Clustered Multi-Task Sequence-to-Sequence Learning for Autonomous Vehicle Repositioning

Lee, Sang‐Min; Lim, Dae-Eun; Kang, Younkook; Kim, Hae-Joong

doi:10.1109/access.2021.3051763

Cited by 13 publications

(5 citation statements)

References 39 publications

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“…Despite the significant progress in this field, due to the diverse and complex problems of air quality predictions, make it is impractical to obtain highly accurate prediction results using a single predictive model [20]. As such, various multitask learning approaches have been proposed over the last five years.…”

Section: Reportedmentioning

confidence: 99%

Attentive Multi-Task Prediction of Atmospheric Particulate Matter: Effect of the COVID-19 Pandemic

et al. 2022

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Air pollution, especially the continual increase in atmospheric particulate matter (PM), is a global environmental challenge. To reduce the PM concentration, a remarkable amount of machine learningbased research has been proposed. However, increasing the accuracy of the predictions and providing clear interpretations of the predictions are challenging. In particular, no studies have addressed models that predict and interpret PM before and during the COVID-19 pandemic. In this paper, we present a two-step predictive and explainable model to obtain insights into reducing PM. We first use attentive multi-task learning to predict the air quality of cities. To accurately predict the concentration of particles with sizes of ∼10 µm or ≤2.5 µm (PM 10 and PM 2.5 , respectively), we demonstrate a performance difference between single-task and multi-task learning, as well as among the state-of-the art methods. The proposed attentive model with multi-task learning outperformed the others in terms of accuracy performance. We then used Shapley additive explanations, a representative explainable artificial intelligence framework, to interpret and determine the significance of features for predicting PM 10 and PM 2.5 . We demonstrated the superiority of the proposed approach in predicting and explaining both PM 10 and PM 2.5 concentrations, and observed a statistically significant difference in air pollution before and during the COVID-19 pandemic.

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Section: Reportedmentioning

confidence: 99%

Attentive Multi-Task Prediction of Atmospheric Particulate Matter: Effect of the COVID-19 Pandemic

et al. 2022

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“…Finally, we used min-max scaling, which realizes equal scaling for independent variables. After conducting transformations for all of the independent variables, we obtained both the accurate performance and fast convergence speed of the prediction models because the transformed data set reduced the sparse area in the data space [33].…”

Section: Data Description and Transformationmentioning

confidence: 99%

“…independent variables, we obtained both the accurate performance and fast con speed of the prediction models because the transformed data set reduced the sp in the data space [33].…”

Section: Exploratory Data Analysismentioning

confidence: 99%

Evaluation and Interpretation of Tourist Satisfaction for Local Korean Festivals Using Explainable AI

Hoon-Seong

Lee

2021

Sustainability

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In this paper, we propose using explainable artificial intelligence (XAI) techniques to predict and interpret the effects of local festival components on tourist satisfaction. We use data-driven analytics, including prediction, interpretation, and utilization phases, to help festivals establish a tourism strategy. Ultimately, this study aims to identify the most significant variables in local tourism strategy and to predict tourist satisfaction. To do so, we conducted an experimental study to compare the prediction accuracy of representative predictive algorithms. We then built a surrogate model based on a game theory-based framework, known as SHapley Additive exPlanations (SHAP), to understand the prediction results and to obtain insight into how tourist satisfaction with local festivals can be improved. Tourist data were collected from local festivals in South Korea over a period of 12 years. We conclude that the proposed predictive and interpretable strategy can identify the strengths and weaknesses of each local festival, allowing festival planners and administrators to enhance their tourist satisfaction rates by addressing the identified weaknesses.

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“…The retrieval-based method mainly completes the reply matching of each dialogue turn, a discriminative model. The neural generation-based models mainly include Sequence-to-Sequence (Seq2Seq) Models [ 3 , 4 , 5 ], Dialogue Context [ 6 ], Response Diversity [ 7 , 8 ], Topic and Personality [ 9 , 10 ], Outside Knowledge Base. Dialogue context, Response Diversity, Topic or Personality, and other methods adopt multi-classification of contextual dialogues and then select and integrate the best alternative answers to return.…”

Section: Introductionmentioning

confidence: 99%

Human–Machine Multi-Turn Language Dialogue Interaction Based on Deep Learning

Yang

et al. 2022

Micromachines

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During multi-turn dialogue, with the increase in dialogue turns, the difficulty of intention recognition and the generation of the following sentence reply become more and more difficult. This paper mainly optimizes the context information extraction ability of the Seq2Seq Encoder in multi-turn dialogue modeling. We fuse the historical dialogue information and the current input statement information in the encoder to capture the context dialogue information better. Therefore, we propose a BERT-based fusion encoder ProBERT-To-GUR (PBTG) and an enhanced ELMO model 3-ELMO-Attention-GRU (3EAG). The two models mainly enhance the contextual information extraction capability of multi-turn dialogue. To verify the effectiveness of the two proposed models, we demonstrate the effectiveness of our model by combining data based on the LCCC-large multi-turn dialogue dataset and the Naturalconv multi-turn dataset. The experimental comparison results show that, in the multi-turn dialogue experiments of the open domain and fixed topic, the two Seq2Seq coding models proposed are significantly improved compared with the current state-of-the-art models. For specified topic multi-turn dialogue, the 3EAG model has the average BLEU value reaches the optimal 32.4, which achieves the best language generation effect, and the BLEU value in the actual dialogue verification experiment also surpasses 31.8. for open-domain multi-turn dialogue. The average BLEU value of the PBTG model reaches 31.8, the optimal 31.8 achieves the best language generation effect, and the BLEU value in the actual dialogue verification experiment surpasses 31.2. So, the 3EAG model is more suitable for fixed-topic multi-turn dialogues for the two tasks. The PBTG model is more muscular in open-domain multi-turn dialogue tasks; therefore, our model is significant for promoting multi-turn dialogue research.

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Clustered Multi-Task Sequence-to-Sequence Learning for Autonomous Vehicle Repositioning

Cited by 13 publications

References 39 publications

Attentive Multi-Task Prediction of Atmospheric Particulate Matter: Effect of the COVID-19 Pandemic

Attentive Multi-Task Prediction of Atmospheric Particulate Matter: Effect of the COVID-19 Pandemic

Evaluation and Interpretation of Tourist Satisfaction for Local Korean Festivals Using Explainable AI

Human–Machine Multi-Turn Language Dialogue Interaction Based on Deep Learning

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