Authorship verification is a crucial task in digital forensic investigations,where it is often necessary to determine whether a specific individual wrote a particular piece of text.Convolutional Neural Networks(CNNs)h...Authorship verification is a crucial task in digital forensic investigations,where it is often necessary to determine whether a specific individual wrote a particular piece of text.Convolutional Neural Networks(CNNs)have shown promise in solving this problem,but their performance highly depends on the choice of hyperparameters.In this paper,we explore the effectiveness of hyperparameter tuning in improving the performance of CNNs for authorship verification.We conduct experiments using a Hyper Tuned CNN model with three popular optimization algorithms:Adaptive Moment Estimation(ADAM),StochasticGradientDescent(SGD),andRoot Mean Squared Propagation(RMSPROP).The model is trained and tested on a dataset of text samples collected from various authors,and the performance is evaluated using accuracy,precision,recall,and F1 score.We compare the performance of the three optimization algorithms and demonstrate the effectiveness of hyperparameter tuning in improving the accuracy of the CNN model.Our results show that the Hyper Tuned CNN model with ADAM Optimizer achieves the highest accuracy of up to 90%.Furthermore,we demonstrate that hyperparameter tuning can help achieve significant performance improvements,even using a relatively simple model architecture like CNNs.Our findings suggest that the choice of the optimization algorithm is a crucial factor in the performance of CNNs for authorship verification and that hyperparameter tuning can be an effective way to optimize this choice.Overall,this paper demonstrates the effectiveness of hyperparameter tuning in improving the performance of CNNs for authorship verification in digital forensic investigations.Our findings have important implications for developing accurate and reliable authorship verification systems,which are crucial for various applications in digital forensics,such as identifying the author of anonymous threatening messages or detecting cases of plagiarism.展开更多
有组织科研团队建设有赖于对科研合作现象和规律的科学认识。常用于科研合作模式研究的合著者网络默认同一成果的合作者间贡献均等,但这通常与科研合作实践相左。作者贡献声明数据的出现为揭示更细粒度的合作实践提供了重要素材。为此,...有组织科研团队建设有赖于对科研合作现象和规律的科学认识。常用于科研合作模式研究的合著者网络默认同一成果的合作者间贡献均等,但这通常与科研合作实践相左。作者贡献声明数据的出现为揭示更细粒度的合作实践提供了重要素材。为此,本研究提出一种利用贡献声明数据构建的新型合作网络——合贡献者网络,为深入研究科研合作问题提供新工具。本研究以PLoS(Public Library of Science)上的药学论文数据为例,以合著者网络为基准,从合贡献者网络的网络结构特征入手,认识此新型合作网络的物理性质;选取当前重要研究方向之一的“合作群体识别”为切入点,进一步认识合贡献者网络的应用价值。研究结果表明:①在网络结构形态上,合贡献者网络比合著者网络更稀疏;②在合作群体识别上,两种网络的群体识别结果部分一致,重合度约为57%;约32%的合作群体在合贡献者网络上发生了重组;③合贡献者网络中的合作群体发文主题比合著者网络更为聚焦,但检验结果并不显著。总体来看,在本研究的数据集上,合贡献者网络较之合著者网络显示出更良好的社区结构;合贡献者网络有助于识别出更细粒度的合作群体,且在所识别的合作群体上发文主题的一致性更高。展开更多
We examine the association between network centrality and research using the accounting research community setting.We establish co-authorship network using papers published in the five top accounting journals from 198...We examine the association between network centrality and research using the accounting research community setting.We establish co-authorship network using papers published in the five top accounting journals from 1980 to 2016.We find that the co-authorship network in accounting is a“small world”with some most connected authors playing a key role in connecting others.We use machine learning to label published papers with multiple topics and find patterns in topics over time.More importantly,we find that co-authorship network centrality is positively associated with future research productivity and topic innovation and that the impact of centrality on productivity is higher with more senior authors.Further,centrality of an author’s co-authors also has an incrementally positive impact.We conclude that network centrality positively influences research output.展开更多
基金Prince Sultan University for funding this publication’s Article Process Charges(APC).
文摘Authorship verification is a crucial task in digital forensic investigations,where it is often necessary to determine whether a specific individual wrote a particular piece of text.Convolutional Neural Networks(CNNs)have shown promise in solving this problem,but their performance highly depends on the choice of hyperparameters.In this paper,we explore the effectiveness of hyperparameter tuning in improving the performance of CNNs for authorship verification.We conduct experiments using a Hyper Tuned CNN model with three popular optimization algorithms:Adaptive Moment Estimation(ADAM),StochasticGradientDescent(SGD),andRoot Mean Squared Propagation(RMSPROP).The model is trained and tested on a dataset of text samples collected from various authors,and the performance is evaluated using accuracy,precision,recall,and F1 score.We compare the performance of the three optimization algorithms and demonstrate the effectiveness of hyperparameter tuning in improving the accuracy of the CNN model.Our results show that the Hyper Tuned CNN model with ADAM Optimizer achieves the highest accuracy of up to 90%.Furthermore,we demonstrate that hyperparameter tuning can help achieve significant performance improvements,even using a relatively simple model architecture like CNNs.Our findings suggest that the choice of the optimization algorithm is a crucial factor in the performance of CNNs for authorship verification and that hyperparameter tuning can be an effective way to optimize this choice.Overall,this paper demonstrates the effectiveness of hyperparameter tuning in improving the performance of CNNs for authorship verification in digital forensic investigations.Our findings have important implications for developing accurate and reliable authorship verification systems,which are crucial for various applications in digital forensics,such as identifying the author of anonymous threatening messages or detecting cases of plagiarism.
文摘有组织科研团队建设有赖于对科研合作现象和规律的科学认识。常用于科研合作模式研究的合著者网络默认同一成果的合作者间贡献均等,但这通常与科研合作实践相左。作者贡献声明数据的出现为揭示更细粒度的合作实践提供了重要素材。为此,本研究提出一种利用贡献声明数据构建的新型合作网络——合贡献者网络,为深入研究科研合作问题提供新工具。本研究以PLoS(Public Library of Science)上的药学论文数据为例,以合著者网络为基准,从合贡献者网络的网络结构特征入手,认识此新型合作网络的物理性质;选取当前重要研究方向之一的“合作群体识别”为切入点,进一步认识合贡献者网络的应用价值。研究结果表明:①在网络结构形态上,合贡献者网络比合著者网络更稀疏;②在合作群体识别上,两种网络的群体识别结果部分一致,重合度约为57%;约32%的合作群体在合贡献者网络上发生了重组;③合贡献者网络中的合作群体发文主题比合著者网络更为聚焦,但检验结果并不显著。总体来看,在本研究的数据集上,合贡献者网络较之合著者网络显示出更良好的社区结构;合贡献者网络有助于识别出更细粒度的合作群体,且在所识别的合作群体上发文主题的一致性更高。
文摘We examine the association between network centrality and research using the accounting research community setting.We establish co-authorship network using papers published in the five top accounting journals from 1980 to 2016.We find that the co-authorship network in accounting is a“small world”with some most connected authors playing a key role in connecting others.We use machine learning to label published papers with multiple topics and find patterns in topics over time.More importantly,we find that co-authorship network centrality is positively associated with future research productivity and topic innovation and that the impact of centrality on productivity is higher with more senior authors.Further,centrality of an author’s co-authors also has an incrementally positive impact.We conclude that network centrality positively influences research output.
