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arXiv 2022年

作者： Deng, Zhi-Hong Wang, Chang-Dong Huang, Ling Lai, Jian-Huang Yu, Philip S. School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Information Security Technology Guangzhou China College of Mathematics and Informatics South China Agricultural University Guangzhou China Guangdong Provincial Key Laboratory of Public Finance and Taxation with Big Data Application China University of Illinois at Chicago ChicagoIL United States

Session-based recommendation tries to make use of anonymous session data to deliver high-quality recommendation under the condition that user-profiles and the complete historical behavioral data of a target user are unavailable. Previous works consider each session individually and try to capture user interests within a session. Despite their encouraging results, these models can only perceive intra-session items and cannot draw upon the massive historical relational information. To solve this problem, we propose a novel method named G3SR (Global Graph Guided Session-based Recommendation). G3SR decomposes the session-based recommendation workflow into two steps. First, a global graph is built upon all session data, from which the global item representations are learned in an unsupervised manner. Then, these representations are refined on session graphs under the graph networks, and a readout function is used to generate session representations for each session. Extensive experiments on two real-world benchmark datasets show remarkable and consistent improvements of the G3SR method over the state-of-the-art methods, especially for cold items. Copyright © 2022, The Authors. All rights reserved.

关键词： Graph neural networks

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Learning High-Resolution and Efficient Non-local Features for Brain Glioma Segmentation in MR Images 23rd

Learning High-Resolution and Efficient Non-local Features fo...

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23rd International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2020

作者： Jia, Haozhe Xia, Yong Cai, Weidong Huang, Heng Research and Development Institute of Northwestern Polytechnical University in Shenzhen Shenzhen518057 China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xian710072 China School of Computer Science University of Sydney SydneyNSW2006 Australia Department of Electrical and Computer Engineering University of Pittsburgh PittsburghPA15261 United States JD Finance America Corporation Mountain ViewCA94043 United States

ISBN: (纸本)9783030597184

Brain glioma segmentation using multi-parametric magnetic resonance (MR) imaging has significant clinical value. Although 3D convolutional neural networks (CNNs) have become increasingly prevalent in delivering this segmentation task, these models still suffer from an insufficient ability to high-resolution feature representation for small and irregular regions, limited local receptive fields, and poor long-range dependencies. In this paper, we propose a 3D High-resolution and Non-local Feature Network (HNF-Net) for brain glioma segmentation using multi-parametric MR imaging. We construct HNF-Net based mainly on the parallel multi-scale fusion (PMF) module, which helps produce strong high-resolution feature representation and aggregate multi-scale contextual information. We also introduce the expectation-maximization attention (EMA) module to HNF-Net, aiming to capture the long-range dependent contextual information and reduce the feature redundancy in a lightweight fashion. We evaluated our HNF-Net on the BraTS 2019 Challenge dataset against eight top-ranking methods listed on the challenge leaderboard. Our results suggest that the proposed HNF-Net achieves improved overall performance over these methods, and our ablation study demonstrates the effectiveness of the PMF module and EMA module. © 2020, Springer Nature Switzerland AG.

关键词： Magnetic resonance imaging

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Joint gaze estimation and facial expression for student engagement prediction in collaborative learning

Joint gaze estimation and facial expression for student enga...

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IEEE International Conference on Teaching, Assessment and Learning for engineering (TALE)

作者： Yi Chen Jin Zhou Jing Gao Ge Gao Sai Wang Wei Zhang School of Computer Science Central China Normal University Wuhan P.R.China School of Computer Science Wuhan University Wuhan P.R.China National Engineering Laboratory of Educational Big Data Application Technology Central China Normal University Wuhan P.R.China

ISBN: (纸本)9781665436885

Estimating and improving students' engagement in a collaborative learning environment is an important component in the field of learning research. Collaborative learning is a strategy of learning activities employed by small groups in which cooperative learning behaviors are closely related to other members or objects in the group. Researchers showed that students who are actively involved in class learn more. Therefore, gaze behavior and facial expression are important nonverbal indicators in cooperative learning environments. In this paper, we proposed a multimodal deep neural network (MDNN) to solve the engagement prediction problem in collaborative learning. We combined facial expression and gaze direction as individual streams of MDNN to predict engagement levels in collaborative learning environments. Our multi-modal solution was evaluated in a real collaborative environment with a significant accuracy of 74%. The results show that the model can accurately predict students' performance in the collaborative learning environment.

