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13th International Scientific Conference on Future Information Technology (FutureTech)

作者： Liu, Minjie Liu, Jin Ren, Haoliang Shanghai Maritime Univ Coll Informat Engn Shanghai Peoples R China

ISBN: (纸本)9789811313288;9789811313271

Generating natural answer is an important task in question answering systems. QA systems are usually designed to response right answers as well as friendly natural sentences to users. In this paper, we apply sequence to sequence model based on LSTM in Chinese natural answer generation. By taking advantage of LSTM in context inference, we build such a model that can learn from question-answer pairs (One question sentence and one answer entity pair), and finally generate natural answer sentences. Experiments show that our model has achieved good effectiveness.

关键词： Natural answer generation sequence to sequence model Long short-term memory

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Body posture prediction based on the sequence to sequence model 6

Body posture prediction based on the sequence to sequence mo...

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6th IEEE International Conference on Cloud Computing and Intelligence Systems (IEEE CCIS)

作者： Chen, Qili Jiang, Jinjin Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Beijing Informat Sci & Technol Univ Dept Automat Beijing 100192 Peoples R China Beijing Key Lab Computat Intelligence & Intellige Beijing 100124 Peoples R China

ISBN: (纸本)9781728138633

The prediction of body posture is necessary for preventing the falls of elderly people. The prediction accuracy of traditional methods will get worse when the predicting time becomes longer. This paper focuses on the body posture prediction accuracy of the sequence to sequence model. The posture model of waist, foot, and shoulders was built and tested. The experiments show that sequence to sequence model has a high prediction performance.

关键词： Deep learning framework sequence to sequence model posture prediction

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A Novel Deep Learning Attention Based sequence to sequence model for Automatic Abstractive Text Summarization

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International Journal of Information Technology (Singapore) 2024年第6期16卷 3597-3603页

作者： Abd Algani, Yousef Methkal Department of Mathematics The Arab Academic College for Education in Israel-Haifa Haifa Israel

Abstractive text summarization is one of the trending topics in the field of natural language processing (NLP). In this type of text summarization, new sentences are generated from the original text, irrespective of whether these sentences exist in the original corpus. There are several existing sequence-to-sequence models for performing abstractive text summarization, but they are equipped with challenges such as redundancy, lack of vocabulary distribution, and irrelevant results. To overcome this, the given paper introduces a novel attention-based sequence-to-sequence model for automatic summarization of abstractive text. The proposed sequence-to-sequence model comprises a Bi-LSTM (bidirectional long-short-term memory) encoder, a unidirectional LSTM decoder, an attention mechanism, and a word embedding layer to compute the probability distribution of each word in the original document. This leads to the feature extraction of words semantically, which results in higher relevance. The performance of the proposed model is validated by taking the CNN/Daily Mail dataset into consideration and assessed using Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics. The results show that the proposed model achieves higher ROUGE-1, ROUGE-2, and ROUGE-L values as compared to the existing baseline models. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： Abstractive text summarization Attention mechanism LSTM sequence to sequence model Word embedded layer

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Deep Keyphrase Generation with a Convolutional sequence to sequence model

Deep Keyphrase Generation with a Convolutional Sequence to S...

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4th International Conference on Systems and Informatics (ICSAI)

作者： Yong Zhang Yang Fang Xiao Weidong Natl Univ Def Technol Syst Engn Coll Changsha Hunan Peoples R China

