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arXiv 2022年

作者： Luo, Hongming Zhou, Fei Lam, Kin-Man Qiu, Guoping College of Electronics and Information Engineering Shenzhen University China Department of Electronic and Information Engineering The Hong Kong Polytechnic University China Peng Cheng Laboratory Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen China Shenzhen Key Laboratory of Digital Creative Technology China Shenzhen Institute for Artificial Intelligence and Robotics for Society China Guangdong-Hong Kong Joint Laboratory for Big Data Imaging and Communication China

User videos shared on social media platforms usually suffer from degradations caused by unknown proprietary processing procedures, which means that their visual quality is poorer than that of the originals. This paper presents a new general video restoration framework for the restoration of user videos shared on social media platforms. In contrast to most deep learning-based video restoration methods that perform end-to-end mapping, where feature extraction is mostly treated as a black box, in the sense that what role a feature plays is often unknown, our new method, termed Video restOration through adapTive dEgradation Sensing (VOTES), introduces the concept of a degradation feature map (DFM) to explicitly guide the video restoration process. Specifically, for each video frame, we first adaptively estimate its DFM to extract features representing the difficulty of restoring its different regions. We then feed the DFM to a convolutional neural network (CNN) to compute hierarchical degradation features to modulate an end-to-end video restoration backbone network, such that more attention is paid explicitly to potentially more difficult to restore areas, which in turn leads to enhanced restoration performance. We will explain the design rationale of the VOTES framework and present extensive experimental results to show that the new VOTES method outperforms various state-of-the-art techniques both quantitatively and qualitatively. In addition, we contribute a large scale real-world database of user videos shared on different social media platforms. Codes and datasets are available at https://***/luohongming/***. Copyright © 2022, The Authors. All rights reserved.

关键词： Restoration

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Study of Region Convolutional Neural Network Deep Learning for Fire Accident Detection 6th

Study of Region Convolutional Neural Network Deep Learning f...

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6th International Conference on Advanced Intelligent Systems and Informatics, AISI 2020

作者： Jean dAmour, Ntawiheba Chang, Kuo-Chi Li, Pei-Qiang Zhou, Yu-Wen Wang, Hsiao-Chuan Lin, Yuh-Chung Chu, Kai-Chun Hsu, Tsui-Lien School of Information Science and Engineering Fuzhou University Fujian University of Technology No. 33 Xuefu South Road New District FuzhouFujian350118 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China Institute of Environmental Engineering National Taiwan University Taipei Taiwan Department of Business Management Fujian University of Technology Fuzhou China Institute of Construction Engineering and Management National Central University Taoyuan Taiwan College of Mechanical & Electrical Engineering National Taipei University of Technology Taipei Taiwan

ISBN: (纸本)9783030586683

Fires accident is one of the disasters which take human life, infrastructure destruction due to its violence or to the delay for the rescue. Object detection is one of the popular topics in recent years, which can play the robust impact for detecting fire and more efficient to provide information to this disaster. However, this study presents the fire detection processed using region convolution neural network. We will train images of different objects in fire using ground truth labeling. After labeling images and determining the region of interest (ROI), the features are extracted from training data, and the detector will be trained and will work to each and image of fire. To validate the effectiveness of this system the algorithm demonstrates images taken from our dataset. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Convolution

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Consistent and Complementary Graph Regularized Multi-view Subspace Clustering

arXiv

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arXiv 2020年

作者： Zheng, Qinghai Zhu, Jihua Li, Zhongyu Pang, Shanmin Wang, Jun Chen, Lei School of Software Engineering Xi’an Jiaotong University Xi’an710049 China Shanghai Institute for Advanced Communication and Data Science School of Communication and Information Engineering Shanghai University Shanghai200444 China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing210023 China

This study investigates the problem of multi-view clustering, where multiple views contain consistent information and each view also includes complementary information. Exploration of all information is crucial for good multi-view clustering. However, most traditional methods blindly or crudely combine multiple views for clustering and are unable to fully exploit the valuable information. Therefore, we propose a method that involves consistent and complementary graph-regularized multi-view subspace clustering (GRMSC), which simultaneously integrates a consistent graph regularizer with a complementary graph regularizer into the objective function. In particular, the consistent graph regularizer learns the intrinsic affinity relationship of data points shared by all views. The complementary graph regularizer investigates the specific information of multiple views. It is noteworthy that the consistent and complementary regularizers are formulated by two different graphs constructed from the first-order proximity and second-order proximity of multiple views, respectively. The objective function is optimized by the augmented Lagrangian multiplier method in order to achieve multi-view clustering. Extensive experiments on six benchmark datasets serve to validate the effectiveness of the proposed method over other state-of-the-art multi-view clustering methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Clustering algorithms

