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Last Utterance-Context Attention Model for Multi-Turn Response Generation

Last Utterance-Context Attention Model for Multi-Turn Respon...

引用

international symposium on distributed computing and applications to business, engineering and science (DCABES)

作者： Guodong Zhang Li Mao Jun Sun School of Internet of things engineering Jiangnan University Wuxi China

ISBN: (数字)9781728197241

ISBN: (纸本)9781728197258

Recently, conversation response generation task is attracting the attention of more and more researchers. Different from single-turn response generation, multi-turn response generation not only focuses on fluency, but also needs to make use of contextual information. Therefore, we believe that an appropriate response should be coherent to the last utterance, and take conversation history into consideration at the same time. We propose a Last Utterance-Context Attention model. The last utterance attention calculates each word in last utterance and form them as a vector. Representation of each utterance is processed by the context attention and formed as a vector as well. Then the two vectors are concatenated as a context vector for decoding the response. In addition, we also apply the multi-head self-attention mechanism to focus more on the key words in each utterance. Both automatic and human evaluation results show that our model outperform baseline models for multi-turn response generation.

关键词： Decoding Context modeling Coherence Task analysis Mathematical model Encoding Deep learning

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Optimal Capacity Planning of Isolated Multi-energy Microgrid Considering Multi-dimensional Uncertainties

Optimal Capacity Planning of Isolated Multi-energy Microgrid...

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international symposium on distributed computing and applications to business, engineering and science (DCABES)

作者： Xianqing Chen Wei Dong Qiang Gao Qiang Yang College of Electrical Engineering Zhejiang University Hangzhou China State Grid Zhejiang Taizhou Power Supply Company Taizhou China

ISBN: (数字)9781728197241

ISBN: (纸本)9781728197258

In recent years, microgrid technology has gradually been proved to be an effective method to solve the power supply problem in remote areas. The optimization goal of the planning model is to minimize the investment, operation and maintenance costs of the microgrid system, and the constraints include system power balance constraint, `abandon wind and light' constraint, energy supply reliability constraint, etc. Then a mixed-integer nonlinear programming model is established, and the particle swarm optimization algorithm is used to find the optimal solution of this problem, after calculation, the optimal capacities of the WT, PV and battery in the microgrid are obtained. Simulation results indicate that the optimal configuration capacity obtained can minimize the total cost of the system while ensuring energy supply reliability and reducing wind and light abandonment as much as possible.

关键词： Microgrids Batteries Optimization Capacity planning Wind power generation Photovoltaic systems State of charge

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Siamese Network cooperating with Multi-head Attention for semantic sentence matching

Siamese Network cooperating with Multi-head Attention for se...

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international symposium on distributed computing and applications to business, engineering and science (DCABES)

作者： Zhao Yuan Sun Jun School of Internet of things Engineering Jiangnan University Wuxi China

ISBN: (数字)9781728197241

ISBN: (纸本)9781728197258

To compare a pair of sentences is a fundamental technology in many NLP tasks. According to the difference between the pair of sentence, we divide semantic sentence matching into two situations: Situation A is that the pair of sentences are worded with a context relationship, Situation B is that two are equal in semantics. Models for Situation A works in Situation B too, so prior deep work mostly model each sentence's representation considering the interaction of the other sentence simultaneously. However, models designed for Situation A bring redundant information for Situation B. In this paper, for sentence pairs with equivalence, we present a deep architecture with comparison-interaction separated to match two sentences, which based on Siamese network for comparison and multi-head attention for interaction information between sentence pairs. Experimental results on the latest Chinese sentence matching datasets outline the effectiveness of our approach.

关键词： Semantics Training Task analysis Encoding Natural languages Feature extraction Computer architecture

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CNN Hyperparameter Optimization Based on CNN Visualization and Perception Hash Algorithm

CNN Hyperparameter Optimization Based on CNN Visualization a...

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international symposium on distributed computing and applications to business, engineering and science (DCABES)

作者： Yifeng Wang Yang Wang Hongyi Li Zhuoxi Cai Xiaohan Tang Yin Yang School of Science Harbin Institute of Technology Shenzhen China

ISBN: (数字)9781728197241

ISBN: (纸本)9781728197258

In this paper, the network structure and the optimal hyperparameter selection which affect the performance of the model are obtained through the analysis of the convolutional neural network model with mathematical interpretation and visualization. In the study, we used visual methods such as deconvolution and Guided Grad-CAM to display the network structure, parameter changes, and the learning process of the model convolutional layer. Simultaneously, we developed CNN hyperparameters optimization strategy based on the perceptual hash algorithm according to its training characteristics. This method significantly improves the accuracy of image classification of the model and the generalization ability of the model and also provides certain theoretical support for the optimization and understanding of deep learning models in practical application. In addition, the hyperparameter optimization method based on the deep learning model feature map reconstruction visualization proposed in this paper also provides a good idea for the formulation of model training strategies.

