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The application of artificial intelligence-assisted technology in cultural and creative product design

SCIENTIFIC REPORTS 2024年第1期14卷 1-18页

作者： Liang, Jing Jiangxi Inst Fash Technol Sch Fash Media Nanchang 330000 Peoples R China

This study proposes a novel artificial intelligence (AI)-assisted design model that combines variational autoencoders (VAE) with reinforcement learning (RL) to enhance innovation and efficiency in cultural and creative product design. By introducing AI-driven decision support, the model streamlines the design workflow and significantly improves design quality. The study establishes a comprehensive framework and applies the model to four distinct design tasks, with extensive experiments validating its performance. Key factors, including creativity, cultural adaptability, and practical application, are evaluated through structured surveys and expert feedback. The results reveal that the VAE + RL model surpasses alternative approaches across multiple criteria. Highlights include a user satisfaction rate of 95%, a Structural Similarity Index (SSIM) score of 0.92, model accuracy of 93%, and a loss reduction to 0.07. These findings confirm the model's superiority in generating high-quality designs and achieving high user satisfaction. Additionally, the model exhibits strong generalization capabilities and operational efficiency, offering valuable insights and data support for future advancements in cultural product design technology.

关键词： Artificial intelligence Cultural and creative product design variational autoencoder Reinforcement learning Design optimization

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Energy Theft Detection Model Based on VAE-GAN for Imbalanced Dataset

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ENERGIES 2023年第3期16卷 1109-1109页

作者： Sun, Youngghyu Lee, Jiyoung Kim, Soohyun Seon, Joonho Lee, Seongwoo Kyeong, Chanuk Kim, Jinyoung Kwangwoon Univ Dept Elect Convergence Engn Seoul 01897 South Korea

Energy theft causes a lot of economic losses every year. In the practical environment of energy theft detection, it is required to solve imbalanced data problem where normal user data are significantly larger than energy theft data. In this paper, a variational autoencoder-generative adversarial network (VAE-GAN)-based energy theft-detection model is proposed to overcome the imbalanced data problem. In the proposed model, the VAE-GAN generates synthetic energy theft data with the features of real energy theft data for augmenting the energy theft dataset. The obtained balanced dataset is applied to train a detector which is designed as one-dimensional convolutional neural network. The proposed model is simulated on the practical dataset for comparing with various generative models to evaluate their performance. From simulation results, it is confirmed that the proposed model outperforms the other existing models. Additionally, it is shown that the proposed model is also very useful in the environments of extreme data imbalance for a wide variety of applications by analyzing the performance of detector according to the balance rate.

关键词： energy theft imbalanced dataset data augmentation variational autoencoder generative adversarial network

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variational co-embedding learning for attributed network clustering

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KNOWLEDGE-BASED SYSTEMS 2023年第1期270卷

作者： Yang, Shuiqiao Verma, Sunny Cai, Borui Jiang, Jiaojiao Yu, Kun Chen, Fang Yu, Shui Univ New South Wales Sch Comp Sci & Engn Sydney NSW 2052 Australia Deakin Univ Sch Informat Technol Burwood Vic 3125 Australia Macquarie Univ Sch Engn Macquarie Pk NSW 2109 Australia Univ Technol Sydney Data Sci Inst Ultimo NSW 2007 Australia Univ Technol Sydney Sch Comp Sci Ultimo NSW 2007 Australia

Recent developments in attributed network clustering combine graph neural networks and autoencoders for unsupervised learning. Although effective, these techniques suffer from either (a) clustering-unfriendly embedding spaces or (b) limited utilization of attribute information. To address these issues, we propose a novel model called variational Co-embedding Learning Model for Attributed Network Clustering (VCLANC), which utilizes much deeper information from the network by reconstructing both the network structure and the node attributes to perform self-supervised learning. Technically, VCLANC consists of dual variational autoencoders that co-embed nodes and attributes into the same latent space, along with a trainable Gaussian mixture prior that simultaneously performs representation learning and node clustering. To optimize the variational autoencoders and infer the latent variables of embeddings and clustering assignments, we derive a new variational lower bound that maximizes the joint likelihood of the observed network structure and node attributes. Furthermore, we also adopt a mutual distance loss on the cluster centers and a clustering assignment hardening loss on the node embeddings to strengthen clustering quality. Our experimental results on four real-world datasets demonstrate the outstanding performance of VCLANC for attributed network clustering.

