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Leveraging Vision-Language Pre-Trained Model and Contrastive Learning for Enhanced Multimodal Sentiment Analysis
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作者 Jieyu An Wan Mohd Nazmee Wan Zainon Binfen Ding 《Intelligent Automation & Soft Computing》 SCIE 2023年第8期1673-1689,共17页
Multimodal sentiment analysis is an essential area of research in artificial intelligence that combines multiple modes,such as text and image,to accurately assess sentiment.However,conventional approaches that rely on... Multimodal sentiment analysis is an essential area of research in artificial intelligence that combines multiple modes,such as text and image,to accurately assess sentiment.However,conventional approaches that rely on unimodal pre-trained models for feature extraction from each modality often overlook the intrinsic connections of semantic information between modalities.This limitation is attributed to their training on unimodal data,and necessitates the use of complex fusion mechanisms for sentiment analysis.In this study,we present a novel approach that combines a vision-language pre-trained model with a proposed multimodal contrastive learning method.Our approach harnesses the power of transfer learning by utilizing a vision-language pre-trained model to extract both visual and textual representations in a unified framework.We employ a Transformer architecture to integrate these representations,thereby enabling the capture of rich semantic infor-mation in image-text pairs.To further enhance the representation learning of these pairs,we introduce our proposed multimodal contrastive learning method,which leads to improved performance in sentiment analysis tasks.Our approach is evaluated through extensive experiments on two publicly accessible datasets,where we demonstrate its effectiveness.We achieve a significant improvement in sentiment analysis accuracy,indicating the supe-riority of our approach over existing techniques.These results highlight the potential of multimodal sentiment analysis and underscore the importance of considering the intrinsic semantic connections between modalities for accurate sentiment assessment. 展开更多
关键词 Multimodal sentiment analysis vision–language pre-trained model contrastive learning sentiment classification
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Project-based Language Learning: an Activity Theory Analysis in SOE Language Learning
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作者 陈苡晴 《海外英语》 2016年第10期215-217,220,共4页
This study focuses on the effectiveness of the project-based language learning(PBLL) in a college Secretarial Oral English(SOE) Module. Student reflections of the language project work have been analyzed through Activ... This study focuses on the effectiveness of the project-based language learning(PBLL) in a college Secretarial Oral English(SOE) Module. Student reflections of the language project work have been analyzed through Activity Theory. Moreover,Data has been collected and categorized based on the components of complex human activity: the subject, object, tools(signs,symbols, and language), the community in which the activity take place, division of labor, and rules. The findings theoretically support the outcome of project-based language learning which align with the object of the activity. 展开更多
关键词 ACTIVITY THEORY project-based learning SOE language learning
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The impact of ChatGPT on foreign language teaching and learning: Opportunities in education and research 被引量:7
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作者 Wilson Cheong Hin Hong 《教育技术与创新》 2023年第1期37-45,共9页
The revolutionary online application ChatGPT has brought immense concerns to the education field.Foreign language teachers being some of those most reliant on writing assessments were among the most anxious,exacerbate... The revolutionary online application ChatGPT has brought immense concerns to the education field.Foreign language teachers being some of those most reliant on writing assessments were among the most anxious,exacerbated by the extensive media coverage about the much-fantasized functionality of the chatbot.Hence,the article starts by elucidating the mechanisms,functions and common misconceptions about ChatGPT.Issues and risks associated with its usage are discussed,followed by an in-depth discussion of how the chatbot can be harnessed by learners and teachers.It is argued that ChatGPT offers major opportunities for teachers and education institutes to improve second/foreign language teaching and assessments,which similarly provided researchers with an array of research opportunities,especially towards a more personalized learning experience. 展开更多
关键词 Large language model second language education flip classroom personalized learning formative assessment
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Deep Learning-Based Sign Language Recognition for Hearing and Speaking Impaired People
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作者 Mrim M.Alnfiai 《Intelligent Automation & Soft Computing》 SCIE 2023年第5期1653-1669,共17页
