检索结果-内蒙古大学图书馆

20th Chinese National Conference on Computational Linguistics, CCL 2021

作者： Lu, Shan Mao, Cunli Yu, Zhengtao Gao, Shengxiang Huang, Yuxin Wang, Zhenhan Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and Technology Kunming650500 China

The study of part-of-speech tags and dependency parsing of low-resource languages plays an important role in promoting low-resource natural language processing tasks. For low-resource language word embedding representation, the existing work does not make full use of character and sub-word level information encoding, resulting in models that cannot use features of different granularities. For this reason, a word embedding representation that integrates multi-granularity features is proposed, and different language models are used separately to obtained on different semantic information at the character, sub-word and word level. And three granular word embeddings are combined to achieve the purpose of enriching semantic information and alleviate the problem of poor performance of the dependency parsing model caused by the scarcity of annotation data. The part-of-speech tagging and dependency parsing model are further jointly trained, so that the models can share knowledge with each other and reduce the linear transmission of part-of-speech tagging errors on the dependency parsing task. Taking Thai and Vietnamese as the research objects, on the Penn Treebank data set, the proposed method is significantly improved compared to the baseline model UAS, LAS, and POS. © 2021 China National Conference on Computational Linguistics Published under Creative Commons Attribution 4.0 International License

关键词： Embeddings

来源：评论

学校读者我要写书评

暂无评论

Semi-Supervised Chinese-Myanmar Neural Machine Translation based Model-Uncertainty 20

Semi-Supervised Chinese-Myanmar Neural Machine Translation b...

引用

20th Chinese National Conference on Computational Linguistics, CCL 2021

作者： Wang, Linqin Yu, Zhengtao Mao, Cunli Gao, Shengxiang Man, Zhibo Wang, Zhenhan Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and Technology Kunming650500 China

The semi-supervised neural machine translation based on back-translation has achieved remarkable results in low-resource neural machine translation. However, due to the scarcity of Chinese-Myanmar bilingual corpus and large structural differences, the self-attention mechanism at the encoder of traditional Transformer based back-translation cannot effectively distinguish the influence of the noise of pseudo parallel data in translation on sentence encoding, resulting in problems such as omission, multiple translation and mistranslation of the translated text. Therefore, this article proposes a semi-supervised Chinese-Myanmar neural machine translation based model uncertainty. In Transformer network, Monte Carlo dropout based on Variational Inference is used to construct model uncertainty attention mechanism to obtain sentence vector representations that can distinguish noise data. On this basis, it is fused with the sentence encoding vector obtained by the self-attention mechanism, so as to obtain the effective sentence encoding representation. Experimental results show that compared with traditional Transformer based back translation, the BLEU score of the proposed method improves by 4.01 and 1.88 points respectively in Chinese-Myanmar and Myanmar-Chinese translation directions, which fully verify the effectiveness of the proposed method in Chinese-Burmese neural machine translation tasks. © 2021 China National Conference on Computational Linguistics Published under Creative Commons Attribution 4.0 International License

关键词： Neural machine translation

来源：评论

学校读者我要写书评

暂无评论

Low-rank reduced biquaternion tensor ring decomposition and tensor completion

引用

Applied Mathematics and Computation 2025年 504卷

作者： Luo, Hui Liu, Xin Liu, Wei Zhang, Yang School of Computer Science and Engineering Faculty of Innovation Engineering Macau University of Science and Technology China Macau Institute of Systems Engineering Faculty of Innovation Engineering Macau University of Science and Technology China School of Artificial Intelligence at Sun Yat-sen University the Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Department of Mathematics University of Manitoba MB Canada

We define the reduced biquaternion tensor ring (RBTR) decomposition and provide a detailed exposition of the corresponding algorithm RBTR-SVD. Leveraging RBTR decomposition, we propose a novel low-rank tensor completion algorithm RBTR-TV integrating RBTR ranks with total variation (TV) regularization to optimize the process. Numerical experiments on color image and video completion tasks indicate the advantages of our method. © 2025 Elsevier Inc.