关键词： Feature extraction Deconvolution Training Convolution Visualization Data visualization Computational modeling

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distributed Network Data Security Protection for SG-eIoT 15th

Distributed Network Data Security Protection for SG-eIoT

引用

15th international Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2019, co-located with the 5th international Conference on Harmony Search, Soft computing and applications, ICHSA 2019

作者： Chen, Zhiyu Peng, Tianliang Zhang, Yanling Yu, Xuehao Yan, Longchuan Gong, Shaoyan Liu, Xiaolin Li, Limin Song, Zheping Wang, Decheng State Grid Information & Telecommunication Branch Beijing100761 China School of Information Engineering Nanchang Institute of Technology Nanchang330099 China China Electric Power Research Institute Beijing100192 China Global Energy Interconnection Research Institute Beijing102209 China PICC Investment Holding Co. Ltd. Beijing100031 China HangZhou Polytechnic Hangzhou311402 China

ISBN: (纸本)9783030325909

With the continuous advancement of power system reform, grid enterprises relying on new technologies such as big data, cloud computing, artificial intelligence, internet of things (IoT) and blockchain are transforming towards more open energy internet enterprises, which brings more challenges to network security. The construction of SG-eIoT has promoted the circulation of data in all aspects of the system, cleaning up the barriers of data islands and effectively improving the operational efficiency of power company. However, due to the large scale of data access points, complex business operations, and huge amount of upstream and downstream enterprises in the industry chain, security risks may exist in the information circulation process such as being tampered and untrustworthy. Aiming at the problem of information security and credibility in the distributed network environment of power enterprises, this paper studies the application of blockchain technology in business information flow, and constructs a new data management mode of weak service connection to strong data interoperability based on the characteristics of blockchain technology, such as non-tampering property, collective maintenance and transaction non-repudiation, which achieves accurate data protection, and fully spports the construction of world-class energy internet company with excellent competitiveness. © 2020, Springer Nature Switzerland AG.

关键词： Blockchain

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Remaining activity sequence prediction for ongoing process instances 33

Remaining activity sequence prediction for ongoing process i...

引用

33rd international Conference on Software engineering and Knowledge engineering, SEKE 2021

作者： Sun, Xiaoxiao Ying, Yuke Yang, Siqing Shen, Hujun School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China

ISBN: (纸本)1891706527

Remaining activity sequence prediction (i.e., Activity suffix prediction) aims at recommending the most likely future behaviors for ongoing process instances (traces), which enables process managers to rationally allocate resources and detect process deviations in advance. Recently, techniques of neural networks have found promising applications in activity suffix prediction by training a prediction model for next activity and iteratively performing the model to achieve the whole sequence prediction. However, the iterative prediction accumulates the deviations of each iteration and the result also lacks interpretability. In this paper, we propose a novel method to predict activity suffixes from the perspective of control flow and data flow for ongoing traces, where process discovery and trace replay techniques are employed to simulate executions of traces under real conditions and Long Short-Term Memory (LSTM) is applied to characterize the correlation between executed information and future execution. Sequence matching between historical prefix traces and ongoing traces are performed based on the above information to select the optimal-matched (i.e., most similar) activity suffix for ongoing process instances. Experiments on real-life datasets demonstrates that our proposed method outperforms other methods. © 2021 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Forecasting

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Construction of QSAR model between the ligand and γ-Aminobutyric acid type A receptor using support vector regression algorithm

Construction of QSAR model between the ligand and γ-Aminobu...

引用

international symposium on distributed computing and applications to business, engineering and science (DCABES)

作者： Shu Cheng Yanrui Ding School of Science Jiangnan University Wuxi China

ISBN: (数字)9781728197241

ISBN: (纸本)9781728197258

Quantitative structure-activity relationship (QSAR) plays an important role in the prediction of biological activity based on machine learning. According to the characteristics of the binding interface between ligands and the γ-Aminobutyric acid type A (GABAA) receptor, we used random forest feature selection and support vector regression (SVR) to establish three QSAR models. The best QSAR model features include docking ligand molecular descriptors and ligand-receptor interactions. We also used Leave-One-Out-Cross-Validation (LOOCV) to select the appropriate value C = 2, g = 0.0221. The result of cross validation (Q LOO 2 ) is 0.8225, R 2 of test set is 0.8326, and MSE is 0.0910. In addition, we found that BELm2, BELe2, Mor08v, Mor29m, refRMS and intermol _ energy are key features, which helps to build QSAR model more accurately.

