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2021年第四届算法、计算和人工智能国际会议

作者： Lixin Xia Hui Hu Laboratory of Language Engineering and Computing/Center for Lexicographical Studies of Guangdong University of Foreign Studies Center for Lexicographical Studies of Guangdong University of Foreign Studies

This study aims to investigate the factors that determine the presence or absence of the preposition in in the construction difficulties/difficulty(in) doing in China English and American English respectively, and to explore the difference between the two varieties on the prepositional use. The search strings difficulty(in) *ing and [have] difficulties(in) *ing were retrieved in the Corpus of Contemporary American English(COCA) and then in the Corpus of China English(CCE). By analyzing the statistics of the singular form from the two corpora, a conclusion can be made that the construction difficulty in *ing is preferred in formal registers in both English varieties as opposed to informal ones. And the difference on the prepositional use between the two English varieties lies in the process of prepositional gerund being replaced by a directly linked gerund. The results of the plural form indicate that the complexity principle is operative in determining the prepositional use in the construction for the two English varieties. Importantly, the findings of this study have great implications for the assessment of the previous claims about the construction. And the study offers a new insight into the analyses of the construction and the difference between American English and China English in the use of the construction. Last but not the least, the study is meaningful to Natural language Processing with a better understanding of the use of the investigated construction in the two English varieties.

关键词： China English American English grammatical figconstruction Computational linguistics Natural language Processing

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A Classifier Using Online Bagging Ensemble Method for Big Data Stream Learning

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Tsinghua Science and Technology 2019年第4期24卷 379-388页

作者： Yanxia Lv Sancheng Peng Ying Yuan Cong Wang Pengfei Yin Jiemin Liu Cuirong Wang School of Computer Science and Engineering Northeastern University Laboratory of Language Engineering and Computing and also with School of Cyber SecurityGuangdong University of Foreign Studies School of Information Science and Engineering Central South University

By combining multiple weak learners with concept drift in the classification of big data stream learning, the ensemble learning can achieve better generalization performance than the single learning approach. In this paper,we present an efficient classifier using the online bagging ensemble method for big data stream learning. In this classifier, we introduce an efficient online resampling mechanism on the training instances, and use a robust coding method based on error-correcting output codes. This is done in order to reduce the effects of correlations between the classifiers and increase the diversity of the ensemble. A dynamic updating model based on classification performance is adopted to reduce the unnecessary updating operations and improve the efficiency of *** implement a parallel version of EoBag, which runs faster than the serial version, and results indicate that the classification performance is almost the same as the serial one. Finally, we compare the performance of classification and the usage of resources with other state-of-the-art algorithms using the artificial and the actual data sets, respectively. Results show that the proposed algorithm can obtain better accuracy and more feasible usage of resources for the classification of big data stream.

关键词： big data stream classification online bagging ensemble learning concept drift

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The Database and Benchmark For the Source Speaker Tracing Challenge 2024

The Database and Benchmark For the Source Speaker Tracing Ch...

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IEEE Spoken language Technology Workshop

作者： Ze Li Yuke Lin Tian Yao Hongbin Suo Pengyuan Zhang Yanzhen Ren Zexin Cai Hiromitsu Nishizaki Ming Li School of Computer Science Wuhan University Wuhan China Suzhou Municipal Key Laboratory of Multimodal Intelligent Systems Duke Kunshan University Kunshan China AI Center OPPO Beijing China Key Laboratory of Speech Acoustics and Content Understanding Institute of Acoustics CAS China Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Center for Language and Speech Processing Johns Hopkins University USA Integrated Graduate School of Medicine Engineering and Agricultural Sciences University of Yamanashi 4-4-37 Takeda Kofu Yamanashi Japan

