Computational linguistics is an engineering-based scientific discipline.It deals with understanding written and spoken language from a computational viewpoint.Further,the domain also helps construct the artefacts that...Computational linguistics is an engineering-based scientific discipline.It deals with understanding written and spoken language from a computational viewpoint.Further,the domain also helps construct the artefacts that are useful in processing and producing a language either in bulk or in a dialogue setting.Named Entity Recognition(NER)is a fundamental task in the data extraction process.It concentrates on identifying and labelling the atomic components from several texts grouped under different entities,such as organizations,people,places,and times.Further,the NER mechanism identifies and removes more types of entities as per the requirements.The significance of the NER mechanism has been well-established in Natural Language Processing(NLP)tasks,and various research investigations have been conducted to develop novel NER methods.The conventional ways of managing the tasks range from rule-related and hand-crafted feature-related Machine Learning(ML)techniques to Deep Learning(DL)techniques.In this aspect,the current study introduces a novel Dart Games Optimizer with Hybrid Deep Learning-Driven Computational Linguistics(DGOHDL-CL)model for NER.The presented DGOHDL-CL technique aims to determine and label the atomic components from several texts as a collection of the named entities.In the presented DGOHDL-CL technique,the word embed-ding process is executed at the initial stage with the help of the word2vec model.For the NER mechanism,the Convolutional Gated Recurrent Unit(CGRU)model is employed in this work.At last,the DGO technique is used as a hyperparameter tuning strategy for the CGRU algorithm to boost the NER’s outcomes.No earlier studies integrated the DGO mechanism with the CGRU model for NER.To exhibit the superiority of the proposed DGOHDL-CL technique,a widespread simulation analysis was executed on two datasets,CoNLL-2003 and OntoNotes 5.0.The experimental outcomes establish the promising performance of the DGOHDL-CL technique over other models.展开更多
域名系统(DNS)是互联网基础服务,是互联网访问的重要入口,域名隐私保护是DNS安全的研究热点。提出了一种基于用户数据报协议(UDP)的DNS传输中用户隐私保护的加密方法:DNSDEA(DNS data encryption algorithm)。该方法采用PKI加密体系与DN...域名系统(DNS)是互联网基础服务,是互联网访问的重要入口,域名隐私保护是DNS安全的研究热点。提出了一种基于用户数据报协议(UDP)的DNS传输中用户隐私保护的加密方法:DNSDEA(DNS data encryption algorithm)。该方法采用PKI加密体系与DNS协议相融合,不仅解决了域名隐私保护问题,而且与传统DNS体系相兼容,保持了DNS系统的简单、高效的技术特点。与当前的DNS加密方法相比,DNSDEA提高了任务并行的并行化粒度,降低了加密情况下DNS查询的延时。展开更多
基金Princess Nourah Bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R281)Princess Nourah Bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4331004DSR10).
文摘Computational linguistics is an engineering-based scientific discipline.It deals with understanding written and spoken language from a computational viewpoint.Further,the domain also helps construct the artefacts that are useful in processing and producing a language either in bulk or in a dialogue setting.Named Entity Recognition(NER)is a fundamental task in the data extraction process.It concentrates on identifying and labelling the atomic components from several texts grouped under different entities,such as organizations,people,places,and times.Further,the NER mechanism identifies and removes more types of entities as per the requirements.The significance of the NER mechanism has been well-established in Natural Language Processing(NLP)tasks,and various research investigations have been conducted to develop novel NER methods.The conventional ways of managing the tasks range from rule-related and hand-crafted feature-related Machine Learning(ML)techniques to Deep Learning(DL)techniques.In this aspect,the current study introduces a novel Dart Games Optimizer with Hybrid Deep Learning-Driven Computational Linguistics(DGOHDL-CL)model for NER.The presented DGOHDL-CL technique aims to determine and label the atomic components from several texts as a collection of the named entities.In the presented DGOHDL-CL technique,the word embed-ding process is executed at the initial stage with the help of the word2vec model.For the NER mechanism,the Convolutional Gated Recurrent Unit(CGRU)model is employed in this work.At last,the DGO technique is used as a hyperparameter tuning strategy for the CGRU algorithm to boost the NER’s outcomes.No earlier studies integrated the DGO mechanism with the CGRU model for NER.To exhibit the superiority of the proposed DGOHDL-CL technique,a widespread simulation analysis was executed on two datasets,CoNLL-2003 and OntoNotes 5.0.The experimental outcomes establish the promising performance of the DGOHDL-CL technique over other models.
文摘域名系统(DNS)是互联网基础服务,是互联网访问的重要入口,域名隐私保护是DNS安全的研究热点。提出了一种基于用户数据报协议(UDP)的DNS传输中用户隐私保护的加密方法:DNSDEA(DNS data encryption algorithm)。该方法采用PKI加密体系与DNS协议相融合,不仅解决了域名隐私保护问题,而且与传统DNS体系相兼容,保持了DNS系统的简单、高效的技术特点。与当前的DNS加密方法相比,DNSDEA提高了任务并行的并行化粒度,降低了加密情况下DNS查询的延时。
