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"doi": "10.1109/VR50410.2021.00030",
"title": "Detection Thresholds with Joint Horizontal and Vertical Gains in Redirected Jumping",
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"abstract": "Redirected jumping (RDJ) is a locomotion technique that allows users to explore a virtual space that is larger than the available physical space by imperceptibly manipulating users' virtual viewpoints according to different gains. In previous redirected jumping work, different types of gains were imposed separately, without considering the possible interaction effects of horizontal and vertical gains on the jumping distance perception. To figure out how humans perceive distance manipulation when more than one gain is used, in this paper, we explored joint horizontal and vertical gains that manipulate horizontal and vertical distances at the same time during two-legged takeoff jumping in the virtual space. We estimated and analyzed horizontal and vertical detection thresholds by conducting a user study, fitting the data to two-dimensional psychometric functions, and visualizing the fitted 3D plots. We provided quantitative insights into the effects of joint gains on detection thresholds, where the imperceptible range for one gain can be affected by the variation of the other gain. Finally, we designed redirected jumping-based games as applications with joint horizontal and vertical gains and demonstrated the effectiveness of the redirected jumping technique.",
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"content": "Redirected jumping (RDJ) is a locomotion technique that allows users to explore a virtual space that is larger than the available physical space by imperceptibly manipulating users' virtual viewpoints according to different gains. In previous redirected jumping work, different types of gains were imposed separately, without considering the possible interaction effects of horizontal and vertical gains on the jumping distance perception. To figure out how humans perceive distance manipulation when more than one gain is used, in this paper, we explored joint horizontal and vertical gains that manipulate horizontal and vertical distances at the same time during two-legged takeoff jumping in the virtual space. We estimated and analyzed horizontal and vertical detection thresholds by conducting a user study, fitting the data to two-dimensional psychometric functions, and visualizing the fitted 3D plots. We provided quantitative insights into the effects of joint gains on detection thresholds, where the imperceptible range for one gain can be affected by the variation of the other gain. Finally, we designed redirected jumping-based games as applications with joint horizontal and vertical gains and demonstrated the effectiveness of the redirected jumping technique.",
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"abstract": "Point cloud registration is a key task in many computational fields. Previous correspondence matching based methods require the inputs to have distinctive geometric structures to fit a 3D rigid transformation according to point-wise sparse feature matches. However, the accuracy of transformation heavily relies on the quality of extracted features, which are prone to errors with respect to partiality and noise. In addition, they can not utilize the geometric knowledge of all the overlapping regions. On the other hand, previous global feature based approaches can utilize the entire point cloud for the registration, however they ignore the negative effect of non-overlapping points when aggregating global features. In this paper, we present OM-Net, a global feature based iterative network for partial-to-partial point cloud registration. We learn overlapping masks to reject non-overlapping regions, which converts the partial-to-partial registration to the registration of the same shape. Moreover, the previously used data is sampled only once from the CAD models for each object, resulting in the same point clouds for the source and reference. We propose a more practical manner of data generation where a CAD model is sampled twice for the source and reference, avoiding the previously prevalent over-fitting issue. Experimental results show that our method achieves state-of-the-art performance compared to traditional and deep learning based methods. Code is available at https://github.com/megvii-research/OMNet.",
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"abstract": "Point cloud completion has become a popular research area in 3D computer vision. It aims to recover the complete point cloud from its partial observation. However, previous methods either directly predict the whole shape, change the original distribution of points, or have limited performance in reconstructing tiny and detailed object components. In this paper, we propose a novel Patch-based Dual-Path Network (PDP-Net) for point cloud completion, which leverages the advantages of different encoder architectures, with one path providing estimation for the global structure of the missing part, and the other path filling in the details by generating several point cloud patches. We also propose an identifier to retain the original points in the partial point cloud possibly. Comprehensive experiments and robustness tests demonstrate the effectiveness of our method even against different missing scales of the point cloud. Code available at: https://github.com/QifHE/PDP-Net-Public.",
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"affiliation": "Center for Future Media, School of Computer Science and Engineering, University of Electronic Science and Technology of China,China,611731",
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"title": "Development of full-featured ECG system for visual stress induced heart rate variability (HRV) assessment",
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"abstract": "Visual stress which can induce headache, migraines and eyestrain affects our body often detrimentally. Heart rate variability (HRV) analysis is commonly used as a quantitative marker depicting the activity of autonomic nervous system (ANS) that may be related to visual stress. In this paper, we proposed an improved HRV methodology for HRV features extraction and analysis. Firstly, a multi-channel portable ECG device has been developed for signal collection, and then we designed full-featured ECG monitoring system which suitable for real-time ECG display, signal processing, high accuracy R wave detection and HRV analysis in time and frequency domain. Taking consideration of the simplicity and real-time, the design of processing flow includes three stages. The first stage is signal preprocessing, we introduced a simple and reliable method termed the Mathematical Morphology (MM) and Difference Operation Method (DOM) for de-noising and R wave amplification. The second stage is to look for the point R and extract R-R interval series based on the above processing. The last stage focuses on HRV analysis from the aspects of time domain and frequency domain. Moreover, this research investigates the relationship between visual stress and HRV, 15 healthy, right-handed volunteers (all males aged from 19 to 25 years) participated in the experiment; there is significant changes of HRV features of visual stress condition compared to reference state. These results show that the HRV is affected by the presence of visual stress and long-term visual stress may weak the function of ANS, which may enable us for visual stress monitoring and management in daily life.",
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"content": "Visual stress which can induce headache, migraines and eyestrain affects our body often detrimentally. Heart rate variability (HRV) analysis is commonly used as a quantitative marker depicting the activity of autonomic nervous system (ANS) that may be related to visual stress. In this paper, we proposed an improved HRV methodology for HRV features extraction and analysis. Firstly, a multi-channel portable ECG device has been developed for signal collection, and then we designed full-featured ECG monitoring system which suitable for real-time ECG display, signal processing, high accuracy R wave detection and HRV analysis in time and frequency domain. Taking consideration of the simplicity and real-time, the design of processing flow includes three stages. The first stage is signal preprocessing, we introduced a simple and reliable method termed the Mathematical Morphology (MM) and Difference Operation Method (DOM) for de-noising and R wave amplification. The second stage is to look for the point R and extract R-R interval series based on the above processing. The last stage focuses on HRV analysis from the aspects of time domain and frequency domain. Moreover, this research investigates the relationship between visual stress and HRV, 15 healthy, right-handed volunteers (all males aged from 19 to 25 years) participated in the experiment; there is significant changes of HRV features of visual stress condition compared to reference state. These results show that the HRV is affected by the presence of visual stress and long-term visual stress may weak the function of ANS, which may enable us for visual stress monitoring and management in daily life.",
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"affiliation": "Department of Computer Science and Engineering, Pusan National University, Busan, South Korea",
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"proceeding": {
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"title": "2013 Humaine Association Conference on Affective Computing and Intelligent Interaction (ACII)",
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"doi": "10.1109/ACII.2013.54",
"title": "Heart Rate Variability and Skin Conductance Biofeedback: A Triple-Blind Randomized Controlled Study",
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"abstract": "High heart rate variability (HRV) and low skin conductance level (SCL) have been associated with low levels of stress. Biofeedback - providing an individual with online information about his or her own physiological state - may help to change these signals in the desired direction and therewith improve an individual's physical or mental condition. While there is an abundance of biofeedback tools and therapies commercially available, there is a lack of well-controlled validation studies. We here compare changes in several physiological, affective and cognitive variables after administering either a fake or a genuine biofeedback protocol that was aimed at affecting HRV and SCL. Which participants belonged to the genuine biofeedback group and which participants belonged to the control group remained unknown to everyone involved in the study until the very last stage of analysis. We did not find any differences in treatment effect between the two groups of participants. We also did not find correlations between HRV and the physiological, affective and cognitive variables that we measured, but there was some indication of SCL being related to error percentage in a cognitive task. While our study can only show that the studied protocol is not effective on the selected effect measures for the studied group of participants, it adds to the doubt that biofeedback is effective above and beyond non-specific treatment effects.",
"abstracts": [
{
"abstractType": "Regular",
"content": "High heart rate variability (HRV) and low skin conductance level (SCL) have been associated with low levels of stress. Biofeedback - providing an individual with online information about his or her own physiological state - may help to change these signals in the desired direction and therewith improve an individual's physical or mental condition. While there is an abundance of biofeedback tools and therapies commercially available, there is a lack of well-controlled validation studies. We here compare changes in several physiological, affective and cognitive variables after administering either a fake or a genuine biofeedback protocol that was aimed at affecting HRV and SCL. Which participants belonged to the genuine biofeedback group and which participants belonged to the control group remained unknown to everyone involved in the study until the very last stage of analysis. We did not find any differences in treatment effect between the two groups of participants. We also did not find correlations between HRV and the physiological, affective and cognitive variables that we measured, but there was some indication of SCL being related to error percentage in a cognitive task. While our study can only show that the studied protocol is not effective on the selected effect measures for the studied group of participants, it adds to the doubt that biofeedback is effective above and beyond non-specific treatment effects.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "High heart rate variability (HRV) and low skin conductance level (SCL) have been associated with low levels of stress. Biofeedback - providing an individual with online information about his or her own physiological state - may help to change these signals in the desired direction and therewith improve an individual's physical or mental condition. While there is an abundance of biofeedback tools and therapies commercially available, there is a lack of well-controlled validation studies. We here compare changes in several physiological, affective and cognitive variables after administering either a fake or a genuine biofeedback protocol that was aimed at affecting HRV and SCL. Which participants belonged to the genuine biofeedback group and which participants belonged to the control group remained unknown to everyone involved in the study until the very last stage of analysis. We did not find any differences in treatment effect between the two groups of participants. We also did not find correlations between HRV and the physiological, affective and cognitive variables that we measured, but there was some indication of SCL being related to error percentage in a cognitive task. While our study can only show that the studied protocol is not effective on the selected effect measures for the studied group of participants, it adds to the doubt that biofeedback is effective above and beyond non-specific treatment effects.",
