier(GBC).The experimental evaluation is conducted on a synthetically generated dataset comprising one million financial transactions,with 5% labelled as fraudulent,simulating realistic fraud rates and behavioural features,including transaction time,origin,amount,and geo-location.The proposed model demonstrated a significant improvement over baseline classifiers,achieving an accuracy of 99%,a precision of 99%,a recall of 97%,and an F1-score of 99%.Compared to individual models,it yielded a 9% gain in overall detection accuracy.It reduced the false positive rate to below 3.5%,thereby minimising the operational costs associated with manually reviewing false alerts.The model’s interpretability is enhanced by the integration of Shapley Additive Explanations(SHAP)values for feature importance,supporting transparency and regulatory auditability.These results affirm the practical relevance of the proposed system for deployment in real-time fraud detection scenarios such as credit card transactions,mobile banking,and cross-border payments.The study also highlights future directions,including the deployment of lightweight models and the integration of multimodal data for scalable fraud analytics.
文摘The global wave of digitalization has accelerated the process of corporate digital transformation,placing higher demands on financial and accounting management.Since then,accounting work has shifted from the traditional transactional model to a modern value management-oriented model,leading to a transformation of accounting functions.As corporate managers,they must advance their work proactively,empower the modernization and innovation of financial accounting with digital technology,and transition to management accounting to ensure enterprises keep pace with the times.Therefore,this paper explores the current status of corporate financial and accounting work amid the digital wave,identifies the challenges in the transformation of accounting from transactional to value management-oriented,and finally proposes several feasible and effective improvement strategies,aiming to provide more references for relevant practitioners.
基金supported in part by the National Natural Science Foundation of China(61972241)the Natural Science Foundation of Shanghai(24ZR1427500,22ZR1427100)+1 种基金the Key Projects of Natural Science Research in Anhui Higher Education Institutions(2022AH051909)Bengbu University 2021 High-Level Scientific Research and Cultivation Project(2021pyxm04).
文摘The task of detecting fraud in credit card transactions is crucial to ensure the security and stability of a financial system,as well as to enforce customer confidence in digital payment systems.Historically,credit card companies have used rulebased approaches to detect fraudulent transactions,but these have proven inadequate due to the complexity of fraud strategies and have been replaced by much more powerful solutions based on machine learning or deep learning algorithms.Despite significant progress,the current approaches to fraud detection suffer from a number of limitations:for example,it is unclear whether some transaction features are more effective than others in discriminating fraudulent transactions,and they often neglect possible correlations among transactions,even though they could reveal illicit behaviour.In this paper,we propose a novel credit card fraud detection(CCFD)method based on a transaction behaviour-based hierarchical gated network.First,we introduce a feature-oriented extraction module capable of identifying key features from original transactions,and such analysis is effective in revealing the behavioural characteristics of fraudsters.Second,we design a transaction-oriented extraction module capable of capturing the correlation between users’historical and current transactional behaviour.Such information is crucial for revealing users’sequential behaviour patterns.Our approach,called transactional-behaviour-based hierarchical gated network model(TbHGN),extracts two types of new transactional features,which are then combined in a feature interaction module to learn the final transactional representations used for CCFD.We have conducted extensive experiments on a real-world credit card transaction dataset with an increase in average F1 between 1.42%and 6.53%and an improvement in average AUC between 0.63%and 2.78%over the state of the art.
