检索结果-内蒙古大学图书馆

STO-CVAE: state transition-oriented conditional variational autoencoder for data augmentation in disability classification

引用

COMPLEX & INTELLIGENT SYSTEMS 2024年第3期10卷 4201-4222页

作者： Bang, Seong Jin Kang, Min Jung Lee, Min-Goo Lee, Sang Min Kwangwoon Univ Coll Software Convergence 20 Kwangwoon Ro Seoul South Korea Korea Univ Dept Physiol Med Coll Anam Dong 5 Ga Seoul South Korea

The class imbalance problem occurs when there is an unequal distribution of classes in a dataset and is a significant issue in various artificial intelligence applications. This study focuses on the severe multiclass imbalance problem of human activity recognition in rehabilitation exercises for people with disabilities. To overcome this problem, we present a novel human action-centric augmentation method for human skeleton-based pose estimation. This study proposes the state transition-oriented conditional variational autoencoder (STO-CVAE) to capture action patterns in repeated exercises. The proposed approach generates action samples by capturing temporal information of human skeletons to improve the identification of minority disability classes. We conducted experimental studies with a real-world dataset gathered from rehabilitation exercises and confirmed the superiority and effectiveness of the proposed method. Specifically, all investigated classifiers (i.e., random forest, support vector machine, extreme gradient boosting, light gradient boosting machine, and TabNet) trained with the proposed augmentation method outperformed the models trained without augmentation in terms of the F1-score and accuracy, with F1-score showing the most improvement. Overall, the prediction accuracy of most classes was improved;in particular, the prediction accuracy of the minority classes was greatly improved. Hence, the proposed STO-CVAE can be used to improve the accuracy of disability classification in the field of physical medicine and rehabilitation and to provide suitable personal training and rehabilitation exercise programs.

关键词： conditional variational autoencoder Human action recognition Human pose estimation Class imbalance problem

来源：评论

学校读者我要写书评

暂无评论

conditional variational autoencoder for Prediction and Feature Recovery Applied to Intrusion Detection in IoT

引用

SENSORS 2017年第9期17卷 1967页

作者： Lopez-Martin, Manuel Carro, Belen Sanchez-Esguevillas, Antonio Lloret, Jaime Univ Valladolid ETSIT Dpto TSyCeIT Paseo Belen 15 E-47011 Valladolid Spain Univ Politecn Valencia Inst Invest Gest Integrada Zonas Costeras Camino Vera S-N E-46022 Valencia Spain

The purpose of a Network Intrusion Detection System is to detect intrusive, malicious activities or policy violations in a host or host's network. In current networks, such systems are becoming more important as the number and variety of attacks increase along with the volume and sensitiveness of the information exchanged. This is of particular interest to Internet of Things networks, where an intrusion detection system will be critical as its economic importance continues to grow, making it the focus of future intrusion attacks. In this work, we propose a new network intrusion detection method that is appropriate for an Internet of Things network. The proposed method is based on a conditional variational autoencoder with a specific architecture that integrates the intrusion labels inside the decoder layers. The proposed method is less complex than other unsupervised methods based on a variational autoencoder and it provides better classification results than other familiar classifiers. More important, the method can perform feature reconstruction, that is, it is able to recover missing features from incomplete training datasets. We demonstrate that the reconstruction accuracy is very high, even for categorical features with a high number of distinct values. This work is unique in the network intrusion detection field, presenting the first application of a conditional variational autoencoder and providing the first algorithm to perform feature recovery.

关键词： intrusion detection variational methods conditional variational autoencoder feature recovery neural networks

来源：评论

学校读者我要写书评

暂无评论

Molecular generative model based on conditional variational autoencoder for de novo molecular design

引用

JOURNAL OF CHEMINFORMATICS 2018年第1期10卷 1-9页

作者： Lim, Jaechang Ryu, Seongok Kim, Jin Woo Kim, Woo Youn Korea Adv Inst Sci & Technol Dept Chem 291 Daehak Ro Daejeon 34141 South Korea Korea Adv Inst Sci & Technol KI Artificial Intelligence 291 Daehak Ro Daejeon 34141 South Korea

We propose a molecular generative model based on the conditional variational autoencoder for de novo molecular design. It is specialized to control multiple molecular properties simultaneously by imposing them on a latent space. As a proof of concept, we demonstrate that it can be used to generate drug-like molecules with five target properties. We were also able to adjust a single property without changing the others and to manipulate it beyond the range of the dataset.

