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3rd International Conference on Optics and Image Processing, ICOIP 2023

作者： Zhang, Diya Wang, Yumeng Wang, Yanrui Pu, Yile Bi, Zhenbo Rao, Wei School of Information Engineering Zhejiang Ocean University Zhoushan316022 China Department of Physics Key Laboratory of Oceanographic Big Data Mining Application of Zhejiang Province Zhejiang Ocean University Zhoushan316000 China

ISBN: (纸本)9781510667426

In view of the phenomenon of detail loss and edge blurring in color images collected underwater, noise reduction is mostly ignored in underwater image enhancement research. In this paper, an improved image enhancement method based on Retinex algorithm is proposed. First,the original underwater image is transferred from RGB channels to HSI color space, and the saturation component is adaptively linearly stretched by gamma correction, The improved Retinex decomposition network based on homomorphic filtering is used to decompose and enhance the Lightness component separately. Eventually, the fused image is transferred from HSI to RGB color space, and the processed image is output. According to the experimental results, this algorithm can not only eliminate the halo artifacts, but also improve the brightness and contrast of the image, restore the original details of the image, and effectively remove the image noise. It greatly improves the visual effect and objective evaluation results of images. © 2023 SPIE. All rights reserved.

关键词： Image enhancement

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Pulsed polarized vortex beam enabled by metafiber lasers

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PhotoniX 2024年第1期5卷 110-120页

作者： Chenxi Zhang Lei Zhang He Zhang Bo Fu Jiyong Wang Min Qiu Key Laboratory of Precision Opto-Mechatronics Technology of Education Ministry School of Instrumentation and Optoelectronic EngineeringBeihang UniversityBeijing 100191China Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province School of EngineeringWestlake University18 Shilongshan RoadHangzhou 310024Zhejiang ProvinceChina Institute of Advanced Technology Westlake Institute for Advanced Study18 Shilongshan RoadHangzhou 310024Zhejiang ProvinceChina QianYuan National Laboratory Hangzhou 310000China Key Laboratory of Big Data-Based Precision Medicine Ministry of Industry and Information Technology School of Engineering MedicineBeihang UniversityBeijing 100191China Ministry of Education Engineering Research Center of Smart Microsensors and Microsystems School of Electronics and InformationHangzhou Dianzi UniversityHangzhou 310018China Westlake Institute for Optoelectronics FuyangHangzhou 311421China

Pulsed polarized vortex beams,a special form of structured light,are generated by tailoring the light beam spatiotemporally and witness the growing application demands in nonlinear optics such as ultrafast laser processing and surface plasma ***,existing techniques for generating polarized vortex beams suffer from either low compactness due to the use of bulky components or limited controlment of pulse ***,an all-fiber technique combining plasmonic metafibers with mode conversion method is harnessed to generate high-performance pulsed polarized vortex *** metafibers are utilized as saturable absorbers to produce Q-switched pulses with micro-second duration,while the offset splicing method is employed to partially convert the fundamental transverse mode(LP_(01))to higher-order mode(LP_(11)).Eventually,a polarized vortex beams laser is achieved at the telecom band with a repetition frequency of 116.0 *** impact of geometrical parameters including period of metafibers and offset of splicing on the spatiotemporal properties of pulsed polarized vortex beams is systematically *** findings could pave the way for design,control and generation of all-fiber pulsed polarized vortex beams,and also offer insights into the development of other types of structured laser sources.

关键词： Metafiber Q-switched pulse Polarized vortex beam

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Fine-Grained Tuple Transfer for Pipelined Query Execution on CPU-GPU Coprocessor 28th

Fine-Grained Tuple Transfer for Pipelined Query Execution o...