关键词： Numerical methods

来源：评论

学校读者我要写书评

暂无评论

Burmese Image Text Recognition Method Fused with Multi-layer Semantic Feature Maps 20

Burmese Image Text Recognition Method Fused with Multi-layer...

引用

20th Chinese National Conference on Computational Linguistics, CCL 2021

作者： Liu, Fuhao Mao, Cunli Yu, Zhengtao Gao, Shengxiang Wang, Linqin Xie, Xuyang Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and Technology Kunming650500 China

Due to the special structure of character combinations in Burmese language, there are great difficulties in the research of image text recognition. Using directly the existing methods to recognize Burmese images has the problems of missing characters and poor recognition under complex background. Therefore, this paper proposes a Burmese image text recognition method based on fused with multi-layer semantic feature maps. It uses deep convolutional networks to obtain multi-layer image features and fuses them to obtain multi-layer semantic information, which alleviates the problem of feature loss in the case of Burmese combining characters. In addition, during the training phase, the MIX UP strategy is used to optimize the network parameters, which improves the generalization ability of the model and reduces the model's dependence on training samples in the testing phase. The experimental results show that the accuracy of the proposed method is improved by 2.2% compared with the baseline model. © 2021 China National Conference on Computational Linguistics Published under Creative Commons Attribution 4.0 International License

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Machine learning and role of artificial intelligence in optimizing work performance and employee behavior

引用

Materials Today: Proceedings 2022年 51卷 2327-2331页

作者： K.K. Ramachandran A. Apsara Saleth Mary Shibani Hawladar D. Asokk Bandi Bhaskar J.R. Pitroda Professor Management Dr G R D College of Science India Faculty Centre for Tourism and Hotel Management Madurai Kamaraj University Tamil Nadu India Research Scholar BS Abdur Rahman Crescent Institute of Science and Technology Chennai India Faculty BS Abdur Rahman Crescent Institute of Science and Technology Chennai India School of Computer Science and Artificial Intelligence SR University Telangana India Civil Engineering Department BVM Engineering College Gujarat Technological University India

In today’s business world, firms are using analytics to diving deeper into their data to improve productivity, acquire a competitive edge, and boost their bottom lines. That is why businesses are eager to integrate machine learning (ML) and artificial intelligence (AI) because they want a faster, more accurate result. In addition, machine learning and artificial intelligence improved productivity by reducing repetitive tasks. As a result, nearly every sector in the world intends to use artificial intelligence and ML. artificial intelligence and ML have various roles in business, ranging from enhancing employee and customer interactions to discovering patterns in massive amounts of data to automating monotonous activities. The goal is to seize the opportunity that artificial intelligence provides in employee success and productivity. Implementing artificial intelligence and machine learning may be a vital tool for any company seeking quantitative help in their decision-making since it can analyze vast quantities of data and spit forth trend directions and actionable suggestions. AI helps employees and businesses to set and achieve better goals results. This paper highlights the impact of using artificial intelligence and ML to enhance employee behaviour and work outcome. This research also provides insights into artificial intelligence, which will usher in a new era in industry.

关键词： artificial intelligence Machine learning Productivity Employee behaviour Automation

来源：评论

学校读者我要写书评

暂无评论

Penetration trajectory optimization for the hypersonic gliding vehicle encountering two interceptors

arXiv

引用

arXiv 2022年

作者： Shen, Zhipeng Yu, Jianglong Dong, Xiwang Hua, Yongzhao Ren, Zhang School of Automation Science and Electrical Engineering Science and Technology on Aircraft Control Laboratory Beihang University Beijing100191 China Institute of Artificial Intelligence Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China

The penetration trajectory optimization problem for the hypersonic gliding vehicle (HGV) encountering two interceptors is investigated. The HGV penetration trajectory optimization problem considering the terminal target area is formulated as a nonconvex optimal control problem. The nonconvex optimal control problem is transformed into a second-order cone programming (SOCP) problem, which can be solved by state-of-the-art interior-point methods. In addition, a penetration strategy that only requires the initial line-of-sight angle information of the interceptors is proposed. The convergent trajectory obtained by the proposed method allows the HGV to evade two interceptors and reach the target area successfully. Furthermore, a successive SOCP method with a variable trust region is presented, which is critical to balancing the trade-off between time consumption and optimality. Finally, the effectiveness and performance of the proposed method are verified by numerical simulations. Copyright © 2022, The Authors. All rights reserved.