关键词： Deep learning Neural networks Education Collaboration Estimation Predictive models Collaborative work

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Unsupervised machine learning algorithm for face morphing

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AIP Conference Proceedings 2023年第1期2782卷

作者： Hitesh Kumar Sharma Mahadev Raman Chadha Gaurav Nagarkoti Prashant Ahlawat School of Computer Science University of Petroleum & Energy Studies (UPES) Energy Acres Bidholi Dehradun-248007 Uttarakhand India Department of Computer Science Quantum University Roorkee India Department of Computer Science and Engineering University Institute of Engineering Chandigarh University Gharaun Mohali India Bachelor of Computer Application GL Bajaj Institute of Management Greater Noida India

Face morphing is the process of morphing or merging the face of the subject image with the face of another test image. Techniques of face morphing have been utilized in quite diverse fields, mostly in the entertainment industry to replace the face of an actor with a resembling actor to complete movies in absence of the actor. Typical face morphing implemented in commercial cinemas are a result of image morphing that is implemented on still images. Face morphing is a complex procedure that encompasses extracting the face marking, followed by the formation of a mesh of triangles that would be further used for morphing the face of a test image to the subject Image. Though there are many different programs with the ability to morph faces the proceeds to select a suitable candidate still largely is done via manual procedures or even if automated, the process is rarely integrated with the face morphing program. For the morphing results to be seamless it should be ensured that there is very less difference between the original image and the morphed image that is, the resemblance between the two images should be very high, therefore there is a brief description in the report about “what is the process of implementing morphing and how such resemblance will be achieved. Our research work aims to design and implement a similar face morphing program that has a model integrated into it for selecting the best suitable candidate for face morphing. Using python programming and concepts of unsupervised learning we aim to create will have the ability to calculate the resemblance between the faces of the subject and various candidates and decide for itself which among different candidates is best suitable for face morphing. Since it finds the most resembling candidate for face morphing, hence the name Doppelgänger.

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Impact of Atmospheric Light on Haze Removal using Single Image

Impact of Atmospheric Light on Haze Removal using Single Ima...

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International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)

作者： Ashok kumar Shrivastava Suryakant Yadav Sanjay Jain Amity University Gwalior Madhya Pradesh India School of Engineering and Technology Noida International University Greater Noida India Computer Science and Application ITM University Gwalior Madhya Pradesh India

ISBN: (数字)9781728185293

ISBN: (纸本)9781728185309

Fog or haze are the common phenomenon which creates unpleasant weather condition. This unpleasant weather condition is not favorable for outdoor photography or imaging, because image characteristics are drastically deteriorated by fog or haze. To enhance the image features, fog or haze removal is required. Fog removal is also required in different applications like recognition, tracking and navigation and consumer electronics. During the fog removal process, estimation of atmospheric light plays main role. In this paper we performed an analysis on an estimation of atmospheric light by taking 0.01%, 0.02% and 0.05% most haze-opaque pixels from foggy image and its effect on results. To perform analysis, we used dark channel prior concept with guided filter for fog or haze removal.

