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Wood-species identification based on terahertz spectral data augmentation and pseudo-label guided deep clustering

WOOD MATERIAL SCIENCE & ENGINEERING 2024年第5期19卷 1004-1014页

作者： Wang, Yuan Wang, Zhi-Gang He, Yi-Hao Avramidis, Stavros Beijing Forestry Univ Sch Technol Beijing Peoples R China Beijing Forestry Univ Res Ctr Biodivers Intelligent Monitoring Beijing Peoples R China Beijing Forestry Univ Joint Int Res Inst Wood Nondestruct Testing & Eval Beijing Peoples R China Univ British Columbia Fac Forestry Dept Wood Sci Vancouver BC Canada Beijing Forestry Univ Sch Technol Beijing 100083 Peoples R China Beijing Forestry Univ Res Ctr Biodivers Intelligent Monitoring Beijing 100083 Peoples R China Beijing Forestry Univ Joint Int Res Inst Wood Nondestruct Testing & Eval Beijing 100083 Peoples R China

In order to address the problem that existing methods combining spectral data and machine learning for wood species identification rely on labeled samples, this study introduces unsupervised learning into the field of wood identification. It proposes a novel wood-identification model called DCVAE (deep conditional variational autoencoder)-PLCAE (pseudo-label convolutional autoencoders). Terahertz time-domain spectra of wood samples at breast height of five broadleaf and five coniferous species were obtained (40 samples of each species of wood, 400 in total). A conditional variational autoencoder was applied to augment the terahertz spectroscopy dataset. Subsequently, a pseudo-label-guided deep clustering model was developed to extract more discriminative deep features. The model was compared with three traditional clustering algorithms and four deep clustering methods. Clustering experiments and visualization results show that the comprehensive clustering performance of DCVAE-PLCAE is better than the other comparative algorithms and that the extracted low-dimensional features has a more straightforward structure. The algorithm in this study can solve the problems of fewer labeled samples and the difficulty of extracting discriminative features by traditional clustering algorithms.

关键词： Wood identification terahertz time domain spectroscopy conditional variational autoencoder pseudo-label

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Vehicle Trajectory Prediction With Multimodal and Dynamics-Aware Interaction Neural Networks

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2024年第12期73卷 18059-18072页

作者： Chang, Yawen Wang, Xudong Shanghai Jiao Tong Univ UM SJTU Joint Inst Shanghai 200240 Peoples R China

Trajectory prediction is essential for improving the reliability and efficiency of vehicle-to-everything (V2X) communications, since link failures and frequent handovers can be alleviated by foreseeing positions of vehicles. Yet, accurate prediction is difficult due to complex interaction and multimodal motions. Deep learning methods are dedicated to addressing these challenging issues and hence surpass traditional physics-based models dramatically. However, current methods mainly model interaction based on spatial relationship while overlooking dynamics-aware interaction, which hinders comprehensive perception. When dealing with multimodality, stochastic latent space without proper goals as regularization is generated to enable one-to-many mapping between historical and future trajectories, which restrains inference accuracy. To tackle these problems, a multimodal and dynamics-aware interaction neural network, MODA, is proposed for vehicle trajectory prediction. To handle dynamics-aware interaction, a graph attention network is conducted. By taking each vehicle as a node and interaction as an edge, self-attention mechanism adaptively calculates edge strength between nodes, which acts as dynamics importance between vehicles for interactive aggregation. To facilitate multimodality, conditional variational autoencoder is incorporated, where supervisory information regularizes the latent space of a recognition network in the training phase for goal orientation. The proposed model is evaluated on two real-world highway datasets: NGSIM I-80 and US-101. The experiments demonstrate that MODA outperforms the state-of-the-art methods by a considerable margin.

关键词： Trajectory Long short term memory Vehicle dynamics Vehicle-to-everything Training Decoding Vehicular ad hoc networks Trajectory prediction dynamics-aware interaction graph attention network multimodality conditional variational autoencoder

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Multimodal Trajectory Prediction for Diverse Vehicle Types in Autonomous Driving with Heterogeneous Data and Physical Constraints

