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
Most transactional memory (TM) research focused on multi-core processors, and others investigated at the clusters, leaving the area of non-uniform memory access (NUMA) system unexplored. The existing TM implementation...Most transactional memory (TM) research focused on multi-core processors, and others investigated at the clusters, leaving the area of non-uniform memory access (NUMA) system unexplored. The existing TM implementations made significant performance degradation on NUMA system because they ignored the slower remote memory access. To solve this problem, a latency-based conflict detection and a forecasting-based conflict prevention method were proposed. Using these techniques, the NUMA aware TM system was presented. By reducing the remote memory access and the abort rate of transaction, the experiment results show that the NUMA aware strategies present good practical TM performance on NUMA system.展开更多
DNS(domain name system) query log analysis has been a popular research topic in recent years. CLOPE, the represented transactional clustering algorithm, could be readily used for DNS query log mining. However, the alg...DNS(domain name system) query log analysis has been a popular research topic in recent years. CLOPE, the represented transactional clustering algorithm, could be readily used for DNS query log mining. However, the algorithm is inefficient when processing large scale data. The MR-CLOPE algorithm is proposed, which is an extension and improvement on CLOPE based on Map Reduce. Different from the previous parallel clustering method, a two-stage Map Reduce implementation framework is proposed. Each of the stage is implemented by one kind Map Reduce task. In the first stage, the DNS query logs are divided into multiple splits and the CLOPE algorithm is executed on each split. The second stage usually tends to iterate many times to merge the small clusters into bigger satisfactory ones. In these two stages, a novel partition process is designed to randomly spread out original sub clusters, which will be moved and merged in the map phrase of the second phase according to the defined merge criteria. In such way, the advantage of the original CLOPE algorithm is kept and its disadvantages are dealt with in the proposed framework to achieve more excellent clustering performance. The experiment results show that MR-CLOPE is not only faster but also has better clustering quality on DNS query logs compared with CLOPE.展开更多
This research is a semi-experimental study (pretest-posttest design with unequal control group). This study aimed to investigate the effectiveness of the group therapy based on transactional analysis in marital burnou...This research is a semi-experimental study (pretest-posttest design with unequal control group). This study aimed to investigate the effectiveness of the group therapy based on transactional analysis in marital burnout and forbearance of married female students. The research on statistical population comprised all married female students studying in humanities subjects in Neishabour Islamic Azad University in the academic year 2015-16. The sample included 24 of these students, who were selected voluntarily and through Random sampling method and were randomly assigned into two experimental and control groups. Measurement tools consisted of Couple Burnout Measure (CBM) and Evans marital forbearance questionnaire. The experimental group received 10 one-half hour sessions of the treatment based on the transactional analysis whereas in the meantime, the control group received nutritional trainings. The results indicated that the therapy based on the transactional analysis had been effective in reducing marital burnout and increasing the forbearance of couples.展开更多
Teacher performance is an important factor to improve education performance. This study aimed to analyze the effect of the work ethics, transformational, and transactional leadership style on teacher performance. The ...Teacher performance is an important factor to improve education performance. This study aimed to analyze the effect of the work ethics, transformational, and transactional leadership style on teacher performance. The experiment was conducted in public high school No. 112 Jakarta. A total of 50 teachers were included as samples, where they were given the questionnaires to be filled by self-rating. Data were analyzed with multiple linear regression model and processed with SPSS version 20. Results showed that transformational leadership, transactional leadership and work ethics have positive and significant impacts on teacher performance both simultaneously and individually.展开更多
