The development of defect prediction plays a significant role in improving software quality. Such predictions are used to identify defective modules before the testing and to minimize the time and cost. The software w...The development of defect prediction plays a significant role in improving software quality. Such predictions are used to identify defective modules before the testing and to minimize the time and cost. The software with defects negatively impacts operational costs and finally affects customer satisfaction. Numerous approaches exist to predict software defects. However, the timely and accurate software bugs are the major challenging issues. To improve the timely and accurate software defect prediction, a novel technique called Nonparametric Statistical feature scaled QuAdratic regressive convolution Deep nEural Network (SQADEN) is introduced. The proposed SQADEN technique mainly includes two major processes namely metric or feature selection and classification. First, the SQADEN uses the nonparametric statistical Torgerson–Gower scaling technique for identifying the relevant software metrics by measuring the similarity using the dice coefficient. The feature selection process is used to minimize the time complexity of software fault prediction. With the selected metrics, software fault perdition with the help of the Quadratic Censored regressive convolution deep neural network-based classification. The deep learning classifier analyzes the training and testing samples using the contingency correlation coefficient. The softstep activation function is used to provide the final fault prediction results. To minimize the error, the Nelder–Mead method is applied to solve non-linear least-squares problems. Finally, accurate classification results with a minimum error are obtained at the output layer. Experimental evaluation is carried out with different quantitative metrics such as accuracy, precision, recall, F-measure, and time complexity. The analyzed results demonstrate the superior performance of our proposed SQADEN technique with maximum accuracy, sensitivity and specificity by 3%, 3%, 2% and 3% and minimum time and space by 13% and 15% when compared with the two state-of-the-art methods.展开更多
Peripheral nerve injury is a serious disease and its repair is challenging. A cable-style autologous graft is the gold standard for repairing long peripheral nerve defects; however, ensuring that the minimum number of...Peripheral nerve injury is a serious disease and its repair is challenging. A cable-style autologous graft is the gold standard for repairing long peripheral nerve defects; however, ensuring that the minimum number of transplanted nerve attains maximum therapeutic effect remains poorly understood. In this study, a rat model of common peroneal nerve defect was established by resecting a 10-mm long right common peroneal nerve. Rats receiving transplantation of the common peroneal nerve in situ were designated as the in situ graft group. Ipsilateral sural nerves（10–30 mm long） were resected to establish the one sural nerve graft group, two sural nerves cable-style nerve graft group and three sural nerves cable-style nerve graft group. Each bundle of the peroneal nerve was 10 mm long. To reduce the barrier effect due to invasion by surrounding tissue and connective-tissue overgrowth between neural stumps, small gap sleeve suture was used in both proximal and distal terminals to allow repair of the injured common peroneal nerve. At three months postoperatively, recovery of nerve function and morphology was observed using osmium tetroxide staining and functional detection. The results showed that the number of regenerated nerve fibers, common peroneal nerve function index, motor nerve conduction velocity, recovery of myodynamia, and wet weight ratios of tibialis anterior muscle were not significantly different among the one sural nerve graft group, two sural nerves cable-style nerve graft group, and three sural nerves cable-style nerve graft group. These data suggest that the repair effect achieved using one sural nerve graft with a lower number of nerve fibers is the same as that achieved using the two sural nerves cable-style nerve graft and three sural nerves cable-style nerve graft. This indicates that according to the ‘multiple amplification＇ phenomenon, one small nerve graft can provide a good therapeutic effect for a large peripheral nerve defect.展开更多