关键词： Attributed network clustering Graph neural network variational autoencoder

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Hybrid Learning Models for IMU-Based HAR with Feature Analysis and Data Correction

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SENSORS 2023年第18期23卷 7802页

作者： Tseng, Yu-Hsuan Wen, Chih-Yu Natl Chung Hsing Univ Dept Comp Sci & Engn Taichung 40227 Taiwan Natl Chung Hsing Univ Dept Elect Engn Taichung 40227 Taiwan Natl Chung Hsing Univ Smart Sustainable New Agr Res Ctr SMARTer Taichung 40227 Taiwan Natl Chung Hsing Univ Innovat & Dev Ctr Sustainable Agr IDCSA Taichung 40227 Taiwan

This paper proposes a novel approach to tackle the human activity recognition (HAR) problem. Four classes of body movement datasets, namely stand-up, sit-down, run, and walk, are applied to perform HAR. Instead of using vision-based solutions, we address the HAR challenge by implementing a real-time HAR system architecture with a wearable inertial measurement unit (IMU) sensor, which aims to achieve networked sensing and data sampling of human activity, data pre-processing and feature analysis, data generation and correction, and activity classification using hybrid learning models. Referring to the experimental results, the proposed system selects the pre-trained eXtreme Gradient Boosting (XGBoost) model and the Convolutional variational autoencoder (CVAE) model as the classifier and generator, respectively, with 96.03% classification accuracy.

关键词： human activity recognition variational autoencoder generative adversarial networks

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Cycle consistent network for end-to-end style transfer TTS training

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NEURAL NETWORKS 2021年 140卷 223-236页

作者： Xue, Liumeng Pan, Shifeng He, Lei Xie, Lei Soong, Frank K. Northwestern Polytech Univ Sch Comp Sci Audio Speech & Language Proc Grp ASLP NPU Xian Peoples R China Microsoft Beijing Peoples R China Microsoft STC Asia Shanghai Peoples R China

In this paper, we propose a cycle consistent network based end-to-end TTS for speaking style transfer, including intra-speaker, inter-speaker, and unseen speaker style transfer for both parallel and unparallel transfers. The proposed approach is built upon a multi-speaker variational autoencoder (VAE) TTS model. The model is usually trained in a paired manner, which means the reference speech is totally paired with the output including speaker identity, text, and style. To achieve a better quality for style transfer, which for most cases is in an unpaired manner, we augment the model with an unpaired path with a separated variational style encoder. The unpaired path takes as input an unpaired reference speech and yields an unpaired output. The unpaired output, which lacks direct ground-truth target, is then successfully constrained by a delicately designed cycle consistent network. Specifically, the unpaired output of the forward transfer is fed into the model again as an unpaired reference input, and after the backward transfer yields an output expected to be the same as the original unpaired reference speech. Ablation study shows the effectiveness of the unpaired path, separated style encoders and cycle consistent network in the proposed model. The final evaluation demonstrates the proposed approach significantly outperforms the Global Style Token (GST) and VAE based systems for all the six style transfer categories, in metrics of naturalness, speech quality, similarity of speaker identity, and similarity of speaking style. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： Speech synthesis End-to-end Style transfer variational autoencoder Cycle consistent

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Exploiting the sparse characteristics in probabilistic feature space for hyperspectral anomaly detection

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JOURNAL OF APPLIED REMOTE SENSING 2021年第4期15卷

作者： Yu, Shaoqi Li, Xiaorun Chen, Shuhan Zhao, Liaoying Zhejiang Univ Dept Elect Engn Hangzhou Zhejiang Peoples R China Hangzhou Dianzi Univ Dept Comp Sci Hangzhou Zhejiang Peoples R China

