This paper proposes an interdisciplinary talent training model that combines foreign language education with area studies.The model aims to cultivate international ocean affairs professionals with cross-cultural commu...This paper proposes an interdisciplinary talent training model that combines foreign language education with area studies.The model aims to cultivate international ocean affairs professionals with cross-cultural communication skills,in-depth regional and country knowledge,and practical expertise in ocean affairs.Additionally,the paper presents specific training pathways and policy recommendations for implementing this model.展开更多
The work in this paper is based on primary research on how to obtain informed consent to medical treatment and or procedure among patients;this study was carried out in Papua New Guinea in both urban and rural health ...The work in this paper is based on primary research on how to obtain informed consent to medical treatment and or procedure among patients;this study was carried out in Papua New Guinea in both urban and rural health settings across customs,cultures,and languages in two provinces,on the basis of qualitative interviews with healthcare professionals including doctors,nurses,other healthcare workers,patients,and traditional healers.We emphasize the views of consent with participants of customs,cultural,and languages regarding informed consent.There are factors between peoples of differing circumstances which can greatly alter how they view consent.Some groups would involve people in the decision-making process that may not traditionally be involved in the decision making of a medical decision.Other groups may dislike certain medical procedures as in Papua New Guinea(PNG).And certain people have different views on what should be disclosed of the patient’s condition.Customs,cultures,and languages are common phenomena which continue to affect the daily lives of many thousands of people.It is unclear in PNG about the characteristics of customs,culture,and language on health care because there is no published information on informed consent and issues that affect the making of informed consent.展开更多
Machine translation of low-resource languages(LRLs)has long been hindered by limited corpora and linguistic complexity.This review summarizes key developments,from traditional methods to recent progress with large lan...Machine translation of low-resource languages(LRLs)has long been hindered by limited corpora and linguistic complexity.This review summarizes key developments,from traditional methods to recent progress with large language models(LLMs),while highlighting ongoing challenges such as data bottlenecks,biases,fairness,and computational costs.Finally,it discusses future directions,including efficient parameter fine-tuning,multimodal translation,and community-driven corpus construction,providing insights for advancing LRL translation research.展开更多
随着钓鱼邮件数量的迅速增加以及对抗技术的不断演进,传统的钓鱼邮件检测方法在效率和准确性方面面临严峻挑战.为此,提出了一种基于大语言模型(large language model,LLM)的钓鱼邮件检测方法,以解决现有系统检测率低、漏报率高及人机交...随着钓鱼邮件数量的迅速增加以及对抗技术的不断演进,传统的钓鱼邮件检测方法在效率和准确性方面面临严峻挑战.为此,提出了一种基于大语言模型(large language model,LLM)的钓鱼邮件检测方法,以解决现有系统检测率低、漏报率高及人机交互性差等问题.通过全面分析钓鱼邮件的关键特征,包括邮件头部字段、正文内容、URL、二维码、附件及HTML页面,利用特征插入算法构建高质量的训练数据集.基于预训练语言模型LLaMA和低秩自适应微调技术(low-rank adaptation,LoRA),在仅更新0.72%模型参数(约50 MB)条件下实现领域知识迁移,获得钓鱼邮件检测大模型.实验结果显示,与传统方法相比,基于大语言模型的检测方法显著提升了检测的准确性与鲁棒性,整体准确率达到94.5%,有效降低了误报率,增强了钓鱼邮件特征的分类与解释能力,提供了更具实用性和可靠性的钓鱼邮件检测方案.展开更多
基金supported by“Dalian Maritime University Teaching Reform Research Fund 2022 Annual Project”(Fund No.XJG2022-96).
文摘This paper proposes an interdisciplinary talent training model that combines foreign language education with area studies.The model aims to cultivate international ocean affairs professionals with cross-cultural communication skills,in-depth regional and country knowledge,and practical expertise in ocean affairs.Additionally,the paper presents specific training pathways and policy recommendations for implementing this model.
文摘The work in this paper is based on primary research on how to obtain informed consent to medical treatment and or procedure among patients;this study was carried out in Papua New Guinea in both urban and rural health settings across customs,cultures,and languages in two provinces,on the basis of qualitative interviews with healthcare professionals including doctors,nurses,other healthcare workers,patients,and traditional healers.We emphasize the views of consent with participants of customs,cultural,and languages regarding informed consent.There are factors between peoples of differing circumstances which can greatly alter how they view consent.Some groups would involve people in the decision-making process that may not traditionally be involved in the decision making of a medical decision.Other groups may dislike certain medical procedures as in Papua New Guinea(PNG).And certain people have different views on what should be disclosed of the patient’s condition.Customs,cultures,and languages are common phenomena which continue to affect the daily lives of many thousands of people.It is unclear in PNG about the characteristics of customs,culture,and language on health care because there is no published information on informed consent and issues that affect the making of informed consent.
基金supported by China Undergraduate Innovation Training Program[Grant No.202410699184]Humanities and Social Sciences Research Project funded by the Ministry of Education of China[Grant No.23YJAZH139].
文摘Machine translation of low-resource languages(LRLs)has long been hindered by limited corpora and linguistic complexity.This review summarizes key developments,from traditional methods to recent progress with large language models(LLMs),while highlighting ongoing challenges such as data bottlenecks,biases,fairness,and computational costs.Finally,it discusses future directions,including efficient parameter fine-tuning,multimodal translation,and community-driven corpus construction,providing insights for advancing LRL translation research.
文摘随着钓鱼邮件数量的迅速增加以及对抗技术的不断演进,传统的钓鱼邮件检测方法在效率和准确性方面面临严峻挑战.为此,提出了一种基于大语言模型(large language model,LLM)的钓鱼邮件检测方法,以解决现有系统检测率低、漏报率高及人机交互性差等问题.通过全面分析钓鱼邮件的关键特征,包括邮件头部字段、正文内容、URL、二维码、附件及HTML页面,利用特征插入算法构建高质量的训练数据集.基于预训练语言模型LLaMA和低秩自适应微调技术(low-rank adaptation,LoRA),在仅更新0.72%模型参数(约50 MB)条件下实现领域知识迁移,获得钓鱼邮件检测大模型.实验结果显示,与传统方法相比,基于大语言模型的检测方法显著提升了检测的准确性与鲁棒性,整体准确率达到94.5%,有效降低了误报率,增强了钓鱼邮件特征的分类与解释能力,提供了更具实用性和可靠性的钓鱼邮件检测方案.