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28th International Conference on database Systems for Advanced Applications, DASFAA 2023

作者： Yang, Zhenhua Pan, Qingfeng Xu, Chen Shanghai Engineering Research Center of Big Data Management East China Normal University Shanghai200062 China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin541004 China

ISBN: (纸本)9783031306365

To leverage the massively parallel capability of GPU for query execution, GPU databases have been studied for over a decade. Recently, researchers proposed to execute queries with both CPU and GPU in a pipelined approach. In the pipelined query execution, the cross-processor tuple transfer plays a crucial role for the overall query execution performance. The state-of-the-art solution achieves cross-processor tuple transfer using a queue-like data structure. However, it is coarse-grained due to the use of a single spin lock to achieve thread-safety. This design causes performance issues as it prevents the threads from accessing the queue simultaneously. In this paper, we propose a fine-grained tuple transfer mechanism. It employs decoupled enqueue/dequeue to enable two threads on different processors to access the queue at the same time. Moreover, this mechanism explores subqueue-based locking to enable the threads on the same processor to access the queue at the same time. In particular, we implement a prototype system, namely π QC, which adopts fine-grained tuple transfer. Our experiments show that π QC achieves an order of magnitude better performance than existing GPU databases such as HeavyDB. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Graphics processing unit

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Review of graph construction and graph learning in stock price prediction 9th

Review of graph construction and graph learning in stock pri...

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9th International Conference on Information Technology and Quantitative Management, ITQM 2022

作者： Wang, Yunong Qu, Yi Chen, Zhensong Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China School of Management and Engineering Capital University of Economics and Business Beijing100070 China

Precise prediction of stock prices leads to more profits and more effective risk prevention, which is of great significance to both investors and regulators. Recent years, various kinds of information not directly-relevant with stock prices have received more attention, such as texts, images or connections. These external information has the potential of reflecting or influencing fluctuations, and thus, given the utilization of advanced analyzing techniques, the forecasting performance of stock prices could be promoted substantially. For instance, graph neural network models have expanded into many other disciplines including stock price prediction, and exhibited impressive representation learning ability. However, in stock markets, well-defined graphs are rarely seen and how to formulate the graph structures needed remains a challenging problem. Towards this end, this article presents a comprehensive overview of graph construction and graph learning in stock price prediction, by reviewing the existing studies, summarizing its general paradigm, special cases and proposing possible prospects. Our research not only systematically reveals the feasible ways of constructing graphs in financial markets, but also provides insights for further implementations of graph learning models into stock prediction tasks. © 2022 The Authors. Published by Elsevier B.V.

关键词： Financial markets

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Variable lanes traffic optimization control method combined with colorful asphalt materials 9

Variable lanes traffic optimization control method combined ...

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9th International Conference on Mechanical engineering, Materials, and Automation Technology, MMEAT 2023

作者： Wang, Ruihan Zhan, Xuejuan Liang, Zijun Li, Yuqi Xiao, Yun Peng, Min School of Urban Construction and Transportation Hefei University China Anhui Province Transportation Big Data Analysis and Application Engineering Laboratory China Key Laboratory of Traffic Information and Safety of Anhui Higher Education Institutes Anhui Sanlian University China

ISBN: (纸本)9781510668584

Along with our country's continuous growth, rising rush hours traffic congestion, many scholars proposed setting variable lanes solution, but due to the variable lanes different times the direction of the lane change, which made the driver can not be correctly into the right lane. So we propose a traffic optimization control method for variable lanes combined with colorful asphalt materials. Firstly, enumeration method is used to list all the direction schemes and phase schemes of the variable lanes, and the optimal scheme is obtained according to the constraints. In order to better distinguish the variable lanes from the conventional lanes, the variable lanes are paved with colorful asphalt environmental materials. Then the phase timing is calculated according to the determined variable lanes, phase scheme and traffic flow. Finally, through VISSIM traffic simulation software to evaluate the results, the results show that combining with colorful asphalt materials clearly calibrate the position of the variable lanes, effective rule on the variable lanes traffic space, and on this basis, through phase timing optimization technology to further improve the intersection traffic efficiency, effectively alleviate the intersection rush hours traffic congestion. It provides a basis for the research of variable lanes optimization control combined with colorful asphalt materials. © 2023 SPIE.

关键词： Traffic congestion

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Design and implementation of fire alarm module for smart home

Design and implementation of fire alarm module for smart hom...