"fno": "5048a289",
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"Heart Rate Variability",
"Biological Control Systems",
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"Atmospheric Measurements",
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"Heart Rate Variability",
"Skin Conductance",
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"Cognitive Performance"
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{
"affiliation": "TNO, Soesterberg, Netherlands",
"fullName": "S. F. Raaijmakers",
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"fullName": "F. W. Steel",
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"affiliation": "TNO, Soesterberg, Netherlands",
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"affiliation": "TNO, Soesterberg, Netherlands",
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"abstract": "Advances in image processing mean it is now possible to measure heart rate variability (HRV) and respiratory rate without contact. The influence of mental workload and dual tasking on HRV and respiratory rate has not been well studied. The objective of the present study was to quantify the relations between workload and dual tasking and HRV and respiratory rate. We measured the weighted workload score of the NASA Task Load Index, the mean oxygenated hemoglobin (oxyHb) concentration, the standard deviation of oxyHb concentration, respiratory data and electrocardiogram data. Subjects performed n-back tasks, Stroop tasks and visual search tasks in low-and high-workload conditions and single-and dual-task conditions. Each task required different attention functions. Respiratory rate was significantly greater in the dual-task conditions than in the single-task conditions, indicating that divided attention increased respiratory rate. The NASA Task Load Index weighted workload score in the Stroop task was higher in the high-workload, dual-task condition than in the low-workload or single-task condition, and oxyHb concentration in the frontal area was lower. This indicates an overload state. In this condition, electrocardiogram data showed distinctive trends. For example, the high-frequency component of electrocardiogram data and mean RR interval were high in the high-workload, dual-task condition. These results suggest that attentional functions and overload in a dual task affected respiratory rate and HRV.",
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"abstract": "The proliferation of information and communication technology (ICT) throughout workplace and home life is thought to increase feelings of being overloaded, drained, and/or burned out. This phenomenon is termed \"technostress.\" In this relatively new line of research, scholars have employed predominantly questionnaire surveys and experiments to investigate the phenomenon. This paper argues for an interpretive, theory building approach for studying techno stress, motivated by two shortcomings of these data collection techniques: questionnaire surveys rely on potentially imperfect participant recall, while experiments cannot find root causes of techno stress during the course of a normal work day. Linking periods of bodily-experienced stress measured by heart rate variability with qualitative data enables an interpretive, theory building approach that allows for a richer understanding of whether and how ICT contributes to stress.",
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"content": "The proliferation of information and communication technology (ICT) throughout workplace and home life is thought to increase feelings of being overloaded, drained, and/or burned out. This phenomenon is termed \"technostress.\" In this relatively new line of research, scholars have employed predominantly questionnaire surveys and experiments to investigate the phenomenon. This paper argues for an interpretive, theory building approach for studying techno stress, motivated by two shortcomings of these data collection techniques: questionnaire surveys rely on potentially imperfect participant recall, while experiments cannot find root causes of techno stress during the course of a normal work day. Linking periods of bodily-experienced stress measured by heart rate variability with qualitative data enables an interpretive, theory building approach that allows for a richer understanding of whether and how ICT contributes to stress.",
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"title": "2022 IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE)",
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"abstract": "Remote measurement of physiological signals through facial videos is an emerging and significant field of research. Through remote photoplethysmography (rPPG), RGB cameras can measure a person’s heart rate variability (HRV) by analyzing subtle light variations on the skin. Fluctuations in HRV readings are caused by imbalances in the autonomic nervous system, such as experiencing a stressful event. This paper presents a novel method for HRV measurement from rPPG signals. We tested the model on 14 subjects participating in stress-inducing tasks. We compared our results against a contact-based ground truth device and demonstrated the potential for an off-the-shelf webcam to provide robust HRV measurement and subsequent stress estimation.",
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"abstract": "Despite its growing popularity, Virtual Reality (VR) has yet to make a significant impact in conventional education due to its high cost, unconvincing learning data, complexity of the technologies and, persistently, cybersickness. To alleviate this dilemma, it is necessary to develop a straightforward and reliable measurement of cybersickness for VR application developers and mainstream educators. The Empatica E4 wearable device and its eco-system were utilized to record Heart Rate Variability (HRV) and Electrodermal Activity (EDA) during customized computer-based and VR tasks with 16 participants. The metrics of S, NNMean, SDNN, RMSSD, and Poincaré Plot in HRV data and SCR width in EDA data were found to be potential indicators of cybersickness. Further research aims to determine a specific cybersickness index.",
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"abstractType": "Regular",
"content": "Despite its growing popularity, Virtual Reality (VR) has yet to make a significant impact in conventional education due to its high cost, unconvincing learning data, complexity of the technologies and, persistently, cybersickness. To alleviate this dilemma, it is necessary to develop a straightforward and reliable measurement of cybersickness for VR application developers and mainstream educators. The Empatica E4 wearable device and its eco-system were utilized to record Heart Rate Variability (HRV) and Electrodermal Activity (EDA) during customized computer-based and VR tasks with 16 participants. The metrics of S, NNMean, SDNN, RMSSD, and Poincaré Plot in HRV data and SCR width in EDA data were found to be potential indicators of cybersickness. Further research aims to determine a specific cybersickness index.",
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"fullName": "Takurou Magaki",
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"affiliation": "Future University Hakodate, Japan",
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"abstract": "Image reconstruction is an important method for texture defect detection, and the existing image reconstruction algorithms based on Autoencoder and GAN cannot suppress the reconstruction of defect information, which affects the detection accuracy. To solve this problem, this paper proposes a novel image reconstruction algorithm based on image inpainting, which includes two modules of defect estimation network and defect inpainting network. Firstly, the defect estimation network used the pre-training model to extract the deep features of the defect image and applied the Gaussian distance to estimate the background area. and then the image inpainting network applied the contextual attention mechanism to repair the non-background area of the defect image. Through the experimental analysis which compared with other state-of-the-art image reconstruction algorithms on the public Mvtec texture dataset, the superiority of the proposed algorithm is effectively verified.",
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"abstract": "Video surveillance contains a lot of facial occlusion, which brings great difficulties to the detection of criminal investigation cases. Current face inpainting algorithms are difficult to meet the uniqueness requirements of face comparison, due to the lack of a priori information within the occluded area. Face sketch drawn by experienced simulated portrait artist according to low-quality video or description of the victim contains lots of useful information. There, this paper proposes a face inpainting algorithm combining face sketch and gate convolution. First, the face sketch, used as guided information, integrates into the occluded face image to complete the missing area. Then, a generative adversarial networks (GAN) with gate convolution is designed for model training, which effectively suppresses the interference of the occlusion area to the inpainting process. The experimental results show that the proposed algorithm obtain the better inpainting results and larger SSIM compared with the other algorithm. The proposed obtain better comprehensive performance.",
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"content": "Video surveillance contains a lot of facial occlusion, which brings great difficulties to the detection of criminal investigation cases. Current face inpainting algorithms are difficult to meet the uniqueness requirements of face comparison, due to the lack of a priori information within the occluded area. Face sketch drawn by experienced simulated portrait artist according to low-quality video or description of the victim contains lots of useful information. There, this paper proposes a face inpainting algorithm combining face sketch and gate convolution. First, the face sketch, used as guided information, integrates into the occluded face image to complete the missing area. Then, a generative adversarial networks (GAN) with gate convolution is designed for model training, which effectively suppresses the interference of the occlusion area to the inpainting process. The experimental results show that the proposed algorithm obtain the better inpainting results and larger SSIM compared with the other algorithm. The proposed obtain better comprehensive performance.",
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"normalizedAbstract": "Video surveillance contains a lot of facial occlusion, which brings great difficulties to the detection of criminal investigation cases. Current face inpainting algorithms are difficult to meet the uniqueness requirements of face comparison, due to the lack of a priori information within the occluded area. Face sketch drawn by experienced simulated portrait artist according to low-quality video or description of the victim contains lots of useful information. There, this paper proposes a face inpainting algorithm combining face sketch and gate convolution. First, the face sketch, used as guided information, integrates into the occluded face image to complete the missing area. Then, a generative adversarial networks (GAN) with gate convolution is designed for model training, which effectively suppresses the interference of the occlusion area to the inpainting process. The experimental results show that the proposed algorithm obtain the better inpainting results and larger SSIM compared with the other algorithm. The proposed obtain better comprehensive performance.",
