Task-based Language Teaching(TBLT)research has provided ample evidence that cognitive complexity is an important aspect of task design that influences learner’s performance in terms of fluency,accuracy,and syntactic ...Task-based Language Teaching(TBLT)research has provided ample evidence that cognitive complexity is an important aspect of task design that influences learner’s performance in terms of fluency,accuracy,and syntactic complexity.Despite the substantial number of empirical investigations into task complexity in journal articles,storyline complexity,one of the features of it,is scarcely investigated.Previous research mainly focused on the impact of storyline complexity on learners’oral performance,but the impact on learners’written performance is less investigated.Thus,this study aims at investigating the effects of narrative complexity of storyline on senior high school students’written performance,as displayed by its complexity,fluency,and accuracy.The present study has important pedagogical implications.That is,task design and assessment should make a distinction between different types of narrative tasks.For example,the task with single or dual storyline.Results on task complexity may contribute to informing the pedagogical choices made by teachers when prioritizing work with a specific linguistic dimension.展开更多
针对现有恶意软件分类方法特征提取的单一性及对通道权重忽视的问题,本文提出了一种基于EfficientNetV2和特征融合的新型分类方法。该方法通过综合利用Byte和Asm文件从多角度提取特征图像,融合生成三通道图像以提供更全面的恶意软件特...针对现有恶意软件分类方法特征提取的单一性及对通道权重忽视的问题,本文提出了一种基于EfficientNetV2和特征融合的新型分类方法。该方法通过综合利用Byte和Asm文件从多角度提取特征图像,融合生成三通道图像以提供更全面的恶意软件特征表达,并采用EfficientNetV2深度学习模型进行分类,更精确地刻画恶意软件间的相似性,从而提高分类准确率。在BIG2015数据集上的实验结果表明,本文方法的分类准确率达到了99.14%,能够有效分类恶意软件家族,凸显了特征融合和深度学习模型在恶意软件分类领域的巨大潜力。Addressing the limitations of singularity of feature extraction and the neglect of channel weights in existing malware classification methods, this paper introduces a novel classification method based on EfficientNetV2 and feature fusion. This method combines Byte and Asm files to extract multi-dimensional feature images, creating three-channel images for a more comprehensive representation of malware features. Utilizing the EfficientNetV2 deep learning model, the approach enhances the accuracy of malware classification by capturing subtle similarities among malware more precisely. Experiments on the BIG2015 dataset demonstrate a classification accuracy of 99.14%, effectively categorizing malware families and highlighting the significant potential of feature fusion and deep learning models in the field of malware classification.展开更多
基于PySide2软件设计,使用Visual Studio Code平台、Python编程语言等技术,实现了对广州新一代双偏振天气雷达基数据及产品生成、运行状态信息及雷达产品传输的自动监控,并针对监控到的雷达运行异常情况同步发出多媒体声音、微信提醒、...基于PySide2软件设计,使用Visual Studio Code平台、Python编程语言等技术,实现了对广州新一代双偏振天气雷达基数据及产品生成、运行状态信息及雷达产品传输的自动监控,并针对监控到的雷达运行异常情况同步发出多媒体声音、微信提醒、手机短信多种方式的报警通知。该软件自投入业务运行以来,运行比较稳定,故障提醒及时准确,极大地缩短值班人员的故障响应时间。展开更多
大脑中动脉M2段(M2 segment of middle cerebral artery, MCA-M2)是颈内动脉系统的重要分支,大脑中动脉M2段闭塞脑梗死导致的神经功能缺损对患者家庭及社会带来了沉重的负担。大脑中动脉M2段闭塞脑梗死的介入治疗目前仍存在争议。目前...大脑中动脉M2段(M2 segment of middle cerebral artery, MCA-M2)是颈内动脉系统的重要分支,大脑中动脉M2段闭塞脑梗死导致的神经功能缺损对患者家庭及社会带来了沉重的负担。大脑中动脉M2段闭塞脑梗死的介入治疗目前仍存在争议。目前有研究表明大脑中动脉M2段闭塞脑梗死血管内治疗优于传统的内科治疗。RAPID软件在急性缺血性脑卒中患者行血管内治疗的决策中提供帮助得到认可,但对于大脑中动脉M2段闭塞脑梗死的血管内治疗适应症的选择缺乏定论。本文就大脑中动脉M2段闭塞脑梗死血管内治疗疗效及Rapid软件筛选合适大脑中动脉M2段闭塞脑梗死介入治疗患者做一综述。The M2 segment of middle cerebral artery (MCA-M2) is an important branch of the internal carotid artery system. Cerebral infarction caused by M2 segment occlusion of MCA-M2 has brought a heavy burden on the family and society. The interventional treatment of M2 segment occlusion of MCA-M2 is still controversial. Current studies have shown that endovascular treatment of M2 segment occlusion of MCA-M2 is superior to traditional medical treatment. RAPID software has been recognized as a helpful tool in the decision-making of endovascular treatment for patients with acute ischemic stroke, but there is a lack of a definite conclusion on the indications for endovascular treatment of M2 segment occlusion of MCA-M2. This article reviews the efficacy of endovascular treatment of M2 segment occlusion of MCA-M2 and the selection of suitable patients for interventional treatment of M2 segment occlusion of MCA-M2 with Rapid software.展开更多
文摘Task-based Language Teaching(TBLT)research has provided ample evidence that cognitive complexity is an important aspect of task design that influences learner’s performance in terms of fluency,accuracy,and syntactic complexity.Despite the substantial number of empirical investigations into task complexity in journal articles,storyline complexity,one of the features of it,is scarcely investigated.Previous research mainly focused on the impact of storyline complexity on learners’oral performance,but the impact on learners’written performance is less investigated.Thus,this study aims at investigating the effects of narrative complexity of storyline on senior high school students’written performance,as displayed by its complexity,fluency,and accuracy.The present study has important pedagogical implications.That is,task design and assessment should make a distinction between different types of narrative tasks.For example,the task with single or dual storyline.Results on task complexity may contribute to informing the pedagogical choices made by teachers when prioritizing work with a specific linguistic dimension.