ISBN: (数字)9798350392258

ISBN: (纸本)9798350392265

Voice conversion (VC) systems can transform audio to mimic another speaker’s voice, thereby attacking speaker verification (SV) systems. However, ongoing studies on source speaker verification (SSV) are hindered by limited data availability and methodological constraints. This paper presents the Source Speaker Tracking Challenge (SSTC) on STL 2024, which aims to fill the gap in the database and benchmark for the SSV task. In this study, we generate a large-scale converted speech database with 16 common VC methods and train a batch of baseline systems based on the MFA-Conformer architecture. In addition, we introduced a related task called conversion method recognition, with the aim of assisting the SSV task. We expect SSTC to be a platform for advancing the development of the SSV task and provide further insights into the performance and limitations of current SV systems against VC attacks. Further details about SSTC can be found here 1 . 1 https://***/

关键词： Databases Conferences Transforms Contrastive learning Benchmark testing Multitasking Calibration

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LLM-driven Effective Knowledge Tracing by Integrating Dual-channel Difficulty

arXiv

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arXiv 2025年

作者： Cen, Jiahui Lin, Jianghao Zhou, Dong Zhong, Weixuan Chen, Jin Yang, Aimin Zhou, Yongmei School of Information Science and Technology Guangdong University of Foreign Studies Guangdong Guangzhou510000 China Laboratory for Language Engineering and Computing Guangdong University of Foreign Studies Guangdong Guangzhou510000 China School of Foreign Languages South China University of Technology 381 Wushan Road Guangdong Guangzhou510641 China School of Computer Science and Intelligence Education Lingnan Normal University Guangdong Zhanjiang524048 China

Knowledge Tracing (KT) is a fundamental technology in intelligent tutoring systems used to simulate changes in students’ knowledge state during learning, track personalized knowledge mastery, and predict performance. However, current KT models face three major challenges: (1) When encountering new questions, models face cold-start problems due to sparse interaction records, making precise modeling difficult;(2) Traditional models only use historical interaction records for student personalization modeling, unable to accurately track individual mastery levels, resulting in unclear personalized modeling;(3) The decision-making process is opaque to educators, making it challenging for them to understand model judgments. To address these challenges, we propose a novel Dual-channel Difficulty-aware Knowledge Tracing (DDKT) framework that utilizes Large language Models (LLMs) and Retrieval-Augmented Generation (RAG) for subjective difficulty assessment, while integrating difficulty bias-aware algorithms and student mastery algorithms for precise difficulty measurement. Our framework introduces three key innovations: (1) Difficulty Balance Perception Sequence (DBPS) - students’ subjective perceptions combined with objective difficulty, measuring gaps between LLM-assessed difficulty, mathematical-statistical difficulty, and students’ subjective perceived difficulty through attention mechanisms;(2) Difficulty Mastery Ratio (DMR) - precise modeling of student mastery levels through different difficulty zones;(3) Knowledge State Update Mechanism - implementing personalized knowledge acquisition through gated networks and updating student knowledge state. The DDKT framework dynamically adjusts question difficulty based on students’ current knowledge state and learning progress, ensuring optimal challenge levels for everyone. Experimental results on two real datasets show our method consistently outperforms nine baseline models, improving AUC metrics by 2% to 8% while effectivel

关键词： Students

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Remote Sensing Image Classification Algorithm Based on Texture Feature and Extreme Learning Machine

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Computers, Materials & Continua 2020年第11期65卷 1385-1395页

作者： Xiangchun Liu Jing Yu Wei Song Xinping Zhang Lizhi Zhao Antai Wang Media Computing Laboratory School of Information EngineeringMinzu University of ChinaBeijing100081China School of Telecommunication Engineering Beijing PolytechnicBeijing100176China National Language Resource Monitoring and Research Center of Minority Languages Minzu University of ChinaBeijing100081China New Jersey Institute of Technology NewarkNJ 07102USA

With the development of satellite technology,the satellite imagery of the earth’s surface and the whole surface makes it possible to survey surface resources and master the dynamic changes of the earth with high efficiency and low *** an important tool for satellite remote sensing image processing,remote sensing image classification has become a hot *** to the natural texture characteristics of remote sensing images,this paper combines different texture features with the Extreme Learning Machine,and proposes a new remote sensing image classification *** experimental tests are carried out through the standard test dataset SAT-4 and *** results show that the proposed method is a simpler and more efficient remote sensing image classification *** also achieves 99.434%recognition accuracy on SAT-4,which is 1.5%higher than the 97.95%accuracy achieved by *** the same time,the recognition accuracy of SAT-6 reaches 99.5728%,which is 5.6%higher than DeepSat’s 93.9%.

