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26th International Conference on Engineering of Complex Computer Systems (ICECCS)

作者： Zhou, Cong Kuang, Li Cent South Univ Sch Comp Sci & Engn Changsha Peoples R China

ISBN: (纸本)9781665401623

Code is a kind of complex data. Recent models learn code representation using global or local aggregation. Global encoding allows all tokens of code to be connected directly and neglects the graph structure. Local encoding focuses on the neighbor nodes when capturing the graph structure but fails to capture long dependencies. In this work, we gather both encoding strategies and investigate different models that combine both global and local representations of code in order to learn code representation better. Specifically, we modify the layer structure based on the sequence-to-sequence model to incorporate a structured model in the encoder and decoder parts, respectively. To further consider different integration ways, we propose four models for method naming. In an extensive evaluation, we demonstrate that our models have a significant improvement on a well-studied dataset of method naming, achieving ROUGE1 score of 54.1, ROUGE-2 score of 26.7, and ROUGE-L score of 54.3, outperforming state-of-the-art models by 2.7, 1.7, and 4.3 points, respectively. Our data and code are available at https://***/zc-work/CGLNaming.

关键词： Code Representation Method Naming sequence-to-sequence Global Encoding Local Encoding

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A Two-stage Chinese text summarization algorithm using keyword information and adversarial learning

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NEUROCOMPUTING 2021年 425卷 117-126页

作者： Deng, Zhenrong Ma, Fuxin Lan, Rushi Huang, Wenming Luo, Xiaonan Guilin Univ Elect Technol Sch Comp Sci & Informat Secur Guilin 541004 Peoples R China East China Normal Univ Shanghai Key Lab Multidimens Informat Proc Shanghai 200241 Peoples R China Guilin Univ Elect Technol Natl & Local Joint Engn Res Ctr Satellite Nav & L Guilin Peoples R China

At present, most Chinese text summarization algorithms use the sequence-to-sequence model, but this model is prone to the problems of unknown words and incomplete content generation. To address these problems, we propose a new two-stage automatic text summarization method using keyword information and adversarial learning in this paper. On the one hand, the proposed method integrates the keyword information into the sequence-to-sequence model. The main information and keywords of the article are considered simultaneously through the attention mechanism to improve the information of summary generation. On the other hand, adversarial learning is introduced into the proposed model to avoid the problem that the semantic vector after passing through the encoder cannot save the context information better. Experiments are carried out on the Chinese dataset LCSTS, and the comparison results show that the proposed method has advantages in abstractive summarization. (c) 2020 Elsevier B.V. All rights reserved.

关键词： Automatic text summarization Keyword information sequence-to-sequence Adversarial learning

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Feature-aware conditional GAN for category text generation

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NEUROCOMPUTING 2023年第1期547卷

作者： Li, Xinze Mao, Kezhi Lin, Fanfan Feng, Zijian Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Nanyang Technol Univ Interdisciplinary Grad Sch Singapore 639798 Singapore

Category text generation receives considerable attentions since it is beneficial for various natural lan-guage processing tasks. Recently, the generative adversarial network (GAN) has attained promising per-formance in text generation, attributed to its adversarial training process. However, there are several issues in text GANs, including discreteness, training instability, mode collapse, lack of diversity and con-trollability etc. To address these issues, this paper proposes a novel GAN framework, the feature-aware conditional GAN (FA-GAN), for controllable category text generation. In FA-GAN, the generator has a sequence-to-sequence structure for improving sentence diversity, which consists of three encoders including a special feature-aware encoder and a category-aware encoder, and one relational-memory-core-based decoder with the Gumbel SoftMax activation function. The discriminator has an additional category classification head. To generate sentences with specified categories, the multi-class classifica-tion loss is supplemented in the adversarial training. Comprehensive experiments have been conducted, and the results show that FA-GAN consistently outperforms 10 state-of-the-art text generation approaches on 6 text classification datasets. The case study demonstrates that the synthetic sentences generated by FA-GAN can match the required categories and are aware of the features of conditioned sen-tences, with good readability, fluency, and text authenticity.& COPY;2023 Elsevier B.V. All rights reserved.

关键词： Category text generation Conditional generative adversarial net Adversarial text generation Generative model sequence-to-sequence Text classification

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A novel ultra short-term wind power prediction model based on double model coordination switching mechanism

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ENERGY 2024年 289卷

作者： Yang, Mao Wang, Da Zhang, Wei Northeast Elect Power Univ Key Lab Modern Power Syst Simulat & Control & Rene Minist Educ Jilin 132000 Peoples R China

Ultra-short-term wind power provides technical support for unit control and scheduling to optimize the power system. Wind fluctuation is the main factor that makes it difficult to accurately predict wind power output. To improve the power prediction accuracy of the wind power fluctuation period, a combined prediction model that can automatically switch between two models is proposed. For small fluctuations in wind speed, we construct a sequence-to-sequence model that integrates temporal and spatial attention mechanisms for power prediction. The prediction model switches to the wind power curve model when the wind speed fluctuates significantly, which reflects the static characteristics of the unit. We determine the appropriate wind speed fluctuation threshold on the verification set, so that the two prediction models can coordinate the switchSimulation experiments were carried out on the data set provided by a wind farm in Inner Mongolia, China. The normalized RMSE of the forecast in the future 4h is 0.1405.