关键词： Molecular design conditional variational autoencoder Deep learning

来源：评论

学校读者我要写书评

暂无评论

Time-varying Item Feature conditional variational autoencoder for Collaborative Filtering

Time-varying Item Feature Conditional Variational Autoencode...

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

NNSPEECH: SPEAKER-GUIDED CONDITIONAL VARIATIONAL AUTOENCODER...

引用

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

来源：评论

学校读者我要写书评

暂无评论

Autonomous Vehicle Path Prediction Using conditional variational autoencoder Networks 1

引用

25th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)

作者： Jagadish, D. N. Chauhan, Arun Mahto, Lakshman Indian Inst Informat Technol Dharwad Dharwad 580009 Karnataka India

ISBN: (数字)9783030757625

ISBN: (纸本)9783030757625;9783030757618

Path prediction of autonomous vehicles is an essential requirement under any given traffic scenario. Trajectory of several agent vehicles in the vicinity of ego vehicle, at least for a short future, is needed to be predicted in order to decide upon the maneuver of the ego vehicle. We explore variational autoencoder networks to obtain multimodal trajectories of agent vehicles. In our work, we condition the network on past trajectories of agents and traffic scenes as well. The latent space representation of traffic scenes is achieved by using another variational autoencoder network. The performance of the proposed networks is compared against a residual baseline model.

关键词： Autonomous vehicle path prediction conditional variational autoencoder Deep learning

来源：评论

学校读者我要写书评

暂无评论

NORMATIVE MODELING VIA conditional variational autoencoder AND ADVERSARIAL LEARNING TO IDENTIFY BRAIN DYSFUNCTION IN ALZHEIMER'S DISEASE 20

NORMATIVE MODELING VIA CONDITIONAL VARIATIONAL AUTOENCODER A...

引用

20th IEEE International Symposium on Biomedical Imaging (ISBI)

作者： Wang, Xuetong Zhou, Rong Zhao, Kanhao Leow, Alex Zhang, Yu He, Lifang Lehigh Univ Dept Comp Sci & Engn Bethlehem PA 18015 USA Lehigh Univ Dept Bioengn Bethlehem PA 18015 USA Univ Illinois Dept Psychiat Chicago IL USA Lehigh Univ Dept Elect & Comp Engn Bethlehem PA USA

ISBN: (纸本)9781665473583

Normative modeling is an emerging and promising approach to effectively study disorder heterogeneity in individual participants. In this study, we propose a novel normative modeling method by combining conditional variational autoencoder with adversarial learning (ACVAE) to identify brain dysfunction in Alzheimer's Disease (AD). Specifically, we first train a conditional VAE on the healthy control (HC) group to create a normative model conditioned on covariates like age, gender and intracranial volume. Then we incorporate an adversarial training process to construct a discriminative feature space that can better generalize to unseen data. Finally, we compute deviations from the normal criterion at the patient level to determine which brain regions were associated with AD. Our experiments on OASIS-3 database show that the deviation maps generated by our model exhibit higher sensitivity to AD compared to other deep normative models, and are able to better identify differences between the AD and HC groups.

关键词： Normative modeling conditional variational autoencoder adversarial learning Alzheimer's disease

来源：评论

学校读者我要写书评

暂无评论

Emotional Dialogue Generation Based on Transformer and conditional variational autoencoder 21

Emotional Dialogue Generation Based on Transformer and Condi...

引用

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

来源：评论

学校读者我要写书评

暂无评论

Recommending Changes on QoE Factors with conditional variational autoencoder 4

Recommending Changes on QoE Factors with Conditional Variati...

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：