关键词： Photography Histograms Navigation Roads Estimation Meteorology Matlab

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Bearing Surface Defect Detection Method Based on Lightweight Reconfiguration Network

SSRN

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SSRN 2021年

作者： Xu, Jiale Danping, Huang Liao, Shipeng Wen, Xiaomei Yuan, Yang Yu, Shaodong School of Mechanical Engineering Sichuan University of Science & Engneering Sichuan Yibin6644000 China Chendu Institute of Computer Application Chinese Academy of Sciences Chengdu610000 China School of Information and Engineering Sichuan Tourism University Chengdu610000 China

Aiming at the problems of low efficiency and poor accuracy of the detection of bearing surface defects, a new detection method is proposed in this paper. The method first used a reconstruction model trained from defect-free bearing image samples to recover the defects in the visual information of the bearing’s surface, and then performed the residual difference between the recovered image and the original image to realize the defect localization and detection. To improve the model detection accuracy and speed, meet the real-time requirements, a bearing surface defect detection model based on lightweight reconstruction network is mainly constructed and developed. The network is a new generative adversarial network model (GAN-IC) with features including the use of parallel convolution instead of the traditional convolutional structure, the introduction of an improved channel attention mechanism between convolutional layers, and the addition of the SSIM Loss function to the L 2 Loss function. Through testing and comparing with advanced reconstruction networks such as DCGAN, U-net, VAE, GAN-IC has been proved to be more suitable for the detection of bearing surface defects in complex backgrounds. © 2021, The Authors. All rights reserved.

关键词： Image reconstruction

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Model-enhanced Vector Index

arXiv

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arXiv 2023年

作者： Zhang, Hailin Wang, Yujing Chen, Qi Chang, Ruiheng Zhang, Ting Miao, Ziming Hou, Yingyan Ding, Yang Miao, Xupeng Wang, Haonan Pang, Bochen Zhan, Yuefeng Sun, Hao Deng, Weiwei Zhang, Qi Yang, Fan Xie, Xing Yang, Mao Cui, Bin School of Computer Science Key Lab of High Confidence Software Technologies Peking University China Microsoft United States Aerospace Information Research Institute Key Laboratory of Target Cognition and Application Technology Chinese Academy of Sciences China Institute of Information Engineering Chinese Academy of Sciences China Carnegie Mellon University United States National University of Singapore Singapore National Engineering Laboratory for Big Data Analysis and Applications Peking University China

Embedding-based retrieval methods construct vector indices to search for document representations that are most similar to the query representations. They are widely used in document retrieval due to low latency and decent recall performance. Recent research indicates that deep retrieval solutions offer better model quality, but are hindered by unacceptable serving latency and the inability to support document updates. In this paper, we aim to enhance the vector index with endto- end deep generative models, leveraging the differentiable advantages of deep retrieval models while maintaining desirable serving efficiency. We propose Modelenhanced Vector Index (MEVI), a differentiable model-enhanced index empowered by a twin-tower representation model. MEVI leverages a Residual Quantization (RQ) codebook to bridge the sequence-to-sequence deep retrieval and embeddingbased models. To substantially reduce the inference time, instead of decoding the unique document ids in long sequential steps, we first generate some semantic virtual cluster ids of candidate documents in a small number of steps, and then leverage the well-adapted embedding vectors to further perform a fine-grained search for the relevant documents in the candidate virtual clusters. We empirically show that our model achieves better performance on the commonly used academic benchmarks MSMARCO Passage and Natural Questions, with comparable serving latency to dense retrieval solutions. Copyright © 2023, The Authors. All rights reserved.

关键词： Embeddings

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A Multi-Step Speed Prediction Method for Traffic Networks Based on MAF-GCN-Seq2Seq Model

A Multi-Step Speed Prediction Method for Traffic Networks Ba...

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Electronic Information Technology and Smart Agriculture (ICEITSA), International Conference on

作者： Yi Long Hongbo Xiao Jianhua Xiao Xiaoning Peng School of Computer Science and Engineering Huaihua University Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua Hunan P. R. China

ISBN: (纸本)9781665464024

Accurately predicting the future trend of traffic flow is of great significance to the planning of urban traffic. The great challenge of predicting traffic flow stems from the complexity and uncertainty of traffic flow. To address the complexity of traffic flow, this paper proposes a MAF-GCN $\text{Seq}2$ Seq model. The model uses a moving average filter to filter the time series data, extracts the spatial features between each node through a graph convolutional neural network, and forms a time series matrix for input into the encoder (GRU), and introduces a self-attention mechanism to mine the spatial features. time-domain features for multi-step traffic speed prediction. In order to verify the advanced nature of the model, this paper uses the traffic flow data set in Shenzhen to predict the traffic flow. Through the comparison experiments, it can be seen that the prediction accuracy of the MAF-GCN $\text{Seq}2$ Seq model reaches 90%, which has obvious advantages compared with other models.

