Web-services are highly distributed programs, and concurrent software is notoriously error-prone. Model checking is a powerful technique to find bugs in concurrent systems. However, the existing model checkers have no...Web-services are highly distributed programs, and concurrent software is notoriously error-prone. Model checking is a powerful technique to find bugs in concurrent systems. However, the existing model checkers have no enough ability to support for the programming languages and communication mechanisms used for Web services. We propose to use Kripke structures as means of modeling Web service. This paper presents an automated way to extract formal models from programs implementing Web services using predicate abstraction for abstract model checking. The abstract models are checked by means of a model checker that implements automatic abstraction refinement. These results enable the verification of the applications that implement Web services.展开更多
The rise of social media platforms has revolutionized communication, enabling the exchange of vast amounts of data through text, audio, images, and videos. These platforms have become critical for sharing opinions and...The rise of social media platforms has revolutionized communication, enabling the exchange of vast amounts of data through text, audio, images, and videos. These platforms have become critical for sharing opinions and insights, influencing daily habits, and driving business, political, and economic decisions. Text posts are particularly significant, and natural language processing (NLP) has emerged as a powerful tool for analyzing such data. While traditional NLP methods have been effective for structured media, social media content poses unique challenges due to its informal and diverse nature. This has spurred the development of new techniques tailored for processing and extracting insights from unstructured user-generated text. One key application of NLP is the summarization of user comments to manage overwhelming content volumes. Abstractive summarization has proven highly effective in generating concise, human-like summaries, offering clear overviews of key themes and sentiments. This enhances understanding and engagement while reducing cognitive effort for users. For businesses, summarization provides actionable insights into customer preferences and feedback, enabling faster trend analysis, improved responsiveness, and strategic adaptability. By distilling complex data into manageable insights, summarization plays a vital role in improving user experiences and empowering informed decision-making in a data-driven landscape. This paper proposes a new implementation framework by fine-tuning and parameterizing Transformer Large Language Models to manage and maintain linguistic and semantic components in abstractive summary generation. The system excels in transforming large volumes of data into meaningful summaries, as evidenced by its strong performance across metrics like fluency, consistency, readability, and semantic coherence.展开更多
We retrospect three abstract models for heat engines which include a classic abstract model in textbook of thermal physics,a primary abstract model for finite-time heat engines,and a refined abstract model for finite-...We retrospect three abstract models for heat engines which include a classic abstract model in textbook of thermal physics,a primary abstract model for finite-time heat engines,and a refined abstract model for finite-time heat engines.The detailed models of heat engines in literature of finite-time thermodynamics may be mapped into the refined abstract model.The future developments based on the refined abstract model are also surveyed.展开更多
Purpose:Mo ve recognition in scientific abstracts is an NLP task of classifying sentences of the abstracts into different types of language units.To improve the performance of move recognition in scientific abstracts,...Purpose:Mo ve recognition in scientific abstracts is an NLP task of classifying sentences of the abstracts into different types of language units.To improve the performance of move recognition in scientific abstracts,a novel model of move recognition is proposed that outperforms the BERT-based method.Design/methodology/approach:Prevalent models based on BERT for sentence classification often classify sentences without considering the context of the sentences.In this paper,inspired by the BERT masked language model(MLM),we propose a novel model called the masked sentence model that integrates the content and contextual information of the sentences in move recognition.Experiments are conducted on the benchmark dataset PubMed 20K RCT in three steps.Then,we compare our model with HSLN-RNN,BERT-based and SciBERT using the same dataset.Findings:Compared with the BERT-based and SciBERT models,the F1 score of our model outperforms them by 4.96%and 4.34%,respectively,which shows the feasibility and effectiveness of the novel model and the result of our model comes closest to the state-of-theart results of HSLN-RNN at present.Research limitations:The sequential features of move labels are not considered,which might be one of the reasons why HSLN-RNN has better performance.Our model is restricted to dealing with biomedical English literature because we use a dataset from PubMed,which is a typical biomedical database,to fine-tune our model.Practical implications:The proposed model is better and simpler in identifying move structures in scientific abstracts and is worthy of text classification experiments for capturing contextual features of sentences.Originality/value:T he study proposes a masked sentence model based on BERT that considers the contextual features of the sentences in abstracts in a new way.The performance of this classification model is significantly improved by rebuilding the input layer without changing the structure of neural networks.展开更多
