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IEEE International Conference on e-Business engineering (ICEBE)

作者： Jiaju Wu Pengshuai Yin Xin Shu Huichou Huang Fei Liu Qingyao Wu School of Software Engineering South China University of Technology China Key Laboratory of Big Data and Intelligent Robot Ministry of Education City University of Hong Kong Hong Kong China Pazhou Lab Guangzhou China

ISBN: (纸本)9781665444194

Lane departure warning is an essential function of driver assistance systems. In this paper, we propose a vision-based lane departure warning framework, which can determine whether the vehicle deviates from the lane only with images as input. Our framework combines the deep learning method and traditional line detection algorithm. Specifically, the framework consists of a Lane Boundaries Localization module, a Lane Boundaries Generator module, and a Departure Warning module. The Lane Boundaries Localization module is a deep neural network for finding the area containing lane Boundaries. Then the Lane Boundaries Generator module performs backbone detection and line detection to generate lane boundaries on the located area. After identifying the lane boundaries, the Departure Warning module can quickly determine whether the vehicle deviates from the lane. Additionally, we implemented and deployed our framework to an Android platform and tested its running speed.

关键词： Location awareness Deep learning Conferences Neural networks Transforms Generators Convolutional neural networks

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Comparative analysis of time irreversibility and amplitude irreversibility based on joint permutation

arXiv

引用

arXiv 2022年

作者： Yao, Wenpo Yao, Wenli Xu, Rongshuang Wang, Jun School of Geographic and Biologic Information Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing210023 China Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Ministry of Education China Department of Hydraulic Engineering School of Civil Engineering Tsinghua University Beijing100084 China School of Applied Meteorology Nanjing University of Information Science & Technology Nanjing210044 China

Although time irreversibility (TIR) and amplitude irreversibility (AIR) are relevant concepts for nonequilibrium analysis, their association has received little attention. This paper conducts a systematic comparative analysis of the relationship between TIR and AIR based on statistical descriptions and numerical simulations. To simplify the quantification of TIR and AIR, the amplitude permutation and global information of the associated vector are combined to produce a joint probability estimation. Chaotic logistic, Henon, and Lorenz series are generated to evaluate TIR and AIR according to surrogate theory, and the distributions of joint permutations for these model series are measured to compare the degree of TIR and AIR. The joint permutation TIR and AIR are then used to investigate nonequilibrium pathological features in epileptic electroencephalography data. Test results suggest that epileptic brain electrical activities, particular those during the onset of seizure, manifest higher nonequilibrium characteristics. According to the statistical definitions and targeted pairs of joint permutations in the chaotic model data, TIR and AIR are fundamentally different nonequilibrium descriptors from time- and amplitude-reversibility, respectively, and thus require different forms of numerical analysis. At the same time, TIR and AIR both provide measures for fluctuation theorems associated with nonequilibrium processes, and have similar probabilistic differences in the pairs of joint permutations and consistent outcomes when used to analyze both the model series and real-world signals. Overall, comparative analysis of TIR and AIR contributes to our understanding of nonequilibrium features and broadens the scope of quantitative nonequilibrium measures. Additionally, the construction of joint permutations contributes to the development of symbolic time series analysis. Copyright © 2022, The Authors. All rights reserved.

关键词： Electroencephalography

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An Implementation of Face Recognition System with Face Presentation Attack Detection on Raspberry Pi

An Implementation of Face Recognition System with Face Prese...

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IEEE International Conference on e-Business engineering (ICEBE)

作者： Wenjun Wang Xiangkang Huang Xin Shu Shuping Li Huichou Huang Qingyao Wu School of Software Engineering South China University of Technology Guangzhou China School of Data Science and Information Engineering Guizhou Minzu University Guiyang China Key Laboratory of Big Data and Intelligent Robot Ministry of Education China City University of Hong Kong Hong Kong China Pazhou Lab Guangzhou China

ISBN: (纸本)9781665444194

With the popularity of unlocking and payment of using the face-swiping function, the era of face recognition has arrived. However, the current face recognition technology can identify the identity of the face image but cannot accurately distinguish the authenticity of the input face on embedded devices. Recently, face recognition with face presentation attack detection has become a research hotspot. In this paper, we develop an intelligent face recognition system on Raspberry Pi to handle this issue. Specifically, we first add a face presentation attack detection process to form a three-stage face recognition method to realize face recognition with face anti-spoofing. Second, we select a face detection model, a face presentation attack detection model and a face recognition model suitable for Raspberry Pi, and transplant these models previously trained in the Python environment to the C++ environment. Then we integrate and deploy these three models on Raspberry Pi and use the NCNN inference framework for efficient reasoning within limited memory and computing resources. Finally, we implement the face recognition system and conduct real-world tests to verify the performance. The experimental results demonstrate that our system can be applied in practice, and can achieve good effects.