A data stream is a massive unbounded sequence of data elements continuouslygenerated at a rapid rate. Due to this reason, most algorithms for data streams sacrifice thecorrectness of their results for fast processing ...A data stream is a massive unbounded sequence of data elements continuouslygenerated at a rapid rate. Due to this reason, most algorithms for data streams sacrifice thecorrectness of their results for fast processing time. The processing time is greatly influenced bythe amount of information that should be maintained. This issue becomes more serious in findingfrequent itemsets or frequency counting over an online transactional data stream since there can bea large number of itemsets to be monitored. We have proposed a method called the estDec method forfinding frequent itemsets over an online data stream. In order to reduce the number of monitoreditemsets in this method, monitoring the count of an itemset is delayed until its support is largeenough to become a frequent itemset in the near future. For this purpose, the count of an itemsetshould be estimated. Consequently, how to estimate the count of an itemset is a critical issue inminimizing memory usage as well as processing time. In this paper, the effects of various countestimation methods for finding frequent itemsets are analyzed in terms of mining accuracy, memoryusage and processing time.展开更多
Transactional memory （TM） is a new promising concurrency-control mechanism that can avoid many of the pitfalls of the traditional lock-based techniques. TM systems handle data races between threads automatically so ...Transactional memory （TM） is a new promising concurrency-control mechanism that can avoid many of the pitfalls of the traditional lock-based techniques. TM systems handle data races between threads automatically so that programmers do not have to reason about the interaction of threads manually. TM provides a programming model that may make the development of multi-threaded programs easier. Much work has been done to explore the various implementation strategies of TM systems and to achieve better performance, but little has been done on how to formally reason about programs using TM and how to make sure that such reasoning is sound. In this paper, we focus on the semantics of transactional memory and present a proof-carrying code （PCC） system for reasoning about programs using TM . We formalize our reasoning with respect to the TM semantics, prove its soundness, and use examples to demonstrate its effectiveness.展开更多
This short article explores the meaning of Shusterman＇s ＂transactional body,＂ derived from his statement that the will is not a purely mental affair that is independent of physical modality but is always an interac...This short article explores the meaning of Shusterman＇s ＂transactional body,＂ derived from his statement that the will is not a purely mental affair that is independent of physical modality but is always an interaction with its environment. While he appreciates the Deweyan interaetional model, he develops the ＂transactional mode,＂ which he implies should expand the body-mind unity to the level of social and cultural conditions. Shusterman finds similar integrations and transactions among the mental, psychophysical and physical levels in Asian philosophical traditions. This article conducts a comparative study between his theory and Mencius＇ understanding of the body. Within the discussion, the analysis of aesthetic experience explicated by Dewey and Shusterman is reviewed as the link to the comparison.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
2021年9月,重庆大学光电工程学院2019级研究生邓培芳以共同第一作者在IEEE Transactions on Geoscience and Remote Sensing期刊上发表了一篇题为“Vision Transformer:An Excellent Teacher for Guiding Small Networks in Remote Sens...2021年9月,重庆大学光电工程学院2019级研究生邓培芳以共同第一作者在IEEE Transactions on Geoscience and Remote Sensing期刊上发表了一篇题为“Vision Transformer:An Excellent Teacher for Guiding Small Networks in Remote Sensing Image Scene Classification”的研究论文。IEEE Transactions on Geoscience and Remote Sensing是IEEE出版的国际性、跨学科期刊,2022年被中国科学院划分为一区Top期刊。邓培芳同学(现在浙江大学攻读博士学位)在2019年暑假期间,加入重庆大学光电工程学院黄鸿教授课题组进行学习,开始本课题的研究工作。展开更多
《中国有色金属学报》和《Transactions of Nonferrous Metals Society of China》(《中国有色金属学报(英文版)》)是中国有色金属学会主办、科学出版社出版的学术期刊。创刊于1991年,主要报道我国有色金属材料、冶金、选矿和化学化工...《中国有色金属学报》和《Transactions of Nonferrous Metals Society of China》(《中国有色金属学报(英文版)》)是中国有色金属学会主办、科学出版社出版的学术期刊。创刊于1991年,主要报道我国有色金属材料、冶金、选矿和化学化工领域的新理论、新技术和新方法。《中国有色金属学报》为月刊,80元/期,全年订价960元。邮发代号:42−218。展开更多