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Learning from Semi-Factuals: A Debiased and Semantic-Aware Framework for Generalized Relation Discovery

arXiv

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arXiv 2024年

作者： Wang, Jiaxin Zhang, Lingling Liu, Jun Guo, Tianlin Wu, Wenjun The School of Computer Science and Technology Xi’an Jiaotong University Shaanxi Xi’an710049 China The Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China National Engineering Lab for Big Data Analytics Xi’an Jiaotong University Shaanxi Xi’an710049 China

We introduce a novel task, called Generalized Relation Discovery (GRD), for open-world relation extraction. GRD aims to identify unlabeled instances in existing pre-defined relations or discover novel relations by assigning instances to clusters as well as providing specific meanings for these clusters. The key challenges of GRD are how to mitigate the serious model biases caused by labeled pre-defined relations to learn effective relational representations and how to determine the specific semantics of novel relations during classifying or clustering unlabeled instances. We then propose a novel framework, SFGRD, for this task to solve the above issues by learning from semi-factuals in two stages. The first stage is semi-factual generation implemented by a tri-view debiased relation representation module, in which we take each original sentence as the main view and design two debiased views to generate semi-factual examples for this sentence. The second stage is semi-factual thinking executed by a dual-space tri-view collaborative relation learning module, where we design a cluster-semantic space and a class-index space to learn relational semantics and relation label indices, respectively. In addition, we devise alignment and selection strategies to integrate two spaces and establish a self-supervised learning loop for unlabeled data by doing semi-factual thinking across three views. Extensive experimental results show that SFGRD surpasses state-of-the-art models in terms of accuracy by 2.36% ∼5.78% and cosine similarity by 32.19%∼ 84.45% for relation label index and relation semantic quality, respectively. To the best of our knowledge, we are the first to exploit the efficacy of semi-factuals in relation extraction. Copyright © 2024, The Authors. All rights reserved.

关键词： Extraction

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Design of a Wideband Phased Array Antenna with Ultrawide Scanning Angle to 70 Degree

Design of a Wideband Phased Array Antenna with Ultrawide Sca...

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International Symposium on Antennas and Propagation (ISAP)

作者： Bei Zhang Xiaofei Xu School of Communication and Engineering Shanghai University Shanghai China Key Laboratory of Specialty Fiber Optics and Optical Access Networks and Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai China

ISBN: (数字)9784885523267

ISBN: (纸本)9781728159096

A wideband phased array antenna (PAA) is designed and numerically demonstrated with ultrawide scanning angle up to 70 degree. A tapered slot antenna (TSA) is chosen as the PAA element which is optimized in a full wave solver. Results shows its capability to provide VSWR≤3 from normal to 70 degree from 2.17GHz to 6.71GHz. To study the broadband radiation patterns at the normal direction, a PAA composed of 9×9 TSA elements is built in the full wave solver. The scanning performances at 6GHz are also studied, with scanning angles from 0 to 70 degree by changing the feeding phases.

关键词： Phased arrays Slot antennas Directive antennas Ultra wideband antennas Broadband communication High frequency Wideband

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CuckooFlow: Achieving Fast Packet Classification for Virtual OpenFlow Switching by Exploiting Network Traffic Locality

CuckooFlow: Achieving Fast Packet Classification for Virtual...

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IEEE International Conference on Big data and Cloud Computing (BdCloud)

作者： Bing Xiong Zhixiong Hu Yao Luo Jin Wang School of Computer & Communication Engineering Changsha University of Science & Technology Changsha China Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science & Technology Changsha China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

The growing trend of network virtualization leads to a widespread deployment of virtual switches in virtualized environments. However, virtual switching is confronted with great performance challenges regarding packet classification especially in OpenFlow-based software defined networks. This paper takes an insight into network traffic locality from the perspective of exact flows and wildcard flows, and propose a fast packet classification scheme CuckooFlow. In particular, we devise an active exact flow cache with cuckoo hashing to predict the respective flow entry for each arrived packet. Subsequently, we formulate its cache hit rate based on packet-in-batch arrivals, and average search length of the packet classification scheme. Finally, our proposed CuckooFlow scheme is evaluated with real network traffic traces by experiments. The experimental results indicate that our proposed scheme outperforms traditional ones Open vSwitch and CuckooDistributor in terms of cache hit rates and average search length, and contributes to promote virtual OpenFlow switching performance.

关键词： Telecommunication traffic Switches Acceleration Classification algorithms Cloud computing Virtualization Decision trees

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Relep: Region and Edge Synergetic Level Set Model Based on Edge-Enhanced Entropy and Variable Exponential P-Laplace Equation

SSRN

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SSRN 2023年

作者： Wu, Jiangfeng Zhou, Zhiheng Zhou, Wenlue Tao, Xiyuan Deng, Ming School of Electronic and Information Engineering South China University of Technology Guangdong Guangzhou China Key Laboratory of Big Data and Intelligent Robot Ministry of Education South China University of Technology Guangzhou China

