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

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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

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Improving Fault Localization Using conditional variational autoencoder

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2022年第8期E105D卷 1490-1494页

作者： Fang, Xianmei Gao, Xiaobo Wang, Yuting Liao, Zhouyu Ma, Yue Hechi Univ Sch Big Data & Comp Sci Hechi Guangxi Peoples R China

Fault localization analyzes the runtime information of two classes of test cases (i.e., passing test cases and failing test cases) to identify suspicious statements potentially responsible for a failure. However, the failing test cases are always far fewer than passing test cases in reality, and the class imbalance problem will affect fault localization effectiveness. To address this issue, we propose a data augmentation approach using conditional variational auto-encoder to synthesize new failing test cases for FL. The experimental results show that our approach significantly improves six state-of-the-art fault localization techniques.

关键词： debugging fault localization conditional variational autoencoder data augmentation

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

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SENSORS 2017年第9期17卷 1967-1967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

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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...

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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

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Emotional Response Generation using conditional variational autoencoder

Emotional Response Generation using Conditional Variational ...

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IEEE International Conference on Big Data and Smart Computing (BigComp)

作者： Lee, Young-Jun Choi, Ho-Jin Korea Adv Inst Sci & Technol Sch Comp Daejeon South Korea

ISBN: (纸本)9781728160344

Neural response generation is to generate human-like response given human utterance by using a deep learning. In the previous studies, expressing emotion in response generation improve user performance, user engagement, and user satisfaction. Also, the conversational agents can communicate with users at the human level. however, the previous emotional response generation model cannot interpret why the model generates such response with emotions. We propose an interpretable emotional response generation model which generates emotional responses by using a latent space. The extraction part is to extract the emotion of input utterance as a vector form by using the Bidirectional GRU based classification model. The generation part is to generate an emotional response to the input utterance by exploiting emotion vector and latent space. All of these parts are jointly optimized at the training process. We will evaluate our model on the emotion-labeled dialogue dataset: DailyDialog.

关键词： Emotional response generation emotion classification model conditional variational autoencoder interpretability

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Intrusion Detection Toward Feature Reconstruction using Huber conditional variational autoencoder 36

Intrusion Detection Toward Feature Reconstruction using Hube...

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36th International Conference on Information Networking (ICOIN)

作者： Razafimahatratra, Fenohasina Lova Rakotomandimby, Miora Fifaliana Wajira, Prasad De Silva Wuhan Univ Technol Sch Informat Engn Wuhan Peoples R China Boroughmarkets Sydney NSW Australia

ISBN: (纸本)9781665413329

autoencoder is recently one of the widely used machine learning approaches where the network is trained to learn the data representation. This paper considers autoencoder for Feature Reconstruction in Intrusion Detection System. Two networks are used to form an autoencoder;the left part, an Encoder network used to learn and compress data in order to reduce feature dimension, and the right part Decoder network, which can be used to reconstruct content into its original format instead of categorizing the data. However, the ability to approximate data reconstructed to the original one in an accurate manner is still a challenging process. Thus, we propose a conditional variational autoencoder with an adaptive loss function named Adaptive Huber CVAE (AH-CVAE). We replace the classical reconstruction loss function with a flexible loss function in order to minimize reconstruction error. Then, this approach is proposed to make an optimal estimation of Intrusion Detection data and achieve an accurate approximation. We conduct our experiment on two datasets, NSL-KDD and UNSW-NB15 Dataset, and compare results with other existing approaches. AH-CVAE can better approximate the original and the reconstructed feature in the intrusion detection dataset.

关键词： Feature Reconstruction Dimensionality reduction autoencoder conditional variational autoencoder Intrusion Detection

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Robust State Estimator Based on conditional variational autoencoder

Robust State Estimator Based on Conditional Variational Auto...