ISBN: (纸本)9781538611074

Keyphrases can provide highly condensed and valuable information that allows users to quickly acquire the main ideas. Most previous studies realize the automatic keyphrase extraction through dividing the source text into multiple chunks and then rank and select the most suitable ones. These approaches ignore the deep semantics behind the text and could not predict the keyphrases not appearing in the source text. A sequence to sequence model to generate keyphrases from vocabulary could solve the issues above. However, traditional sequence to sequence model based on recurrent neural network(RNN) suffers from low efficiency problem. We propose an architecture based entirely on convolutional neural networks. Compared to recurrent models, computations can be completely parallelized over all elements so as to better exploit the GPU hardware. Our use of gated linear units alleviates gradient propagation and we equip each decoder layer with a separate attention model. Moreover, we incorporate a copying mechanism to handle out-of-vocabulary phrases. In experiments, we evaluate our model on six datasets, and our proposed model is demonstrated to outperform state-of-the-art baseline models consistently and significantly, both on extracting the keyphrases existing in the source text and generating the absent keyphrases based on the sematic meaning of the text.

关键词： Keyphrase Generation Convolutional Neural Network sequence to sequence model

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A Causal Long Short-Term Memory sequence to sequence model for TEC Prediction Using GNSS Observations

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REMOTE SENSING 2020年第9期12卷

作者： Kaselimi, Maria Voulodimos, Athanasios Doulamis, Nikolaos Doulamis, Anastasios Delikaraoglou, Demitris Natl Tech Univ Athens Sch Rural & Surveying Engn Athens 15780 Greece Univ West Attica Dept Informat & Comp Engn Athens 12243 Greece

The necessity of predicting the spatio-temporal phenomenon of ionospheric variability is closely related to the requirement of many users to be able to obtain high accuracy positioning with low cost equipment. The Precise Point Positioning (PPP) technique is highly accepted by the scientific community as a means for providing high level of position accuracy from a single receiver. However, its main drawback is the long convergence time to achieve centimeter-level accuracy in positioning. Hereby, we propose a deep learning-based approach for ionospheric modeling. This method exploits the advantages of Long Short-Term Memory (LSTM) Recurrent Neural Networks (RNN) for timeseries modeling and predicts the total electron content per satellite from a specific station by making use of a causal, supervised deep learning method. The scope of the proposed method is to compare and evaluate the between-satellites ionospheric delay estimation, and to aggregate the Total Electron Content (TEC) outcomes per-satellite into a single solution over the station, thus constructing regional TEC models, in an attempt to replace Global Ionospheric Maps (GIM) data. The evaluation of our proposed recurrent method for the prediction of vertical total electron content (VTEC) values is compared against the traditional Autoregressive (AR) and the Autoregressive Moving Average (ARMA) methods, per satellite. The proposed model achieves error lower than 1.5 TECU which is slightly better than the accuracy of the current GIM products which is currently about 2.0-3.0 TECU.

关键词： GNSS PPP ionospheric delay ionospheric electron content LSTM sequence to sequence model deep learning

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An attention-based Bayesian sequence to sequence model for short-term solar power generation prediction within decomposition-ensemble strategy

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JOURNAL OF CLEANER PRODUCTION 2023年 416卷

作者： Xiao, Fei Wang, Xiao-kang Hou, Wen-hui Zhang, Xue-yang Wang, Jian-qiang Cent South Univ Sch Business Changsha 410083 Peoples R China City Univ Hong Kong Dept Adv Design & Syst Engn Kowloon Tong Hong Kong Peoples R China Shenzhen Univ Coll Management Shenzhen 518060 Peoples R China

The utilization of renewable energy has attracted much attention with the deterioration of the environment. Solar energy is widely distributed and has huge reserves. But, solar energy is also volatile and intermittent. This brings difficulties to the large-scale grid connection of solar energy and the distribution of power resources. The key to solve this problem is to provide accurate and stable short-term prediction for solar power generation. For this purpose, we present an attention-based Bayesian sequence to sequence (Seq2Seq) model within decomposition-ensemble strategy. Firstly, an influential factors analysis is performed on the data about meteorological conditions, operation status of the photovoltaic panels and historical solar power generation sequences to obtain the optimal combination of the influential factors. Secondly, this paper proposes a novel decomposition-ensemble framework based on complete ensemble empirical mode decomposition with adaptive noise and independent component analysis to mine the intrinsic modes of the solar power generation time series. Thirdly, this paper presents an attention-based Bayesian Seq2Seq method for modeling the relationships between solar power generation and influential factors. Using a real-world dataset from State Administration of Electricity Investment Science and Technology of China, a case study, comparative analysis and robustness checks are conducted, through which the contributions of the methods in the proposed model and the superior performance of the model are demonstrated.