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"affiliation": "Xi’an University of Posts and Telecommunications,Center for Image and Information Processing,Xi’an,Shaanxi,China,710121",
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"affiliation": "Xi’an University of Posts and Telecommunications,Center for Image and Information Processing,Xi’an,Shaanxi,China,710121",
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"affiliation": "Xi’an University of Posts and Telecommunications,Center for Image and Information Processing,Xi’an,Shaanxi,China,710121",
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"affiliation": "Xi’an University of Posts and Telecommunications,Center for Image and Information Processing,Xi’an,Shaanxi,China,710121",
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"abstract": "Free-form inpainting is the task of adding new content to an image in the regions specified by an arbitrary binary mask. Most existing approaches train for a certain distribution of masks, which limits their generalization capabilities to unseen mask types. Furthermore, training with pixel-wise and perceptual losses often leads to simple textural extensions towards the missing areas instead of semantically meaningful generation. In this work, we propose RePaint: A Denoising Diffusion Probabilistic Model (DDPM) based inpainting approach that is applicable to even extreme masks. We employ a pretrained unconditional DDPM as the generative prior. To condition the generation process, we only alter the reverse diffusion iterations by sampling the unmasked regions using the given image infor-mation. Since this technique does not modify or condition the original DDPM network itself, the model produces high-quality and diverse output images for any inpainting form. We validate our method for both faces and general-purpose image inpainting using standard and extreme masks. Re-Paint outperforms state-of-the-art Autoregressive, and GAN approaches for at least five out of six mask distributions. Github Repository: git.io/RePaint",
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"content": "Free-form inpainting is the task of adding new content to an image in the regions specified by an arbitrary binary mask. Most existing approaches train for a certain distribution of masks, which limits their generalization capabilities to unseen mask types. Furthermore, training with pixel-wise and perceptual losses often leads to simple textural extensions towards the missing areas instead of semantically meaningful generation. In this work, we propose RePaint: A Denoising Diffusion Probabilistic Model (DDPM) based inpainting approach that is applicable to even extreme masks. We employ a pretrained unconditional DDPM as the generative prior. To condition the generation process, we only alter the reverse diffusion iterations by sampling the unmasked regions using the given image infor-mation. Since this technique does not modify or condition the original DDPM network itself, the model produces high-quality and diverse output images for any inpainting form. We validate our method for both faces and general-purpose image inpainting using standard and extreme masks. Re-Paint outperforms state-of-the-art Autoregressive, and GAN approaches for at least five out of six mask distributions. Github Repository: git.io/RePaint",
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"affiliation": "ETH Zürich,Computer Vision Lab,Switzerland",
"fullName": "Fisher Yu",
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"surname": "Yu",
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"affiliation": "ETH Zürich,Computer Vision Lab,Switzerland",
"fullName": "Radu Timofte",
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"affiliation": "ETH Zürich,Computer Vision Lab,Switzerland",
"fullName": "Luc Van Gool",
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"title": "Dual-path Image Inpainting with Auxiliary GAN Inversion",
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"abstract": "Deep image inpainting can inpaint a corrupted image using a feed-forward inference, but still fails to handle large missing area or complex semantics. Recently, GAN inversion based inpainting methods propose to leverage semantic information in pretrained generator (e.g., StyleGAN) to solve the above issues. Different from feed-forward methods, they seek for a closest latent code to the corrupted image and feed it to a pretrained generator. However, inferring the latent code is either time-consuming or inaccurate. In this paper, we develop a dual-path inpainting network with inversion path and feed-forward path, in which inversion path provides auxiliary information to help feed-forward path. We also design a novel deformable fusion module to align the feature maps in two paths. Experiments on FFHQ and LSUN demonstrate that our method is effective in solving the aforementioned problems while producing more realistic results than state-of-the-art methods.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Deep image inpainting can inpaint a corrupted image using a feed-forward inference, but still fails to handle large missing area or complex semantics. Recently, GAN inversion based inpainting methods propose to leverage semantic information in pretrained generator (e.g., StyleGAN) to solve the above issues. Different from feed-forward methods, they seek for a closest latent code to the corrupted image and feed it to a pretrained generator. However, inferring the latent code is either time-consuming or inaccurate. In this paper, we develop a dual-path inpainting network with inversion path and feed-forward path, in which inversion path provides auxiliary information to help feed-forward path. We also design a novel deformable fusion module to align the feature maps in two paths. Experiments on FFHQ and LSUN demonstrate that our method is effective in solving the aforementioned problems while producing more realistic results than state-of-the-art methods.",
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"affiliation": "MoE Key Lab of Artificial Intelligence, Shanghai Jiao Tong University,Department of Computer Science and Engineering",
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"affiliation": "MoE Key Lab of Artificial Intelligence, Shanghai Jiao Tong University,Department of Computer Science and Engineering",
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"fullName": "Jianfu Zhang",
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"affiliation": "MoE Key Lab of Artificial Intelligence, Shanghai Jiao Tong University,Department of Computer Science and Engineering",
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"title": "Image Inpainting with Context Flow Network",
"normalizedTitle": "Image Inpainting with Context Flow Network",
"abstract": "Image inpainting using deep and complicated convolutional neural networks(CNN) has recently produced outstanding results. Several researchers have considered employing large receptive fields and deep networks for long-distance information transfer to obtain semantically coherent inpainting results. As a side effect, these strategies would lead to the loss of detail and other artifacts. Motivated by the attention mechanism and sequence-to-sequence model, a novel convolution structure called context flow module is introduced into a coarse-to-fine two stages network, extracting information from distant regions without extra network layers or details loss. The context flow module in the refinement network can effectively gather both spatial and contextual data in the distance, and flow information to the next layer patch by patch. The coarse and refinement networks' backbones are encoder-decoder architecture stacked with gated and dilated convolutions. The refinement network encloses two extra elements: the context flow module and a feature-sharing space. The coarse network generates semantically consistent images with no gaps. The refinement network enhances the sharpness and enriches the details of the initial results. Moreover, a patch-based GAN is applied to stabilize training and generate semantically reasonable results. Experimental results show that our method excels at the performance of the state-of-the-art methods on faces(CelebA), buildings(Paris Street View), and natural images(Places2) datasets. The proposed context flow module can be easily integrated with any existing networks to improve their inpainting performance.",
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"content": "Image inpainting using deep and complicated convolutional neural networks(CNN) has recently produced outstanding results. Several researchers have considered employing large receptive fields and deep networks for long-distance information transfer to obtain semantically coherent inpainting results. As a side effect, these strategies would lead to the loss of detail and other artifacts. Motivated by the attention mechanism and sequence-to-sequence model, a novel convolution structure called context flow module is introduced into a coarse-to-fine two stages network, extracting information from distant regions without extra network layers or details loss. The context flow module in the refinement network can effectively gather both spatial and contextual data in the distance, and flow information to the next layer patch by patch. The coarse and refinement networks' backbones are encoder-decoder architecture stacked with gated and dilated convolutions. The refinement network encloses two extra elements: the context flow module and a feature-sharing space. The coarse network generates semantically consistent images with no gaps. The refinement network enhances the sharpness and enriches the details of the initial results. Moreover, a patch-based GAN is applied to stabilize training and generate semantically reasonable results. Experimental results show that our method excels at the performance of the state-of-the-art methods on faces(CelebA), buildings(Paris Street View), and natural images(Places2) datasets. The proposed context flow module can be easily integrated with any existing networks to improve their inpainting performance.",
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"normalizedAbstract": "Image inpainting using deep and complicated convolutional neural networks(CNN) has recently produced outstanding results. Several researchers have considered employing large receptive fields and deep networks for long-distance information transfer to obtain semantically coherent inpainting results. As a side effect, these strategies would lead to the loss of detail and other artifacts. Motivated by the attention mechanism and sequence-to-sequence model, a novel convolution structure called context flow module is introduced into a coarse-to-fine two stages network, extracting information from distant regions without extra network layers or details loss. The context flow module in the refinement network can effectively gather both spatial and contextual data in the distance, and flow information to the next layer patch by patch. The coarse and refinement networks' backbones are encoder-decoder architecture stacked with gated and dilated convolutions. The refinement network encloses two extra elements: the context flow module and a feature-sharing space. The coarse network generates semantically consistent images with no gaps. The refinement network enhances the sharpness and enriches the details of the initial results. Moreover, a patch-based GAN is applied to stabilize training and generate semantically reasonable results. Experimental results show that our method excels at the performance of the state-of-the-art methods on faces(CelebA), buildings(Paris Street View), and natural images(Places2) datasets. The proposed context flow module can be easily integrated with any existing networks to improve their inpainting performance.",
"fno": "974400a923",
"keywords": [
"Convolutional Neural Nets",
"Feature Extraction",
"Image Restoration",
"Learning Artificial Intelligence",
"Coarse Network",
"Coarse To Fine Two Stages Network",
"Context Flow Module",
"Context Flow Network",
"Extra Network Layers",
"Flow Information",
"Image Inpainting",
"Long Distance Information Transfer",
"Outstanding Results",
"Refinement Network",
"Semantically Coherent Inpainting Results",
"Semantically Reasonable Results",
"Sequence To Sequence Model",
"Training",
"Convolution",
"Shape",
"Semantics",
"Memory Management",
"Generative Adversarial Networks",
"Feature Extraction",
"Image Inpainting",
"Attention Mechanism",
"Generative Adversarial Network GAN",
"Deep Learning"
],
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{
"affiliation": "School of Journalism and Communication, Shaanxi Normal University,Xi'an,China",
"fullName": "Jianwen Liu",
"givenName": "Jianwen",
"surname": "Liu",
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{
"affiliation": "School of Computer Science, Shaanxi Normal University,Xi'an,China",
"fullName": "Jiarui Xue",
"givenName": "Jiarui",
"surname": "Xue",
"__typename": "ArticleAuthorType"
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{
"affiliation": "School of Journalism and Communication, Shaanxi Normal University,Xi'an,China",