Sign language is mainly utilized in communication with people who have hearing disabilities.Sign language is used to communicate with people hav-ing developmental impairments who have some or no interaction skills.The... Sign language is mainly utilized in communication with people who have hearing disabilities.Sign language is used to communicate with people hav-ing developmental impairments who have some or no interaction skills.The inter-action via Sign language becomes a fruitful means of communication for hearing and speech impaired persons.A Hand gesture recognition systemfinds helpful for deaf and dumb people by making use of human computer interface(HCI)and convolutional neural networks(CNN)for identifying the static indications of Indian Sign Language(ISL).This study introduces a shark smell optimization with deep learning based automated sign language recognition(SSODL-ASLR)model for hearing and speaking impaired people.The presented SSODL-ASLR technique majorly concentrates on the recognition and classification of sign lan-guage provided by deaf and dumb people.The presented SSODL-ASLR model encompasses a two stage process namely sign language detection and sign lan-guage classification.In thefirst stage,the Mask Region based Convolution Neural Network(Mask RCNN)model is exploited for sign language recognition.Sec-ondly,SSO algorithm with soft margin support vector machine(SM-SVM)model can be utilized for sign language classification.To assure the enhanced classifica-tion performance of the SSODL-ASLR model,a brief set of simulations was car-ried out.The extensive results portrayed the supremacy of the SSODL-ASLR model over other techniques. 展开更多
关键词 Sign language recognition deep learning shark smell optimization mask rcnn model disabled people
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FIRST LANGUAGE ACQUISITION AS A MODEL FOR SECOND LANGUAGE ACQUISITION: THE TOTAL PHYSICAL RESPONSE APPROACH IN SECOND LANGUAGE LEARNING AND TEACHING 被引量:11
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作者 井卫华 《外语与外语教学》 1988年第2期12-22,共11页
L1 and L2 acquisition, in some respects, are similar. Language development in children goes hand in hand with physical and cognitive development. Children learn their first language by imitation, but not always and no... L1 and L2 acquisition, in some respects, are similar. Language development in children goes hand in hand with physical and cognitive development. Children learn their first language by imitation, but not always and not only by imitation. There seems to be some "innate capacities" that make children start to speak at the same time they do and in the way they do it. Adults learning a second language usually are controlled more by their motivation. But language input is important for both L1 and L2 acquisition. Though there are differences between CL1 and between CL2 and AL2, the way in which these learners acquire some of the grammatical morphemes is similar. This, together with some other evidence, shows that it is not only children who can acquire language. Adults can also acquire a language. But when adults acquire a language, they should also learn it. Some of the ways in which children acquire their language can be used as a model for L2 acquisition, even for Chinese students whose language is unrelated to English and whose culture is different. Learning the culture of the English-speaking countries will benefit the learning of the language. Like children, listening should also be well in advance of speaking in L2 acquisition. To train listening comprehension skills, Asher’s TPR approach proves more effective. TPR approach is at the moment limited to the beginning stage only. In order for students to gain all the five skills in a second language learning, namely, listening, speaking, reading, writing, and interpreting/translating, other methods should be used at the same time, or at later stages. 展开更多
关键词 THE TOTAL PHYSICAL RESPONSE APPROACH IN SECOND language learning AND TEACHING FIRST language ACQUISITION AS A model FOR SECOND language ACQUISITION TOTAL
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Models for Teaching Culture in Computer-Assisted Language Learning
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作者 YUEJian-ying 《Sino-US English Teaching》 2014年第4期283-289,共7页
CALL (computer-assisted language learning) has tremendously transformed the teaching of language, with its wide application in many aspects of language teaching. However, how to integrate CALL into the teaching of c... CALL (computer-assisted language learning) has tremendously transformed the teaching of language, with its wide application in many aspects of language teaching. However, how to integrate CALL into the teaching of culture is still rarely discussed. The purpose of the paper is to explore feasible models for teaching culture in CALL and their effects on students' acquisition process. Based on two fundamental pedagogical approaches (participatory pedagogy and multiliteracies pedagogy) for teaching culture, this paper proposes three pedagogical models (problem-posing model, web-quest model, and computer-supported collaborative learning model) for teaching culture in CALL. In the end, this paper illustrates a combination of the three models in real CALL setting by a cultural teaching case. A questionnaire survey and interviews are conducted to reflect on students' feedback, which gives an insight into possible adjustments in teaching models and the paper also proposes future possibilities in applying these models into teaching culture 展开更多