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2023 International Conference on Electronic Information engineering and data Processing, EIEDP 2023

作者： Liu, Xiaohong Yan, Kai College of Big Data and Intelligent Engineering Southwest Forestry University Kunming650224 China Key Laboratory of Metallurgical Equipment and Control Technology Ministry of Education Wuhan University of Science and Technology Wuhan430081 China

ISBN: (纸本)9781510666160

With the continuous development of information technology, artificial intelligence fire automatic alarm system is gradually developed. However, the current system is based on a single sensor for identification, when a fire still uses manual active fire extinguishing, low efficiency in handling fire, high failure rate, poor system scalability, poor anti-interference ability, high false alarm rate. At the same time, the recognition accuracy is low, the processing speed and intelligence are also insufficient. Therefore. To address the above problems, this study designs a multi-sensor artificial intelligence fire prevention system using smoke and temperature sensors. The fire prevention system can send alarm signals at the earliest stage of fire and can intelligently extinguish the fire, so that the disaster can be controlled and extinguished by taking measures in advance. Meanwhile, the system can continue to work even if one of the sensors is not working properly. © 2023 SPIE.

关键词： Fires

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PD-Refiner: An Underlying Surface Inheritance Refiner with Adaptive Edge-Aware Supervision for Point Cloud Denoising 24

PD-Refiner: An Underlying Surface Inheritance Refiner with A...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhang, Chengwei Zhang, Xueyi Yue, Xianghu Lao, Mingrui Jiang, Tao Wang, Jiawei Zhang, Fubo Chen, Longyong National Key Laboratory of Microwave Imaging Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Laboratory for Big Data and Decision National University of Defense Technology Hunan Changsha China Department of Electrical and Computer and Engineering National University of Singapore Singapore Singapore National Key Laboratory of Information Systems Engineering National University of Defense Technology Hunan Changsha China School of Software Engineering Tongji University Shanghai China

ISBN: (纸本)9798400706868

Point clouds from real-world scenarios inevitably contain complex noise, significantly impairing the accuracy of downstream tasks. To tackle this challenge, cascading encoder-decoder architecture has become a conventional technical route to iterative denoise. However, circularly feeding the output of denoiser as its input again involves the re-extraction of underlying surface, leading to unstable denoising process and over-smoothed geometric details. To address these issues, we propose a novel denoising paradigm dubbed PD-Refiner that employs a single encoder to model the underlying surface. Then, we leverage several lightweight hierarchical Underlying Surface Inheritance Refiners (USIRs) to inherit and strengthen it, thereby avoiding the re-extraction from the intermediate point cloud. Furthermore, we design adaptive edge-aware supervision to improve the edge awareness of the USIRs, allowing for the adjustment of the denoising preferences from global structure to local details. The results demonstrate that our method not only achieves state-of-the-art performance in terms of denoising stability and efficacy, but also enhances edge clarity and point cloud uniformity. © 2024 ACM.

关键词： edge-aware supervision

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PDEGAN: A Panoramic Style Transfer Based on Generative Adversarial Networks

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第4期9卷 2112-2121页

作者： Wang, Qinghua Long, Xinling Huang, Jingwei Chen, Yang Yang, Lirong Zhang, Fuquan College of Computer and Big Data Minjiang University Fuzhou350108 China Fuzhou Technology Innovation Center of intelligent Manufacturing information System Minjiang University Fuzhou350108 China Fujian Province University Fuzhou350300 China College of Computer and Control Engineering Minjiang University Fuzhou350108 China College of Computer and Big Data Fuzhou University Fuzhou China School of Mechanical and Automotive Engineering Fujian University of Technology Fuzhou China College of foreign languages Dalian Jiaotong University Dalian116028 China Digital Media Art Key Laboratory of Sichuan Province Sichuan Conservatory of Music Chengdu610021 China

