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15th Annual ACM/IEEE International Conference on Human-Robot Interaction (HRI)

作者： Marmpena, Mina Garcia, Fernando Lim, Angelica Univ Plymouth Plymouth Devon England ANYbotics AG Zurich Switzerland Simon Fraser Univ Burnaby BC Canada

ISBN: (纸本)9781450370578

Non-verbal communication that encompasses emotional body language is a crucial aspect of social robotics applications. Deep learning models for the generation of robotic expressions of bodily affect gain more and more ground recently over the hand-coded methods. In this work, we present a conditional variational autoencoder network that generates emotional body language animations of targeted valence and arousal for a Pepper robot, and we conduct a user study to evaluate the interpretability of the generated animations.

关键词： human-robot interaction robotic animation emotional body language deep learning conditional variational autoencoder

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An efficient faults and attacks categorization model in IoT-based cyber physical systems using Dilated CNN and BiLSTM with multi-scale dense Attention module

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2024年第PartB期96卷

作者： Laythkhaleel, Riyam Ibrahim, Abdullahi Abdu Naseri, Raghda Awad Shaban Farhan, Hameed Mutlag Altinbas Univ Elecr & Comp Engn Mahmutbey Dilmenler Caddesi 26 TR-34217 Istanbul Turkiye Tikrit Univ Dent Dept MMH4 876 Tikrit Iraq

The physical process with the digital computing channel and electronic computing is integrated by the Cyber-Physical Systems (CPS). The abnormality and failures are the two main sources that affect the performance of the CPS. In the CPS, the research on security analysis and fault diagnosis has attracted lots of interest from the researcher. However, the existing model does not find the difference between the fault and attacks, the existing approaches need adequate development for identifying the difference between the fault and attack in the Internet of Things (IoT)-CPS. In this research, a deep learning-based approach is developed to detect the attacks and faults in the IoT-CPS for enhancing the security of the network. The IoT-based data is garnered from a standard online source. After collecting the data, the extraction of the deep features is performed using the conditional variational autoencoder (CVA). The attained deep attributes are further taken for the weighted feature fusion process in which the required weights are chosen in an optimal manner using the Enhanced Egret Swarm Optimization (EESO) algorithm. The obtained weighted fused features are inputted into the Dilated Convolutional Neural Network and Bidirectional Long Short-Term Memory with Multi-Scale Dense Attention (DCNN-Bi-LSTM-MSDA). The classification outcomes are obtained from the DCNN-Bi-LSTM-MSDA module. Throughout the result analysis, the accuracy and NPV rate of the designed model is 94.16% and 99.38%. The validation of the fault and attack classification offered by the implemented deep learning-oriented failure and abnormality classification scheme in IoT-based CPS is done against several traditional models.

关键词： Internet of Things Cyber-Physical System Fault and Attack Classification conditional variational autoencoder Enhanced Egret Swarm Optimization Algorithm Dilated Convolutional Neural Network Bidirectional Long Short-Term Memory with Multi-Scale Dense Attention

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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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Highly imbalanced fault diagnosis of turbine blade cracks via deep focal dynamically weighted conditional variational auto-encoder network

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ADVANCED ENGINEERING INFORMATICS 2024年第PartA期62卷

作者： Huang, Xin Zhang, Xiaodong Xiong, Yiwei Fan, Bochao Dai, Fei Xi An Jiao Tong Univ Sch Mech Engn Xian 710049 Peoples R China Xi An Jiao Tong Univ Key Lab Educ Minist Modern Design & Rotor Bearing Syst Xian 710049 Peoples R China

Turbine blade cracks pose a critical threat to the safety of aircraft in flight. However, data collected from turbine blades often exhibit substantial imbalances in real industry settings, posing challenges for effective blade crack diagnosis using intelligent models. To solve this problem, our study presents an intelligent fault diagnosis framework utilizing three-dimensional blade tip clearance (3D-BTC) signal under highly imbalanced samples, the so-called deep focal dynamically weighted conditional variational auto -encoder (DWCVAE-DFL), which can leverage the benefits of cost -sensitive learning methods and deep generative models to effectively tackles the diagnosis complexity of imbalanced class samples, thereby improving the blade crack diagnosis performance. Specifically, the novel deep focal loss (DFL) function is first designed to consider the varying sensitivity of imbalanced class samples in feature extraction and fault classification. The blade -rotor simulation rig is used for validating the effectiveness of our proposed method. Our experimental results indicate that the performance of our proposed method outperforms the latest methods when dealing with highly imbalanced samples.

关键词： Blade crack diagnosis Imbalanced class samples conditional variational autoencoder Deep focal loss Three-dimensional blade tip clearance

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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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Two-stage correction prediction of wind power based on numerical weather prediction wind speed superposition correction and improved clustering

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ENERGY 2024年 302卷

作者： Yang, Mao Guo, Yunfeng Fan, Fulin Huang, Tao Northeast Elect Power Univ Key Lab Modern Power Syst Simulat & Control & Rene Minist Educ Jilin 132012 Peoples R China Harbin Inst Technol Sch Elect Engn & Automat Harbin 150001 Peoples R China Univ Strathclyde Inst Energy & Environm Glasgow G1 1XW Scotland Politecn Torino DOE I-10129 Turin Italy

The unavoidable wind power prediction error poses a challenge for wind power to participate in day-ahead scheduling plan. This paper proposes a two-stage correction method considering NWP wind speed and power correction. Firstly, this paper fully considers the historical similarity of NWP wind speed, and adopts the improved clustering distance for segment matching and wind speed correction based on the extracted most relevant trend component, which enhances the utilization of NWP information and avoids the randomness and uncertainty of the intelligent correction method, and gram angular field (GAF) and stacked autoencoder (SAE) are used for feature extraction and conditional variational autoencoder (CVA) is used to generate specific samples to ensure the adequacy of the clustering process. Secondly, considering the historical similarity of error of predicted power, the power is further corrected based on the proposed weighted double-constraint to realize the secondary calibration. The Proposed method is applied to several wind farms in China to verify its effectiveness. The results show that the proposed method reduces the NRMSE by 3.82 % and NMAE by 3.40 % compared with direct prediction in the wind farms in western Inner Mongolia, which is important for promoting wind power consumption and maintaining the safe and stable operation of the power system.