关键词 CALL (computer-assisted language learning teaching culture pedagogical models
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Personality Trait Detection via Transfer Learning
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作者 Bashar Alshouha Jesus Serrano-Guerrero +2 位作者 Francisco Chiclana Francisco P.Romero Jose A.Olivas 《Computers, Materials & Continua》 SCIE EI 2024年第2期1933-1956,共24页
Personality recognition plays a pivotal role when developing user-centric solutions such as recommender systems or decision support systems across various domains,including education,e-commerce,or human resources.Tra-... Personality recognition plays a pivotal role when developing user-centric solutions such as recommender systems or decision support systems across various domains,including education,e-commerce,or human resources.Tra-ditional machine learning techniques have been broadly employed for personality trait identification;nevertheless,the development of new technologies based on deep learning has led to new opportunities to improve their performance.This study focuses on the capabilities of pre-trained language models such as BERT,RoBERTa,ALBERT,ELECTRA,ERNIE,or XLNet,to deal with the task of personality recognition.These models are able to capture structural features from textual content and comprehend a multitude of language facets and complex features such as hierarchical relationships or long-term dependencies.This makes them suitable to classify multi-label personality traits from reviews while mitigating computational costs.The focus of this approach centers on developing an architecture based on different layers able to capture the semantic context and structural features from texts.Moreover,it is able to fine-tune the previous models using the MyPersonality dataset,which comprises 9,917 status updates contributed by 250 Facebook users.These status updates are categorized according to the well-known Big Five personality model,setting the stage for a comprehensive exploration of personality traits.To test the proposal,a set of experiments have been performed using different metrics such as the exact match ratio,hamming loss,zero-one-loss,precision,recall,F1-score,and weighted averages.The results reveal ERNIE is the top-performing model,achieving an exact match ratio of 72.32%,an accuracy rate of 87.17%,and 84.41%of F1-score.The findings demonstrate that the tested models substantially outperform other state-of-the-art studies,enhancing the accuracy by at least 3%and confirming them as powerful tools for personality recognition.These findings represent substantial advancements in personality recognition,making them appropriate for the development of user-centric applications. 展开更多
关键词 Personality trait detection pre-trained language model big five model transfer learning
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A Survey on Chinese Sign Language Recognition:From Traditional Methods to Artificial Intelligence
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作者 Xianwei Jiang Yanqiong Zhang +1 位作者 Juan Lei Yudong Zhang 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第7期1-40,共40页
Research on Chinese Sign Language(CSL)provides convenience and support for individuals with hearing impairments to communicate and integrate into society.This article reviews the relevant literature on Chinese Sign La... Research on Chinese Sign Language(CSL)provides convenience and support for individuals with hearing impairments to communicate and integrate into society.This article reviews the relevant literature on Chinese Sign Language Recognition(CSLR)in the past 20 years.Hidden Markov Models(HMM),Support Vector Machines(SVM),and Dynamic Time Warping(DTW)were found to be the most commonly employed technologies among traditional identificationmethods.Benefiting from the rapid development of computer vision and artificial intelligence technology,Convolutional Neural Networks(CNN),3D-CNN,YOLO,Capsule Network(CapsNet)and various deep neural networks have sprung up.Deep Neural Networks(DNNs)and their derived models are integral tomodern artificial intelligence recognitionmethods.In addition,technologies thatwerewidely used in the early days have also been integrated and applied to specific hybrid models and customized identification methods.Sign language data collection includes acquiring data from data gloves,data sensors(such as Kinect,LeapMotion,etc.),and high-definition photography.Meanwhile,facial expression recognition,complex background processing,and 3D sign language recognition have also attracted research interests among scholars.Due to the uniqueness and complexity of Chinese sign language,accuracy,robustness,real-time performance,and user independence are significant challenges for future sign language recognition research.Additionally,suitable datasets and evaluation criteria are also worth pursuing. 展开更多