"fullName": "Juan Zhang",
"givenName": "Juan",
"surname": "Zhang",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "School of Computer Science, Shaanxi Normal University,Xi'an,China",
"fullName": "Ying Yang",
"givenName": "Ying",
"surname": "Yang",
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"abstract": "Recently data-driven image inpainting methods have made inspiring progress, impacting fundamental image editing tasks such as object removal and damaged image repairing. These methods are more effective than classic approaches, however, due to memory limitations they can only handle low-resolution inputs, typically smaller than 1K. Meanwhile, the resolution of photos captured with mobile devices increases up to 8K. Naive up-sampling of the low-resolution inpainted result can merely yield a large yet blurry result. Whereas, adding a high-frequency residual image onto the large blurry image can generate a sharp result, rich in details and textures. Motivated by this, we propose a Contextual Residual Aggregation (CRA) mechanism that can produce high-frequency residuals for missing contents by weighted aggregating residuals from contextual patches, thus only requiring a low-resolution prediction from the network. Since convolutional layers of the neural network only need to operate on low-resolution inputs and outputs, the cost of memory and computing power is thus well suppressed. Moreover, the need for high-resolution training datasets is alleviated. In our experiments, we train the proposed model on small images with resolutions 512 × 512 and perform inference on high-resolution images, achieving compelling inpainting quality. Our model can inpaint images as large as 8K with considerable hole sizes, which is intractable with previous learning-based approaches. We further elaborate on the light-weight design of the network architecture, achieving real-time performance on 2K images on a GTX 1080 Ti GPU. Codes are available at: https://github. com/Ascend-Huawei/Ascend-Canada/tree/ master/Models/Research_HiFIll_Model.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Recently data-driven image inpainting methods have made inspiring progress, impacting fundamental image editing tasks such as object removal and damaged image repairing. These methods are more effective than classic approaches, however, due to memory limitations they can only handle low-resolution inputs, typically smaller than 1K. Meanwhile, the resolution of photos captured with mobile devices increases up to 8K. Naive up-sampling of the low-resolution inpainted result can merely yield a large yet blurry result. Whereas, adding a high-frequency residual image onto the large blurry image can generate a sharp result, rich in details and textures. Motivated by this, we propose a Contextual Residual Aggregation (CRA) mechanism that can produce high-frequency residuals for missing contents by weighted aggregating residuals from contextual patches, thus only requiring a low-resolution prediction from the network. Since convolutional layers of the neural network only need to operate on low-resolution inputs and outputs, the cost of memory and computing power is thus well suppressed. Moreover, the need for high-resolution training datasets is alleviated. In our experiments, we train the proposed model on small images with resolutions 512 × 512 and perform inference on high-resolution images, achieving compelling inpainting quality. Our model can inpaint images as large as 8K with considerable hole sizes, which is intractable with previous learning-based approaches. We further elaborate on the light-weight design of the network architecture, achieving real-time performance on 2K images on a GTX 1080 Ti GPU. Codes are available at: https://github. com/Ascend-Huawei/Ascend-Canada/tree/ master/Models/Research_HiFIll_Model.",
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"authors": [
{
"affiliation": "Huawei Technologies Canada Co. Ltd.",
"fullName": "Zili Yi",
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"affiliation": "Huawei Technologies Canada Co. Ltd.",
"fullName": "Qiang Tang",
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"affiliation": "Huawei Technologies Canada Co. Ltd.",
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"title": "Research on Image Inpainting Based on Generative Adversarial Network",
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"abstract": "In recent years, the rapid development of deep learning has achieved remarkable results in many scientific research fields. Especially in the field of computer vision, deep learning has almost reached the highest level of image processing. Related deep learning methods have also been applied to the field of image inpainting, making researchers begin to use deep learning models to solve the problem of digital image inpainting. The generation of the adversarial network model has greatly improved the inpainting technology of digital images. This paper builds an image inpainting framework based on the generative adversarial network. The inpainting process is divided into two parallel stages, namely reconstruction inpainting and generation inpainting. The network structure of this paper is composed of two parts: a generating network and a discriminating network. The generated network generates an image, and the discriminating network judges whether the image generated by the generating network is consistent with the real image. The loss function of the network uses the loss function of WGAN-GP to calculate the loss of parameters, in order to update the network alternately, making the generated image more natural and realistic. This paper uses the Places2 data set to verify the algorithm, and combines the subjective evaluation method with the objective evaluation method to evaluate the quality of the inpainting image. Experiments show that the algorithm inpainting effect is better.",
"abstracts": [
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"content": "In recent years, the rapid development of deep learning has achieved remarkable results in many scientific research fields. Especially in the field of computer vision, deep learning has almost reached the highest level of image processing. Related deep learning methods have also been applied to the field of image inpainting, making researchers begin to use deep learning models to solve the problem of digital image inpainting. The generation of the adversarial network model has greatly improved the inpainting technology of digital images. This paper builds an image inpainting framework based on the generative adversarial network. The inpainting process is divided into two parallel stages, namely reconstruction inpainting and generation inpainting. The network structure of this paper is composed of two parts: a generating network and a discriminating network. The generated network generates an image, and the discriminating network judges whether the image generated by the generating network is consistent with the real image. The loss function of the network uses the loss function of WGAN-GP to calculate the loss of parameters, in order to update the network alternately, making the generated image more natural and realistic. This paper uses the Places2 data set to verify the algorithm, and combines the subjective evaluation method with the objective evaluation method to evaluate the quality of the inpainting image. Experiments show that the algorithm inpainting effect is better.",
"__typename": "ArticleAbstractType"
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"normalizedAbstract": "In recent years, the rapid development of deep learning has achieved remarkable results in many scientific research fields. Especially in the field of computer vision, deep learning has almost reached the highest level of image processing. Related deep learning methods have also been applied to the field of image inpainting, making researchers begin to use deep learning models to solve the problem of digital image inpainting. The generation of the adversarial network model has greatly improved the inpainting technology of digital images. This paper builds an image inpainting framework based on the generative adversarial network. The inpainting process is divided into two parallel stages, namely reconstruction inpainting and generation inpainting. The network structure of this paper is composed of two parts: a generating network and a discriminating network. The generated network generates an image, and the discriminating network judges whether the image generated by the generating network is consistent with the real image. The loss function of the network uses the loss function of WGAN-GP to calculate the loss of parameters, in order to update the network alternately, making the generated image more natural and realistic. This paper uses the Places2 data set to verify the algorithm, and combines the subjective evaluation method with the objective evaluation method to evaluate the quality of the inpainting image. Experiments show that the algorithm inpainting effect is better.",
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"content": "Augmented Reality (AR) has potential in the manufacturing and manual assembly industry. However, the effectiveness of AR as an assistive system depends on several factors, one of which is information presentation. A set of guidelines for the effective interface design of Augmented Reality systems for manual assembly have been proposed. To validate the design guidelines, it was decided to use a simulated AR approach using Virtual Reality (VR). To evaluate the simulated AR system, it is necessary to also simulate the manual assembly task. This paper describes the challenges faced when developing the manual assembly task simulation, particularly when simulating physical interactions in VR using the Unity3D engine.",
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"Augmented Reality",
"Design Engineering",
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"affiliation": "Universiti Teknologi PETRONAS,Computer & Information Sciences Department,Bandar Seri Iskandar,Perak,Malaysia",
"fullName": "Nor Farzana Syaza Jeffri",
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"affiliation": "Universiti Teknologi PETRONAS,Computer & Information Sciences Department,Bandar Seri Iskandar,Perak,Malaysia",
"fullName": "Dayang Rohaya Awang Rambli",
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"abstract": "Transformer has demonstrated promising performance in many 2D vision tasks. However, it is cumbersome to compute the self-attention on large-scale point cloud data because point cloud is a long sequence and unevenly distributed in 3D space. To solve this issue, existing methods usually compute self-attention locally by grouping the points into clusters of the same size, or perform convolutional self-attention on a discretized representation. However, the former results in stochastic point dropout, while the latter typically has narrow attention fields. In this paper, we propose a novel voxel-based architecture, namely Voxel Set Transformer (VoxSeT), to detect 3D objects from point clouds by means of set-to-set translation. VoxSeT is built upon a voxel-based set attention (VSA) module, which reduces the self-attention in each voxel by two cross-attentions and models features in a hidden space induced by a group of latent codes. With the VSA module, VoxSeT can manage voxelized point clusters with arbitrary size in a wide range, and process them in parallel with linear complexity. The proposed VoxSeT integrates the high performance of transformer with the efficiency of voxel-based model, which can be used as a good alternative to the convolutional and point-based backbones. VoxSeT reports competitive results on the KITTI and Waymo detection benchmarks. The source codes can be found at https://github.com/skyhehe123/VoxSeT.",
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"affiliation": "The Hong Kong Polytechnic University",
"fullName": "Chenhang He",
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"affiliation": "The Hong Kong Polytechnic University",
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"affiliation": "The Hong Kong Polytechnic University",
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"affiliation": "The Hong Kong Polytechnic University",