关键词： Image classification gray level co-occurrence matrix extreme learning machine

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A method of English-Chinese language recognition and its application in oral English learning system 4

A method of English-Chinese language recognition and its app...

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4th International Conference on Electronic Information Technology and Computer engineering, EITCE 2020

作者： Li, XinGuang Chen, Shuai Zhou, ZhiChao Long, XiaoLan Chen, ZeMing Wu, WeiYuan Laboratory of Language Engineering and Computing Guangdong University of Foreign Studies Guangzhou China Guangdong University of Foreign Studies Guangzhou China South China University of Technology Guangzhou China

ISBN: (纸本)9781450387811

In this paper, Gammatone Frequency Cepstrum Coefficients (GFCC) and Shifted Delta Cepstra (SDC) hybrid model were used to extract the speech feature parameters, and Gaussian Mixture Model-Universal Background Model (GMM-UBM) was used for language recognition. Taking oral English speeches of Chinese students as a corpus, we developed an English-Chinese language recognition module and applied it to the oral English learning system. The module could identify whether the student's answer language was English, which could increase the intelligence of the learning system. The experiment results showed the language recognition system introduced in this paper had a higher recognition accuracy and enhanced the function of the learning system. © 2020 ACM.

关键词： Learning systems

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Visual language Model based Cross-modal Semantic Communication Systems

arXiv

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arXiv 2024年

作者： Jiang, Feibo Tang, Chuanguo Dong, Li Wang, Kezhi Yang, Kun Pan, Cunhua Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China School of Information Science and Engineering Hunan Normal University Changsha China Changsha Social Laboratory of Artificial Intelligence Hunan University of Technology and Business Changsha China The Department of Computer Science Brunel University London United Kingdom The School of Computer Science and Electronic Engineering University of Essex ColchesterCO4 3SQ United Kingdom Changchun Institute of Technology China The National Mobile Communications Research Laboratory Southeast University Nanjing210096 China

Semantic Communication (SC) has emerged as a novel communication paradigm in recent years, successfully transcending the Shannon physical capacity limits through innovative semantic transmission concepts. Nevertheless, extant Image Semantic Communication (ISC) systems face several challenges in dynamic environments, including low semantic density, catastrophic forgetting, and uncertain Signal-to-Noise Ratio (SNR). To address these challenges, we propose a novel Vision-language Model-based Cross-modal Semantic Communication (VLM-CSC) system. The VLM-CSC comprises three novel components: (1) Cross-modal Knowledge Base (CKB) is used to extract high-density textual semantics from the semantically sparse image at the transmitter and reconstruct the original image based on textual semantics at the receiver. The transmission of high-density semantics contributes to alleviating bandwidth pressure. (2) Memory-assisted Encoder and Decoder (MED) employ a hybrid long/short-term memory mechanism, enabling the semantic encoder and decoder to overcome catastrophic forgetting in dynamic environments when there is a drift in the distribution of semantic features. (3) Noise Attention Module (NAM) employs attention mechanisms to adaptively adjust the semantic coding and the channel coding based on SNR, ensuring the robustness of the CSC system. The experimental simulations validate the effectiveness, adaptability, and robustness of the CSC system. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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Keyword-based Indonesian text summarization 4th

Keyword-based Indonesian text summarization

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4th International Conference on Intelligent computing, Communication and Devices, ICCD 2018