关键词 Chinese Sign language Recognition deep neural networks artificial intelligence transfer learning hybrid network models
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On the Teaching Design of Graduates’EAP Course:Enhancing Language Proficiency and Critical Thinking Skills
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作者 LIU Yuan 《Sino-US English Teaching》 2024年第5期238-241,共4页
This paper explores the integration of the bridge-in,objectives,pre-assessment,participatory activities,post-assessment and summary(BOPPPS)teaching model within the context of the post-graduates Academic English cours... This paper explores the integration of the bridge-in,objectives,pre-assessment,participatory activities,post-assessment and summary(BOPPPS)teaching model within the context of the post-graduates Academic English course.It discusses how this structured approach can effectively enhance students’language proficiency,foster critical thinking skills,and align with the multifaceted objectives of advanced English language education.The study provides a detailed examination of each BOPPPS component as applied to the post-graduates Academic English curriculum,supported by theoretical underpinnings and practical implications. 展开更多
关键词 Academic English course BOPPPS teaching model language proficiency critical thinking active learning
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Development of Fundamental Competencies for Working Persons Through Project-Based Learning 被引量:1
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作者 Toshihiro Ioi 《Management Studies》 2014年第3期159-167,共9页
Universities teach mainly specialized subjects and the liberal arts. Society expects university students to gain certain basic skills important when working for a company. These skills can be divided broadly into acti... Universities teach mainly specialized subjects and the liberal arts. Society expects university students to gain certain basic skills important when working for a company. These skills can be divided broadly into action, thinking, and teamwork. The purpose of this paper is to propose a method of project-based education for developing fundamental competencies for working persons. Many studies have been reported on educational methods with project management techniques, but few have considered project-based education aiming at improving fundamental competencies for working persons. If these competencies can be developed through project-based education, it will be possible to develop not only teamwork skills, but also a wide range of skills involving action as well as thinking. The traditional Japanese university curriculum comprises specialized subjects and the liberal arts. The author proposes the addition of project-based education to develop basic skills needed in the workforce. This research proposes an education model for basic competency training and examines the educational outcomes by studying results of a cooking tool project assigned to university students. The model includes Teoriya Resheniya Izobretatelskikh Zadatch (TRIZ, a Russian acronym for the theory of inventive problem solving), a World Cafe, and the SECI process (a process of knowledge creation comprised of socialization, externalization, combination, and internalization in knowledge management) in the hope that this model will be conducive to implementing effective project-based learning. This research concludes that it is possible to develop the basic skills needed by university students in society through project-based learning under a basic skills education model. 展开更多
关键词 Keywords: FUNDAMENTAL competency project-based learning world caf6 SECI process cooking tool project maturity model TRIZ
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Cross-Language Transfer Learning-based Lhasa-Tibetan Speech Recognition
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作者 Zhijie Wang Yue Zhao +3 位作者 Licheng Wu Xiaojun Bi Zhuoma Dawa Qiang Ji 《Computers, Materials & Continua》 SCIE EI 2022年第10期629-639,共11页
As one of Chinese minority languages,Tibetan speech recognition technology was not researched upon as extensively as Chinese and English were until recently.This,along with the relatively small Tibetan corpus,has resu... As one of Chinese minority languages,Tibetan speech recognition technology was not researched upon as extensively as Chinese and English were until recently.This,along with the relatively small Tibetan corpus,has resulted in an unsatisfying performance of Tibetan speech recognition based on an end-to-end model.This paper aims to achieve an accurate Tibetan speech recognition using a small amount of Tibetan training data.We demonstrate effective methods of Tibetan end-to-end speech recognition via cross-language transfer learning from three aspects:modeling unit selection,transfer learning method,and source language selection.Experimental results show that the Chinese-Tibetan multi-language learning method using multilanguage character set as the modeling unit yields the best performance on Tibetan Character Error Rate(CER)at 27.3%,which is reduced by 26.1%compared to the language-specific model.And our method also achieves the 2.2%higher accuracy using less amount of data compared with the method using Tibetan multi-dialect transfer learning under the same model structure and data set. 展开更多