The long transaction latency and low throughput of blockchain are the key challenges affecting the large-scale adoption of blockchain technology. Sharding technology is a primary solution by divides the blockchain net...The long transaction latency and low throughput of blockchain are the key challenges affecting the large-scale adoption of blockchain technology. Sharding technology is a primary solution by divides the blockchain network into multiple independent shards for parallel transaction processing. However, most existing random or modular schemes fail to consider the transactional relationships between accounts, which leads to a high proportion of cross-shard transactions, thereby increasing the communication overhead and transaction confirmation latency between shards. To solve this problem, this paper proposes a blockchain sharding algorithm based on account degree and frequency (DFSA). The algorithm takes into account both account degree and weight relationships between accounts. The blockchain transaction network is modeled as an undirected weighted graph, and community detection algorithms are employed to analyze the correlations between accounts. Strong-correlated accounts are grouped into the same shard, and a multi-shard blockchain network is constructed. Additionally, to further reduce the number of cross-shard transactions, this paper designs a random redundancy strategy based on account correlation, which randomly selects strong-correlated accounts and stores them redundantly in another shard, thus original cross-shard transactions can be verified and confirmed within the same shard. Simulation experiments demonstrate that DFSA outperforms the random sharding algorithm (RSA), modular sharding algorithm (MSA), and label propagation algorithm (LPA) in terms of cross-shard transaction proportion, latency, and throughput. Therefore, DFSA can effectively reduce cross-shard transaction proportion and lower transaction confirmation latency.展开更多
Cyber-Physical System (CPS) devices are increasing exponentially. Lacking confidentiality creates a vulnerable network. Thus, demanding the overall system with the latest and robust solutions for the defence mechanism...Cyber-Physical System (CPS) devices are increasing exponentially. Lacking confidentiality creates a vulnerable network. Thus, demanding the overall system with the latest and robust solutions for the defence mechanisms with low computation cost, increased integrity, and surveillance. The proposal of a mechanism that utilizes the features of authenticity measures using the Destination Sequence Distance Vector (DSDV) routing protocol which applies to the multi-WSN (Wireless Sensor Network) of IoT devices in CPS which is developed for the Device-to-Device (D2D) authentication developed from the local-chain and public chain respectively combined with the Software Defined Networking (SDN) control and monitoring system using switches and controllers that will route the packets through the network, identify any false nodes, take preventive measures against them and preventing them for any future problems. Next, the system is powered by Blockchain cryptographic features by utilizing the TrustChain features to create a private, secure, and temper-free ledger of the transactions performed inside the network. Results are achieved in the legitimate devices connecting to the network, transferring their packets to their destination under supervision, reporting whenever a false node is causing hurdles, and recording the transactions for temper-proof records. Evaluation results based on 1000+ transactions illustrate that the proposed mechanism not only outshines most aspects of Cyber-Physical systems but also consumes less computation power with a low latency of 0.1 seconds only.展开更多
BACKGROUND There is a lack of study on vitamin D and calcium levels in epileptic patients receiving therapy,despite the growing recognition of the importance of bone health in individuals with epilepsy.Associations on...BACKGROUND There is a lack of study on vitamin D and calcium levels in epileptic patients receiving therapy,despite the growing recognition of the importance of bone health in individuals with epilepsy.Associations one statistical method for finding correlations between variables in big datasets is called association rule mining(ARM).This technique finds patterns of common items or events in the data set,including associations.Through the analysis of patient data,including demographics,genetic information,and reactions with previous treatments,ARM can identify harmful drug reactions,possible novel combinations of medicines,and trends which connect particular individual features to treatment outcomes.AIM To investigate the evidence on the effects of anti-epileptic drugs(AEDs)on calcium metabolism and supplementing with vitamin D to help lower the likelihood of bone-related issues using ARM technique.METHODS ARM technique was used to analyze patients’behavior on calcium metabolism,vitamin D and anti-epileptic medicines.Epileptic sufferers of both sexes who attended neurological outpatient and in patient department clinics were recruited for the study.There were three patient groups:Group 1 received one AED,group 2 received two AEDs,and group 3 received more than two AEDs.The