作者： Liu, Jinru Liu, Wuying Laboratory of Language Engineering and Computing Guangdong University of Foreign Studies GuangzhouGuangdong510420 China Engineering Research Center for Cyberspace Content Security Guangdong University of Foreign Studies GuangzhouGuangdong510420 China

ISBN: (纸本)9789811394058

Automatic summarization is a hot research topic in the field of natural language processing. Now the automatic summarization technology is mostly for majority languages such as English and Chinese, while less for the rare languages such as Indonesian. We aim to analyze the development trends of summarization and explore the processing methods for Indonesian summarization. This paper introduces the implementation process and experimental results of Indonesian text summarization based on the keyword frequency extraction method. The experimentation compares the RUGOE-2 result of our keyword-based system with that of the PSKSUMSUM one in Indonesian text summarization. The experimental results show that the keyword-based Indonesian text summarization is more effective. © Springer Nature Singapore Pte Ltd 2020.

关键词： Processing

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A Method for Automatic English Oral Fluency Scoring

A Method for Automatic English Oral Fluency Scoring

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International Congress on Image and Signal Processing, BioMedical engineering and Informatics (CISP-BMEI)

作者： Su-Mei Li Zi-Xi Zhu Xiao-Ning Li Xin-Guang Li Laboratory of Language Engineering and Computing Guangdong University of Foreign Studies Guangzhou Guangdong China Experimental teaching center Guangdong University of Foreign Studies Guangzhou Guangdong China School of Information Science and Technology Guangdong University of Foreign Studies Guangzhou Guangdong China School of Computer Science and Technology South China Business College (SCBC) of Guangdong University of Foreign Studies Guangzhou Guangdong China

ISBN: (数字)9798331507398

ISBN: (纸本)9798331507404

In order to address the issue of incomplete feature extraction in traditional oral fluency scoring methods, a method based on feature fusion is proposed in the paper, which integrates traditional acoustic features and deep features of audio to complete the scoring of oral fluency. For the audio to be tested, IS10_paraling features are extracted as traditional acoustic features, and a fine-tuned wav2vec2.0 pre-trained model is used to extract deep features of the audio. These two types of features are concatenated to form fusion features, which are then input into a multilayer perceptron scoring model to obtain the oral fluency score. Experimental results on the speechocean762 dataset show that compared to scoring methods based on single types of audio features, the fusion feature-based method improves scoring performance. The multilayer perceptron scoring model based on fusion features achieves a Pearson correlation coefficient of 0.744 with human scoring, demonstrating the effectiveness of the proposed method.

关键词： Correlation coefficient Training Human-machine systems Data visualization Multilayer perceptrons Feature extraction Acoustics User experience Informatics Spectrogram

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A practice on neural machine translation from Indonesian to Chinese 4th

A practice on neural machine translation from Indonesian to ...

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4th International Conference on Intelligent computing, Communication and Devices, ICCD 2018

作者： Bai, Lin Liu, Wuying Laboratory of Language Engineering and Computing Guangdong University of Foreign Studies GuangzhouGuangdong510420 China Engineering Research Center for Cyberspace Content Security Guangdong University of Foreign Studies GuangzhouGuangdong510420 China

ISBN: (纸本)9789811394058

Machine translation is used to implement the translation between different languages. Neural machine translation is one of the most popular machine translation methods which have made a great progress in recent years especially on universal languages. However, domestic translation software for non-universal languages is limited and also needs improving. In this paper, we carry out a practice on neural machine translation from Indonesian to Chinese. We first build a bilingual corpus of Indonesian and Chinese. After that, we train translation models using neural machine translation method, with traditional statistical machine translation models as baselines. In the later stages of our practice, we develop a Web software, named as Lore Translator, basing on our so-far best translation model. The performance of our model is comparable to previous work. © Springer Nature Singapore Pte Ltd 2020.

关键词： Neural machine translation

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