关键词： Wind power Ultra-short-term prediction Switching mechanism Wind farm sequence-to-sequence Wind speed-power curve

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Grammatical Error Correction with Denoising Autoencoder

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2021年第8期12卷 821-826页

作者： Pajak, Krzysztof Gonczarek, Adam LangMedia Sp Zoo Mariana Rapackiego 5 PL-53021 Wroclaw Poland Alphamoon Sp Zoo Grabarska 1 PL-50079 Wroclaw Poland

A denoising autoencoder sequence-to-sequence model based on transformer architecture proved to be useful for underlying tasks such as summarization, machine translation, or question answering. This paper investigates the possibilities of using this model type for grammatical error correction and introduces a novel method of remark-based model checkpoint output combining. This approach was evaluated by the BEA 2019 shared task. It was able to achieve state-of-the-art F-score results on the test set 73.90 and development set 56.58. This was done without any GEC-specific pre-training, but only by fine-tuning the autoencoder model and combining checkpoint outputs. This proves that an efficient model solving GEC might be trained in a matter of hours using a single GPU.

关键词： Denoising autoencoder transformer sequence-to-sequence grammatical error correction model ensembling error remarks filtering fine-tuning

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A task-oriented neural dialogue system capable of knowledge accessing

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JOURNAL OF INFORMATION SECURITY AND APPLICATIONS 2023年 76卷

作者： Liu, Mengjuan Liu, Jiang Liu, Chenyang Yeh, Kuo-Hui Univ Elect Sci & Technol China Network & Data Secur Key Lab Sichuan Prov Chengdu 610054 Peoples R China Natl Dong Hwa Univ Dept Informat Management Hualien 974301 Taiwan

As one of the most important AI applications, task-oriented dialogue systems have been a hot topic in this field. These systems are primarily designed to fulfill tasks and provide answers to queries, such as checking the weather or booking flights. However, there are two major challenges in implementing task-oriented dialogue systems: selecting the right knowledge from the relevant knowledge base and combining the knowledge to produce grammatically correct and fluent replies. In this paper, we present a task-oriented dialogue system using an end-to-end trainable neural model. This dialogue system consists of four components: an encoder for encoding the dialogue history, a three-hop memory network for storing the relevant knowledge entries and dialogue history words, a knowledge screener for selecting the knowledge that is really related to the dialogue history, and a decoder for generating the replies. Finally, we validate the proposed dialogue system on a benchmark dataset (KVRET). The experimental results show that the proposed model generates more accurate replies than baselines in automatic and human evaluations. Also, we verify the superiorities of the two improvements by ablation experiments.

关键词： Task-oriented dialogue sequence-to-sequence Memory network Knowledge base

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FlexSleepTransformer: a transformer-based sleep staging model with flexible input channel configurations

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SCIENTIFIC REPORTS 2024年第1期14卷 1-15页

作者： Guo, Yanchen Nowakowski, Maciej Dai, Weiying SUNY Binghamton Sch Comp Binghamton NY 13902 USA United Hlth Serv Hosp Inc Sleep Med Binghamton NY 13790 USA

Clinical sleep diagnosis traditionally relies on polysomnography (PSG) and expert manual classification of sleep stages. Recent advancements in deep learning have shown promise in automating sleep stage classification using a single PSG channel. However, variations in PSG acquisition devices and environments mean that the number of PSG channels can differ across sleep centers. To integrate a sleep staging method into clinical practice effectively, it must accommodate a flexible number of PSG channels. In this paper, we proposed FlexSleepTransformer, a transformer-based model designed to handle varying number of input channels, making it adaptable to diverse sleep staging datasets. We evaluated FlexSleepTransformer using two distinct datasets: the public SleepEDF-78 dataset and the local SleepUHS dataset. Notably, FlexSleepTransformer is the first model capable of simultaneously training on datasets with differing number of PSG channels. Our experiments showed that FlexSleepTransformer trained on both datasets together achieved 98% of the accuracy compared to models trained on each dataset individually. Furthermore, it outperformed models trained exclusively on one dataset when tested on the other dataset. Additionally, FlexSleepTransformer surpassed state-of-the-art CNN and RNN-based models on both datasets. Due to its adaptability with varying channels numbers, FlexSleepTransformer holds significant potential for clinical adoption, especially when trained with data from a wide range of sleep centers.

