Worst-case execution time (WCET) analysis of multi-threaded software is still a challenge. This comes mainly from the fact that synchronization has to be taken into account. In this paper, we focus on this issue and o...Worst-case execution time (WCET) analysis of multi-threaded software is still a challenge. This comes mainly from the fact that synchronization has to be taken into account. In this paper, we focus on this issue and on automatically calculating and incorporating stalling times (e.g. caused by lock contention) in a generic graph model. The idea that thread interleavings can be studied with a matrix calculus is novel in this research area. Our sparse matrix representations of the program are manipulated using an extended Kronecker algebra. The resulting graph represents multi-threaded programs similar as CFGs do for sequential programs. With this graph model, we are able to calculate the WCET of multi-threaded concurrent programs including stalling times which are due to synchronization. We employ a generating function-based approach for setting up data flow equations which are solved by well-known elimination-based dataflow analysis methods or an off-the-shelf equation solver. The WCET of multi-threaded programs can finally be calculated with a non-linear function solver.展开更多
With regard to the failure and cancellation of business logic of web services composition(WSC),this paper propose a novel web services transaction compensation mechanism based on paired net which can dynamically estab...With regard to the failure and cancellation of business logic of web services composition(WSC),this paper propose a novel web services transaction compensation mechanism based on paired net which can dynamically establish agile compensation-triggered process(CSCP-Nets),and satisfy prospective compensation requirements.The related execution semantics of five usual composition compensation patterns based on paired net are analyzed in the situations of successful execution,failure compensation and failure recovery.Paired net based application of trip reservation process(TRP) shows that it is feasible.展开更多
This study explores the impact of hyperparameter optimization on machine learning models for predicting cardiovascular disease using data from an IoST(Internet of Sensing Things)device.Ten distinct machine learning ap...This study explores the impact of hyperparameter optimization on machine learning models for predicting cardiovascular disease using data from an IoST(Internet of Sensing Things)device.Ten distinct machine learning approaches were implemented and systematically evaluated before and after hyperparameter tuning.Significant improvements were observed across various models,with SVM and Neural Networks consistently showing enhanced performance metrics such as F1-Score,recall,and precision.The study underscores the critical role of tailored hyperparameter tuning in optimizing these models,revealing diverse outcomes among algorithms.Decision Trees and Random Forests exhibited stable performance throughout the evaluation.While enhancing accuracy,hyperparameter optimization also led to increased execution time.Visual representations and comprehensive results support the findings,confirming the hypothesis that optimizing parameters can effectively enhance predictive capabilities in cardiovascular disease.This research contributes to advancing the understanding and application of machine learning in healthcare,particularly in improving predictive accuracy for cardiovascular disease management and intervention strategies.展开更多
文摘Worst-case execution time (WCET) analysis of multi-threaded software is still a challenge. This comes mainly from the fact that synchronization has to be taken into account. In this paper, we focus on this issue and on automatically calculating and incorporating stalling times (e.g. caused by lock contention) in a generic graph model. The idea that thread interleavings can be studied with a matrix calculus is novel in this research area. Our sparse matrix representations of the program are manipulated using an extended Kronecker algebra. The resulting graph represents multi-threaded programs similar as CFGs do for sequential programs. With this graph model, we are able to calculate the WCET of multi-threaded concurrent programs including stalling times which are due to synchronization. We employ a generating function-based approach for setting up data flow equations which are solved by well-known elimination-based dataflow analysis methods or an off-the-shelf equation solver. The WCET of multi-threaded programs can finally be calculated with a non-linear function solver.
基金supported by National High Technologies Research and Development Program of China (863 Program)(No. 2008BAH24B03)National Natural Science Foundation of China(Nos. 60673122 and 60940033)+2 种基金Postdoctoral Science Foundation of China (No. 20080440121)Natural Science Foundation of Hunan Province (Nos. 06017089 and 60940033)Science and Technology Planning Project of Hunan Province (No. 2010GK3020)
文摘With regard to the failure and cancellation of business logic of web services composition(WSC),this paper propose a novel web services transaction compensation mechanism based on paired net which can dynamically establish agile compensation-triggered process(CSCP-Nets),and satisfy prospective compensation requirements.The related execution semantics of five usual composition compensation patterns based on paired net are analyzed in the situations of successful execution,failure compensation and failure recovery.Paired net based application of trip reservation process(TRP) shows that it is feasible.
基金supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU),Grant Number IMSIU-RG23151.
文摘This study explores the impact of hyperparameter optimization on machine learning models for predicting cardiovascular disease using data from an IoST(Internet of Sensing Things)device.Ten distinct machine learning approaches were implemented and systematically evaluated before and after hyperparameter tuning.Significant improvements were observed across various models,with SVM and Neural Networks consistently showing enhanced performance metrics such as F1-Score,recall,and precision.The study underscores the critical role of tailored hyperparameter tuning in optimizing these models,revealing diverse outcomes among algorithms.Decision Trees and Random Forests exhibited stable performance throughout the evaluation.While enhancing accuracy,hyperparameter optimization also led to increased execution time.Visual representations and comprehensive results support the findings,confirming the hypothesis that optimizing parameters can effectively enhance predictive capabilities in cardiovascular disease.This research contributes to advancing the understanding and application of machine learning in healthcare,particularly in improving predictive accuracy for cardiovascular disease management and intervention strategies.