关键词： Numerical methods

来源：评论

学校读者我要写书评

暂无评论

Deep learning-driven multi-user wavelet NOMA for user centric cell-free massive MIMO communications

引用

Alexandria engineering Journal 2025年 128卷 533-543页

作者： Arshad, Rabia Baig, Sobia Aslam, Saad Ahmad, Muneeb Mumtaz, Shahid Department of Computer Science University of Central Punjab Avenue 1 Khayaban-e-Jinnah Road Johar Town Punjab Lahore54000 Pakistan Department of Electrical and Computer Engineering Energy Research Center COMSATS University 1.5 KM Defence Road off Raiwand Road LDA Avenue Phase 1 Punjab Lahore54000 Pakistan School of Computing and Artificial Intelligence Faculty of Engineering and Technology Sunway University Petaling Jaya Selangor47500 Malaysia Department of IT Convergence Engineering Kumoh National Institute of Technology Gumi39177 Korea Republic of Department of Engineering Nottingham Trent University 50 Shakespeare St NottinghamNG1 4BU United Kingdom

The evolution of Cell-Free Massive MIMO (CF-mMIMO) systems in the sixth-generation (6G) communications brings notable benefits including enhanced capacity, broader coverage, and greater reliability. However, these advanced systems may be subjected to critical challenges like, exponential growth in the user connectivity, precise channel estimation, and effective mitigation of the inter-user interference. This article addresses these challenges through Deep Learning (DL) for accurate channel estimation and a robust Wavelet Transform based Non-Orthogonal Multiple Access (NOMA) scheme to mitigate the inter-user interference in a user-centric CF-mMIMO system. By eliminating the reliance on pilot-assisted channel estimation, the DL-based approach achieves higher accuracy and lowers transmission overhead in a multi-user scenario. The results highlight the superiority of DL-based channel estimation for a CF-mMIMO system employing wavelet NOMA scheme over traditional methods, showing a 17% reduction in bit error rate (BER) and a 15% improvement in achievable sum-rate. © 2025 The Authors

关键词： Multiresolution analysis

来源：评论

学校读者我要写书评

暂无评论

Predicting the Impacts of the Environment on the Probability and Severity of Electrical Injuries by AIML

Predicting the Impacts of the Environment on the Probability...

引用

New Frontiers in Communication, Automation, Management and Security (ICCAMS), International Conference on

作者： Araddhana Arvind Deshmukh K Parish Venkata Kumar B.Anjanee Kumar Pramoda Patro Pavitar Parkash Singh N. Ashokkumar Department of Artificial Intelligence and Data science Marathwada Mitra Mandal College of Engineering Pune India Department of Computer Applications Velagapudi Ramakrishna Siddhartha Engineering College (Autonomous) Vijayawada India Electronics and Communication Engineering Dadi institute of engineering and technology Visakhapatnam India Department of Mathematics Koneru Lakshmaiah Education Foundation Hyderabad India Department of Management Lovely Professional University Phagwara India Department of ECE Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College) Tirupati India

ISBN: (数字)9798350317060

ISBN: (纸本)9798350317077

The acceleration of global warming and environmental degradation has been attributed to industrial growth and fossil fuel utilization. Consequently, this phenomenon has resulted in a rise in the frequency and intensity of natural disasters. Due to their predominantly outdoor installation, generators are subject to significant impacts. The occurrence of natural disasters has led to an increase in accidents associated with electrical equipment. This paper analyses statistical data on electrical fires, electric shock accidents, and electricity-related accidents caused by climate change in domestic settings to determine the associated risk *** this study, we propose a risk assessment model for electrical fires, explicitly focusing on every month. Our approach involves analysing data on electrical hazards in different regional areas, particularly metropolitan cities, considering climatic conditions such as temperature and humidity. Additionally, we aim to evaluate the accident risk rating associated with natural disasters, specifically those related to low and high-voltage equipment. In this study, a risk analysis model has been developed to assess electrical hazards in different regions and equipment types. The model presented herein serves as a foundational predictive tool. Hence, offering electrical security amenities in the forthcoming period is feasible by giving a risk estimation map delineating potential dangers specific to each geographical area and wiring category via web platforms or mobile applications utilizing the investigation data provided. Additionally, it is imperative to enhance the resilience and dependability of electrical components as a proactive measure against electrocute ions resulting from climate change-induced emergencies.