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SENSORS 2024年第22期24卷 7323页

作者： Ge, Maoning Ohtani, Kento Ding, Ming Niu, Yingjie Zhang, Yuxiao Takeda, Kazuya Nagoya Univ Grad Sch Informat Furo ChoChikusa Ward Nagoya 4648601 Japan Zhejiang Fubang Technol Inc Ningbo R&D Campus Block A Ningbo 315048 Peoples R China RoboSense Technol Co Ltd 701 Block B800 Naxian Rd Pudong 200131 Shanghai Peoples R China Nagoya Univ Tier IV Inc Open Innovat Ctr 1-3Mei-eki 1 ChomeNakamura Ward Nagoya 4506610 Japan

The accurate prediction of vehicle behavior is crucial for autonomous driving systems, impacting their safety and efficiency in complex urban environments. To address the challenge of multi-agent trajectory prediction, we propose a novel model integrating multiple input modalities, including historical trajectories, map data, vehicle features, and interaction information. Our approach employs a conditional variational autoencoder (CVAE) framework with a decoder that predicts control actions using the Gaussian Mixture Model (GMM) and then converts these actions into dynamically feasible trajectories through a bicycle model. Evaluated on the nuScenes dataset, the model achieves great performance across key metrics, including minADE5 of 1.26 and minFDE5 of 2.85, demonstrating robust performance across various vehicle types and prediction horizons. These results indicate that integrating multiple data sources, physical models, and probabilistic methods significantly improves trajectory prediction accuracy and reliability for autonomous driving. Our approach generates diverse yet realistic predictions, capturing the multimodal nature of future outcomes while adhering to Physical Constraints and vehicle dynamics.

关键词： multi-agent trajectory prediction multimodal learning conditional variational autoencoder Gaussian Mixture Model autonomous driving

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Solving two-stage stochastic integer programs via representation learning

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NEURAL NETWORKS 2025年 188卷 107446页

作者： Wu, Yaoxin Cao, Zhiguang Song, Wen Zhang, Yingqian Eindhoven Univ Technol Dept Ind Engn & Innovat Sci NL-5600 MB Eindhoven Netherlands Singapore Management Univ Singapore Sch Comp & Informat Syst Singapore 178902 Singapore Shandong Univ Inst Marine Sci & Technol Qingdao 266237 Peoples R China

Solving stochastic integer programs (SIPs) is extremely intractable due to the high computational complexity. To solve two-stage SIPs efficiently, we propose a conditional variational autoencoder (CVAE) for scenario representation learning. A graph convolutional network (GCN) based VAE embeds scenarios into a low-dimensional latent space, conditioned on the deterministic context of each instance. With the latent representations of stochastic scenarios, we perform two auxiliary tasks: objective prediction and scenario contrast, which predict scenario objective values and the similarities between them, respectively. These tasks further integrate objective information into the representations through gradient backpropagation. Experiments show that the learned scenario representations can help reduce scenarios in SIPs, facilitating high-quality solutions in a short computational time. This superiority generalizes well to instances of larger sizes, more scenarios, and various distributions.

关键词： Stochastic integer programs conditional variational autoencoder Semi-supervised learning Contrastive learning

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Few-shot learning with representative global prototype

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NEURAL NETWORKS 2024年 180卷 106600页

作者： Liu, Yukun Shi, Daming Lin, Hexiu Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China

Few-shot learning is often challenged by low generalization performance due to the model is mostly learned with the base classes only. To mitigate the above issues, a few-shot learning method with representative global prototype is proposed in this paper. Specifically, to enhance generalization to novel class, we propose a strategy for jointly training base and novel classes. This process produces prototypes characterizing the class information called representative global prototypes. Additionally, to avoid the problem of data imbalance and prototype bias caused by newly added categories of sparse samples, a novel sample synthesis method is proposed for augmenting more representative samples of novel class. Finally, representative samples and non-representative samples with high uncertainty are selected to enhance the representational and discriminative abilities of the global prototype. Intensive experiments have been conducted on two popular benchmark datasets, and the experimental results show that this method significantly improves the classification ability of few-shot learning tasks and achieves state-of-the-art performance.