关键词 Cross-language transfer learning low-resource language modeling unit Tibetan speech recognition
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Project-based Learning (PBL) for Enhancing Chinese College Students' English Performance
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作者 Yujie Wang Hongbo Song 《Sino-US English Teaching》 2005年第5期59-64,共6页
Project-based learning (PBL) as an instructional approach focuses on student-centeredness through contextualizing learning by presenting students with real world issues and practices that aim at achieving enduring l... Project-based learning (PBL) as an instructional approach focuses on student-centeredness through contextualizing learning by presenting students with real world issues and practices that aim at achieving enduring learning effects. As a challenge to traditional lecture-based instruction, PBL may generate positive backwash assessment effects via documenting the process of developing authentic language skills. These skills-oriented outcomes are achieved through proper curricula in appropriate learning situations via various dimensions. However, problems may occur such as management, pressure from administration, evaluation doing their selected projects. teacher/student attitude, class size and proper group process and criteria, and students' likely frustrations in 展开更多
关键词 project project-based learning assessment and evaluation College English authentic language use learning performance
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Towards Realizing Sign Language-to-Speech Conversion by Combining Deep Learning and Statistical Parametric Speech Synthesis
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作者 Xiaochun An Hongwu Yang Zhenye Gan 《国际计算机前沿大会会议论文集》 2016年第1期176-178,共3页
This paper realizes a sign language-to-speech conversion system to solve the communication problem between healthy people and speech disorders. 30 kinds of different static sign languages are firstly recognized by com... This paper realizes a sign language-to-speech conversion system to solve the communication problem between healthy people and speech disorders. 30 kinds of different static sign languages are firstly recognized by combining the support vector machine (SVM) with a restricted Boltzmann machine (RBM) based regulation and a feedback fine-tuning of the deep model. The text of sign language is then obtained from the recognition results. A context-dependent label is generated from the recognized text of sign language by a text analyzer. Meanwhile,a hiddenMarkov model (HMM) basedMandarin-Tibetan bilingual speech synthesis system is developed by using speaker adaptive training.The Mandarin speech or Tibetan speech is then naturally synthesized by using context-dependent label generated from the recognized sign language. Tests show that the static sign language recognition rate of the designed system achieves 93.6%. Subjective evaluation demonstrates that synthesized speech can get 4.0 of the mean opinion score (MOS). 展开更多
关键词 Deep learning Support vector machine Static SIGN language recognition Context-dependent LABEL Hidden Markov model Mandarin-Tibetan BILINGUAL SPEECH synthesis
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Progress in Neural NLP:Modeling,Learning,and Reasoning 被引量:10
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作者 Ming Zhou Nan Duan +1 位作者 Shujie Liu Heung-Yeung Shum 《Engineering》 SCIE EI 2020年第3期275-290,共16页
Natural language processing(NLP)is a subfield of artificial intelligence that focuses on enabling computers to understand and process human languages.In the last five years,we have witnessed the rapid development of N... Natural language processing(NLP)is a subfield of artificial intelligence that focuses on enabling computers to understand and process human languages.In the last five years,we have witnessed the rapid development of NLP in tasks such as machine translation,question-answering,and machine reading comprehension based on deep learning and an enormous volume of annotated and unannotated data.In this paper,we will review the latest progress in the neural network-based NLP framework(neural NLP)from three perspectives:modeling,learning,and reasoning.In the modeling section,we will describe several fundamental neural network-based modeling paradigms,such as word embedding,sentence embedding,and sequence-to-sequence modeling,which are widely used in modern NLP engines.In the learning section,we will introduce widely used learning methods for NLP models,including supervised,semi-supervised,and unsupervised learning;multitask learning;transfer learning;and active learning.We view reasoning as a new and exciting direction for neural NLP,but it has yet to be well addressed.In the reasoning section,we will review reasoning mechanisms,including the knowledge,existing non-neural inference methods,and new neural inference methods.We emphasize the importance of reasoning in this paper because it is important for building interpretable and knowledgedriven neural NLP models to handle complex tasks.At the end of this paper,we will briefly outline our thoughts on the future directions of neural NLP. 展开更多