文摘Low data encryption efficiency and inadequate security are two issues with the current blockchain cross-chain transaction protection schemes.To address these issues,a blockchain cross-chain transaction protection scheme based on Fully Homomorphic Encryption(FHE)is proposed.In the proposed scheme,the functional relationship is established by Box-Muller,Discrete Gaussian Distribution Function(DGDF)and Uniform Random Distribution Func-tion(URDF)are used to improve the security and efficiency of key generation.Subsequently,the data preprocessing function is introduced to perform cleaning,deduplication,and normalization operations on the transaction data of multi-key signature,and it is classified into interactive data and asset data,so as to perform different homomorphic operations in the FHE encryption stage.Ultimately,in the FHE encryption stage,homomorphic multiplication and homomorphic addition are used targeted for the interactive data and asset data,thereby reducing the computational complexity and enhancing the FHE encryption efficiency.The significance of the proposed scheme is proved by experimental results:Firstly,the multi-key generation function and its specific sampling method and transformation ensure the security and efficiency of key generation.Data preprocessing can also accelerate the FHE encryption process by eliminating invalid data and redundancy,so the FHE encryption efficiency is significantly improved.Secondly,the FHE encryption method based on discrete logarithm problem enhances the security of transaction data and can effectively resist multiple attacks.In addition,the preprocessed data also has good performance in capacity storage.The proposed scheme has significant impacts on key indicators such as encryption efficiency and security,it provides a new reference for blockchain cross-chain transaction protection technology and has an important impact on the security improvement of various cross-chain transaction data.
文摘In CSCW system, there are many long-time, cooperative, interactive transactions. Traditional transaction model and advanced transaction model do not effectively support transaction processing in CSCW system. In this paper, a semantics-based cooperative transaction model(SCTM) is put forward. This model is based on the semantics information of cooperative process and data objects, and can satisfy the demands of transaction processing in CSCW system.
基金supported by the National Research Foundation of Korea(NRF)funded by the Korea government(RS-2023-00249743)Additionally,this research was supported by the Global-Learning&Academic Research Institution for Master’s,PhD Students,and Postdocs(LAMP)Program of the National Research Foundation of Korea(NRF)grant funded by the Ministry of Education(RS-2024-00443714)This research was also supported by the“Research Base Construction Fund Support Program”funded by Jeonbuk National University in 2025.
文摘Traditional anomaly detection methods often assume that data points are independent or exhibit regularly structured relationships,as in Euclidean data such as time series or image grids.However,real-world data frequently involve irregular,interconnected structures,requiring a shift toward non-Euclidean approaches.This study introduces a novel anomaly detection framework designed to handle non-Euclidean data by modeling transactions as graph signals.By leveraging graph convolution filters,we extract meaningful connection strengths that capture relational dependencies often overlooked in traditional methods.Utilizing the Graph Convolutional Networks(GCN)framework,we integrate graph-based embeddings with conventional anomaly detection models,enhancing performance through relational insights.Ourmethod is validated on European credit card transaction data,demonstrating its effectiveness in detecting fraudulent transactions,particularly thosewith subtle patterns that evade traditional,amountbased detection techniques.The results highlight the advantages of incorporating temporal and structural dependencies into fraud detection,showcasing the robustness and applicability of our approach in complex,real-world scenarios.
文摘Cloud computing is the highly demanded technology nowadays.Due to the service oriented architecture,seamless accessibility and other advantages of this advent technology,many transaction rich applications are making use of it.At the same time,it is vulnerable to hacks and threats.Hence securing this environment is of at most important and many research works are being reported focusing on it.This paper proposes a safe storage mechanism using Elliptic curve cryptography(ECC)for the Transaction Rich Applications(TRA).With ECC based security scheme,the security level of the protected system will be increased and it is more suitable to secure the delivered data in the portable devices.The proposed scheme shields the aligning of different kind of data elements to each provider using an ECC algorithm.Analysis,comparison and simulation prove that the proposed system is more effective and secure for the Transaction rich applications in Cloud.
文摘An integrated method for concurrency control in parallel real-time databases has been proposed in this paper. The nested transaction model has been investigated to offer more atomic execution units and finer grained control within in a transaction. Based on the classical nested locking protocol and the speculative concurrency control approach, a two-shadow adaptive concurrency control protocol, which combines the Sacrifice based Optimistic Concurrency Control (OPT-Sacrifice) and High Priority two-phase locking (HP2PL) algorithms together to support both optimistic and pessimistic shadow of each sub-transaction, has been proposed to increase the likelihood of successful timely commitment and to avoid unnecessary replication overload.