It is difficult to formalize the causes of vulnerability, and there is no effective model to reveal the causes and characteristics of vulnerability. In this paper, a vulnerability model construction method is proposed...It is difficult to formalize the causes of vulnerability, and there is no effective model to reveal the causes and characteristics of vulnerability. In this paper, a vulnerability model construction method is proposed to realize the description of vulnerability attribute and the construction of a vulnerability model. A vulnerability model based on chemical abstract machine（CHAM） is constructed to realize the CHAM description of vulnerability model, and the framework of vulnerability model is also discussed. Case study is carried out to verify the feasibility and effectiveness of the proposed model. In addition, a prototype system is also designed and implemented based on the proposed vulnerability model. Experimental results show that the proposed model is more effective than other methods in the detection of software vulnerabilities.展开更多
Software engineering has been taught at many institutions as individual course for many years. Recently, many higher education institutions offer a BSc degree in Software Engineering. Software engineers are required, ...Software engineering has been taught at many institutions as individual course for many years. Recently, many higher education institutions offer a BSc degree in Software Engineering. Software engineers are required, especially at the small enterprises, to play many roles, and sometimes simultaneously. Beside the technical and managerial skills, software engineers should have additional intellectual skills such as domain-specific abstract thinking. Therefore, software engineering curriculum should help the students to build and improve their skills to meet the labor market needs. This study aims to explore the perceptions of software engineering students on the influence of learning software modeling and design on their domain-specific abstract thinking. Also, we explore the role of the course project in improving their domain-specific abstract thinking. The study results have shown that, most of the surveyed students believe that learning and practicing modeling and design concepts contribute to their ability to think abstractly on specific domain. However, this finding is influenced by the students’ lack of the comprehension of some modeling and design aspects (e.g., generalization). We believe that, such aspects should be introduced to the students at early levels of software engineering curriculum, which certainly will improve their ability to think abstractly on specific domain.展开更多
This article contains a system conversion from object oriented design into Software Product Line (SPL) using delta modeling of Abstract Behavioral Specification (ABS). ABS is a modeling language which targets system w...This article contains a system conversion from object oriented design into Software Product Line (SPL) using delta modeling of Abstract Behavioral Specification (ABS). ABS is a modeling language which targets system with high level of variety and supports SPL development with delta modeling. The case study of this thesis is a digital library system called Library Automation and Digital Archive (LONTAR). Originally, LONTAR only uses SOAP-based web service. With ABS, LONTAR will be converted into SPL and implement another web service called REST. The motivation of this conversion of LONTAR from object oriented into SPL is because it is easier to develop system with ABS than using regular object oriented. Product definition in ABS is relatively easier than creating a new subclass and do customization to make it works well.展开更多
任务的目的是识别对话中的关键信息并生成一段简短的文本.由于对话具有非正式化和动态交互性质,导致对话文本信息稀疏、关键信息分散.然而,现有模型未能实现对对话中主题特征信息的有效挖掘,缺乏对核心话语的识别,忽略了附加特征融合过...任务的目的是识别对话中的关键信息并生成一段简短的文本.由于对话具有非正式化和动态交互性质,导致对话文本信息稀疏、关键信息分散.然而,现有模型未能实现对对话中主题特征信息的有效挖掘,缺乏对核心话语的识别,忽略了附加特征融合过程中的噪声问题.针对上述问题,本文提出一种结合主题挖掘与话语中心性的对话摘要模型DS-TMUC(Dialogue Summarization model combining Topic Mining and Utterance Centrality).首先,提出一种主题特征提取模块,该模块引入嵌入式主题模型来有效地挖掘对话中可解释的潜在主题信息,为抽象对话摘要过程提供更丰富的语义信息.其次,提出一种特征动态融合模块,设计特征感知网络为融合特征去除噪声以增强特征的表征能力,利用多头注意力捕捉特征之间的语义关联性,并且使用门控机制进行过滤融合,从而增强特征之间的有效融合.再次,提出一种话语赋权模块,设计无监督聚类方法计算话语中心性权重为话语赋权,通过引导模型选择核心话语,进而提高模型对对话上下文建模的有效性.在SAMSum和DialogSum数据集上的实验结果表明,DS-TMUC模型的总体性能优于对比模型.展开更多
模型的编码器输出中包含冗余信息,导致生成内容存在语义不相关和偏离主旨等问题,提出了一个结合关键词信息和门控单元的预训练文本摘要模型BGUK(BERT with Gated Unit and Keywords)。首先,该模型使用BERT对源文本进行编码,并引入了门...模型的编码器输出中包含冗余信息,导致生成内容存在语义不相关和偏离主旨等问题,提出了一个结合关键词信息和门控单元的预训练文本摘要模型BGUK(BERT with Gated Unit and Keywords)。首先,该模型使用BERT对源文本进行编码,并引入了门控单元进行语义提取和冗余信息的过滤。其次,将主题关键词信息合并到模型中解决生成摘要偏离主旨的问题。最后,加入覆盖率机制来减少生成摘要时出现的重复。实验结果表明BGUK生成了更符合主题的高质量的摘要,同时ROUGE得分也超过了基线模型。展开更多
基金the National Natural Science Foundation of China (60663005, 60563005)the Natural Science Foundation of Guangxi Province (0542036, 0728093, 0728089)
文摘Web-services are highly distributed programs, and concurrent software is notoriously error-prone. Model checking is a powerful technique to find bugs in concurrent systems. However, the existing model checkers have no enough ability to support for the programming languages and communication mechanisms used for Web services. We propose to use Kripke structures as means of modeling Web service. This paper presents an automated way to extract formal models from programs implementing Web services using predicate abstraction for abstract model checking. The abstract models are checked by means of a model checker that implements automatic abstraction refinement. These results enable the verification of the applications that implement Web services.
