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Recommending Changes on QoE Factors with conditional variational autoencoder 4

Recommending Changes on QoE Factors with Conditional Variati...

4th ACM SIGCOMM FlexNets Workshop on Flexible Networks, Artificial Intelligence Supported Network Flexibility and Agility (FlexNets)

作者： Ickin, Selim Ericsson AB Stockholm Sweden

ISBN: (纸本)9781450386340

Increasing complexity in management of immense number of network elements and their dynamically changing environment necessitates machine learning based recommendation models to guide human experts in setting appropriate network configurations to sustain end-user Quality of Experience (QoE). In this paper, we present and demonstrate a generative conditional variational autoencoder (CVAE)-based technique to reconstruct realistic underlying QoE factors together with improvement suggestions in a video streaming use case. Based on our experiment setting consisting of a set of what-if scenarios, our approach pinpointed the potential required changes on the QoE factors to improve the estimated video Mean Opinion Scores (MOS).

关键词： Machine Learning conditional variational autoencoder Quality of Experience

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NNSPEECH: SPEAKER-GUIDED conditional variational autoencoder FOR ZERO-SHOT MULTI-SPEAKER TEXT-TO-SPEECH 47

NNSPEECH: SPEAKER-GUIDED CONDITIONAL VARIATIONAL AUTOENCODER...

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47th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Zhao, Botao Zhang, Xulong Wang, Jianzong Cheng, Ning Xiao, Jing Ping An Technol Shenzhen Co Ltd Shenzhen Guangdong Peoples R China Fudan Univ Inst Sci & Technol Brain Inspired Intelligence Shanghai Peoples R China

ISBN: (纸本)9781665405409

Multi-speaker text-to-speech (TTS) using a few adaption data is a challenge in practical applications. To address that, we propose a zero-shot multi-speaker TTS, named nnSpeech, that could synthesis a new speaker voice without fine-tuning and using only one adaption utterance. Compared with using a speaker representation module to extract the characteristics of new speakers, our method bases on a speaker-guided conditional variational autoencoder and can generate a variable Z, which contains both speaker characteristics and content information. The latent variable Z distribution is approximated by another variable conditioned on reference mel-spectrogram and phoneme. Experiments on the English corpus, Mandarin corpus, and cross-dataset proves that our model could generate natural and similar speech with only one adaption speech.

关键词： zero-shot multi-speaker text-to-speech conditional variational autoencoder

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A conditional variational autoencoder Algorithm for Reconstructing Defect Data of Magnetic Flux Leakage 32

A Conditional Variational Autoencoder Algorithm for Reconstr...

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32nd Chinese Control And Decision Conference (CCDC)

作者： Lu, Senxiang Wu, Jing Zhang, Jianan Northeastern Univ Sch Informat Sci & Engn Shenyang 110004 Peoples R China Liaoning Prov Inst Measurement Shenyang 110004 Peoples R China

ISBN: (纸本)9781728158556

Magnetic flux leakage (MFL) testing is widely used in pipeline nondestructive testing. The acquisition quality of MFL signal is an important prerequisite for the accuracy of pipeline detection and evaluation. However, data loss often occurs in the process of detection, due to the interference of external environmental factors. Therefore, it is important to reconstruct the missing data. This paper analyzes four types of MFL data loss, and presents a conditional variational autoencoder algorithm to reconstruct defect data of MFL for these types of loss. The algorithm effectiveness is tested by comparing with traditional variational autoencoder method. The results demonstrate that the proposed method can improve the accuracy of reconstructing defect data loss.

关键词： Magnetic Flux Leakage Data Loss conditional variational autoencoder Defect Data Reconstructing

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Radio Galaxy Morphology Simulation via Residual conditional variational autoencoder 15

Radio Galaxy Morphology Simulation via Residual Conditional ...

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15th International Conference on Computational Intelligence and Security (CIS)

作者： Ma, Zhixian Zhu, Jie Zhu, Yongkai Xu, Haiguang Shanghai Jiao Tong Univ Dept Elect Engn Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Dept Astrophys Shanghai 200240 Peoples R China

ISBN: (纸本)9781728160924

We propose a radio galaxy morphology simulation approach by using a conditional variational autoencoder composed of residual convolutional blocks namely ResCVAE. It estimates the distribution of the existed morphology-labeled radio galaxy images by mapping them to a latent low-dimension space, and simulates new images according to the obtained distribution. From designed experiments we find that the pixelwise cross-entropy (PCE) loss, as a component of the optimization objective for the ResCVAE, trains the network parameters to obtain lower reconstruction loss and more accurate simulation performance than the mean squared error (MSE) loss function under the same training settings. In addition, the strategies of applying condition and residual convolutional blocks are also evaluated and compared to the other networks, which suggests a domination of our proposed approach.

关键词： image simulation pattern analysis conditional variational autoencoder residual

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Time-varying Item Feature conditional variational autoencoder for Collaborative Filtering

Time-varying Item Feature Conditional Variational Autoencode...