基金supported by State Grid Shanxi Electric Power Company Science and Technology Project“Research on key technologies of carbon tracking and carbon evaluation for new power system”(Grant:520530230005)。
文摘With the introduction of the“dual carbon”goal and the continuous promotion of low-carbon development,the integrated energy system(IES)has gradually become an effective way to save energy and reduce emissions.This study proposes a low-carbon economic optimization scheduling model for an IES that considers carbon trading costs.With the goal of minimizing the total operating cost of the IES and considering the transferable and curtailable characteristics of the electric and thermal flexible loads,an optimal scheduling model of the IES that considers the cost of carbon trading and flexible loads on the user side was established.The role of flexible loads in improving the economy of an energy system was investigated using examples,and the rationality and effectiveness of the study were verified through a comparative analysis of different scenarios.The results showed that the total cost of the system in different scenarios was reduced by 18.04%,9.1%,3.35%,and 7.03%,respectively,whereas the total carbon emissions of the system were reduced by 65.28%,20.63%,3.85%,and 18.03%,respectively,when the carbon trading cost and demand-side flexible electric and thermal load responses were considered simultaneously.Flexible electrical and thermal loads did not have the same impact on the system performance.In the analyzed case,the total cost and carbon emissions of the system when only the flexible electrical load response was considered were lower than those when only the flexible thermal load response was taken into account.Photovoltaics have an excess of carbon trading credits and can profit from selling them,whereas other devices have an excess of carbon trading and need to buy carbon credits.
基金supported by Researchers Supporting Project(No.RSP-2020/102)King Saud University,Riyadh,Saudi Arabiathe National Natural Science Foundation of China(Nos.61802031,61772454,61811530332,61811540410)+4 种基金the Natural Science Foundation of Hunan Province,China(No.2019JGYB177)the Research Foundation of Education Bureau of Hunan Province,China(No.18C0216)the“Practical Innovation and Entrepreneurial Ability Improvement Plan”for Professional Degree Graduate students of Changsha University of Science and Technology(No.SJCX201971)Hunan Graduate Scientific Research Innovation Project,China(No.CX2019694)This work is also supported by the Programs of Transformation and Upgrading of Industries and Information Technologies of Jiangsu Province(No.JITC-1900AX2038/01).
文摘As the typical peer-to-peer distributed networks, blockchain systemsrequire each node to copy a complete transaction database, so as to ensure newtransactions can by verified independently. In a blockchain system (e.g., bitcoinsystem), the node does not rely on any central organization, and every node keepsan entire copy of the transaction database. However, this feature determines thatthe size of blockchain transaction database is growing rapidly. Therefore, with thecontinuous system operations, the node memory also needs to be expanded tosupport the system running. Especially in the big data era, the increasing networktraffic will lead to faster transaction growth rate. This paper analyzes blockchaintransaction databases and proposes a storage optimization scheme. The proposedscheme divides blockchain transaction database into cold zone and hot zone usingexpiration recognition method based on Least Recently Used (LRU) algorithm. Itcan achieve storage optimization by moving unspent transaction outputs outsidethe in-memory transaction databases. We present the theoretical analysis on theoptimization method to validate the effectiveness. Extensive experiments showour proposed method outperforms the current mechanism for the blockchaintransaction databases.
基金supported in part by the National Natural Science Foundation of China (62272078)the CAAI-Huawei MindSpore Open Fund (CAAIXSJLJJ-2021-035A)the Doctoral Student Talent Training Program of Chongqing University of Posts and Telecommunications (BYJS202009)。
文摘Cryptocurrency, as a typical application scene of blockchain, has attracted broad interests from both industrial and academic communities. With its rapid development, the cryptocurrency transaction network embedding(CTNE) has become a hot topic. It embeds transaction nodes into low-dimensional feature space while effectively maintaining a network structure,thereby discovering desired patterns demonstrating involved users' normal and abnormal behaviors. Based on a wide investigation into the state-of-the-art CTNE, this survey has made the following efforts: 1) categorizing recent progress of CTNE methods, 2) summarizing the publicly available cryptocurrency transaction network datasets, 3) evaluating several widely-adopted methods to show their performance in several typical evaluation protocols, and 4) discussing the future trends of CTNE. By doing so, it strives to provide a systematic and comprehensive overview of existing CTNE methods from static to dynamic perspectives,thereby promoting further research into this emerging and important field.