关键词： Face recognition Computational modeling Conferences Memory management C++ languages Cognition Organ transplantation

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Evaluating the Long-Term Effects of Near-Natural Restoration on Post-Fire Forest Dynamics in a Wildland-Urban Interface Landscape

SSRN

引用

SSRN 2023年

作者： Xiao, Jiangtao Lin, Yang Zhou, Wangming Qiao, Zeyu Chang, Yu Yu, Xinran Li, Yuanyuan Ren, Ping Fang, Lei Key Lab of Land Resources Evaluation and Monitoring in Southwest China Ministry of Education Sichuan Normal University Chengdu610066 China School of Geography and Resources Sciences Sichuan Normal University Chengdu610066 China CAS Key Laboratory of Forest and Ecology Management Institute of Applied Ecology Chinese Academy of Sciences Shenyang110016 China School of Land and Resources Surveying and Mapping Engineering Shandong Agriculture and Engineering University Jinan250100 China Big Data Research Center for Ecology and Environment Shandong University Qingdao266237 China Environment Research Institute Shandong University Qingdao266237 China

Wildland fire is an important disturbance agent that can drastically alter forest structures and erode forest ecosystem services. Forests in the wildland-urban interface (WUI) are of high value but vulnerable to fires due to abundant fire ignitions and flammable forest fuels. It is critically important to quickly restore the post-fire WUI forest landscape to maintain ecosystem service provision following near-natural restoration strategies. To evaluate the long-term effects of near-natural restoration on the WUI forest landscape, which are largely unclear, we used a wildfire near Shenyang City in northeast China as an example and investigated the post-fire forest dynamics under two different scenarios (i.e., natural succession and near-natural restoration) based on the forest landscape model. The results demonstrated that artificial plantations can significantly accelerate the restoration process in terms of forest biomass, species biodiversity, and age structure. Under the near-natural restoration scenario, the biomass of the burned area can be quickly restored, but biomass under both scenarios tended to equal out at the end of the simulation. At the species level, the biomass of pioneer tree species such as Chinese pine and Black locust decreased under the near-natural restoration scenario, while the biomass of other species started to increase, not only in terms of biomass, but the results also showed that post-fire planting accelerated the restoration of forest biodiversity and increased biodiversity. By 2070, the Shannon‒Wiener index was predicted to be 1.49 under natural succession and remained at 2.02 under near-natural restoration. In terms of age structure, near-natural restoration shortens the evolution time of fire trails to mature forests. In summary, near-natural restoration accelerates forest recovery in post-fire WUI areas. Our results highlighted the impact of near-natural restoration on forest restoration to inform post-fire forest management practi

关键词： Biomass

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Robust Low-rank Deep Feature Recovery in CNNs: Toward Low Information Loss and Fast Convergence

Robust Low-rank Deep Feature Recovery in CNNs: Toward Low In...

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IEEE International Conference on data Mining (ICDM)

作者： Jiahuan Ren Zhao Zhang Jicong Fan Haijun Zhang Mingliang Xu Meng Wang School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) & Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China School of Data Science The Chinese University of Hong Kong (Shenzhen) & Shenzhen Research Institute of Big Data Shenzhen China Harbin Institute of Technology (Shenzhen) Shenzhen China School of Information Engineering Zhengzhou University Zhengzhou China

ISBN: (纸本)9781665423991

Convolutional Neural Networks (CNNs)-guided deep models have obtained impressive performance for image representation, however the representation ability may still be restricted and usually needs more epochs to make the model converge in training, due to the useful information loss during the convolution and pooling operations. We therefore propose a general feature recovery layer, termed Low-rank Deep Feature Recovery (LDFR), to enhance the representation ability of the convolutional features by seamlessly integrating low-rank recovery into CNNs, which can be easily extended to all existing CNNs-based models. To be specific, to recover the lost information during the convolution operation, LDFR aims at learning the low-rank projections to embed the feature maps onto a low-rank subspace based on some selected informative convolutional feature maps. Such low-rank recovery operation can ensure all convolutional feature maps to be reconstructed easily to recover the underlying subspace with more useful and detailed information discovered, e.g., the strokes of characters or the texture information of clothes can be enhanced after LDFR. In addition, to make the learnt low-rank subspaces more powerful for feature recovery, we design a fusion strategy to obtain a generalized subspace, which averages over all learnt sub-spaces in each LDFR layer, so that the convolutional feature maps in test phase can be recovered effectively via low-rank embedding. Extensive results on several image datasets show that existing CNNs-based models equipped with our LDFR layer can obtain better performance.