Nowadays, the low-rank representation (LRR) and deep learning-based methods have received much attention in anomaly detection for hyperspectral images (HSIs). However, most of these methods mainly focus on the powerful reconstruction capability of the neural networks while ignoring the potential probability distribution of both anomalies and background pixels. To solve the problem, we propose a sparse component extraction-based probability distribution representation detector (SC-PDRD) framework, which integrates the characteristic of the sparse component obtained by the LRR model with the powerful probability representation ability of the variational autoencoder (VAE) network. The LRR model effectively separates the anomaly component from the background, which also serves as the prior anomaly distribution for each pixel. Moreover, the VAE architecture tries to recover the potential anomaly distribution using the sparse detection map in the feature space. In addition, we employ the Chebyshev neighborhood to leverage spatial information. The modified Wasserstein distance measures the distance between the test pixel and its neighborhood. The final detection map is attained by combining the prior anomalous degree of the anomalies with the output of the VAE network. Experimental results on three real HSIs demonstrate the effectiveness and superiority of SCPDRD. (C) 2021 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： hyperspectral anomaly detection hyperspectral imagery variational autoencoder probability distribution intrinsic structure

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Towards physical interaction-based sequential mobility assistance using latent generative model of movement state

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ADVANCED ROBOTICS 2021年第1期35卷 64-79页

作者： Itadera, Shunki Kobayashi, Taisuke Nakanishi, Jun Aoyama, Tadayoshi Hasegawa, Yasuhisa Nagoya Univ Dept Micronano Mech Sci & Engn Nagoya Aichi Japan Nara Inst Sci & Technol Div Informat Sci Nara Japan Meijo Univ Dept Mech Engn Nagoya Aichi Japan

In this paper, we address sequential mobility assistance for daily elderly care through physical human-robot interaction. The goal of this work is to develop a robotic assistive system to provide physical support in daily life such as movement transition, e.g. sit-to-stand and walking. Using a mobile human support robotic platform, we propose an unsupervised learning-based approach to providing desirable physical support through an adaptive impedance parameter selection strategy according to the recognized user's movement state in an online manner. Using a latent generative model with a long short-term memory-based variational autoencoder, we first estimate the probability of the user's current movement state based on the sensory information in a low dimensional latent space. Then, the desired impedance parameters are selected adaptively according to the estimated movement state. One of the benefits of such an unsupervised learning approach is that no labeling is necessary in the training phase. Furthermore, our proposed framework is capable of detecting possible novel states such as falling over based on the obtained latent space. In order to demonstrate the proof of concept of our proposed approach, we present the experimental results of performance evaluations of online movement state recognition as well as novel movement detection.

关键词： Physical human– robot interaction mobility assistance movement detection latent generative model variational autoencoder

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Disentangling cobionts and contamination in long-read genomic data using sequence composition

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G3-GENES GENOMES GENETICS 2024年第11期14卷 jkae187页

作者： Weber, Claudia C. Wellcome Genome Campus Tree Life Wellcome Sanger Inst Hinxton CB10 1SA England

The recent acceleration in genome sequencing targeting previously unexplored parts of the tree of life presents computational challenges. Samples collected from the wild often contain sequences from several organisms, including the target, its cobionts, and contaminants. Effective methods are therefore needed to separate sequences. Though advances in sequencing technology make this task easier, it remains difficult to taxonomically assign sequences from eukaryotic taxa that are not well represented in databases. Therefore, reference-based methods alone are insufficient. Here, I examine how we can take advantage of differences in sequence composition between organisms to identify symbionts, parasites, and contaminants in samples, with minimal reliance on reference data. To this end, I explore data from the Darwin Tree of Life project, including hundreds of high-quality HiFi read sets from insects. Visualizing two-dimensional representations of read tetranucleotide composition learned by a variational autoencoder can reveal distinct components of a sample. Annotating the embeddings with additional information, such as coding density, estimated coverage, or taxonomic labels allows rapid assessment of the contents of a dataset. The approach scales to millions of sequences, making it possible to explore unassembled read sets, even for large genomes. Combined with interactive visualization tools, it allows a large fraction of cobionts reported by reference-based screening to be identified. Crucially, it also facilitates retrieving genomes for which suitable reference data are absent.