关键词： Automatic sleep staging Transformer sequence-to-sequence Multi-channel Multi-dataset Deep neural network

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Multiscale Self-Attention Architecture in Temporal Neural Network for Nonintrusive Load Monitoring

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2023年 72卷

作者： Shan, Zihan Si, Gangquan Qu, Kai Wang, Qianyue Kong, Xiangguang Tang, Yu Yang, Chen Xi An Jiao Tong Univ Res Ctr Informat Fus & Intelligent Control Xian 710115 Peoples R China Xi An Jiao Tong Univ Sch Elect Engn Xian 710115 Peoples R China

Nonintrusive load monitoring (NILM) constitutes a significant function of the smart grid in the future. The purpose is to ameliorate the consumption and supply of electricity by disaggregating the total load to the appliance-level load without intrusive monitoring. Recently, energy disaggregation is improved with the emergence of deep learning, but the imbalanced datasets and long sequences bring multiple difficulties to model training. The distribution of ON/OFF states and the limitation of the model lead to massive false-positive samples and undetected events. To tackle these problems, we proposed a multiscale self-attention network (MSANet) to utilize the global temporal correlation and local sequential features. Specifically, the dilated window self-attention mechanism is proposed to compute the local attention, and the multibranch structure is to exploit sequential features of different scales. Furthermore, the embedding of global temporal information is introduced to improve global contextual awareness, and subtask networks are designed for different tasks, respectively, to alleviate the effect of imbalance. The proposed model is evaluated on the reference energy disaggregation dataset (REDD) and U.K.-domestic appliance-level electricity (DALE) dataset and shows outstanding performance on the mean absolute error (MAE) and F1 score compared with baseline algorithms.

关键词： Transformers Hidden Markov models Computational modeling Load modeling Deep learning Neural networks Load monitoring energy disaggregation nonintrusive load monitoring (NILM) self-attention sequence-to-sequence

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Natural Language Processing for Covid-19 Consulting System

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Procedia computer science 2023年 218卷 1335-1341页

作者： Sushreeta Tripathy Rishabh Singh Mousim Ray Dept. of CA ITER S'O'A(DU) Bhubaneswar India. Dept. of CSE ITER S'O'A(DU) Bhubaneswar India.

The world was taken aback when the Covid-19 pandemic hit in 2019. Ever since precautions have been taken to prevent the spreading or mutating of the virus, but the virus still keeps spreading and mutating. Scientists predict that the virus is going to stay for a long time but with reduced effectiveness. Recognizing the symptoms of the virus is essential in order to provide proper treatment for the virus. Visiting hospitals for consultation becomes quite difficult when people are supposed to maintain social distancing. Recently neural network generative models have shown impressive abilities in developing chatbots. However, using these neural network generative models that lack the required Covid specific knowledge to develop a Covid consulting system makes them difficult to be scaled. In order to bridge the gap between patients and a limited number of doctors we have proposed a Covid consulting agent by integrating the medical knowledge of Covid-19 with the neural network generative models. This system will automatically scan patient's dialogues seeking for a consultation to recognize the symptoms for Covid-19. The transformer and pretrained systems of BERT-GPT and GPT were fine-tuned CovidDialog-English dataset to generate responses for Covid-19 which were doctor-like and clinically meaningful to further solve the problem of the surging demand for medical consultations compared to the limited number of medical professionals. The results are evaluated and compared using multiple evaluation metrics which are NIST-, perplexity, BLEU-, METEOR, Entropy- and Dist-. In this paper, we also hope to prove that the results obtained from the automated dialogue systems were significantly similar to human evaluation. Furthermore, the evaluation shows that state-of-the-art BERT-GPT performs better.

关键词： NLP covid-19 dialogue system sequence-to-sequence transformer

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Automatic Analyses of Dysarthric Speech based on Distinctive Features

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APSIPA TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING 2023年第3期12卷

作者： Wong, Ka Ho Meng, Helen Mei-Ling Chinese Univ Hong Kong Dept Syst Engn & Engn Management Hong Kong Peoples R China

Dysathria is a neuromotor disorder that causes the individual to speak with imprecise articulation. This paper presents an automatic analysis framework for dysarthric speech, using a linguistically motivated representation based on distinctive features. Our framework includes a seq2seq phonetic decoder for Cantonese dysarthric speech. The manually or automatically transcribed phones can be mapped into a representation that consists of 21 distinctive features (DF). The DFs between the transcribed phones and canonical phones are compared in order to identify articulatory error rate (AER) for each DF. This forms an AER profile for a given set of dysarthric recordings from a speaker. Experiments show that the difference between the AER profile derived from manual versus automatic phonetic transcription is relatively small with a root mean squared error (RMSE) of 0.053 for the word-reading task and 0.085 for the sentence-reading task in CU DYS. In addition, the correlations between the AER profiles are high, at 0.97 and 0.95 for the two tasks respectively. These results reflect the viability of the proposed framework as an automated means of processing dysarthric speech to achieve articulatory analyses described by DFs. The AER profile is intuitive and interpretable, for pinpointing problem areas in articulation.

关键词： Dysarthric distinctive features recognition sequence-to-sequence

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