researchers analyzed the alkaline phosphatase,ionized calcium,total calcium,phosphorus,vitamin D levels,or parathyroid hormone values.RESULTS A total of 150 patients,aged 12 years to 60 years,were studied,with 50 in each group(1,2,and 3).60%were men,this gender imbalance may affect the study’s findings,as women have different bone metabolism dynamics influenced by hormonal variations,including menopause.The results may not fully capture the distinct effects of AEDs on female patients.A greater equal distribution of women should be the goal of future studies in order to offer a complete comprehension of the metabolic alterations brought on by AEDs.86 patients had generalized epilepsy,64 partial.42%of patients had AEDs for>5 years.Polytherapy reduced calcium and vitamin D levels compared to mono and dual therapy.Polytherapy elevated alkaline phosphatase and phosphorus levels.CONCLUSION ARM revealed the possible effects of variables like age,gender,and polytherapy on parathyroid hormone levels in individuals taking antiepileptic medication.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
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
In the contemporary era,characterized by the Internet and digitalization as fundamental features,the operation and application of digital currency have gradually developed into a comprehensive structural system.This s...In the contemporary era,characterized by the Internet and digitalization as fundamental features,the operation and application of digital currency have gradually developed into a comprehensive structural system.This system restores the essential characteristics of currency while providing auxiliary services related to the formation,circulation,storage,application,and promotion of digital currency.Compared to traditional currency management technologies,big data analysis technology,which is primarily embedded in digital currency systems,enables the rapid acquisition of information.This facilitates the identification of standard associations within currency data and provides technical support for the operational framework of digital currency.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
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
With the gradual application of central bank digital currency(CBDC)in China,it brings new payment methods,but also potentially derives new money laundering paths.Two typical application scenarios of CBDC are considere...With the gradual application of central bank digital currency(CBDC)in China,it brings new payment methods,but also potentially derives new money laundering paths.Two typical application scenarios of CBDC are considered,namely the anonymous transaction scenario and real-name transaction scenario.First,starting from the interaction network of transactional groups,the degree distribution,density,and modularity of normal and money laundering transactions in two transaction scenarios are compared and analyzed,so as to clarify the characteristics and paths of money laundering transactions.Then,according to the two typical application scenarios,different transaction datasets are selected,and different models are used to train the models on the recognition of money laundering behaviors in the two datasets.Among them,in the anonymous transaction scenario,the graph convolutional neural network is used to identify the spatial structure,the recurrent neural network is fused to obtain the dynamic pattern,and the model ChebNet-GRU is constructed.The constructed ChebNet-GRU model has the best effect in the recognition of money laundering behavior,with a precision of 94.3%,a recall of 59.5%,an F1 score of 72.9%,and a microaverage F1 score of 97.1%.While in the real-name transaction scenario,the traditional machine learning method is far better than the deep learning method,and the micro-average F1 score of the random forest and XGBoost models both reach 99.9%,which can effectively identify money laundering in currency transactions.展开更多
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Projects(61003075, 61170261) supported by the National Natural Science Foundation of China
文摘Most transactional memory (TM) research focused on multi-core processors, and others investigated at the clusters, leaving the area of non-uniform memory access (NUMA) system unexplored. The existing TM implementations made significant performance degradation on NUMA system because they ignored the slower remote memory access. To solve this problem, a latency-based conflict detection and a forecasting-based conflict prevention method were proposed. Using these techniques, the NUMA aware TM system was presented. By reducing the remote memory access and the abort rate of transaction, the experiment results show that the NUMA aware strategies present good practical TM performance on NUMA system.