Panoramic image plays an extremely important role in the application of 3D, but in some special scenarios, the depth information is also needed as an auxiliary, but to obtain the panoramic depth information needs a high cost, it is extremely difficult to obtain. Therefore, this paper proposes a panoramic depth estimation method called PDEGAN (Panoramic Depth Estimation Generative Adversarial Network). This method first enhances the style migration, transfers the input panoramic image to a style closer to the panoramic depth image domain, and then combines the conversion results with the original panoramic image as the input data for further depth estimation training. By using the style closer to the depth estimation, the texture, color and other characteristics of the image can be adjusted, so as to better highlight the depth information of the object in the image, so that the generator network is more focused on performing the image translation task, reduce the impact of style differences on the image translation task, and improve the generalization ability of the model. At the same time, the model will also use multi-scale feature fusion, combined with the attention mechanism, to better capture the depth information of different scales, viewing angles and channels. Not only that, but also by introducing depth gradient loss, surface normal loss to improve the accuracy and stability of depth estimation. To this end, we also made a set of related datasets for the related experiments of PDEGAN, and named it PDE Images. Finally, it is shown that the method proposed in this paper has more accurate estimation results than the traditional Pix2Pix image conversion model. © 2024, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Generative adversarial networks

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CLCoSum: Curriculum Learning-Based Code Summarization for Code Language Models

CLCoSum: Curriculum Learning-Based Code Summarization for Co...

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IEEE/ACM International Conference on Program Comprehension (ICPC)

作者： Hongkui He Jiexin Wang Liuwen Cao Yi Cai School of Software Engineering South China University of Technology Key Laboratory of Big Data and Intelligent Robot (SCUT) MOE of China

ISBN: (数字)9798331502232

ISBN: (纸本)9798331502249

The code summarization task aims to automatically generate natural language descriptions for code snippets. Recently, pre-trained code language models (CLMs) have demonstrated outstanding performance on code summarization. Additionally, researchers have shown that there is a strong correlation between code function names and summaries, and poorly defined function names lead to worse summaries generated by models. To mitigate this issue, in this paper, we propose CLCoSum, a curriculum learning-based code summarization method for CLMs that improves their performance in poorly named function scenarios. CLCoSum helps CLMs avoid overreliance on function names when they are poorly defined. First, CLCoSum employs data augmentation operators on function names to generate semantically equivalent poorly named codes, which are considered harder data and assist in reducing the model's reliance on unclear function names. Subsequently, CLCoSum uses a curriculum learning paradigm to allow the model to learn these harder codes in an organized way during finetuning. This approach enables CLMs to progress from easier to more difficult training data, similar to the human learning process. Extensive experiments on two existing datasets for Java and Python demonstrate that CLCoSum boosts the performance of various CLMs in code summarization. Specifically, the improvements in BLEU-4 score range from approximately 5% to $\mathbf{2 0. 8 \%}$ . The fine-tuning speed of CLCoSum on the augmented dataset is also competitive. Our code and data are available at https://***/KuiH/CLCoSum.

关键词： Java Codes Correlation Natural languages Training data data augmentation data models Python

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Contrastive Learning for Robust Android Malware Familial Classification

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IEEE Transactions on Dependable and Secure Computing 2022年 1-14页

作者： Wu, Yueming Dou, Shihan Zou, Deqing Yang, Wei Qiang, Weizhong Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai China University of Texas at Dallas Dallas USA National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Due to its open-source nature, Android operating system has been the main target of attackers to exploit. Malware creators always perform different code obfuscations on their apps to hide malicious activities. Features extracted from these obfuscated samples through program analysis contain many useless and disguised features, which leads to many false negatives. To address the issue, in this paper, we demonstrate that obfuscation-resilient malware family analysis can be achieved through contrastive learning. The key insight behind our analysis is that contrastive learning can be used to reduce the difference introduced by obfuscation while amplifying the difference between malware and other types of malware. Based on the proposed analysis, we design a system that can achieve robust and interpretable classification of Android malware. To achieve robust classification, we perform contrastive learning on malware samples to learn an encoder that can automatically extract robust features from malware samples. To achieve interpretable classification, we transform the function call graph of a sample into an image by centrality analysis. Then the corresponding heatmaps can be obtained by visualization techniques. These heatmaps can help users understand why the malware is classified as this family. We implement IFDroid and perform extensive evaluations on two datasets. Experimental results show that IFDroid is superior to state-of-the-art Android malware familial classification systems. Moreover, IFDroid is capable of maintaining a 98.4% F1 on classifying 69,421 obfuscated malware samples. IEEE

关键词： Semantics

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