关键词： Predictive models Atomic measurements Support vector machines Testing Reliability Eigenvalues and eigenfunctions Training

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Performance Comparison of Small Object Detection Algorithms of UAV based Aerial Images

Performance Comparison of Small Object Detection Algorithms ...

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international symposium on distributed computing and applications to business, engineering and science (DCABES)

作者： Hao Xu Yuan Cao Qian Lu Qiang Yang College of Electrical Engineering Zhejiang University Hangzhou China State Grid Jiangsu Electric Power CO. Ltd. Research Institute Nanjing China

ISBN: (数字)9781728197241

ISBN: (纸本)9781728197258

Traffic controls in modern society are part of urban management. With the assistance of unmanned aerial vehicles (UAVs) equipped with mounted cameras, researchers could capture aerial (bird-view) images from appropriate altitude. The perspective in aerial images makes appearances of objects squat, although aerial images can supply more contextual information about the environment by a broader view angle, the object instances may be detected by mistake. This fact diminishes the aerial images that can be fed to a network with higher dimensions that increases the computational cost to prevent the diminishing of pixels belonging to small objects. To compare model performance on small objects with aerial images, this study trains and tests two object detectors, i.e. YOLOv4 and YOLOv3, on the AU-AIR dataset, and exploited the parameterization of YOLO based models for small object detection. Finally, the key numerical results and observations are presented.

关键词： Object detection Training Traffic control Detectors Computational modeling Unmanned aerial vehicles Surveillance

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Research on GAN-based Container Code Images Generation Method

Research on GAN-based Container Code Images Generation Metho...

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international symposium on distributed computing and applications to business, engineering and science (DCABES)

作者： Yan Liang Hanbing Yao School of Computer Science and Technology Wuhan University of Technology Wuhan China

ISBN: (数字)9781728197241

ISBN: (纸本)9781728197258

Recognizing images based on deep learning algorithms requires sufficient samples as a training dataset. In the port field, there is also a lack of container image datasets for deep learning research. This paper proposes a model based on GAN's container box character sample extended dataset (C-SAGAN), and addresses the problems of container box code character defaced and corrupt caused by the port environment, the generative adversarial network is trained with a small amount of real images to generate container character samples. The C-SAGAN model introduces class tags and self-attention in the generator and discriminator. The class tags can control the image generation process. The self-attention mechanism can extract image features based on global information and generate image samples with clear details. The experimental results show that the quality of the samples generated by the generative adversarial network model proposed in this paper is excellent. The samples are used in the CRNN model as the training dataset and the real images are used as the test sets, won the high recognition rate.

关键词： Containers Training Generators Generative adversarial networks Image recognition Gallium nitride Character recognition

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Research on Fishery Trajectory Analysis and Fishing Ground Discrimination Based on CNN

Research on Fishery Trajectory Analysis and Fishing Ground D...

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international symposium on distributed computing and applications to business, engineering and science (DCABES)

作者： Wenjin Xu Qin Xie Yue Ma College of Information Science and Technology Qingdao University of Science & Technology Qingdao China

ISBN: (数字)9781728197241

ISBN: (纸本)9781728197258

With the continuous progress of science and technology, people's production and lifestyle which has lasted for thousands of years are also changing quietly. More and more things, people, data, and the Internet are connected together, which also makes the power of big data calculation and mining increase exponentially. The navigation data of Zhejiang coastal fishermen are recorded by satellite. Using big data calculation can combine the ocean climate information, fishing vessel location information, and the data formed by fishing grounds into a big fishery data platform. After many captains use the data provided by this platform for fishing guidance, they have achieved a very good catch. They use scientific and technological innovation to change fishing methods so that fishermen can no longer rely on heaven for food. The risk is difficult to predict. The research in this paper is to find the common characteristics of fishing grounds by analyzing the navigation data of fishermen in the Zhejiang coastal area recorded by satellite and at the same time, expand the search range to estimate the new fishing ground location. This work can be used to mine data by using convolutional neural network.

关键词： Aquaculture Data mining Boats Satellites Satellite navigation systems Clustering algorithms Trajectory

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