关键词： variational autoencoder data visualization unsupervised learning long-read sequencing

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Sentiment Analysis-Based Automatic Stress and Emotion Recognition using Weighted Fused Fusion-Based Cascaded DTCN with Attention Mechanism from EEG Signal

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JOURNAL OF INFORMATION & KNOWLEDGE MANAGEMENT 2024年第5期23卷

作者： Kathole, Atul B. Lonare, Savita Dharmale, Gulbakshee Katti, Jayashree Vhatkar, Kapil Kimbahune, Vinod V. Dr DY Patil Inst Technol Pune Comp Engn Pimpri 411018 Maharashtra India Pimpri Chinchwad Coll Engn Nigdi IT Dept Pimpri 411044 Maharashtra India

When loaded with difficulties in fulfilling daily requirements, a lot of people in today's world experience an emotional pressure known as stress. Stress that lasts for a short duration of time has more advantages as they are good for mental health. But, the persistence of stress for a long duration of time may lead to serious health impacts in individuals, such as high blood pressure, cardiovascular disease, stroke and so on. Long-term stress, if unidentified and not treated, may also result in personality disorder, depression and anxiety. The initial detection of stress has become more important to prevent the health issues that arise due to stress. Detection of stress based on brain signals for analysing the emotion in humans leads to accurate detection outcomes. Using EEG-based detection systems and disease, disability and disorders can be identified from the brain by utilising the brain waves. Sentiment Analysis (SA) is helpful in identifying the emotions and mental stress in the human brain. So, a system to accurately and precisely detect depression in human based on their emotion through the utilisation of SA is of high necessity. The development of a reliable and precise Emotion and Stress Recognition (ESR) system in order to detect depression in real-time using deep learning techniques with the aid of Electroencephalography (EEG) signal-based SA is carried out in this paper. The essentials needed for performing stress and emotion detection are gathered initially from benchmark databases. Next, the pre-processing procedures, like the removal of artifacts from the gathered EEG signal, are carried out on the implemented model. The extraction of the spectral attributes is carried out from the pre-processed. The extracted spectral features are considered the first set of features. Then, with the aid of a Conditional variational autoencoder (CVA), the deep features are extracted from the pre-processed signals forming a second set of features. The weights are opti

关键词： Stress recognition emotion recognition spectral feature extraction variational autoencoder weighted feature fusion adaptive egret swarm optimisation algorithm cascaded deep temporal convolution network with attention mechanism

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A Deep Learning Algorithm for High-Dimensional Exploratory Item Factor Analysis

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PSYCHOMETRIKA 2021年第1期86卷 1-29页

作者： Urban, Christopher J. Bauer, Daniel J. Univ North Carolina Chapel Hill NC 27515 USA

Marginal maximum likelihood (MML) estimation is the preferred approach to fitting item response theory models in psychometrics due to the MML estimator's consistency, normality, and efficiency as the sample size tends to infinity. However, state-of-the-art MML estimation procedures such as the Metropolis-Hastings Robbins-Monro (MH-RM) algorithm as well as approximate MML estimation procedures such as variational inference (VI) are computationally time-consuming when the sample size and the number of latent factors are very large. In this work, we investigate a deep learning-based VI algorithm for exploratory item factor analysis (IFA) that is computationally fast even in large data sets with many latent factors. The proposed approach applies a deep artificial neural network model called an importance-weighted autoencoder (IWAE) for exploratory IFA. The IWAE approximates the MML estimator using an importance sampling technique wherein increasing the number of importance-weighted (IW) samples drawn during fitting improves the approximation, typically at the cost of decreased computational efficiency. We provide a real data application that recovers results aligning with psychological theory across random starts. Via simulation studies, we show that the IWAE yields more accurate estimates as either the sample size or the number of IW samples increases (although factor correlation and intercepts estimates exhibit some bias) and obtains similar results to MH-RM in less time. Our simulations also suggest that the proposed approach performs similarly to and is potentially faster than constrained joint maximum likelihood estimation, a fast procedure that is consistent when the sample size and the number of items simultaneously tend to infinity.

关键词： Deep learning artificial neural network variational inference variational autoencoder importance sampling importance weighted autoencoder item response theory categorical factor analysis latent variable modeling

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