关键词： GAF-SAE feature integration conditional variational autoencoder Improved clustering distance Weighted double-constraint mechanism NWP wind speed correction Two-stage calibration

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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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SECT: A Successively conditional Transformer for Controllable Paraphrase Generation

SECT: A Successively Conditional Transformer for Controllabl...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) / IEEE World Congress on Computational Intelligence (IEEE WCCI) / International Joint Conference on Neural Networks (IJCNN) / IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Xue, Tang Zhao, Yuran Yang, Chaoqi Liu, Gongshen Li, Xiaoyong Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai Peoples R China Univ Illinois Dept Comp Sci Champaign IL USA

ISBN: (数字)9781728186719

ISBN: (纸本)9781728186719

Paraphrase generation has consistently been a challenging area in the field of NLP. Despite the considerable achievements made by previous work, existing methods lack a flexible way to include multiple controllable attributes to enhance the diversity of paraphrased sentences. To overcome this challenge, we propose a Successively conditional Transformer (SECT) to tackle this task. SECT is based on a combination of conditional variational autoencoder (CVAE) and Transformer framework to generate diversified words. More specifically, our SECT deploys multi-head attention and memory gate mechanism to keep the interaction between each of the attributes and the corresponding encoder layer hidden state. To address the problem of absorbing flexible attributes, we apply a successive structure to our SECT, which enables the framework to couple the CVAE latent variables with the encoder layer hidden states progressively. In addition, our SECT is trained by minimizing a tailor-designed loss for producing paraphrased sentences as required. Finally, we conduct extensive experiments to substantiate the validity and effectiveness of our proposed model. The results show that SECT significantly outperforms the existing state-of-the-art approaches and generates more diverse paraphrased sentences.

关键词： paraphrase generation conditional variational autoencoder transformer successive structure

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Geometry Aligned variational Transformer for Image-conditioned Layout Generation 22

Geometry Aligned Variational Transformer for Image-condition...

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30th ACM International Conference on Multimedia (MM)

作者： Cao, Yunning Ma, Ye Zhou, Min Liu, Chuanbin Xie, Hongtao Ge, Tiezheng Jiang, Yuning Univ Sci & Technol China Hefei Peoples R China Alibaba Grp Beijing Peoples R China

ISBN: (纸本)9781450392037

Layout generation is a novel task in computer vision, which combines the challenges in both object localization and aesthetic appraisal, widely used in advertisements, posters, and slides design. An accurate and pleasant layout should consider both the intra-domain relationship within layout elements and the inter-domain relationship between layout elements and the image. However, most previous methods simply focus on image-content-agnostic layout generation, without leveraging the complex visual information from the image. To this end, we explore a novel paradigm entitled image-conditioned layout generation, which aims to add text overlays to an image in a semantically coherent manner. Specifically, we propose an Image-Conditioned variational Transformer (ICVT) that autoregressively generates various layouts in an image. First, self-attention mechanism is adopted to model the contextual relationship within layout elements, while cross-attention mechanism is used to fuse the visual information of conditional images. Subsequently, we take them as building blocks of conditional variational autoencoder (CVAE), which demonstrates appealing diversity. Second, in order to alleviate the gap between layout elements domain and visual domain, we design a Geometry Alignment module, in which the geometric information of the image is aligned with the layout representation. In addition, we construct a large-scale advertisement poster layout designing dataset with delicate layout and saliency map annotations. Experimental results show that our model can adaptively generate layouts in the non-intrusive area of the image, resulting in a harmonious layout design.

关键词： image-conditioned layout generation conditional variational autoencoder Transformer cross attention

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Style-driven Handwritten Character Generation 30

Style-driven Handwritten Character Generation

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30th IEEE Signal Processing and Communications Applications Conference (SIU)

作者： Ucar, Ozdenur Genc, Yakup Gebze Tech Univ Bilgisayar Muhendisligi Bolumu Gebze Turkey

ISBN: (纸本)9781665450928

Handwritten character generation is a popular topic with a variety of applications. Many methods of character generation have been proposed in the literature, but few of these methods focus on preserving the writer's style while producing handwritten characters. Representing handwriting styles involves the challenge of representing both the style of each character and the overall style of the writer. In this study, unlike the studies in the literature, it is tried to produce the characters of the person that we have not seen yet by extracting the handwriting style of the person with limited data. While producing handwritten characters, spline curves of the characters were used as input, as well as handwritten images. In the developed method, a multi-tasking learning network is proposed by using the conditional Variable autoencoder (CVAE) model, which is one of the deep learning methods. In the experiments carried out, three different models were compared and the performance of the conditionally Variable autoencoder (CVAE) network trained with spline curves gave better results compared to other models.

关键词： Handwritting Generation conditional variational autoencoder Writing Style Spline Curve

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