关键词 Natural language processing Deep learning modeling learning and reasoning
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Incorporating Linguistic Rules in Statistical Chinese Language Model for Pinyin-to-character Conversion 被引量:2
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作者 刘秉权 Wang +2 位作者 Xiaolong Wang Yuying 《High Technology Letters》 EI CAS 2001年第2期8-13,共6页
An N-gram Chinese language model incorporating linguistic rules is presented. By constructing elements lattice, rules information is incorporated in statistical frame. To facilitate the hybrid modeling, novel methods ... An N-gram Chinese language model incorporating linguistic rules is presented. By constructing elements lattice, rules information is incorporated in statistical frame. To facilitate the hybrid modeling, novel methods such as MI-based rule evaluating, weighted rule quantification and element-based n-gram probability approximation are presented. Dynamic Viterbi algorithm is adopted to search the best path in lattice. To strengthen the model, transformation-based error-driven rules learning is adopted. Applying proposed model to Chinese Pinyin-to-character conversion, high performance has been achieved in accuracy, flexibility and robustness simultaneously. Tests show correct rate achieves 94.81% instead of 90.53% using bi-gram Markov model alone. Many long-distance dependency and recursion in language can be processed effectively. 展开更多
关键词 Chinese Pinyin-to-character conversion Rule-based language model N-gram language model Hybrid language model Element lattice Transformation-based error-driven learning
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Word Embeddings and Semantic Spaces in Natural Language Processing
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作者 Peter J. Worth 《International Journal of Intelligence Science》 2023年第1期1-21,共21页
One of the critical hurdles, and breakthroughs, in the field of Natural Language Processing (NLP) in the last two decades has been the development of techniques for text representation that solves the so-called curse ... One of the critical hurdles, and breakthroughs, in the field of Natural Language Processing (NLP) in the last two decades has been the development of techniques for text representation that solves the so-called curse of dimensionality, a problem which plagues NLP in general given that the feature set for learning starts as a function of the size of the language in question, upwards of hundreds of thousands of terms typically. As such, much of the research and development in NLP in the last two decades has been in finding and optimizing solutions to this problem, to feature selection in NLP effectively. This paper looks at the development of these various techniques, leveraging a variety of statistical methods which rest on linguistic theories that were advanced in the middle of the last century, namely the distributional hypothesis which suggests that words that are found in similar contexts generally have similar meanings. In this survey paper we look at the development of some of the most popular of these techniques from a mathematical as well as data structure perspective, from Latent Semantic Analysis to Vector Space Models to their more modern variants which are typically referred to as word embeddings. In this review of algoriths such as Word2Vec, GloVe, ELMo and BERT, we explore the idea of semantic spaces more generally beyond applicability to NLP. 展开更多
关键词 Natural language Processing Vector Space models Semantic Spaces Word Embeddings Representation learning Text Vectorization Machine learning Deep learning
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Intelligent Sign Multi-Language Real-Time Prediction System with Effective Data Preprocessing
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作者 Doaa E. Elmatary Doaa M. Maher Areeg Tarek Ibrahim 《Journal of Computer and Communications》 2023年第10期120-134,共15页
A multidisciplinary approach for developing an intelligent sign multi-language recognition system to greatly enhance deaf-mute communication will be discussed and implemented. This involves designing a low-cost glove-... A multidisciplinary approach for developing an intelligent sign multi-language recognition system to greatly enhance deaf-mute communication will be discussed and implemented. This involves designing a low-cost glove-based sensing system, collecting large and diverse datasets, preprocessing the data, and using efficient machine learning models. Furthermore, the glove is integrated with a user-friendly mobile application called “Life-sign” for this system. The main goal of this work is to minimize the processing time of machine learning classifiers while maintaining higher accuracy performance. This is achieved by using effective preprocessing algorithms to handle noisy and inconsistent data. Testing and iterating approaches have been applied to various classifiers to refine and improve their accuracy in the recognition process. Additionally, the Extra Trees (ET) classifier has been identified as the best algorithm, with results proving successful gesture prediction at an average accuracy of about 99.54%. A smart optimization feature has been implemented to control the size of data transferred via Bluetooth, allowing for fast recognition of consecutive gestures. Real-time performance has been measured through extensive experimental testing on various consecutive gestures, specifically referring to Arabic Sign Language (ArSL). The results have demonstrated that the system guarantees consecutive gesture recognition with a lower delay of 50 milliseconds. 展开更多