关键词： Few-shot learning Representative global prototype conditional variational autoencoder Sample synthesis

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EmoMusicTV: Emotion-Conditioned Symbolic Music Generation With Hierarchical Transformer VAE

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IEEE TRANSACTIONS ON MULTIMEDIA 2024年 26卷 1076-1088页

作者： Ji, Shulei Yang, Xinyu Xi An Jiao Tong Univ Sch Comp Sci & Technol Xian 710049 Peoples R China

Emotion is one of the most crucial attributes of music. However, due to the scarcity of emotional music datasets, emotion-conditioned symbolic music generation using deep learning techniques has not been investigated in depth. In particular, no study explores conditional music generation with the guidance of emotion, and few studies adopt time-varying emotional conditions. To address these issues, first, we endow three public lead sheet datasets with fine-grained emotions by automatically computing the valence labels from the chord progressions. Second, we propose a novel and effective encoder-decoder architecture named EmoMusicTV to explore the impact of emotional conditions on multiple music generation tasks and to capture the rich variability of musical sequences. EmoMusicTV is a transformer-based variational autoencoder (VAE) that contains a hierarchical latent variable structure to model holistic properties of the music segments and short-term variations within bars. The piece-level and bar-level emotional labels are embedded in their corresponding latent spaces to guide music generation. Third, we pretrain EmoMusicTV with the lead sheet continuation task to further improve its performance on conditional melody or harmony generation. Experimental results demonstrate that EmoMusicTV outperforms previous methods on three tasks, i.e., melody harmonization, melody generation given harmony, and lead sheet generation. Ablation studies verify the significant roles of emotional conditions and hierarchical latent variable structure on conditional music generation. Human listening shows that the lead sheets generated by EmoMusicTV are closer to the ground truth (GT) and perform slightly worse than the GT in conveying emotional polarity.

关键词： Symbolic music generation lead sheets emotional condition conditional variational autoencoder transformer

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Synthetic and privacy-preserving traffic trace generation using generative AI models for training Network Intrusion Detection Systems

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JOURNAL OF NETWORK AND COMPUTER APPLICATIONS 2024年 229卷

作者： Aceto, Giuseppe Giampaolo, Fabio Guida, Ciro Izzo, Stefano Pescape, Antonio Piccialli, Francesco Prezioso, Edoardo Univ Naples Federico II Dept Elect Engn & Informat Technol DIETI Via Claudio 21 I-80125 Naples Italy Univ Naples Federico II Dept Math & Applicat R Caccioppoli DMA Via Cinthia I-80126 Naples Italy Univ Bergamo Via Caniana 2 I-24127 Bergamo Italy

Network Intrusion Detection Systems (NIDS) are crucial tools for protecting networked devices from cyberattacks. Recent development in the field of Artificial Intelligence (AI) has provided tremendous advantages in implementing NIDSs able to monitor network traffic and block cyberattacks in real-time. In the literature, it is widely recognized that the effective training of a NIDS requires a large quantity of labeled traffic, representative of attacks. Nonetheless, the availability of public and abundant datasets remains remarkably restricted due to the cost of gathering and labeling real traffic traces and privacy concerns for sharing them. To tackle these challenges, in this paper we present a generative AI model capable of synthesizing anonymized traffic traces from real ones, thus dealing with privacy, abundance, and representativeness. The proposal is based on a conditional variational autoencoder (CVAE) and a preprocessing procedure specifically designed for the generation of new traffic traces. To validate our solution, we conduct an extensive empirical study leveraging three recent and publicly -available datasets, containing benign and malicious traffic. The validation is carried out from both the perspectives of classification performance of a robust NIDS and the quality of synthetic data, in comparison to the utilization of real data. We compare our CVAE with two state-of-the-art AI -based traffic data generators and prove that, trained with traces emitted by our generative model, a NIDS has a limited F1score loss compared to training on real data;competing models instead struggle or fail to generate traces that are as effective for NIDS training and as statistically similar to the original. We make the synthetic datasets available in both PCAP and tabular formats, to facilitate the reproducibility of our findings and encourage further exploration in the field of generative AI for networking.

关键词： Network Intrusion Detection System User privacy Traffic trace generation Traffic malware classification Generative artificial intelligence conditional variational autoencoder

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Enhancement of Track Damage Identification by Data Fusion of Vibration-Based Image Representation

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JOURNAL OF NONDESTRUCTIVE EVALUATION 2024年第1期43卷 1-13页

作者： Wang, Shaohua Tang, Lihua Dou, Yinling Li, Zhaoyu Aw, Kean C. Univ Auckland Dept Mech & Mechatron Engn 5 Grafton Rd Auckland 1010 New Zealand Chengdu Vocat & Tech Coll Ind Chengdu 610031 Peoples R China China Railway Eryuan Engn Grp Co Ltd Inst Sci & Technol Chengdu 610031 Peoples R China