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IEEE International Conference on Big Data (Big Data)

作者： Kim, Jeeyung Seoul Natl Univ Datamining Lab Ind Engn Seoul South Korea

ISBN: (纸本)9781728108582

The factors impacting on what users purchase can be classified as an internal factor which is time-invariant user's unique taste and an external factor which is time-varying item characteristic. However, the current recommendation system has a limitation of making recommendations based only on the user's history without taking into account the item features trends of the time, which prevents precise recommendations. In this paper, the recommendation system that reflects inter-items trends of time-based bin is proposed. We focus on creating a hybrid recommender system that could effectively combine time-varying content data with the users history data. Specifically, we use conditional variational autoencoder (VAE) to add a time dynamic item features to user-item implicit feedback data. In this case, distributed representation of items in the specific period is used as a condition that is added to input and latent variable of VAE respectively. In detail, the distributed representation per time bin can be extracted using ISTM. By applying a condition into VAE, a hybrid recommendation system can be created to reflect the item time-varying features. The proposed model in this paper differs from current studies in that it reflects the changing characteristics inherent of the products and utilizes it for recommendation. The Movielens 1M data and Amazon women's clothing dataset are used for the evaluation of the proposed model.

关键词： recommender system conditional variational autoencoder long short term memory

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A Hybrid Model for QoS Prediction based on Improved conditional variational autoencoder 20

A Hybrid Model for QoS Prediction based on Improved Conditio...

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20th IEEE Int Symposium on Parallel and Distributed Processing with Applicat / 15th IEEE Int Conf on Social Comp and Networking / 12th IEEE Int Conf on Big Data and Cloud Comp / 12th IEEE Int Conf on Sustainable Comp and Commun

作者： Wu, Mengwei Lu, Qin Wang, Yingxue Wang, Yichao Chen, Huanyu Li, Weixiao Qilu Univ Technol Shandong Acad Sci Sch Comp Sci & Technol Jinan Shandong Peoples R China

ISBN: (纸本)9781665464970

As a nonfunctional attribute of Web services, Quality of Service (QoS) is a crucial criterion that researchers utilize to develop personalized service recommendation. The QoS history is thin and contains noisy data because of the network environment and other inevitable factors. In order to increase the effectiveness of Web service recommendation, it is essential to accurately predict the missing QoS values. Therefore, a model for QoS prediction based on improved conditional variational autoencoder is proposed in order to improve the prediction accuracy of missing QoS. The Bias based Singular Value Decomposition (BiasSVD) is employed by the model to pre-populate the initial sparse QoS matrix with missing values. By improved conditional variational autoencoder, the pre-filled matrix is learned and the QoS is reconstructed. Then, the users' plausible similar neighbors are filtered using the Pearson Correlation Coefficient and Independent Sample T-test. The missing QoS is predicted by combining the reconstruction matrix. Last but not least, the experimental results on the actual dataset WSDream demonstrate that the model improves the QoS prediction accuracy to considerable degree.

关键词： Service recommendation QoS prediction conditional variational autoencoder BiasSVD Independent Sample T-test

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Emotional Dialogue Generation Based on Transformer and conditional variational autoencoder 21

Emotional Dialogue Generation Based on Transformer and Condi...

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21st IEEE International Conference on Ubiquitous Computing and Communications / 5th IEEE International Conference on Data Science and Computational Intelligence / 21st IEEE International Conference on Computer and Information Technology

作者： Lin, Hongquan Deng, Zhenrong Wuzhou Univ Sch Data Sci & Software Engn Wuzhou Peoples R China Guilin Univ Elect Technol Sch Comp Sci & Inform Secur Guilin Peoples R China

ISBN: (纸本)9781665477260

Human-machine dialogue is one of the most challenging tasks in the field of natural language processing, and it is also the basis for the realization of a human-machine inclusive society in the future. At present, the generative dialogue model based on deep learning is prone to generate general responses with single content and no meaningful information, and the existing research considers emotional factors relatively little. Aiming at the shortcomings of existing methods, an emotional dialogue generation model based on Transformer and conditional variational autoencoder(CVAE) is proposed, which is intended to improve the diversity of response information and embed emotional factors in the generated response. This model uses Transformer to extract the semantic features of the text sequence to improve the utilization of the semantic information of the text sequence. To increase the diversity of the response information, the latent variable of the conditional variational self-encoder is introduced into the decoder. In addition, in order to enhance the empathy ability of the model, an emotion perception encoder is used to encode user emotion information, and a pre-trained emotion classification model based on BERT is proposed to detect the emotion information implicit in the utterance. Experiments have shown that the proposed model has a stronger generation ability, more diverse information to generate responses, and a stronger empathy ability.