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IEEE-Power-and-Energy-Society General Meeting (PESGM)

作者： Wang, Lei Qi, Junjian South Dakota State Univ Elect Engn & Comp Sci Brookings SD 57007 USA

ISBN: (纸本)9798350381849;9798350381832

In this paper, we propose a novel, model-free robust state estimator based on the conditional variational autoencoder (CVAE). Unlike traditional model-based methods such as weighted least squares, our approach eliminates the need for prior knowledge of the power system model. Once adequately trained, our estimator can accurately estimate the system states based on measurements. As CVAE provides a probability distribution over the output data, the inherent uncertainty modeling makes the proposed estimator robust to bad data. The effectiveness and robustness of the proposed state estimator are demonstrated on the IEEE 14-bus system.

关键词： Bad data detection conditional variational autoencoder deep learning model-free robust state estimation

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Autonomous Vehicle Path Prediction Using conditional variational autoencoder Networks 1

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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

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SINGING FUNDAMENTAL FREQUENCY CONTOUR GENERATION USING GENERALIZED COMMAND-RESPONSE MODEL AND SCORE-conditional variational autoencoder 31

SINGING FUNDAMENTAL FREQUENCY CONTOUR GENERATION USING GENER...

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IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP)

作者： Seki, Shogo Taga, Haruka Toda, Tomoki NTT Corp NTT Commun Sci Labs Atsugi Kanagawa Japan Nagoya Univ Grad Sch Informat Nagoya Aichi Japan Nagoya Univ Informat Technol Ctr Nagoya Aichi Japan

ISBN: (纸本)9781728163383

This paper proposes a method for achieving physically motivated and interpretable control of fundamental frequency (F0) contour generation in singing aid systems for laryngectomees. Recently proposed variational autoencoder (VAE)-based method, VAE-SPACE, has successfully generated singing F0 contours from musical scores. However, VAE-SPACE can generate physically deviated F0 contours. Moreover, to represent fluctuations in F0 contours, VAE-SPACE requires manual adjustment of noise components used as the input with musical scores. To address these issues, the proposed method 1) introduces a generalized command-response (GCR) model to represent an F0 contour as an approximation of a physical F0 production mechanism, and 2) employs a conditional VAE (CVAE) to treat musical scores and the noise components separately. The experimental results reveal that the proposed method achieves comparable performance as VAE-SPACE without the manual adjustment of noise components and makes it possible to control F0 contours more intuitively by using the trained GCR model.

关键词： Singing aid system F0 contour generation Generalized command-response model conditional variational autoencoder

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Improving Item Cold-start Recommendation via Model-agnostic conditional variational autoencoder 22

Improving Item Cold-start Recommendation via Model-agnostic ...

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45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)

作者： Zhao, Xu Ren, Yi Du, Ying Zhang, Shenzheng Wang, Nian Tencent News Beijing Peoples R China

ISBN: (纸本)9781450387323

Embedding & MLP has become a paradigm for modern large-scale recommendation system. However, this paradigm suffers from the cold-start problem which will seriously compromise the ecological health of recommendation systems. This paper attempts to tackle the item cold-start problem by generating enhanced warmed-up ID embeddings for cold items with historical data and limited interaction records. From the aspect of industrial practice, we mainly focus on the following three points of item cold-start: 1) How to conduct cold-start without additional data requirements and make strategy easy to be deployed in online recommendation scenarios. 2) How to leverage both historical records and constantly emerging interaction data of new items. 3) How to model the relationship between item ID and side information stably from interaction data. To address these problems, we propose a model-agnostic conditional variational autoencoder based Recommendation(CVAR) framework with some advantages including compatibility on various backbones, no extra requirements for data, utilization of both historical data and recent emerging interactions. CVAR uses latent variables to learn a distribution over item side information and generates desirable item ID embeddings using a conditional decoder. The proposed method is evaluated by extensive offline experiments on public datasets and online A/B tests on Tencent News recommendation platform, which further illustrate the advantages and robustness of CVAR.

关键词： Cold-Start Recommendation conditional variational autoencoder Item ID embedding

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