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"abstract": "In the future, analysis of social networks will conceivably move from graphs to hypergraphs. However, theory has not yet caught up with this type of data organizational structure. By introducing and analyzing a general model of preferential attachment hypergraphs, this paper makes a step towards narrowing this gap. We consider a random preferential attachment model H(p,Y) for network evolution that allows arrivals of both nodes and hyperedges of random size. At each time step t, two possible events may occur: (1) [vertex arrival event:] with probability p > 0 a new vertex arrives and a new hyperedge of size y<sub>t</sub>, containing the new vertex and Y<sub>t</sub>-1 existing vertices, is added to the hypergraph; or (2) [hyperedge arrival event:] with probability 1-p, a new hyperedge of size Y<sub>t</sub>, containing Y<sub>t</sub> existing vertices, is added to the hypergraph. In both cases, the involved existing vertices are chosen independently at random according to the preferential attachment rule, i.e., with probability proportional to their degree, where the degree of a vertex is the number of edges containing it. Assuming general restrictions on the distribution of Y<sub>t</sub>, we prove that the H(p,Y) model generates power law networks, i.e., the expected fraction of nodes with degree k is proportional to k<sup>-1-Γ</sup>, where Γ = lim<sub>t→∞</sub> [(Σ<sup>t-1</sup>i=0 E[Yi])/(t(E[Yt]-ρ))] ∈ (0, ∞). This extends the special case of preferential attachment graphs, where Y<sub>t</sub> = 2 for every t, yielding Γ = 2/ (2- p). Therefore, our results show that the exponent of the degree distribution is sensitive to whether one considers the structure of a social network to be a hypergraph or a graph. We discuss, and provide examples for, the implications of these considerations.",
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"content": "In the future, analysis of social networks will conceivably move from graphs to hypergraphs. However, theory has not yet caught up with this type of data organizational structure. By introducing and analyzing a general model of preferential attachment hypergraphs, this paper makes a step towards narrowing this gap. We consider a random preferential attachment model H(p,Y) for network evolution that allows arrivals of both nodes and hyperedges of random size. At each time step t, two possible events may occur: (1) [vertex arrival event:] with probability p > 0 a new vertex arrives and a new hyperedge of size y<sub>t</sub>, containing the new vertex and Y<sub>t</sub>-1 existing vertices, is added to the hypergraph; or (2) [hyperedge arrival event:] with probability 1-p, a new hyperedge of size Y<sub>t</sub>, containing Y<sub>t</sub> existing vertices, is added to the hypergraph. In both cases, the involved existing vertices are chosen independently at random according to the preferential attachment rule, i.e., with probability proportional to their degree, where the degree of a vertex is the number of edges containing it. Assuming general restrictions on the distribution of Y<sub>t</sub>, we prove that the H(p,Y) model generates power law networks, i.e., the expected fraction of nodes with degree k is proportional to k<sup>-1-Γ</sup>, where Γ = lim<sub>t→∞</sub> [(Σ<sup>t-1</sup>i=0 E[Yi])/(t(E[Yt]-ρ))] ∈ (0, ∞). This extends the special case of preferential attachment graphs, where Y<sub>t</sub> = 2 for every t, yielding Γ = 2/ (2- p). Therefore, our results show that the exponent of the degree distribution is sensitive to whether one considers the structure of a social network to be a hypergraph or a graph. We discuss, and provide examples for, the implications of these considerations.",
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"content": "Graph generative models have broad applications in biology, chemistry and social science. However, modelling and understanding the generative process of graphs is challenging due to the discrete and high-dimensional nature of graphs, as well as permutation invariance to node orderings in underlying graph distributions. Current leading autoregressive models fail to capture the permutation invariance nature of graphs for the reliance on generation ordering and have high time complexity. Here, we propose a continuous-time generative diffusion process for permutation invariant graph generation to mitigate these issues. Specifically, we first construct a forward diffusion process defined by a stochastic differential equation (SDE), which smoothly converts graphs within the complex distribution to random graphs that follow a known edge probability. Solving the corresponding reverse-time SDE, graphs can be generated from newly sampled random graphs. To facilitate the reverse-time SDE, we newly design a position-enhanced graph score network, capturing the evolving structure and position information from perturbed graphs for permutation equivariant score estimation. Under the evaluation of comprehensive metrics, our proposed generative diffusion process achieves competitive performance in graph distribution learning. Experimental results also show that GraphGDP can generate high-quality graphs in only 24 function evaluations, much faster than previous autoregressive models.",
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"authors": [
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"affiliation": "Beihang University,SKLSDE,Beijing,China",
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"affiliation": "Beihang University,SKLSDE,Beijing,China",
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"affiliation": "University of Central Florida,Florida,USA",
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"affiliation": "Beihang University,SKLSDE,Beijing,China",
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"abstract": "As the field of brain monitoring is evolving rapidly, there is an increasing demand of finding innovative ways to handle relevant signals. Especially electroencephalogram (EEG) signals provide a non-invasive way of diagnostic inference of brain's functionality. Nevertheless, EEG signals are often corrupted by impulsive noise, thus prior denoising is required for accurate analysis and decision making. On the other hand, EEG signals admit naturally a representation in the form of graphs, with the electrodes corresponding to the nodes of the graph and the edges expressing the connectivity strength. To this end, graph signal processing (GSP) is a versatile tool, which enables the representation and analysis of graph-structured signals, whose interdependencies are encoded in the form of an appropriate adjacency matrix. To address the denoising of graph-structured signals, under impulsive noise conditions, this work introduces a regularized graph filtering scheme based on fractional lower order moments, coupled with distinct adjacency matrices inspired both by statistical approaches and visibility graphs that are better capable of capturing the topological and functional connectivity between the distinct nodes. The experimental evaluation on real EEG signals recorded in epileptic and non-epileptic seizures, reveals the effects of the adjacency matrix choice on the denoising performance.",
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"content": "As the field of brain monitoring is evolving rapidly, there is an increasing demand of finding innovative ways to handle relevant signals. Especially electroencephalogram (EEG) signals provide a non-invasive way of diagnostic inference of brain's functionality. Nevertheless, EEG signals are often corrupted by impulsive noise, thus prior denoising is required for accurate analysis and decision making. On the other hand, EEG signals admit naturally a representation in the form of graphs, with the electrodes corresponding to the nodes of the graph and the edges expressing the connectivity strength. To this end, graph signal processing (GSP) is a versatile tool, which enables the representation and analysis of graph-structured signals, whose interdependencies are encoded in the form of an appropriate adjacency matrix. To address the denoising of graph-structured signals, under impulsive noise conditions, this work introduces a regularized graph filtering scheme based on fractional lower order moments, coupled with distinct adjacency matrices inspired both by statistical approaches and visibility graphs that are better capable of capturing the topological and functional connectivity between the distinct nodes. The experimental evaluation on real EEG signals recorded in epileptic and non-epileptic seizures, reveals the effects of the adjacency matrix choice on the denoising performance.",
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"abstract": "Given a stream of heterogeneous edges, comprising different types of nodes and edges, which arrive in an interleaved fashion to multiple different graphs evolving simultaneously, how can we spot the anomalous graphs in real-time using only constant memory? This problem is motivated by and generalizes from its application in security to host-level advanced persistent threat (APT) detection. In this talk, I will introduce STREAMSPOT, a clustering based anomaly detection approach for streaming heterogeneous graphs that addresses challenges in two key fronts: (1) heterogeneity, and (2) streaming nature. Specifically, we introduce a new similarity function for heterogeneous graphs that compares two graphs based on their relative frequency of local substructures, represented as short strings. This function lends itself to a vector representation of each graph, which is (a) fast to compute, and (b) amenable to a sketched version with bounded size that preserves the aforementioned similarity. STREAMSPOT exhibits desirable properties that a streaming application requires-it is (i) fully-streaming; processing the stream one edge at a time as it arrives, (ii) memory-efficient; requiring constant space for the sketches and the clustering, (iii) fast; taking constant time to update the graph sketches and the cluster summaries that can process over 100K edges per second, and (iv) online; scoring and flagging anomalies in real time. Experiments on datasets containing simulated system-call flow graphs from normal browser activity and various attack scenarios (ground truth) show that STREAMSPOT is high-performance; achieving above 95% detection accuracy with small delay, and competitive response time and memory usage.",
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"abstract": "The computer-assisted radiologic informative report is currently emerging in dental practice to facilitate dental care and reduce time consumption in manual panoramic radiographic interpretation. However, the amount of dental radiographs for training is very limited, particularly from the point of view of deep learning. This study aims to utilize recent self-supervised learning methods like SimMIM and UM-MAE to increase the model efficiency and understanding of the limited number of dental radiographs. We use the Swin Transformer for teeth numbering, detection of dental restorations, and instance segmentation tasks. To the best of our knowledge, this is the first study that applied self-supervised learning methods to Swin Transformer on dental panoramic radiographs. Our results show that the SimMIM method obtained the highest performance of 90.4% and 88.9% on detecting teeth and dental restorations and instance segmentation, respectively, increasing the average precision by 13.4 and 12.8 over the random initialization baseline. Moreover, we augment and correct the existing dataset of panoramic radiographs. The code and the dataset are available at https://github.com/AmaniHAlmalki/DentalMIM.",
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"abstract": "Legal text contains various challenges in automated processing, compounded by the lack of detailed resources available for them. However, the ability of process such texts automatically is highly sought after. In this paper we try to parse a set of contract documents and identify key legal terminologies present in them, with the help of four text processing methods from different backgrounds: Tsetlin Machines, BERT, CNNBiLSTM and FastText. We show that the TM based approach works at par with other popular methods, with the added benefit of making available important clause literals that can act as specific linguistic cues to legal terminology.",
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"abstract": "Identifying academic plagiarism is a pressing task for educational and research institutions, publishers, and funding agencies. Current plagiarism detection systems reliably find instances of copied and moderately reworded text. However, reliably detecting concealed plagiarism, such as strong paraphrases, translations, and the reuse of nontextual content and ideas is an open research problem. In this paper, we extend our prior research on analyzing mathematical content and academic citations. Both are promising approaches for improving the detection of concealed academic plagiarism primarily in Science, Technology, Engineering and Mathematics (STEM). We make the following contributions: i) We present a two-stage detection process that combines similarity assessments of mathematical content, academic citations, and text. ii) We introduce new similarity measures that consider the order of mathematical features and outperform the measures in our prior research. iii) We compare the effectiveness of the math-based, citation-based, and text-based detection approaches using confirmed cases of academic plagiarism. iv) We demonstrate that the combined analysis of math-based and citation-based content features allows identifying potentially suspicious cases in a collection of 102K STEM documents. Overall, we show that analyzing the similarity of mathematical content and academic citations is a striking supplement for conventional text-based detection approaches for academic literature in the STEM disciplines. The data and code of our study are openly available at https://purl.org/hybridPD.",