基金Project(61103046) supported in part by the National Natural Science Foundation of ChinaProject(B201312) supported by DHU Distinguished Young Professor Program,China+1 种基金Project(LY14F020007) supported by Zhejiang Provincial Natural Science Funds of ChinaProject(2014A610072) supported by the Natural Science Foundation of Ningbo City,China
文摘DNS(domain name system) query log analysis has been a popular research topic in recent years. CLOPE, the represented transactional clustering algorithm, could be readily used for DNS query log mining. However, the algorithm is inefficient when processing large scale data. The MR-CLOPE algorithm is proposed, which is an extension and improvement on CLOPE based on Map Reduce. Different from the previous parallel clustering method, a two-stage Map Reduce implementation framework is proposed. Each of the stage is implemented by one kind Map Reduce task. In the first stage, the DNS query logs are divided into multiple splits and the CLOPE algorithm is executed on each split. The second stage usually tends to iterate many times to merge the small clusters into bigger satisfactory ones. In these two stages, a novel partition process is designed to randomly spread out original sub clusters, which will be moved and merged in the map phrase of the second phase according to the defined merge criteria. In such way, the advantage of the original CLOPE algorithm is kept and its disadvantages are dealt with in the proposed framework to achieve more excellent clustering performance. The experiment results show that MR-CLOPE is not only faster but also has better clustering quality on DNS query logs compared with CLOPE.
文摘This research is a semi-experimental study (pretest-posttest design with unequal control group). This study aimed to investigate the effectiveness of the group therapy based on transactional analysis in marital burnout and forbearance of married female students. The research on statistical population comprised all married female students studying in humanities subjects in Neishabour Islamic Azad University in the academic year 2015-16. The sample included 24 of these students, who were selected voluntarily and through Random sampling method and were randomly assigned into two experimental and control groups. Measurement tools consisted of Couple Burnout Measure (CBM) and Evans marital forbearance questionnaire. The experimental group received 10 one-half hour sessions of the treatment based on the transactional analysis whereas in the meantime, the control group received nutritional trainings. The results indicated that the therapy based on the transactional analysis had been effective in reducing marital burnout and increasing the forbearance of couples.
文摘Teacher performance is an important factor to improve education performance. This study aimed to analyze the effect of the work ethics, transformational, and transactional leadership style on teacher performance. The experiment was conducted in public high school No. 112 Jakarta. A total of 50 teachers were included as samples, where they were given the questionnaires to be filled by self-rating. Data were analyzed with multiple linear regression model and processed with SPSS version 20. Results showed that transformational leadership, transactional leadership and work ethics have positive and significant impacts on teacher performance both simultaneously and individually.
文摘A data stream is a massive unbounded sequence of data elements continuouslygenerated at a rapid rate. Due to this reason, most algorithms for data streams sacrifice thecorrectness of their results for fast processing time. The processing time is greatly influenced bythe amount of information that should be maintained. This issue becomes more serious in findingfrequent itemsets or frequency counting over an online transactional data stream since there can bea large number of itemsets to be monitored. We have proposed a method called the estDec method forfinding frequent itemsets over an online data stream. In order to reduce the number of monitoreditemsets in this method, monitoring the count of an itemset is delayed until its support is largeenough to become a frequent itemset in the near future. For this purpose, the count of an itemsetshould be estimated. Consequently, how to estimate the count of an itemset is a critical issue inminimizing memory usage as well as processing time. In this paper, the effects of various countestimation methods for finding frequent itemsets are analyzed in terms of mining accuracy, memoryusage and processing time.
基金Supported by the National Natural Science Foundation of China under Grant Nos.60673126 and 90718026, and Intel China Research Center.
文摘Transactional memory （TM） is a new promising concurrency-control mechanism that can avoid many of the pitfalls of the traditional lock-based techniques. TM systems handle data races between threads automatically so that programmers do not have to reason about the interaction of threads manually. TM provides a programming model that may make the development of multi-threaded programs easier. Much work has been done to explore the various implementation strategies of TM systems and to achieve better performance, but little has been done on how to formally reason about programs using TM and how to make sure that such reasoning is sound. In this paper, we focus on the semantics of transactional memory and present a proof-carrying code （PCC） system for reasoning about programs using TM . We formalize our reasoning with respect to the TM semantics, prove its soundness, and use examples to demonstrate its effectiveness.
文摘This short article explores the meaning of Shusterman＇s ＂transactional body,＂ derived from his statement that the will is not a purely mental affair that is independent of physical modality but is always an interaction with its environment. While he appreciates the Deweyan interaetional model, he develops the ＂transactional mode,＂ which he implies should expand the body-mind unity to the level of social and cultural conditions. Shusterman finds similar integrations and transactions among the mental, psychophysical and physical levels in Asian philosophical traditions. This article conducts a comparative study between his theory and Mencius＇ understanding of the body. Within the discussion, the analysis of aesthetic experience explicated by Dewey and Shusterman is reviewed as the link to the comparison.