关键词： Short-term solar power generation prediction Decomposition and ensemble sequence to sequence model Attention mechanism Bayesian optimization

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DEASeq2Seq: An attention based sequence to sequence model for short-term metro passenger flow prediction within decomposition-ensemble strategy

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2023年 146卷

作者： Huang, Hao Mao, Jiannan Lu, Weike Hu, Guojing Liu, Lan Southwest Jiaotong Univ Sch Transportat & Logist Chengdu 610031 Peoples R China Soochow Univ Sch Rail Transportationat Suzhou 215131 Peoples R China Alabama Transportat Inst Tuscaloosa AL 35487 USA Suzhou Univ Sci & Technol Sch Civil Engn Suzhou 215131 Peoples R China Southwest Jiaotong Univ Natl & Local Joint Engn Lab Integrated Intelligen Chengdu 610031 Peoples R China

Short-term passenger flow prediction has practical significance for metro management and operation. However, the complex nonlinear and non-stationary characteristics make it challenging to detect evolution characteristics of passenger flow. To address this problem, a hybrid short-term metro passenger flow prediction model named decomposition ensemble attention sequence to sequence (DEASeq2Seq) is proposed in this paper. The proposed DEASeq2Seq includes three phases: decomposition, ensemble, and prediction. First, complete empirical mode decomposition with adaptive noise (CEEMDAN) is utilized to decompose the original passenger flow data into several intrinsic mode functions (IMFs) and a residue. Second, recurrence quantification analysis (RQA) is performed to reconstruct the decomposed modes into a stochastic part, a deterministic part, and a trend part via determinism evaluation. Third, a Seq2Seq model with the attention mechanism is proposed to execute multistep prediction for short-term passenger flow and explore the influence mechanism of the reconstructed components on the prediction targets. The real dataset from Chengdu metro, China, is used to verify the proposed model. The experiment results show that the proposed DEASeq2Seq model outperforms the benchmark models. Further model interpretations are conducted to analyze the impacts of decomposition strategy, ensemble strategy, and attention mechanism.

关键词： Short-term passenger flow prediction Empirical mode decomposition Recurrence quantification analysis sequence to sequence model Attention mechanism

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Fault diagnostics on railway switches and crossings using a bat-optimised sequence-to-sequence neural model

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INTERNATIONAL JOURNAL OF SENSOR NETWORKS 2024年第4期44卷 226-236页

作者： Deng, Xiaoyun Liuzhou Railway Vocat Tech Coll Sch Urban Rail Transportat Liuzhou 545616 Peoples R China

Railway switch and crossing requires large and complex structures, components, and technologies like electronic device control and switches to improve the railway network process. The large volume of components requires frequent monitoring and diagnosis systems to diagnose the faults. The fault diagnosis and identification system monitors the track circuits to ensure the railway network's safety and availability. The traditional fault diagnosis system requires fine-tuning methodologies to handle the complex switch-crossing information. Therefore, this paper uses the bat optimised sequence to sequence neural model (BOS2SNM) to handle the task according to the available measurement signals. The neural model identifies the faults from the temporal and spatial dependences, and the model learns from the data. During this process, the network function is optimised by applying the bat optimisation algorithm that minimises the deviation between the output values. The introduced system efficiency is analysed using the publically available dataset information and respective performance metrics. The experimental outcomes of the suggested BOS2SNM attain a high fault detection rate of 98.5%, fault diagnosis accuracy rate of 97.9%, prediction rate of 96.7%, precision rate of 95.6%, and F1-score rate of 94.5% compared to existing techniques.