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"title": "CNN Application in Detection of Privileged Documents in Legal Document Review",
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"abstract": "Protecting privileged communications and data from disclosure is paramount for legal teams. Legal advice, such as attorney-client communications or litigation strategy are typically exempt from disclosure in litigations or regulatory events and are vital to the attorney-client relationship. To protect this information from disclosure, companies and outside counsel often review vast amounts of documents to determine those that contain privileged material. This process is extremely costly and time consuming. As data volumes increase, legal counsel normally employs methods to reduce the number of documents requiring review while balancing the need to ensure the protection of privileged information. Keyword searching is relied upon as a method to target privileged information and reduce document review populations. Keyword searches are effective at casting a wide net but often return overly inclusive results – most of which do not contain privileged information. To overcome the weaknesses of keyword searching, legal teams increasingly are using machine learning techniques to target privileged information. In these studies, classic text classification techniques are applied to build classification models to identify privileged documents. In this paper, the authors propose a different method by applying machine learning / convolutional neural network techniques (CNN) to identify privileged documents. Our proposed method combines keyword searching with CNN. For each keyword term, a CNN model is created using the context of the occurrences of the keyword. In addition, a method was proposed to select reliable privileged (positive) training keyword occurrences from labeled positive training documents. Extensive experiments were conducted, and the results show that the proposed methods can significantly reduce false positives while still capturing most of the true positives.",
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"content": "Protecting privileged communications and data from disclosure is paramount for legal teams. Legal advice, such as attorney-client communications or litigation strategy are typically exempt from disclosure in litigations or regulatory events and are vital to the attorney-client relationship. To protect this information from disclosure, companies and outside counsel often review vast amounts of documents to determine those that contain privileged material. This process is extremely costly and time consuming. As data volumes increase, legal counsel normally employs methods to reduce the number of documents requiring review while balancing the need to ensure the protection of privileged information. Keyword searching is relied upon as a method to target privileged information and reduce document review populations. Keyword searches are effective at casting a wide net but often return overly inclusive results – most of which do not contain privileged information. To overcome the weaknesses of keyword searching, legal teams increasingly are using machine learning techniques to target privileged information. In these studies, classic text classification techniques are applied to build classification models to identify privileged documents. In this paper, the authors propose a different method by applying machine learning / convolutional neural network techniques (CNN) to identify privileged documents. Our proposed method combines keyword searching with CNN. For each keyword term, a CNN model is created using the context of the occurrences of the keyword. In addition, a method was proposed to select reliable privileged (positive) training keyword occurrences from labeled positive training documents. Extensive experiments were conducted, and the results show that the proposed methods can significantly reduce false positives while still capturing most of the true positives.",
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"fullName": "Rishi Chhatwal",
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"abstract": "This paper details the design of an algorithm for automatically manipulating the important aesthetic element of video, visual tempo. Automatic injection, detection and repair of such aesthetic elements, it is argued, is vital to the next generation of amateur multimedia authoring tools. We evaluate the performance of the algorithm on a battery of synthetic data and demonstrate its ability to return the visual tempo of the final media a considerable degree closer to the target signal. The novelty of this work lies chiefly in the systematic manipulation of this high level aesthetic element of video.",
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"title": "Image Aesthetics Assessment Using Graph Attention Network",
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"abstract": "Aspect ratio and spatial layout are two of the principal factors influencing the aesthetic value of a photograph. However, incorporating these into the traditional convolution-based frameworks for the task of image aesthetics assessment is problematic. The aspect ratio of the photographs gets distorted while they are resized/cropped to a fixed dimension to facilitate training batch sampling. On the other hand, the convolutional filters process information locally and are limited in their ability to model the global spatial layout of a photograph. In this work, we present a two-stage framework based on graph neural networks and address both these problems jointly. First, we propose a feature-graph representation in which the input image is modelled as a graph, maintaining its original aspect ratio and resolution. Second, we propose a graph neural network architecture that takes this feature-graph and captures the semantic relationship between different regions of the input image using visual attention. Our experiments show that the proposed framework advances the state-of-the-art results in aesthetic score regression on the Aesthetic Visual Analysis (AVA) benchmark. Our code is publicly available for comparisons and further explorations.<sup>1</sup>",
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"abstract": "In this work we consider the problem of generating aesthetically pleasing photography, sometimes termed photographic fine art (PFA). We cast this problem as a generative modeling task and use a conditional GAN framework. Recent works have shown that conditioning based on semantic information is beneficial for improving photo-realism. In this work we propose a novel GAN architecture which is able to generate photo-realistic images with a specified aesthetic quality by conditioning on both semantic and aesthetic information. To condition the generator, we propose a modified conditional batch normalization layer. To condition the discriminator, we use a joint probabilistic model of semantics and aesthetics to estimate the compatibility between an image (either real or generated) and the conditioning variable. We show quantitatively and qualitatively that our model, called PFAGAN, is able to generate images conditioned on semantic categories and aesthetic scores.",
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"title": "Investigating aesthetics to afford more ‘felt’ knowledge and ‘meaningful’ navigation interface designs",
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"abstract": "Aesthetically manipulating the visual variables of a navigation interface design has the potential for substantial improvements in the interpretation of, and subsequent navigational choices made resulting from that design. This paper reports on a study that explores how an `optimal' path is understood across fifteen different types of route map designs for ten cities (approximately 150 route map designs in total). We are interested in how participants make sense of the route map, and subsequently choose an optimal pathway. The findings show that participants who experience certain aesthetically designed route maps are more inclined to meaningfully link information and create connections. By more deeply understanding people's perceptions of the aesthetics of a navigation problem space - particularly the ways in which people value and connect with aesthetic elements and how these impact the decisions made - a novel insight into individuals' understanding of data visualisation and how aesthetics affect is achieved.",
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"affiliation": "Universitat Polit?cnica de Catalunya",
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"affiliation": "Universitat Polit?cnica de Catalunya",
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"abstract": "Hand-held transparent displays are important infrastructure for augmented reality applications. Truly transparent displays are not yet feasible in hand-held form, and a promising alternative is to simulate transparency by displaying the image the user would see if the display were not there. Previous simulated transparent displays have important limitations, such as being tethered to auxiliary workstations, requiring the user to wear obtrusive head-tracking devices, or lacking the depth acquisition support that is needed for an accurate transparency effect for close-range scenes.We describe a general simulated transparent display and three prototype implementations (P1, P2, and P3), which take advantage of emerging mobile devices and accessories. P1 uses an off-theshelf smartphone with built-in head-tracking support; P1 is compact and suitable for outdoor scenes, providing an accurate transparency effect for scene distances greater than 6m. P2 uses a tablet with a built-in depth camera; P2 is compact and suitable for short-distance indoor scenes, but the user has to hold the display in a fixed position. P3 uses a conventional tablet enhanced with on-board depth acquisition and head tracking accessories; P3 compensates for user head motion and provides accurate transparency even for closerange scenes. The prototypes are hand-held and self-contained, without the need of auxiliary workstations for computation.",
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"content": "Hand-held transparent displays are important infrastructure for augmented reality applications. Truly transparent displays are not yet feasible in hand-held form, and a promising alternative is to simulate transparency by displaying the image the user would see if the display were not there. Previous simulated transparent displays have important limitations, such as being tethered to auxiliary workstations, requiring the user to wear obtrusive head-tracking devices, or lacking the depth acquisition support that is needed for an accurate transparency effect for close-range scenes.We describe a general simulated transparent display and three prototype implementations (P1, P2, and P3), which take advantage of emerging mobile devices and accessories. P1 uses an off-theshelf smartphone with built-in head-tracking support; P1 is compact and suitable for outdoor scenes, providing an accurate transparency effect for scene distances greater than 6m. P2 uses a tablet with a built-in depth camera; P2 is compact and suitable for short-distance indoor scenes, but the user has to hold the display in a fixed position. P3 uses a conventional tablet enhanced with on-board depth acquisition and head tracking accessories; P3 compensates for user head motion and provides accurate transparency even for closerange scenes. The prototypes are hand-held and self-contained, without the need of auxiliary workstations for computation.",
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"abstract": "We present a novel dynamic load-balancing algorithm based on data repartitioning for parallel particle tracing in flow visualization. Instead of static data assignment, we dynamically repartition the data into blocks and reassign the blocks to processes to balance the workload distribution among the processes. Block repartitioning is performed based on a dynamic workload estimation method that predicts the workload in the flow field on the fly as the input. In our approach, we allow data duplication in the repartitioning, enabling the same data blocks to be assigned to multiple processes. Load balance is achieved by regularly exchanging the blocks (together with the particles in the blocks) among processes according to the output of the data repartitioning. Compared with other load-balancing algorithms, our approach does not need any preprocessing on the raw data and does not require any dedicated process for work scheduling, while it has the capability to balance uneven workload efficiently. Results show improved load balance and high efficiency of our method on tracing particles in both steady and unsteady flow.",