In this paper, the vibration-based image representation and data fusion demonstrates distinctive benefit in feature extraction, yielding superior performance for damage identification in railway engineering. Specifically, based on vehicle-track coupled dynamics, the rail vibration datasets under diverse fastener damage conditions are generated. By converting 1-D vibration signals into 2-D grayscale images with recurrence plots (RPs) and the aid of conditional variational autoencoder (CVAE), the acceleration RPs and displacement RPs are fused for enhancing feature extraction. It is demonstrated that detecting the variation in texture patterns and color distribution of the vibration-based images facilitates effective damage identification, mitigating the sensitivity of damage recognition to the deterioration of track irregularity. The results show that the displacement RPs characterised by quasi-static features are more suitable for fastener damage identification. Further, by employing the data fusion that combines both the random dynamic features of the acceleration RPs and quasi-static features of the displacement RPs, the tolerance of measurement range for accurate fastener damage identification can be extended. The robustness of the proposed method is validated after testing different sampling frequencies and additional noise.

关键词： Rail track Damage identification Vibration-based image representation Data fusion Recurrence plot conditional variational autoencoder

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Anomaly detection method based on temporal spatial information enhancement

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MEASUREMENT SCIENCE AND TECHNOLOGY 2024年第3期35卷 035410-035410页

作者： Wang, Lin Wang, Xiangjun Li, Mingyang Liu, Feng Tianjin Univ State Key Lab Precis Measuring Technol & Instrumen Tianjin 300072 Peoples R China Tianjin Univ Key Lab MOEMS Minist Educ Tianjin 300072 Peoples R China Tianjin Univ Sch Precis Instrument & Optoelect Engn Tianjin 300072 Peoples R China

The proposed Anomaly Detection Method Based on Temporal Spatial Information Enhancement addresses the limitations of unsupervised techniques in detecting abnormal events beyond boundaries and limited samples. It incorporates a Serial Depth Separable Residual Block (Serial Block) as the backbone for predicting future frame. Additionally, a DenseReserve Subsample Module (DRSM) facilitates feature scale scaling, and a U-shaped Pyramid Attention Module (UPAM) guides feature fusion and enhances spatial details. During the prediction stage, reconstructed optical flow information aids in distinguishing abnormal and normal event features, with the abnormal score determined by a weighted fusion of optical flow reconstruction error and prediction error for future frames. Experimental results demonstrate the method's outstanding performance, achieving area under the curve metrics of 99.7%, 92.1%, and 78.3% on UCSD Ped2, CUHK Avenue, and ShahanghaiTech datasets, respectively. This method offers significant advancements in detecting anomalous events in complex and multi-scene surveillance videos, with improved operational speed and reduced parameters.

关键词： visual anomaly detection unsupervised paradigm conditional variational autoencoder optical flow reconstruct

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An Emotional Dialogue System Using conditional Generative Adversarial Networks with a Sequence-to-Sequence Transformer Encoder

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ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING 2024年第12期23卷 1-23页

作者： Huang, Wen-chieh Hsueh, Yu-ling Natl Chung Cheng Univ Dept Comp Sci & Informat Engn Minhsiung Chiayi Taiwan Natl Chung Cheng Univ Adv Inst Mfg High Tech Innovat Minhsiung Chiayi Taiwan

Understanding the expression of emotion and generating appropriate responses are key steps toward constructing emotional, conversational agents. In this article, we propose a framework for single-turn emotional conversation generation, and there are three main components in our model, namely, a sequence-to-sequence model with stacked encoders, a conditional variational autoencoder, and conditional generative adversarial networks. For the sequence-to-sequence model with stacked encoders, we designed a two-layer encoder by combining Transformer with gated recurrent units-based neural networks. Because of the flexibility of the sequence-to-sequence model, we adopted a conditional variational autoencoder in our framework, which uses latent variables to learn a distribution over potential responses and generates diverse responses. Furthermore, we regard a conditional variational autoencoder-based, sequence-to-sequence model as the generative model, and the training of the generative model is assisted by both a content discriminator and an emotion classifier, which assists our model in promoting content information and emotion expression. We use automated evaluation and human evaluation to evaluate our model and baselines on the NII Test Collections for IR Systems short-text conversation task Chinese emotional conversation generation Subtask dataset [44], and the experimental results demonstrate that our proposed framework can generate semantically reasonable and emotionally appropriate responses.

关键词： Chinese conversation generation emotional intelligence natural lan- guage processing deep learning gated recurrent units sequence-to-sequence attention mechanism trans- former model conditional adversarial generative nets conditional variational autoencoder

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