关键词 Hand Gesture Translator Sign Multi-language Machine learning models Deaf-Mute Community
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Measuring the model risk-adjusted performance of machine learning algorithms in credit default prediction
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作者 Andrés Alonso Robisco JoséManuel CarbóMartínez 《Financial Innovation》 2022年第1期1930-1964,共35页
Implementing new machine learning(ML)algorithms for credit default prediction is associated with better predictive performance;however,it also generates new model risks,particularly concerning the supervisory validati... Implementing new machine learning(ML)algorithms for credit default prediction is associated with better predictive performance;however,it also generates new model risks,particularly concerning the supervisory validation process.Recent industry surveys often mention that uncertainty about how supervisors might assess these risks could be a barrier to innovation.In this study,we propose a new framework to quantify model risk-adjustments to compare the performance of several ML methods.To address this challenge,we first harness the internal ratings-based approach to identify up to 13 risk components that we classify into 3 main categories—statistics,technology,and market conduct.Second,to evaluate the importance of each risk category,we collect a series of regulatory documents related to three potential use cases—regulatory capital,credit scoring,or provisioning—and we compute the weight of each category according to the intensity of their mentions,using natural language processing and a risk terminology based on expert knowledge.Finally,we test our framework using popular ML models in credit risk,and a publicly available database,to quantify some proxies of a subset of risk factors that we deem representative.We measure the statistical risk according to the number of hyperparameters and the stability of the predictions.The technological risk is assessed through the transparency of the algorithm and the latency of the ML training method,while the market conduct risk is quantified by the time it takes to run a post hoc technique(SHapley Additive exPlanations)to interpret the output. 展开更多
关键词 Artificial intelligence Machine learning Credit risk INTERPRETABILITY BIAS Internal ratings based model IRB model Natural language processing NLP
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Building a Model for Teaching Korean in Universities under the Vision of Experiential Learning
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作者 Zhenying Ju 《Journal of Contemporary Educational Research》 2020年第7期1-6,共6页
The rapid development of the economy and the continuous improvement of the education system made the state begin to pay more attention to the talents in the education sector,especially in the context of the developmen... The rapid development of the economy and the continuous improvement of the education system made the state begin to pay more attention to the talents in the education sector,especially in the context of the development of economic globalization,the demand for talents in foreign languages is increasing.China and South Korea are closely connected,so the demand for Korean language talents in our country is increasing,and many universities have established Korean language majors,and is constantly exploring teaching models and methods to enhance Korean language teaching,among which,experiential teaching being the university’s Korean language teaching is the important ways and means.This paper mainly analyzes the construction of the model of Korean language teaching in universities under experiential learning. 展开更多
关键词 Experiential learning UNIVERSITIES Korean language teaching model construction
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E-Learning系统中课程知识本体的构建与实现 被引量:5
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作者 刘光蓉 杜小勇 +1 位作者 王琰 崔建伟 《情报学报》 CSSCI 北大核心 2009年第4期499-508,共10页
本文提出了一套指导E-Learning系统中课程知识本体构建的原理和规则。以C语言程序设计课程为例,按照教学步骤和教学规律,通过对课程知识点中核心概念集的抽取及其概念之间关系的建立,形成了C语言程序设计课程知识本体,该本体由183个概念... 本文提出了一套指导E-Learning系统中课程知识本体构建的原理和规则。以C语言程序设计课程为例,按照教学步骤和教学规律,通过对课程知识点中核心概念集的抽取及其概念之间关系的建立,形成了C语言程序设计课程知识本体,该本体由183个概念、130个上下位关系、48个属性组成。采用标准的OWL本体描述语言对其进行定义和描述,在Prot(?)g(?)中能正确运行,表明建立的本体模型是正确合理的。课程知识本体的成功构建为基于本体的E-Learning系统奠定了基础。最后,介绍了以课程知识本体为内核开发的E-Learning系统,该系统主要实现了课程知识本体的管理及基于本体的可视化资源检索。 展开更多
关键词 E-learning 知识本体 建模元语 知识粒度 OWL
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