"abstracts": [
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"content": "We present a novel dynamic load-balancing algorithm based on data repartitioning for parallel particle tracing in flow visualization. Instead of static data assignment, we dynamically repartition the data into blocks and reassign the blocks to processes to balance the workload distribution among the processes. Block repartitioning is performed based on a dynamic workload estimation method that predicts the workload in the flow field on the fly as the input. In our approach, we allow data duplication in the repartitioning, enabling the same data blocks to be assigned to multiple processes. Load balance is achieved by regularly exchanging the blocks (together with the particles in the blocks) among processes according to the output of the data repartitioning. Compared with other load-balancing algorithms, our approach does not need any preprocessing on the raw data and does not require any dedicated process for work scheduling, while it has the capability to balance uneven workload efficiently. Results show improved load balance and high efficiency of our method on tracing particles in both steady and unsteady flow.",
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"title": "Edge-Level Explanations for Graph Neural Networks by Extending Explainability Methods for Convolutional Neural Networks",
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"abstract": "Graph Neural Networks (GNNs) are deep learning models that take graph data as inputs, and they are applied to various tasks such as traffic prediction and molecular property prediction. However, owing to the complexity of the GNNs, it has been difficult to analyze which parts of inputs affect the GNN model’s outputs. In this study, we extend explainability methods for Convolutional Neural Networks (CNNs), such as Local Interpretable Model-Agnostic Explanations (LIME), Gradient-Based Saliency Maps, and Gradient-Weighted Class Activation Mapping (Grad-CAM) to GNNs, and predict which edges in the input graphs are important for GNN decisions. The experimental results indicate that the LIME-based approach is the most efficient explainability method for multiple tasks in the real-world situation, outperforming even the state-of-the-art method in GNN explainability.",
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"title": "2021 IEEE International Performance, Computing, and Communications Conference (IPCCC)",
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"doi": "10.1109/IPCCC51483.2021.9679362",
"title": "Accelerate Graph Neural Network Training by Reusing Batch Data on GPUs",
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"abstract": "With the increasing adoption of graph neural networks (GNNs) in the graph-based deep learning community, various graph programming frameworks and models have been developed to improve the productivity of GNNs. The current GNN frameworks choose GPU as an essential tool to accelerate GNN training. However, it is still challenging to train GNNs on large graphs with limited GPU memory. Unlike traditional neural networks, generating mini-batch data by sampling in GNNs requires some complicated tasks such as traversing the graph to select neighboring nodes and gathering their features. This process takes up most of the training and we find the main bottleneck comes from transferring nodes features from CPU to GPU through limited bandwidth. In this paper, We propose a method Reusing Batch Data for the problem of data transmission. This method utilizes the similarity between adjacent mini-batches to reduce repeated data transmission from CPU to GPU. Furthermore, to reduce the overhead introduced by this method, we design a fast algorithm based on GPU to detect repeated nodes’ data and achieve shorter additional computation time. Evaluations on three representative GNN models show that our method can reduce transmission time by up to 60% and speed the end-to-end GNN training by up to 1.79× over the state-ofthe-art baselines. Besides, Reusing Batch Data can effectively save GPU memory footprint by about 19% to 40% while still reducing the training time compared to the static cache strategy.",
"abstracts": [
{
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"content": "With the increasing adoption of graph neural networks (GNNs) in the graph-based deep learning community, various graph programming frameworks and models have been developed to improve the productivity of GNNs. The current GNN frameworks choose GPU as an essential tool to accelerate GNN training. However, it is still challenging to train GNNs on large graphs with limited GPU memory. Unlike traditional neural networks, generating mini-batch data by sampling in GNNs requires some complicated tasks such as traversing the graph to select neighboring nodes and gathering their features. This process takes up most of the training and we find the main bottleneck comes from transferring nodes features from CPU to GPU through limited bandwidth. In this paper, We propose a method Reusing Batch Data for the problem of data transmission. This method utilizes the similarity between adjacent mini-batches to reduce repeated data transmission from CPU to GPU. Furthermore, to reduce the overhead introduced by this method, we design a fast algorithm based on GPU to detect repeated nodes’ data and achieve shorter additional computation time. Evaluations on three representative GNN models show that our method can reduce transmission time by up to 60% and speed the end-to-end GNN training by up to 1.79× over the state-ofthe-art baselines. Besides, Reusing Batch Data can effectively save GPU memory footprint by about 19% to 40% while still reducing the training time compared to the static cache strategy.",
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"normalizedAbstract": "With the increasing adoption of graph neural networks (GNNs) in the graph-based deep learning community, various graph programming frameworks and models have been developed to improve the productivity of GNNs. The current GNN frameworks choose GPU as an essential tool to accelerate GNN training. However, it is still challenging to train GNNs on large graphs with limited GPU memory. Unlike traditional neural networks, generating mini-batch data by sampling in GNNs requires some complicated tasks such as traversing the graph to select neighboring nodes and gathering their features. This process takes up most of the training and we find the main bottleneck comes from transferring nodes features from CPU to GPU through limited bandwidth. In this paper, We propose a method Reusing Batch Data for the problem of data transmission. This method utilizes the similarity between adjacent mini-batches to reduce repeated data transmission from CPU to GPU. Furthermore, to reduce the overhead introduced by this method, we design a fast algorithm based on GPU to detect repeated nodes’ data and achieve shorter additional computation time. Evaluations on three representative GNN models show that our method can reduce transmission time by up to 60% and speed the end-to-end GNN training by up to 1.79× over the state-ofthe-art baselines. Besides, Reusing Batch Data can effectively save GPU memory footprint by about 19% to 40% while still reducing the training time compared to the static cache strategy.",
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"Graph Theory",
"Graphics Processing Units",
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"Accelerate Graph Neural Network Training",
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"Current GNN Frameworks",
"Traditional Neural Networks",
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"authors": [
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"affiliation": "National University of Defense Technology,National Key Laboratory of Parallel and Distributed Processing School of Computer,Changsha,China",
"fullName": "Zhejiang Ran",
"givenName": "Zhejiang",
"surname": "Ran",
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{
"affiliation": "National University of Defense Technology,National Key Laboratory of Parallel and Distributed Processing School of Computer,Changsha,China",
"fullName": "Zhiquan Lai",
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"surname": "Lai",
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{
"affiliation": "National University of Defense Technology,National Key Laboratory of Parallel and Distributed Processing School of Computer,Changsha,China",
"fullName": "Lizhi Zhang",
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"affiliation": "National University of Defense Technology,National Key Laboratory of Parallel and Distributed Processing School of Computer,Changsha,China",
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"doi": "10.1109/TPSISA52974.2021.00002",
"title": "Membership Inference Attack on Graph Neural Networks",
"normalizedTitle": "Membership Inference Attack on Graph Neural Networks",
"abstract": "Graph Neural Networks (GNNs), which generalize traditional deep neural networks on graph data, have achieved state-of-the-art performance on several graph analytical tasks. We focus on how trained GNN models could leak information about the member nodes that they were trained on. We introduce two realistic settings for performing a membership inference (MI) attack on GNNs. While choosing the simplest possible attack model that utilizes the posteriors of the trained model (black-box access), we thoroughly analyze the properties of GNNs and the datasets which dictate the differences in their robustness towards MI attack. While in traditional machine learning models, overfitting is considered the main cause of such leakage, we show that in GNNs the additional structural information is the major contributing factor. We support our findings by extensive experiments on four representative GNN models. To prevent MI attacks on GNN, we propose two effective defenses that significantly decreases the attacker's inference by up to 60% without degradation to the target model's performance. Our code is available at https://github.com/iyempissy/rebMIGraph.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Graph Neural Networks (GNNs), which generalize traditional deep neural networks on graph data, have achieved state-of-the-art performance on several graph analytical tasks. We focus on how trained GNN models could leak information about the member nodes that they were trained on. We introduce two realistic settings for performing a membership inference (MI) attack on GNNs. While choosing the simplest possible attack model that utilizes the posteriors of the trained model (black-box access), we thoroughly analyze the properties of GNNs and the datasets which dictate the differences in their robustness towards MI attack. While in traditional machine learning models, overfitting is considered the main cause of such leakage, we show that in GNNs the additional structural information is the major contributing factor. We support our findings by extensive experiments on four representative GNN models. To prevent MI attacks on GNN, we propose two effective defenses that significantly decreases the attacker's inference by up to 60% without degradation to the target model's performance. Our code is available at https://github.com/iyempissy/rebMIGraph.",
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"normalizedAbstract": "Graph Neural Networks (GNNs), which generalize traditional deep neural networks on graph data, have achieved state-of-the-art performance on several graph analytical tasks. We focus on how trained GNN models could leak information about the member nodes that they were trained on. We introduce two realistic settings for performing a membership inference (MI) attack on GNNs. While choosing the simplest possible attack model that utilizes the posteriors of the trained model (black-box access), we thoroughly analyze the properties of GNNs and the datasets which dictate the differences in their robustness towards MI attack. While in traditional machine learning models, overfitting is considered the main cause of such leakage, we show that in GNNs the additional structural information is the major contributing factor. We support our findings by extensive experiments on four representative GNN models. To prevent MI attacks on GNN, we propose two effective defenses that significantly decreases the attacker's inference by up to 60% without degradation to the target model's performance. Our code is available at https://github.com/iyempissy/rebMIGraph.",
"fno": "162300a011",