Image segmentation under conditions such as noise and intensity inhomogeneity poses a significant challenge for models. Hybrid level set approaches integrating area and edge information show promise in addressing these challenges. In this study, we propose a novel hybrid level set model, called region and edge synergetic level set model based on edge-enhanced entropy and variable exponential p-Laplace equation (RELEP), which is based on an edge-enhanced entropy and a variable exponential p-Laplace equation. Firstly, a new probability distribution is defined to enhance the edge extraction ability of image entropy in the presence of image intensity inhomogeneity and noise, termed as edge-enhanced entropy. The feature map obtained after edge-enhanced entropy processing is then used to co-fit the energy function with the original map to form the region term. Furthermore, the incorporation of edge-enhanced entropy into the p-Laplace equation leads to the formation of a variable exponential p-Laplace equation edge term. This integration facilitates the acquisition of precise and smooth boundaries, while simultaneously bolstering the robustness of the model. Finally, by incorporating the region term and edge term into the variational level set framework, the RELEP model is proposed. Experimental results on both synthetic and natural images demonstrate that the proposed RELEP model outperforms several representative and state-of-the-art models in terms of segmentation performance. © 2023, The Authors. All rights reserved.

关键词： Image segmentation

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Surface crack width measurement based on digital image processing

Surface crack width measurement based on digital image proce...

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Chinese Control and Decision Conference, CCDC

作者： Zongyuan Xie Hongyan Ma Yongxue Ye Jiaming Dou Shengyan Li Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture Beijing China National Virtual Simulation Experimental Center for Smart City Education Beijing China

ISBN: (纸本)9781665478977

Cracks are common and dangerous hazards in buildings. Aiming at the defects of low accuracy and poor feasibility in traditional crack width detection, proposing a crack width measurement method based on an orthogonal skeleton line. Firstly, the crack image is processed by grayscale. Secondly, the processed image is median filtering and threshold segmentation. Finally, the skeleton and outline of the crack image were extracted by this method, and the measurement and comparison of simulated cracks and actual cracks are completed. The simulation results show that this method has high accuracy and feasibility and can meet the accuracy of engineering practice.

关键词： Image segmentation Simulation Buildings Process control Position measurement Gray-scale Skeleton

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Image Denoising Method Based on Channel Compression and Split-Attention Mechanism

Image Denoising Method Based on Channel Compression and Spli...

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Computer engineering and Intelligent Control (ICCEIC) International Conference on

作者： Hao Sun Xiaoyan Qiao Yantai Key Laboratory of Big Data Modeling and Intelligent Computing Immersion School of Computer Science and Technology Shandong Technology and Business University Technology and Evaluation Shandong Engineering Research Center Yantai Shandong

ISBN: (数字)9798331507992

ISBN: (纸本)9798331508005

In the image restoration task, how to make full use of spatial and channel feature information to improve the reconstruction quality of the model without significantly increasing the computational complexity is an important research issue. To address this challenge, this paper proposes a CS module (Channel Compression and Split-Attention) specifically for optimizing image denoising tasks. The module first compresses the features through a preliminary convolution operation, then introduces the split attention convolution mechanism to split the features by channel and dynamically weight them, thereby enhancing the model's adaptive attention to different feature channels and improving feature expression capabilities. Through experiments on multiple image denoising datasets, the results show that the CS module significantly improves the image reconstruction quality, especially in terms of peak signal-to-noise ratio (PSNR). At the same time, the introduction of the CS module does not lead to a significant increase in computational complexity, maintaining a high computational efficiency, which verifies the practicality and superiority of the module in image denoising tasks.

关键词： Image coding PSNR Convolution Computational modeling Refining Image restoration Computational efficiency Computational complexity Image reconstruction Image denoising

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ANTI: An Adaptive Network Traffic Indexing Algorithm for High-Speed Networks

ANTI: An Adaptive Network Traffic Indexing Algorithm for Hig...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Jiale Chen Xingshu Chen Liangguo Chen Xiao Lan Yonggang Luo School of Cyber Science and Engineering Sichuan University China Key Laboratory of Data Protection and Intelligent Management Ministry of Education (Sichuan University) China Cyber Science Research Institute Sichuan University China

Network packets record communication behaviors and details, which is important for security audits, attack detection, and forensic analysis. For the effectiveness and timeliness of security analysis, it is necessary to fully store network packets and build an efficient packet index. However, the existing packet indexing algorithms based on the radix tree ignore the distribution characteristics of network traffic and use internal nodes with the same capacity for index construction, resulting in wasted disk space and poor retrieval performance. As a solution, $w$ e propose ANTI, an adaptive network traffic indexing algorithm similar to Adaptive Radix Tree, which can adaptively switch internal nodes with different capacity according to the density of network traffic and compress the common prefix and distinct suffix of traffic attributes to balance the index construction performance and space utilization. We also implement a packet-aware network traffic archiving and indexing system to achieve full packet archival, efficient indexing, and fast retrieval. Finally, we empirically evaluate ANTI in IPv4 (IPv6) traffic scenarios, and the results confirm the effectiveness of ANTI as well as the benefit of adopting ANTI for enhancing indexing and retrieval performance compared with other state-of-art algorithms.

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