关键词： Training Uncertainty Time series analysis Predictive models Feature extraction Data models Spatial databases

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Extremely Large-Scale Dynamic Metasurface Antennas (XL-DMAs): Near-Field Modeling and Channel Estimation

arXiv

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arXiv 2024年

作者： Yang, Songjie Lyu, Wanting Ning, Boyu Xiu, Yue Xiong, Youzhi Chen, Hua Assi, Chadi Yuen, Chau The National Key Laboratory of Wireless Communications University of Electronic Science and Technology of China Chengdu611731 China College of Air Traffic Management Civil Aviation Flight University of China Sichuan618311 China College of Physics and Electronic Engineering Sichuan Normal University Chengdu610101 China The Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo315211 China The Zhejiang Key Laboratory of Mobile Network Application Technology Ningbo315211 China Concordia University MontrealQCH3G 1M8 Canada The School of Electrical and Electronics Engineering Nanyang Technological University Singapore

Dynamic metasurface antennas (DMAs) represent a novel transceiver array architecture for extremely large-scale (XL) communications, offering the advantages of reduced power consumption and lower hardware costs compared to conventional arrays. This paper focuses on near-field channel estimation for XL-DMAs. We begin by analyzing the near-field characteristics of uniform planar arrays (UPAs) and introducing the Oblong Approx. model. This model decouples elevation-azimuth (EL-AZ) parameters for XL-DMAs, providing an effective means to characterize the near-field effect. It offers simpler mathematical expressions than the second-order Taylor expansion model, all while maintaining negligible model errors for oblong-shaped arrays. Building on the Oblong Approx. model, we propose an EL-AZ-decoupled estimation framework that involves near- and far-field parameter estimation for AZ/EL and EL/AZ directions, respectively. The former is formulated as a distributed compressive sensing problem, addressed using the proposed off-grid distributed orthogonal least squares algorithm, while the latter involves a straightforward parallelizable search. Crucially, we illustrate the viability of decoupled EL-AZ estimation for near-field UPAs, exhibiting commendable performance and linear complexity correlated with the number of metasurface elements. Moreover, we design an measurement matrix optimization method with the Lorentzian constraint on DMAs and highlight the estimation performance degradation resulting from this constraint. Copyright © 2024, The Authors. All rights reserved.

关键词： Channel estimation

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Large-Scale Target Detection and Classification Based on Improved Candidate Regions 11th

Large-Scale Target Detection and Classification Based on Imp...

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11th International Conference on Image and Graphics, ICIG 2021

作者： Xi, Runping Han, Qianqian Jia, Gaoyun Kou, Xuefeng School of Computer Science and Techology Northwest Polytechnical University Xi’an710072 China National Engineering Laboratory of Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi’an710129 China Shaanxi Provincial Key Laboratory of Speech and Image Information Processing Xi’an710129 China Shaanxi Baocheng Aviation instrument Co. Ltd. Baoji China

ISBN: (纸本)9783030873578

Target detection of aerial images has become a frontier subject of concern in the image processing field. Using existing method to detect and classify large-scale building objects in aerial images, the accuracy is still a little low. This is mainly because the current method does not make full use of the prior information of the target to be detected, so there are too much redundant information in the candidate box. In this paper, our own dataset were built and then utilize the Hough transform to filter out the images that may exist in the sequence image. For images with dense lines or circles, it is possible that there is an artificial building target which will be detected, otherwise it is excluded directly. Besides, this paper exploits significance analysis from the filtered image and then extract the area of interest where the potential target is located. The results of the above-mentioned processing lay a good foundation for the subsequent detection and classification which can help improve the accuracy. © 2021, Springer Nature Switzerland AG.

关键词： Antennas

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