文摘针对现有恶意软件分类方法特征提取的单一性及对通道权重忽视的问题,本文提出了一种基于EfficientNetV2和特征融合的新型分类方法。该方法通过综合利用Byte和Asm文件从多角度提取特征图像,融合生成三通道图像以提供更全面的恶意软件特征表达,并采用EfficientNetV2深度学习模型进行分类,更精确地刻画恶意软件间的相似性,从而提高分类准确率。在BIG2015数据集上的实验结果表明,本文方法的分类准确率达到了99.14%,能够有效分类恶意软件家族,凸显了特征融合和深度学习模型在恶意软件分类领域的巨大潜力。Addressing the limitations of singularity of feature extraction and the neglect of channel weights in existing malware classification methods, this paper introduces a novel classification method based on EfficientNetV2 and feature fusion. This method combines Byte and Asm files to extract multi-dimensional feature images, creating three-channel images for a more comprehensive representation of malware features. Utilizing the EfficientNetV2 deep learning model, the approach enhances the accuracy of malware classification by capturing subtle similarities among malware more precisely. Experiments on the BIG2015 dataset demonstrate a classification accuracy of 99.14%, effectively categorizing malware families and highlighting the significant potential of feature fusion and deep learning models in the field of malware classification.
文摘基于PySide2软件设计,使用Visual Studio Code平台、Python编程语言等技术,实现了对广州新一代双偏振天气雷达基数据及产品生成、运行状态信息及雷达产品传输的自动监控,并针对监控到的雷达运行异常情况同步发出多媒体声音、微信提醒、手机短信多种方式的报警通知。该软件自投入业务运行以来,运行比较稳定,故障提醒及时准确,极大地缩短值班人员的故障响应时间。
文摘大脑中动脉M2段(M2 segment of middle cerebral artery, MCA-M2)是颈内动脉系统的重要分支,大脑中动脉M2段闭塞脑梗死导致的神经功能缺损对患者家庭及社会带来了沉重的负担。大脑中动脉M2段闭塞脑梗死的介入治疗目前仍存在争议。目前有研究表明大脑中动脉M2段闭塞脑梗死血管内治疗优于传统的内科治疗。RAPID软件在急性缺血性脑卒中患者行血管内治疗的决策中提供帮助得到认可,但对于大脑中动脉M2段闭塞脑梗死的血管内治疗适应症的选择缺乏定论。本文就大脑中动脉M2段闭塞脑梗死血管内治疗疗效及Rapid软件筛选合适大脑中动脉M2段闭塞脑梗死介入治疗患者做一综述。The M2 segment of middle cerebral artery (MCA-M2) is an important branch of the internal carotid artery system. Cerebral infarction caused by M2 segment occlusion of MCA-M2 has brought a heavy burden on the family and society. The interventional treatment of M2 segment occlusion of MCA-M2 is still controversial. Current studies have shown that endovascular treatment of M2 segment occlusion of MCA-M2 is superior to traditional medical treatment. RAPID software has been recognized as a helpful tool in the decision-making of endovascular treatment for patients with acute ischemic stroke, but there is a lack of a definite conclusion on the indications for endovascular treatment of M2 segment occlusion of MCA-M2. This article reviews the efficacy of endovascular treatment of M2 segment occlusion of MCA-M2 and the selection of suitable patients for interventional treatment of M2 segment occlusion of MCA-M2 with Rapid software.