关键词： Training Image recognition Convolution Conferences Image representation data mining Convolutional neural networks

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Subject Event Extraction from Chinese Court Verdict Case via Frame-filling

Subject Event Extraction from Chinese Court Verdict Case via...

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IEEE International Conference on Big knowledge (ICBK)

作者： Gongqing Wu Shengjie Hu Yinghuan Wang Zan Zhang Xianyu Bao Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education Hefei University of Technology Hefei China Shenzhen Academy of Inspection and Quarantine Shenzhen China

ISBN: (数字)9781728181561

ISBN: (纸本)9781728181578

At present, the query and acquisition of the fragmented knowledge in Chinese court verdicts mainly adopt the class case retrieval method based on the search engine and the rough extraction method for a part of the data in court verdicts. These traditional methods cannot structurally extract fragmented knowledge in Chinese court verdicts and meet the needs of people for the follow-up analysis of court verdicts. Thus, in this paper, we present a structured subject event extraction method (SEE) for Chinese court verdict cases combining with techniques of event extraction (EE) and attribute-value pair extraction (AVPE). Specifically, we provide a subject event representation frame for organizing fragmented knowledge in Chinese court verdict cases. Then, we extract subject events from the unstructured cases based on the trained sequence labeling models and constructed heuristic rules, and fill them into the subject event representation frame in the form of attribute-value pairs (AVPs). The experimental results show that SEE can efficiently and automatically extract subject events from Chinese court verdict cases and visually display them via frame-filling, which promotes the efficiency of people in searching for legal materials and facilitates further research and analysis.

关键词： Feature extraction data mining labeling Information retrieval Semantics Machine learning knowledge engineering

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Task-wise Sampling Convolutions for Arbitrary Oriented Object Detection in Aerial Images

arXiv

引用

arXiv 2022年

作者： Huang, Zhanchao Li, Wei Xia, Xiang-Gen Wang, Hao Tao, Ran The Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education The Academy of Digital China China Fuzhou University Fuzhou350108 China The National & Local Joint Engineering Research Center of Satellite Geospatial Information Technology Fuzhou University Fuzhou350108 China The School of Information and Electronics Beijing Institute of Technology Beijing Key Lab of Fractional Signals and Systems Beijing100081 China The Department of Electrical and Computer Engineering University of Delaware NewarkDE19716 United States

Arbitrary-oriented object detection (AOOD) has been widely applied to locate and classify objects with diverse orientations in remote sensing images. However, the inconsistent features for the localization and classification tasks in AOOD models may lead to ambiguity and low-quality object predictions, which constrains the detection performance. In this paper, an AOOD method called task-wise sampling convolutions (TS-Conv) is proposed. TS-Conv adaptively samples task-wise features from respective sensitive regions and maps these features together in alignment to guide a dynamic label assignment for better predictions. Specifically, sampling positions of the localization convolution in TS-Conv is supervised by the oriented bounding box (OBB) prediction associated with spatial coordinates. While sampling positions and convolutional kernel of the classification convolution are designed to be adaptively adjusted according to different orientations for improving the orientation robustness of features. Furthermore, a dynamic task-aware label assignment (DTLA) strategy is developed to select optimal candidate positions and assign labels dynamicly according to ranked task-aware scores obtained from TS-Conv. Extensive experiments on several public datasets covering multiple scenes, multimodal images, and multiple categories of objects demonstrate the effectiveness, scalability and superior performance of the proposed TS-Conv. © 2022, CC BY-NC-ND.

关键词： Convolution

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Exploring A CAM - Based Approach for Weakly Supervised Fire Detection Task

Exploring A CAM - Based Approach for Weakly Supervised Fire ...