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.
文摘2021年9月,重庆大学光电工程学院2019级研究生邓培芳以共同第一作者在IEEE Transactions on Geoscience and Remote Sensing期刊上发表了一篇题为“Vision Transformer:An Excellent Teacher for Guiding Small Networks in Remote Sensing Image Scene Classification”的研究论文。IEEE Transactions on Geoscience and Remote Sensing是IEEE出版的国际性、跨学科期刊,2022年被中国科学院划分为一区Top期刊。邓培芳同学(现在浙江大学攻读博士学位)在2019年暑假期间,加入重庆大学光电工程学院黄鸿教授课题组进行学习,开始本课题的研究工作。
文摘《中国有色金属学报》和《Transactions of Nonferrous Metals Society of China》(《中国有色金属学报(英文版)》)是中国有色金属学会主办、科学出版社出版的学术期刊。创刊于1991年,主要报道我国有色金属材料、冶金、选矿和化学化工领域的新理论、新技术和新方法。《中国有色金属学报》为月刊,80元/期,全年订价960元。邮发代号:42−218。
基金supported by the National Natural Science Foundation of China(Grant No.61802301)awarded to J.Lithe Postgraduate Innovation Fund Project of Xi’an Shiyou University(Grant No.YCX2513159).
文摘The long transaction latency and low throughput of blockchain are the key challenges affecting the large-scale adoption of blockchain technology. Sharding technology is a primary solution by divides the blockchain network into multiple independent shards for parallel transaction processing. However, most existing random or modular schemes fail to consider the transactional relationships between accounts, which leads to a high proportion of cross-shard transactions, thereby increasing the communication overhead and transaction confirmation latency between shards. To solve this problem, this paper proposes a blockchain sharding algorithm based on account degree and frequency (DFSA). The algorithm takes into account both account degree and weight relationships between accounts. The blockchain transaction network is modeled as an undirected weighted graph, and community detection algorithms are employed to analyze the correlations between accounts. Strong-correlated accounts are grouped into the same shard, and a multi-shard blockchain network is constructed. Additionally, to further reduce the number of cross-shard transactions, this paper designs a random redundancy strategy based on account correlation, which randomly selects strong-correlated accounts and stores them redundantly in another shard, thus original cross-shard transactions can be verified and confirmed within the same shard. Simulation experiments demonstrate that DFSA outperforms the random sharding algorithm (RSA), modular sharding algorithm (MSA), and label propagation algorithm (LPA) in terms of cross-shard transaction proportion, latency, and throughput. Therefore, DFSA can effectively reduce cross-shard transaction proportion and lower transaction confirmation latency.
基金funded by Ajman University,AU-Funded Research Grant 2023-IRG-ENIT-22.
文摘Cyber-Physical System (CPS) devices are increasing exponentially. Lacking confidentiality creates a vulnerable network. Thus, demanding the overall system with the latest and robust solutions for the defence mechanisms with low computation cost, increased integrity, and surveillance. The proposal of a mechanism that utilizes the features of authenticity measures using the Destination Sequence Distance Vector (DSDV) routing protocol which applies to the multi-WSN (Wireless Sensor Network) of IoT devices in CPS which is developed for the Device-to-Device (D2D) authentication developed from the local-chain and public chain respectively combined with the Software Defined Networking (SDN) control and monitoring system using switches and controllers that will route the packets through the network, identify any false nodes, take preventive measures against them and preventing them for any future problems. Next, the system is powered by Blockchain cryptographic features by utilizing the TrustChain features to create a private, secure, and temper-free ledger of the transactions performed inside the network. Results are achieved in the legitimate devices connecting to the network, transferring their packets to their destination under supervision, reporting whenever a false node is causing hurdles, and recording the transactions for temper-proof records. Evaluation results based on 1000+ transactions illustrate that the proposed mechanism not only outshines most aspects of Cyber-Physical systems but also consumes less computation power with a low latency of 0.1 seconds only.