关键词： Emotional dialogue generation conditional variational autoencoder Transformer BERT Emotion analysis

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Crustal model in eastern Qinghai-Tibet plateau and western Yangtze craton based on conditional variational autoencoder

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PHYSICS OF THE EARTH AND PLANETARY INTERIORS 2020年 309卷 106584-106584页

作者： Cheng, Xianqiong Jiang, Kezhi Chengdu Univ Technol Coll Geophys Chengdu 610059 Peoples R China Chengdu Univ Technol Coll Engn & Technol Leshan 614000 Peoples R China

Eastern margin of the Qinghai-Tibet plateau and western margin of the Yangtze craton has a complex crust mantle structure and is an important region for studying the crust-mantle deformation mechanism. Because the Rayleigh surface wave group velocity has a strong constraint on the structure of the crust and upper mantle, a lot of researches have used the Rayleigh surface wave group velocity to invert the crust-mantle structure in this region, but the layered multi-parameter joint inversion and the uncertainty of the inversion result evaluation is still a research hotspot. Motived by conditional variational autoencoder (CVAE) which is composed of a decoding and encoding process that uses a deep neural network to predict the variational distribution of parameters, we propose a crustal model by CVAE to attain the forward and inversion relationship between the group velocity of Rayleigh wave and the crustal model. In which the CVAE decoding corresponds to the forward process and the encoding corresponds to the inverse process. Moreover, the inversion results of this method are constrained from three aspects as follows: The network inputs and outputs have the same group velocities;The decoded outputs are normal distribution of hidden variables representing the crustal model;The mean values of the decoded output during CVAE training are the sampled crustal models. The proposed method can invert multi-parameters and evaluate uncertainty of the inversion efficiently. Based on the latest group velocity model, we invert for mean and variance of thickness in the upper, middle, and lower sedimentary cover, and of thickness, P-wave velocity, S-wave velocity and density in the upper, middle, and lower crystalline crust for eastern Qinghai-Tibet plateau and western Yangtze craton. Compared with the existing crustal models obtained by different methods, the results of this study are in good agreement. We conclude that CVAE-based group velocity inversion of the crustal model is a fe

关键词： Crustal model conditional variational autoencoder Eastern Qinghai-Tibet plateau and western Yangtze craton Data-driven

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Vitruvio: conditional variational autoencoder to generate building meshes via single perspective sketches

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AUTOMATION IN CONSTRUCTION 2024年 166卷

作者： Tono, Alberto Huang, Heyaojing Agrawal, Ashwin Fischer, Martin Stanford Univ Palo Alto CA USA Computat Design Inst San Francisco CA USA

At the beginning of a project, architects convey design ideas via quick 2D diagrams, front views, floor plans, and sketches. Consequently, many stakeholders have difficulty visualizing the 3D representation of the building mass, leading to varied interpretations thus inhibiting a shared understanding of the design. To alleviate the challenge, this paper proposes a deep learning-based method, Vitruvio, for creating a 3D model from a single perspective sketch. This method allows designers to automatically generate 3D representations in real-time based on their initial sketches and thus communicate effectively and intuitively to the client. Vitruvio adapts the Occupancy Network to perform single view reconstruction (SVR), a technique for creating 3D representations from a single image. Vitruvio achieves: (1) an 18% increase in the reconstruction accuracy and (2) a 26% reduction in the inference time compared to the Occupancy Network on one thousand buildings provided by the New York municipality. This research investigates the effect that the building orientation has on the reconstruction quality, discovering that Vitruvio can capture fine-grain details in complex buildings when their native orientation is preserved during training, as opposed to the SVR's standard practice that aligns every building to its canonical pose. The code is available here https://***/CDInstitute/Vitruvio.

关键词： Artificial intelligence Neural -aided design Deep generative design Deep generative modeling conditional variational autoencoder Sketch -based modeling

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Driving Style-Based conditional variational autoencoder for Prediction of Ego Vehicle Trajectory

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IEEE ACCESS 2021年 9卷 169348-169356页

作者： Kim, Dongchan Shon, Hyukju Kweon, Nahyun Choi, Seungwon Yang, Chanuk Huh, Kunsoo Hanyang Univ Dept Automot Engn Seoul 04763 South Korea Hanyang Univ Dept Automot Engn Automot Comp Convergence Seoul 04763 South Korea Hanyang Univ Dept Automot Elect Control Engn Seoul 04763 South Korea

Trajectory prediction of the ego vehicle is essential for advanced driver assistance systems to function properly. By recognizing various driving styles and predicting trajectories reflecting them, the prediction performance is enhanced, and a personalized trajectory can be generated. Therefore, we propose to combine driving style recognition and trajectory prediction tasks using only in-vehicle CAN-bus sensor data for possible application to normal vehicles. The DeepConvLstm network was utilized for driving style recognition, and a generative-based model was used for trajectory prediction. The classified driving style was added as a condition to the network. In addition, the past trajectory of the ego vehicle is estimated and utilized as an additional input for performance improvement. The performance of the proposed method is analyzed in terms of the root mean squared error (RMSE) and mean absolute error (MAE) compared with the case wherein the driving style and the past trajectory are not conditioned or given, respectively. The results demonstrate the effectiveness of the proposed method.

关键词： Trajectory Feature extraction Vehicles Training Global Positioning System Data models Task analysis Driving style recognition trajectory prediction conditional variational autoencoder CAN data Hardware-in-the-Loop simulation

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