Software defect prediction plays a very important role in software quality assurance,which aims to inspect as many potentially defect-prone software modules as possible.However,the performance of the prediction model ...Software defect prediction plays a very important role in software quality assurance,which aims to inspect as many potentially defect-prone software modules as possible.However,the performance of the prediction model is susceptible to high dimensionality of the dataset that contains irrelevant and redundant features.In addition,software metrics for software defect prediction are almost entirely traditional features compared to the deep semantic feature representation from deep learning techniques.To address these two issues,we propose the following two solutions in this paper:(1)We leverage a novel non-linear manifold learning method-SOINN Landmark Isomap(SL-Isomap)to extract the representative features by selecting automatically the reasonable number and position of landmarks,which can reveal the complex intrinsic structure hidden behind the defect data.(2)We propose a novel defect prediction model named DLDD based on hybrid deep learning techniques,which leverages denoising autoencoder to learn true input features that are not contaminated by noise,and utilizes deep neural network to learn the abstract deep semantic features.We combine the squared error loss function of denoising autoencoder with the cross entropy loss function of deep neural network to achieve the best prediction performance by adjusting a hyperparameter.We compare the SL-Isomap with seven state-of-the-art feature extraction methods and compare the DLDD model with six baseline models across 20 open source software projects.The experimental results verify that the superiority of SL-Isomap and DLDD on four evaluation indicators.展开更多
Objective:This paper is to investigate functional connectivity differences between the abacus experts and control groups by using nonlinear processing method spatiotemporal Lyapunov exponent.Methods:11 right-handed he...Objective:This paper is to investigate functional connectivity differences between the abacus experts and control groups by using nonlinear processing method spatiotemporal Lyapunov exponent.Methods:11 right-handed healthy control children and 12 abacus children were undergone functional magnetic resonance imaging(fMRI).After preprocessing fMRI data with SPM,linear and nonlinear methods for connectivity analysis were both employed.Results:Connectivity differences between the two groups were statistically P<0.05 by the correlation method,while the P value by the nonlinear method were P<0.01.Conclusion:There are significant differences between the two groups in functional connectivity of bilateral occipital lobes.The nonlinear method proposed here seems to be more specific compared with the common linear correlation method.展开更多
多任务学习是一种联合多个任务同时学习来增强模型表示和泛化能力的手段,任务之间的相关性是多任务学习的基本因素.为解决任务差异的内在冲突会损害部分任务的预测的问题,提出一种基于相关性学习层(correlation learning layer,CLL)的...多任务学习是一种联合多个任务同时学习来增强模型表示和泛化能力的手段,任务之间的相关性是多任务学习的基本因素.为解决任务差异的内在冲突会损害部分任务的预测的问题,提出一种基于相关性学习层(correlation learning layer,CLL)的多任务学习模型,并将该模型作为新的代理模型应用于贝叶斯优化算法中,以期解决昂贵的优化问题.在传统的多任务学习网络后面增加相关性学习层,使已经完成初步共享学习的任务在该层进行优化共享,令多个任务所学习到的知识充分交互起来.根据不同的基于参数的共享机制,构建带有相关性层的LeNet和径向基函数(radial basis function,RBF)多任务学习模型,在多任务版本的美国国家标准与技术研究所(Mixed National Institute of Standards and Technology,MNIST)数据集和任务相关性可控制的综合数据集上进行实验,验证了所提出的基于相关性层的多任务学习模型的有效性.将所提多任务学习网络作为代理模型应用于贝叶斯优化算法中,不仅能减少模型对目标问题的评价次数,还能成倍地扩充训练数据数量,进而提升模型的性能.展开更多
文摘The development of defect prediction plays a significant role in improving software quality. Such predictions are used to identify defective modules before the testing and to minimize the time and cost. The software with defects negatively impacts operational costs and finally affects customer satisfaction. Numerous approaches exist to predict software defects. However, the timely and accurate software bugs are the major challenging issues. To improve the timely and accurate software defect prediction, a novel technique called Nonparametric Statistical feature scaled QuAdratic regressive convolution Deep nEural Network (SQADEN) is introduced. The proposed SQADEN technique mainly includes two major processes namely metric or feature selection and classification. First, the SQADEN uses the nonparametric statistical Torgerson–Gower scaling technique for identifying the relevant software metrics by measuring the similarity using the dice coefficient. The feature selection process is used to minimize the time complexity of software fault prediction. With the selected metrics, software fault perdition with the help of the Quadratic Censored regressive convolution deep neural network-based classification. The deep learning classifier analyzes the training and testing samples using the contingency correlation coefficient. The softstep activation function is used to provide the final fault prediction results. To minimize the error, the Nelder–Mead method is applied to solve non-linear least-squares problems. Finally, accurate classification results with a minimum error are obtained at the output layer. Experimental evaluation is carried out with different quantitative metrics such as accuracy, precision, recall, F-measure, and time complexity. The analyzed results demonstrate the superior performance of our proposed SQADEN technique with maximum accuracy, sensitivity and specificity by 3%, 3%, 2% and 3% and minimum time and space by 13% and 15% when compared with the two state-of-the-art methods.