关键词： railway switch and crossing fault diagnosis bat algorithm sequence to sequence model neural networks

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Enhancing Maritime Data Integration for Platform Services With sequence-to-sequence models and Statistical Refinement

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IEEE ACCESS 2025年 13卷 58636-58648页

作者： Hwang, Hyoseong Wong, Richard Lim, Ducsun Kang, Jonggu Joe, Inwhee Hanyang Univ Dept Comp Software Engn Seoul 04763 South Korea HD Hyundai Marine Solut Digital Technol Ctr Seongnam 13553 South Korea Sungshin Womens Univ Sch AI Convergence Seoul 02844 South Korea

The increasing adoption of IoT devices on ships and the expansion of platform services present a critical challenge in integrating heterogeneous ship data models into a unified platform data model. The task involves mapping ship Domain-Specific Language (DSL) descriptions to platform indices, complicated by variability and class imbalance. To address these challenges, this paper proposes a framework that combines a sequence-to-sequence model with statistical vectorization techniques. The model generates structured mapping classes, offering flexibility to accommodate diverse equipment and attributes, while training exclusively on connected data mitigates class imbalance. Subsequently, statistical vectorization techniques are applied to identify the correct match among the classified candidates, while ensuring that unconnected data is excluded. This two-step approach enhances recall and guarantees accurate relationships between ship DSLs and platform data indices. The proposed framework is validated using real-world data from 52 ships. Experimental results demonstrate that the sequence-to-sequence model with statistical refinement outperformed single-step and discriminative methods in handling class imbalance and variations when mapping ship DSLs to a unified platform data model. Our method achieved a recall of 89.14 and an $\text {F}_{\beta }$ -Score of 87.12, which are 4.15 and 1.91 points higher, respectively, than the reference classification method.

关键词： Marine vehicles Data models Data integration DSL Biological system modeling Accuracy Transformers Seaports Interoperability Computational modeling Data collection domain specific languages (DSL) sequence to sequence model data platform

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Creating a Parallel Corpus for the Kazakh Sign Language and Learning

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APPLIED SCIENCES-BASEL 2025年第5期15卷

作者： Yerimbetova, Aigerim Sakenov, Bakzhan Sambetbayeva, Madina Daiyrbayeva, Elmira Berzhanova, Ulmeken Othman, Mohamed Inst Informat & Computat Technol Lab Comp Engn Intelligent Syst Alma Ata 050010 Kazakhstan Univ Illinois Global Educ & Training Champaign IL 61820 USA LN Gumilyov Eurasian Natl Univ Dept Informat Syst Astana 010008 Kazakhstan Satbayev Univ Dept Software Engn Alma Ata 050013 Kazakhstan Al Farabi Kazakh Natl Univ Fac Informat Technol Alma Ata 050038 Kazakhstan Univ Putra Malaysia Fac Comp Sci & Informat Technol Dept Commun Technol & Network Serdang 43400 Malaysia

Kazakh Sign Language (KSL) is a crucial communication tool for individuals with hearing and speech impairments. Deep learning, particularly Transformer models, offers a promising approach to improving accessibility in education and communication. This study analyzes the syntactic structure of KSL, identifying its unique grammatical features and deviations from spoken Kazakh. A custom parser was developed to convert Kazakh text into KSL glosses, enabling the creation of a large-scale parallel corpus. Using this resource, a Transformer-based machine translation model was trained, achieving high translation accuracy and demonstrating the feasibility of this approach for enhancing communication accessibility. The research highlights key challenges in sign language processing, such as the limited availability of annotated data. Future work directions include the integration of video data and the adoption of more comprehensive evaluation metrics. This paper presents a methodology for constructing a parallel corpus through gloss annotations, contributing to advancements in sign language translation technology.

关键词： Kazakh sign language parallel corpus sign language machine translation deep learning sequence to sequence model

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