文摘The rise of social media platforms has revolutionized communication, enabling the exchange of vast amounts of data through text, audio, images, and videos. These platforms have become critical for sharing opinions and insights, influencing daily habits, and driving business, political, and economic decisions. Text posts are particularly significant, and natural language processing (NLP) has emerged as a powerful tool for analyzing such data. While traditional NLP methods have been effective for structured media, social media content poses unique challenges due to its informal and diverse nature. This has spurred the development of new techniques tailored for processing and extracting insights from unstructured user-generated text. One key application of NLP is the summarization of user comments to manage overwhelming content volumes. Abstractive summarization has proven highly effective in generating concise, human-like summaries, offering clear overviews of key themes and sentiments. This enhances understanding and engagement while reducing cognitive effort for users. For businesses, summarization provides actionable insights into customer preferences and feedback, enabling faster trend analysis, improved responsiveness, and strategic adaptability. By distilling complex data into manageable insights, summarization plays a vital role in improving user experiences and empowering informed decision-making in a data-driven landscape. This paper proposes a new implementation framework by fine-tuning and parameterizing Transformer Large Language Models to manage and maintain linguistic and semantic components in abstractive summary generation. The system excels in transforming large volumes of data into meaningful summaries, as evidenced by its strong performance across metrics like fluency, consistency, readability, and semantic coherence.
基金the National Natural Science Foundation of China(Grant Nos.11975050 and 11675017).
文摘We retrospect three abstract models for heat engines which include a classic abstract model in textbook of thermal physics,a primary abstract model for finite-time heat engines,and a refined abstract model for finite-time heat engines.The detailed models of heat engines in literature of finite-time thermodynamics may be mapped into the refined abstract model.The future developments based on the refined abstract model are also surveyed.
基金supported by the project “The demonstration system of rich semantic search application in scientific literature” (Grant No. 1734) from the Chinese Academy of Sciences
文摘Purpose:Mo ve recognition in scientific abstracts is an NLP task of classifying sentences of the abstracts into different types of language units.To improve the performance of move recognition in scientific abstracts,a novel model of move recognition is proposed that outperforms the BERT-based method.Design/methodology/approach:Prevalent models based on BERT for sentence classification often classify sentences without considering the context of the sentences.In this paper,inspired by the BERT masked language model(MLM),we propose a novel model called the masked sentence model that integrates the content and contextual information of the sentences in move recognition.Experiments are conducted on the benchmark dataset PubMed 20K RCT in three steps.Then,we compare our model with HSLN-RNN,BERT-based and SciBERT using the same dataset.Findings:Compared with the BERT-based and SciBERT models,the F1 score of our model outperforms them by 4.96%and 4.34%,respectively,which shows the feasibility and effectiveness of the novel model and the result of our model comes closest to the state-of-theart results of HSLN-RNN at present.Research limitations:The sequential features of move labels are not considered,which might be one of the reasons why HSLN-RNN has better performance.Our model is restricted to dealing with biomedical English literature because we use a dataset from PubMed,which is a typical biomedical database,to fine-tune our model.Practical implications:The proposed model is better and simpler in identifying move structures in scientific abstracts and is worthy of text classification experiments for capturing contextual features of sentences.Originality/value:T he study proposes a masked sentence model based on BERT that considers the contextual features of the sentences in abstracts in a new way.The performance of this classification model is significantly improved by rebuilding the input layer without changing the structure of neural networks.