文摘BACKGROUND There is a lack of study on vitamin D and calcium levels in epileptic patients receiving therapy,despite the growing recognition of the importance of bone health in individuals with epilepsy.Associations one statistical method for finding correlations between variables in big datasets is called association rule mining(ARM).This technique finds patterns of common items or events in the data set,including associations.Through the analysis of patient data,including demographics,genetic information,and reactions with previous treatments,ARM can identify harmful drug reactions,possible novel combinations of medicines,and trends which connect particular individual features to treatment outcomes.AIM To investigate the evidence on the effects of anti-epileptic drugs(AEDs)on calcium metabolism and supplementing with vitamin D to help lower the likelihood of bone-related issues using ARM technique.METHODS ARM technique was used to analyze patients’behavior on calcium metabolism,vitamin D and anti-epileptic medicines.Epileptic sufferers of both sexes who attended neurological outpatient and in patient department clinics were recruited for the study.There were three patient groups:Group 1 received one AED,group 2 received two AEDs,and group 3 received more than two AEDs.The researchers analyzed the alkaline phosphatase,ionized calcium,total calcium,phosphorus,vitamin D levels,or parathyroid hormone values.RESULTS A total of 150 patients,aged 12 years to 60 years,were studied,with 50 in each group(1,2,and 3).60%were men,this gender imbalance may affect the study’s findings,as women have different bone metabolism dynamics influenced by hormonal variations,including menopause.The results may not fully capture the distinct effects of AEDs on female patients.A greater equal distribution of women should be the goal of future studies in order to offer a complete comprehension of the metabolic alterations brought on by AEDs.86 patients had generalized epilepsy,64 partial.42%of patients had AEDs for>5 years.Polytherapy reduced calcium and vitamin D levels compared to mono and dual therapy.Polytherapy elevated alkaline phosphatase and phosphorus levels.CONCLUSION ARM revealed the possible effects of variables like age,gender,and polytherapy on parathyroid hormone levels in individuals taking antiepileptic medication.
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.
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In the contemporary era,characterized by the Internet and digitalization as fundamental features,the operation and application of digital currency have gradually developed into a comprehensive structural system.This system restores the essential characteristics of currency while providing auxiliary services related to the formation,circulation,storage,application,and promotion of digital currency.Compared to traditional currency management technologies,big data analysis technology,which is primarily embedded in digital currency systems,enables the rapid acquisition of information.This facilitates the identification of standard associations within currency data and provides technical support for the operational framework of digital currency.
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.
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.
基金supported by the National Science Foundation of China(No.61602536)the Emerging Interdisciplinary Project of Central University of Finance and Economics(CUFE),and Financial Sustainable Development Research Team.
文摘With the gradual application of central bank digital currency(CBDC)in China,it brings new payment methods,but also potentially derives new money laundering paths.Two typical application scenarios of CBDC are considered,namely the anonymous transaction scenario and real-name transaction scenario.First,starting from the interaction network of transactional groups,the degree distribution,density,and modularity of normal and money laundering transactions in two transaction scenarios are compared and analyzed,so as to clarify the characteristics and paths of money laundering transactions.Then,according to the two typical application scenarios,different transaction datasets are selected,and different models are used to train the models on the recognition of money laundering behaviors in the two datasets.Among them,in the anonymous transaction scenario,the graph convolutional neural network is used to identify the spatial structure,the recurrent neural network is fused to obtain the dynamic pattern,and the model ChebNet-GRU is constructed.The constructed ChebNet-GRU model has the best effect in the recognition of money laundering behavior,with a precision of 94.3%,a recall of 59.5%,an F1 score of 72.9%,and a microaverage F1 score of 97.1%.While in the real-name transaction scenario,the traditional machine learning method is far better than the deep learning method,and the micro-average F1 score of the random forest and XGBoost models both reach 99.9%,which can effectively identify money laundering in currency transactions.