基金supported by the National Basic Research Program of China(973 Program),No.2014CB542200a grant from the Ministry of Education Innovation Team,No.IRT1201+2 种基金the National Natural Science Foundation of China,No.31271284,31171150,81171146,30971526,31100860,31040043,31640045,31671246a grant from the Educational Ministry New Century Excellent Talents Support Project in China,No.BMU20110270a grant from the National Key Research and Development Program in China,No.2016YFC1101604
文摘Peripheral nerve injury is a serious disease and its repair is challenging. A cable-style autologous graft is the gold standard for repairing long peripheral nerve defects; however, ensuring that the minimum number of transplanted nerve attains maximum therapeutic effect remains poorly understood. In this study, a rat model of common peroneal nerve defect was established by resecting a 10-mm long right common peroneal nerve. Rats receiving transplantation of the common peroneal nerve in situ were designated as the in situ graft group. Ipsilateral sural nerves（10–30 mm long） were resected to establish the one sural nerve graft group, two sural nerves cable-style nerve graft group and three sural nerves cable-style nerve graft group. Each bundle of the peroneal nerve was 10 mm long. To reduce the barrier effect due to invasion by surrounding tissue and connective-tissue overgrowth between neural stumps, small gap sleeve suture was used in both proximal and distal terminals to allow repair of the injured common peroneal nerve. At three months postoperatively, recovery of nerve function and morphology was observed using osmium tetroxide staining and functional detection. The results showed that the number of regenerated nerve fibers, common peroneal nerve function index, motor nerve conduction velocity, recovery of myodynamia, and wet weight ratios of tibialis anterior muscle were not significantly different among the one sural nerve graft group, two sural nerves cable-style nerve graft group, and three sural nerves cable-style nerve graft group. These data suggest that the repair effect achieved using one sural nerve graft with a lower number of nerve fibers is the same as that achieved using the two sural nerves cable-style nerve graft and three sural nerves cable-style nerve graft. This indicates that according to the ‘multiple amplification＇ phenomenon, one small nerve graft can provide a good therapeutic effect for a large peripheral nerve defect.
基金This work is supported in part by the National Science Foundation of China(Grant Nos.61672392,61373038)in part by the National Key Research and Development Program of China(Grant No.2016YFC1202204).
文摘Software defect prediction plays a very important role in software quality assurance,which aims to inspect as many potentially defect-prone software modules as possible.However,the performance of the prediction model is susceptible to high dimensionality of the dataset that contains irrelevant and redundant features.In addition,software metrics for software defect prediction are almost entirely traditional features compared to the deep semantic feature representation from deep learning techniques.To address these two issues,we propose the following two solutions in this paper:(1)We leverage a novel non-linear manifold learning method-SOINN Landmark Isomap(SL-Isomap)to extract the representative features by selecting automatically the reasonable number and position of landmarks,which can reveal the complex intrinsic structure hidden behind the defect data.(2)We propose a novel defect prediction model named DLDD based on hybrid deep learning techniques,which leverages denoising autoencoder to learn true input features that are not contaminated by noise,and utilizes deep neural network to learn the abstract deep semantic features.We combine the squared error loss function of denoising autoencoder with the cross entropy loss function of deep neural network to achieve the best prediction performance by adjusting a hyperparameter.We compare the SL-Isomap with seven state-of-the-art feature extraction methods and compare the DLDD model with six baseline models across 20 open source software projects.The experimental results verify that the superiority of SL-Isomap and DLDD on four evaluation indicators.
文摘Objective:This paper is to investigate functional connectivity differences between the abacus experts and control groups by using nonlinear processing method spatiotemporal Lyapunov exponent.Methods:11 right-handed healthy control children and 12 abacus children were undergone functional magnetic resonance imaging(fMRI).After preprocessing fMRI data with SPM,linear and nonlinear methods for connectivity analysis were both employed.Results:Connectivity differences between the two groups were statistically P<0.05 by the correlation method,while the P value by the nonlinear method were P<0.01.Conclusion:There are significant differences between the two groups in functional connectivity of bilateral occipital lobes.The nonlinear method proposed here seems to be more specific compared with the common linear correlation method.
文摘多任务学习是一种联合多个任务同时学习来增强模型表示和泛化能力的手段,任务之间的相关性是多任务学习的基本因素.为解决任务差异的内在冲突会损害部分任务的预测的问题,提出一种基于相关性学习层(correlation learning layer,CLL)的多任务学习模型,并将该模型作为新的代理模型应用于贝叶斯优化算法中,以期解决昂贵的优化问题.在传统的多任务学习网络后面增加相关性学习层,使已经完成初步共享学习的任务在该层进行优化共享,令多个任务所学习到的知识充分交互起来.根据不同的基于参数的共享机制,构建带有相关性层的LeNet和径向基函数(radial basis function,RBF)多任务学习模型,在多任务版本的美国国家标准与技术研究所(Mixed National Institute of Standards and Technology,MNIST)数据集和任务相关性可控制的综合数据集上进行实验,验证了所提出的基于相关性层的多任务学习模型的有效性.将所提多任务学习网络作为代理模型应用于贝叶斯优化算法中,不仅能减少模型对目标问题的评价次数,还能成倍地扩充训练数据数量,进而提升模型的性能.