引用

IEEE International Conference on e-Business engineering (ICEBE)

作者： Lvlong Lai Jian Chen Huichou Huang Qingyao Wu School of Software Engineering South China University of Technology Guangzhou China Key Laboratory of Big Data and Intelligent Robot Ministry of Education City University of Hong Kong Hong Kong China Pazhou Lab Guangzhou China

ISBN: (纸本)9781665444194

Most existing works in fire detection literature use available detectors like Faster RCNN, SSD, YOLO, etc. to localize the fire in images. These approaches work well but require object-level annotation for training, which is created manually and is very expensive. In this paper, we explore the weakly supervised fire detection task (WSFD) in which only the image-level annotation is given. We propose an approach based on class activation map (CAM). The CAM-based approach firstly trains a deep neural network as the classifier for identifying fire and non-fire images. For a fire image in the inference stage, it uses the classifier to create a CAM and then further generates the bounding boxes according to the CAM. To evaluate the effectiveness of our approach, we collect and construct a benchmark dataset named WS-FireNet and conduct comprehensive experiments on it. The experiment results show that in a way the performance of our approach is satisfactory.

关键词： Training Deep learning Annotations IEEE Sections Conferences Neural networks Detectors

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More Grounded Image Captioning by Distilling Image-Text Matching Model

More Grounded Image Captioning by Distilling Image-Text Matc...

引用

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yuanen Zhou Meng Wang Daqing Liu Zhenzhen Hu Hanwang Zhang Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) School of Computer Science and Information Engineering Hefei University of Technology University of Science and Technology of China Nanyang Technological University

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Visual attention not only improves the performance of image captioners, but also serves as a visual interpretation to qualitatively measure the caption rationality and model transparency. Specifically, we expect that a captioner can fix its attentive gaze on the correct objects while generating the corresponding words. This ability is also known as grounded image captioning. However, the grounding accuracy of existing captioners is far from *** improve the grounding accuracy while retaining the captioning quality, it is expensive to collect the word-region alignment as strong *** this end, we propose a Part-of-Speech (POS) enhanced image-text matching model (SCAN[24]): POS-SCAN, as the effective knowledge distillation for more grounded image captioning. The benefits are two-fold: 1) given a sentence and an image, POS-SCAN can ground the objects more accurately than SCAN; 2) POS-SCAN serves as a word-region alignment regularization for the captioner's visual attention module. By showing benchmark experimental results, we demonstrate that conventional image captioners equipped with POS-SCAN can significantly improve the grounding accuracy without strong supervision. Last but not the least, we explore the indispensable Self-Critical Sequence Training (SCST[46]) in the context of grounded image captioning and show that the image-text matching score can serve as a reward for more grounded captioning 1 .

关键词： Visualization Grounding Task analysis Training Measurement Computational modeling Image edge detection

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Integrated Sensing and Communication Signals Toward 5G-A and 6G: A Survey

arXiv

引用

arXiv 2023年

作者： Wei, Zhiqing Qu, Hanyang Wang, Yuan Yuan, Xin Wu, Huici Du, Ying Han, Kaifeng Zhang, Ning Feng, Zhiyong The Key Laboratory of Universal Wireless Communications Ministry of Education School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China The National Engineering Lab for Mobile Network Technologies Beijing University of Posts and Telecommunications Beijing100876 China Peng Cheng Laboratory Shenzhen518066 China Data61 CSIRO Sydney Australia The Department of Electrical and Computer Engineering University of Windsor WindsorONN9B 3P4 Canada The University of Science and Technology of China China Academy of Information and Communications Technology Beijing100191 China China Academy of Information and Communications Technology Beijing100191 China

Integrated sensing and communication (ISAC) has the advantages of efficient spectrum utilization and low hardware cost. It is promising to be implemented in the fifth-generation-advanced (5G-A) and sixth-generation (6G) mobile communication systems, having the potential to be applied in intelligent applications requiring both communication and high-accurate sensing capabilities. As the fundamental technology of ISAC, ISAC signal directly impacts the performance of sensing and communication. This article systematically reviews the literature on ISAC signals from the perspective of mobile communication systems, including ISAC signal design, ISAC signal processing and ISAC signal optimization. We first review the ISAC signal design based on 5G, 5G-A and 6G mobile communication systems. Then, radar signal processing methods are reviewed for ISAC signals, mainly including the channel information matrix method, spectrum lines estimator method and super resolution method. In terms of signal optimization, we summarize peak-to-average power ratio (PAPR) optimization, interference management, and adaptive signal optimization for ISAC signals. This article may provide the guidelines for the research of ISAC signals in 5G-A and 6G mobile communication *** Codes 94-02 Copyright © 2023, The Authors. All rights reserved.

关键词： Orthogonal frequency division multiplexing

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