关键词： Wiring Temperature distribution Electric potential Analytical models Disasters Urban areas Humidity

来源：评论

学校读者我要写书评

暂无评论

Enhancing Natural Language Processing in Multilingual Chatbots for Cross-Cultural Communication

Enhancing Natural Language Processing in Multilingual Chatbo...

引用

Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), International Conference on

作者： Myagmarsuren Orosoo Indrajit Goswami Fredrick Ruban Alphonse Gulnaz Fatma Manikandan Rengarajan B Kiran Bala Mongolian National University of Education General Management Strategy and HRM Alliance School of Business Alliance University Bengaluru Department of Languages Institute of Finance and International Management Bengaluru Dept. of English Jazan University Jazan Saudi Arabia Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science K.Ramakrishnan college of Engineering Trichy

In order to promote smooth cross-cultural communication, this research focuses on improving Natural Language Processing (NLP) features within multilingual Chabots. The need for efficient communication across various linguistic and cultural barriers has grown as the world becomes more interconnected. In this context, multilingual Chabot's are essential tools that facilitate easy communication between users of various cultural backgrounds. In order to better understand and respond to a wide range of linguistic details, idioms, and cultural allusions, the research explores ways to improve NLP algorithms. This research investigates how machine learning techniques can be integrated to improve the Chabot's ability to adapt to various linguistic patterns, enabling it to acquire new language skills over time. By implementing an extensive and flexible approach, the proposed approach seeks to transform Natural Language Processing in Multilingual Chatbots for Cross-Cultural Communication. In order to identify language-specific patterns and specifications, the methodology starts with the collection of a diverse dataset that is representative of linguistic variations and cultural contexts. It then uses advanced linguistic analysis techniques like phonetic analysis. The next step is cultural context modelling, which entails building a database enhanced with cultural allusions and language variations unique to a given context that are obtained from various sources. Transfer learning, reinforcement learning for dialogue, and modern algorithms such as Multilingual Unsupervised and Supervised Embeddings (MUSE) for cross-lingual embeddings are all included in machine learning integration. In order to obtain information about language precision and cultural sensitivity, the methodology incorporates interactive surveys and sentiment analysis into a strong user feedback mechanism. The proposed model achieved 98% performance.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Vietnamese event detection based on Chinese information and Vietnamese syntax guidance 20

Vietnamese event detection based on Chinese information and ...

引用

20th Chinese National Conference on Computational Linguistics, CCL 2021

作者： Chen, Long Guo, Junjun Zhang, Yafei Gao, Shengxiang Yu, Zhengtao Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and Technology Kunming650500 China

Current event detection models based on deep learning rely on a sufficient amount of labeled data and only focus on specific in-formation such as trigger words. However, the scarcity of annotation data for Vietnamese news events and the ambiguity of event types have brought great challenges to Vietnamese event detection. According to the fea-ture that sentences expressing the same viewpoint but different languages usually have the same or similar semantic components, this paper proposes a Vietnamese event detection framework that combines Chinese information and Vietnamese syntax. First, use the shared encoder strategy and cross-attention network to integrate Chinese semantic information into Vietnamese, then use graph convolutional network to obtain Vietnamese representation based on Vietnamese dependency syntactic information. Finally, the Vietnamese semantic representation based on Chinese event type information is extracted through the event type perception network to realize Vietnamese news event detection. Experimental results show that under the guidance of Chinese information and Vietnamese syntax, Vietnamese event detection has achieved good results. © 2021 China National Conference on Computational Linguistics Published under Creative Commons Attribution 4.0 International License

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：