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"normalizedAbstract": "Glycans play an indispensable role in various bio-logical processes, such as cancer and autoimmune diseases. The function of glycan is closely determined by its structure. Due to the branch and nonlinear properties of glycans, previous research treats the glycans graph structure as a topological graph to represent glycans data effectively. Graph neural networks (GNNs) are an efficient graph mining method and have many applications in bioinformatics. Therefore, researchers have successfully used handcrafted GNNs to predict glycan immunogenicity. However, a GNN architecture contains many different components, and designing GNN architectures for specific graphs in the bioinformatics field is time-consuming and expert-dependent. To address this challenge, we propose an efficient automatic graph neural network method called EAGNN that can efficiently and automatically construct GNN architecture for glycan immunogenicity prediction. We design an effective graph attention pooling (GAP) search space. We use differential architecture search to efficiently create the optimal GNN architecture in the search space to build the GNN model for glycan immunogenicity prediction. We test EAGNN on the data set SugarBase based on the glycan immunogenicity prediction task. The experiment results show that EAGNN can work more superiorly than the baseline model and achieve the best performance.",
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"Bioinformatics",
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"affiliation": "Drexel University,College of Computing & Informatics,USA",
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"abstracts": [
{
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"content": "Graph neural networks (GNNs) are effective models to address learning problems on graphs and have been successfully applied to numerous domains. To improve the productivity of implementing GNNs, various GNN programming frameworks have been developed. Both the effectiveness (accuracy, loss, etc) and the performance (latency, bandwidth, etc) are essential metrics to evaluate the implementation of GNNs. There are many comparative studies related to the effectiveness of different GNN models on domain tasks. However, the performance characteristics of different GNN frameworks are still lacking. In this study, we evaluate the effectiveness and performance of six popular GNN models, GCN, GIN, GAT, GraphSAGE, MoNet, and GatedGCN, across several common benchmarks under two popular GNN frameworks, PyTorch Geometric and Deep Graph Library. We analyze the training time, GPU utilization, and memory usage of different evaluation settings and the performance of models across different hardware configurations under the two frameworks. Our evaluation provides in-depth observations of performance bottlenecks of GNNs and the performance differences between the two popular GNN frameworks. Our work helps GNN researchers understand the performance differences of the popular GNN frameworks, and gives guidelines for developers to find potential performance bugs of frameworks and optimization possibilities of GNNs.",
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"title": "Temporal Collaborative Filtering with Graph Convolutional Neural Networks",
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"abstract": "Temporal collaborative filtering (TCF) methods aim at modelling non-static aspects behind recommender systems, such as the dynamics in users' preferences and social trends around items. State-of-the-art TCF methods employ recurrent neural networks (RNNs) to model such aspects. These methods deploy matrix-factorization-based (MF -based) approaches to learn the user and item representations. Recently, graph-neural-network-based (GNN-based) approaches have shown improved performance in providing accurate recommendations over traditional MF -based approaches in non-temporal CF settings. Motivated by this, we propose a novel TCF method that leverages GNNs to learn user and item representations, and RNNs to model their temporal dynamics. A challenge with this method lies in the increased data sparsity, which negatively impacts obtaining meaningful quality representations with GNNs. To overcome this challenge, we train a GNN model at each time step using a set of observed interactions accumulated time-wise. Comprehensive experiments on real-world data show the improved performance obtained by our method over several state-of-the-art temporal and non-temporal CF models.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Temporal collaborative filtering (TCF) methods aim at modelling non-static aspects behind recommender systems, such as the dynamics in users' preferences and social trends around items. State-of-the-art TCF methods employ recurrent neural networks (RNNs) to model such aspects. These methods deploy matrix-factorization-based (MF -based) approaches to learn the user and item representations. Recently, graph-neural-network-based (GNN-based) approaches have shown improved performance in providing accurate recommendations over traditional MF -based approaches in non-temporal CF settings. Motivated by this, we propose a novel TCF method that leverages GNNs to learn user and item representations, and RNNs to model their temporal dynamics. A challenge with this method lies in the increased data sparsity, which negatively impacts obtaining meaningful quality representations with GNNs. To overcome this challenge, we train a GNN model at each time step using a set of observed interactions accumulated time-wise. Comprehensive experiments on real-world data show the improved performance obtained by our method over several state-of-the-art temporal and non-temporal CF models.",
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"authors": [
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"affiliation": "Vrije Universiteit Brussel,ETRO Department,Brussels, Leuven,Belgium,B-3001",
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"affiliation": "Vrije Universiteit Brussel,ETRO Department,Brussels, Leuven,Belgium,B-3001",
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"affiliation": "Vrije Universiteit Brussel,ETRO Department,Brussels, Leuven,Belgium,B-3001",
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"abstract": "With the gradual expansion of our insurance company business, the risks are increasing, and the problem of insolvency of insurance companies is becoming apparent. Therefore, the research on the solvency adequacy ratio has become one of the hot topics in the academic field. Since the commissioning of the second generation solvency supervision system (C-ROSS) in 2015, a series of changes have taken place in the solvency of insurance companies. This paper firstly applied the data of 50 groups of small and medium-sized non life insurance companies from 2010 to 2014 based on the new changes after the C-ROSS trial operation to analyze the internal factors of solvency adequacy by using the data of 40 groups of them based on the multivariate statistical analysis. Then, the validity of the model was verified by 10 other groups of data. Through verification, the model can describe the solvency adequacy rate of small and medium-sized non-life insurance companies in China well. Finally, some meaningful policy suggestions were put forward.",
"abstracts": [
{
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"content": "With the gradual expansion of our insurance company business, the risks are increasing, and the problem of insolvency of insurance companies is becoming apparent. Therefore, the research on the solvency adequacy ratio has become one of the hot topics in the academic field. Since the commissioning of the second generation solvency supervision system (C-ROSS) in 2015, a series of changes have taken place in the solvency of insurance companies. This paper firstly applied the data of 50 groups of small and medium-sized non life insurance companies from 2010 to 2014 based on the new changes after the C-ROSS trial operation to analyze the internal factors of solvency adequacy by using the data of 40 groups of them based on the multivariate statistical analysis. Then, the validity of the model was verified by 10 other groups of data. Through verification, the model can describe the solvency adequacy rate of small and medium-sized non-life insurance companies in China well. Finally, some meaningful policy suggestions were put forward.",
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"normalizedAbstract": "With the gradual expansion of our insurance company business, the risks are increasing, and the problem of insolvency of insurance companies is becoming apparent. Therefore, the research on the solvency adequacy ratio has become one of the hot topics in the academic field. Since the commissioning of the second generation solvency supervision system (C-ROSS) in 2015, a series of changes have taken place in the solvency of insurance companies. This paper firstly applied the data of 50 groups of small and medium-sized non life insurance companies from 2010 to 2014 based on the new changes after the C-ROSS trial operation to analyze the internal factors of solvency adequacy by using the data of 40 groups of them based on the multivariate statistical analysis. Then, the validity of the model was verified by 10 other groups of data. Through verification, the model can describe the solvency adequacy rate of small and medium-sized non-life insurance companies in China well. Finally, some meaningful policy suggestions were put forward.",
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"Insurance",
"Small To Medium Enterprises",
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"abstract": "This paper discusses different machine learning models that can be used for insurance underwriting. The underwriting process for insurance is conventionally done by well-trained individuals, but the whole process is time-consuming and tedious, especially as the applications are getting increasingly more complex nowadays. The advancement in technology has enabled us to seek an alternative solution to this tedious and labor-intensive process of underwriting. Many believe that tools from Artificial Intelligence and more specifically, methods in Machine Learning, can provide time-efficient and accurate evaluations of risk. Using the publicly available dataset from Prudential Insurance, several feature engineering methods are implemented and tested for their usefulness. Specifically, kth nearest neighbor, multinomial logistic regression, random forest, and gradient boosting trees are fitted, and gradient boosting is shown to have the best performance and robustness.",
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"abstract": "In order to improve the accuracy of agricultural insurance cost prediction and the satisfaction of insurance demand prediction, a new agricultural insurance demand prediction model based on support vector machine is proposed in this paper. Firstly, the large number rule is used to calculate the characteristics of the sample data, and the agricultural insurance demand data are fully mined according to the feature ranking results. Secondly, based on the mining results, the range transformation method is used to preprocess the historical agricultural insurance data. Finally, the weight of insurance data is calculated, and the global and local kernel functions are combined to complete the construction of support vector machine. Input insurance demand data samples into support vector machine, and the result is the prediction result of agricultural insurance demand. The comparative experimental results show that compared with other traditional prediction models, this model can accurately predict the cost of agricultural insurance, and has high user satisfaction. Therefore, it shows that the practical application performance of the model in this paper is strong.",
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{
"abstractType": "Regular",
"content": "In order to improve the accuracy of agricultural insurance cost prediction and the satisfaction of insurance demand prediction, a new agricultural insurance demand prediction model based on support vector machine is proposed in this paper. Firstly, the large number rule is used to calculate the characteristics of the sample data, and the agricultural insurance demand data are fully mined according to the feature ranking results. Secondly, based on the mining results, the range transformation method is used to preprocess the historical agricultural insurance data. Finally, the weight of insurance data is calculated, and the global and local kernel functions are combined to complete the construction of support vector machine. Input insurance demand data samples into support vector machine, and the result is the prediction result of agricultural insurance demand. The comparative experimental results show that compared with other traditional prediction models, this model can accurately predict the cost of agricultural insurance, and has high user satisfaction. Therefore, it shows that the practical application performance of the model in this paper is strong.",
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"Costing",
"Data Mining",
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"Agricultural Insurance Demand Prediction Model",
"Feature Ranking",
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