基金Supported by the National Natural Science Foundation of China(61202110 and 61502205)the Project of Jiangsu Provincial Six Talent Peaks(XYDXXJS-016)
文摘It is difficult to formalize the causes of vulnerability, and there is no effective model to reveal the causes and characteristics of vulnerability. In this paper, a vulnerability model construction method is proposed to realize the description of vulnerability attribute and the construction of a vulnerability model. A vulnerability model based on chemical abstract machine（CHAM） is constructed to realize the CHAM description of vulnerability model, and the framework of vulnerability model is also discussed. Case study is carried out to verify the feasibility and effectiveness of the proposed model. In addition, a prototype system is also designed and implemented based on the proposed vulnerability model. Experimental results show that the proposed model is more effective than other methods in the detection of software vulnerabilities.
文摘Software engineering has been taught at many institutions as individual course for many years. Recently, many higher education institutions offer a BSc degree in Software Engineering. Software engineers are required, especially at the small enterprises, to play many roles, and sometimes simultaneously. Beside the technical and managerial skills, software engineers should have additional intellectual skills such as domain-specific abstract thinking. Therefore, software engineering curriculum should help the students to build and improve their skills to meet the labor market needs. This study aims to explore the perceptions of software engineering students on the influence of learning software modeling and design on their domain-specific abstract thinking. Also, we explore the role of the course project in improving their domain-specific abstract thinking. The study results have shown that, most of the surveyed students believe that learning and practicing modeling and design concepts contribute to their ability to think abstractly on specific domain. However, this finding is influenced by the students’ lack of the comprehension of some modeling and design aspects (e.g., generalization). We believe that, such aspects should be introduced to the students at early levels of software engineering curriculum, which certainly will improve their ability to think abstractly on specific domain.
文摘This article contains a system conversion from object oriented design into Software Product Line (SPL) using delta modeling of Abstract Behavioral Specification (ABS). ABS is a modeling language which targets system with high level of variety and supports SPL development with delta modeling. The case study of this thesis is a digital library system called Library Automation and Digital Archive (LONTAR). Originally, LONTAR only uses SOAP-based web service. With ABS, LONTAR will be converted into SPL and implement another web service called REST. The motivation of this conversion of LONTAR from object oriented into SPL is because it is easier to develop system with ABS than using regular object oriented. Product definition in ABS is relatively easier than creating a new subclass and do customization to make it works well.
文摘任务的目的是识别对话中的关键信息并生成一段简短的文本.由于对话具有非正式化和动态交互性质,导致对话文本信息稀疏、关键信息分散.然而,现有模型未能实现对对话中主题特征信息的有效挖掘,缺乏对核心话语的识别,忽略了附加特征融合过程中的噪声问题.针对上述问题,本文提出一种结合主题挖掘与话语中心性的对话摘要模型DS-TMUC(Dialogue Summarization model combining Topic Mining and Utterance Centrality).首先,提出一种主题特征提取模块,该模块引入嵌入式主题模型来有效地挖掘对话中可解释的潜在主题信息,为抽象对话摘要过程提供更丰富的语义信息.其次,提出一种特征动态融合模块,设计特征感知网络为融合特征去除噪声以增强特征的表征能力,利用多头注意力捕捉特征之间的语义关联性,并且使用门控机制进行过滤融合,从而增强特征之间的有效融合.再次,提出一种话语赋权模块,设计无监督聚类方法计算话语中心性权重为话语赋权,通过引导模型选择核心话语,进而提高模型对对话上下文建模的有效性.在SAMSum和DialogSum数据集上的实验结果表明,DS-TMUC模型的总体性能优于对比模型.
文摘模型的编码器输出中包含冗余信息,导致生成内容存在语义不相关和偏离主旨等问题,提出了一个结合关键词信息和门控单元的预训练文本摘要模型BGUK(BERT with Gated Unit and Keywords)。首先,该模型使用BERT对源文本进行编码,并引入了门控单元进行语义提取和冗余信息的过滤。其次,将主题关键词信息合并到模型中解决生成摘要偏离主旨的问题。最后,加入覆盖率机制来减少生成摘要时出现的重复。实验结果表明BGUK生成了更符合主题的高质量的摘要,同时ROUGE得分也超过了基线模型。
