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IEEE International Conference on Multimedia and Expo (ICME)

作者： Si Li Jiaxing Liu Peilin Li Dichucheng Li Xinlu Liu Yongwei Gao Wei Li School of Computer Science and Technology Fudan University China School of Statistics and Information Shanghai University of International Business and Economics China Shanghai Key Laboratory of Intelligent Information Processing Fudan University China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Drum Source Separation (DSS) aims to separate drum mixtures into individual drum sounds, such as kick and snare. Deep neural network methods have been successfully applied for source separation. However, due to the limited size of existing datasets and the strongly overlap of drums in frequency and time, these methods still have certain shortcomings. To address these challenges, we construct a large drum sound dataset and propose a novel training objective to improve performance of DSS task. The training objective leverages three temporal-frequency statistical descriptors (spectral centroid, spectral spread, and spectral flux) to separate drum sources. Our experimental results demonstrate that our method can make a SDR improvement of 0.98 dB on UNet and 1.07 dB on MERT. Furthermore, our method achieves consistent improvements in low-resource and cross-dataset scenarios. Our code and dataset are available at https://***/150042/Drum-Separation-TF.

关键词： Training Deep learning Adaptation models Time-frequency analysis Visualization Tracking loops Source separation

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A study on the prediction model of dam seepage volume based on isolated forest-multiple stepwise linear regression 4

A study on the prediction model of dam seepage volume based ...

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4th International Conference on computer Engineering and Application, ICCEA 2023

作者： Chen, Guanyu Zhang, Suli Zhang, Chenlin Qian, XiaoChun Changchun University of Technology School of Computer Science & Engineering Jilin Provincial Key Laboratory of Changbai Historical Culture and Vr Reconstruction Technology Jilin Prov. Changbai Mt. Ecol. Culture and Virtual Reality Simulation Engineering Research Center Changchun China Jilin Institute of Chemical Technology School of Information and Control Engineering Jilin Provincial Key Laboratory of Changbai Historical Culture and Vr Reconstruction Technology Jilin Prov. Changbai Mt. Ecol. Culture and Virtual Reality Simulation Engineering Research Center Changchun China

ISBN: (纸本)9798350347548

In the study of hydraulic seepage prediction, it is often necessary to target multiple feature dimensions. In this paper, a variety of traditional machine learning algorithms based on stepwise regression filtering features are used to train a dam seepage prediction model based on isolated forest processing of data outliers. The results show that the algorithm can be applied to predict the seepage data of a reservoir more accurately, and further suggest improvements to the dam's seepage control function and observation facilities. The experimental results show that the correlation coefficient of the linear regression model is significantly improved when the training sample ratio is 0.8 after the isolated forest treatment of outliers and stepwise regression screening features. © 2023 IEEE.

关键词： Forecasting

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Prompt learning in computer vision: a survey

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Frontiers of information technology & Electronic Engineering 2024年第1期25卷 42-63页

作者： Yiming LEI Jingqi LI Zilong LI Yuan CAO Hongming SHAN Shanghai Key Laboratory of Intelligent Information Processing School of Computer ScienceFudan UniversityShanghai 200438China Institute of Science and Technology for Brain-Inspired Intelligence Fudan UniversityShanghai 200433China MOE Frontiers Center for Brain Science Fudan UniversityShanghai 200433China Shanghai Center for Brain Science and Brain-Inspired Technology Shanghai 201210China

Prompt learning has attracted broad attention in computer vision since the large pre-trained visionlanguagemodels (VLMs) exploded. Based on the close relationship between vision and language information builtby VLM, prompt learning becomes a crucial technique in many important applications such as artificial intelligencegenerated content (AIGC). In this survey, we provide a progressive and comprehensive review of visual promptlearning as related to AIGC. We begin by introducing VLM, the foundation of visual prompt learning. Then, wereview the vision prompt learning methods and prompt-guided generative models, and discuss how to improve theefficiency of adapting AIGC models to specific downstream tasks. Finally, we provide some promising researchdirections concerning prompt learning.

关键词： Prompt learning Visual prompt tuning(VPT) Image generation Image classification Artificial intelligence generated content(AIGC)

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A Local Regression-Based SLA-Aware Energy-Efficient Virtual Machine Consolidation Algorithm in Data Centers

A Local Regression-Based SLA-Aware Energy-Efficient Virtual ...

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International Conference on Big Data Computing and Communications (BIGCOM)

作者： Xiaodong Wu Ronghai Wang Guoxin Lin School of Mathmatics and Computer Science Quanzhou Normal University Fujian Provincial Key Laboratory of Data-Intensive Computing Key Laboratory of Intelligent Computing and Information Processing Fujian Province University Fujian Provincial Big Data Research Institute of Intelligent Manufacturing Quanzhou China

Virtual Machine Consolidation (VMC) is an effective means to improve the resource utilization and reduce energy consumption of data centers. However, the VMC optimization that only considers energy consumption may lead to SLA (Service-Level Agreements) violations and thus fail to guarantee the QoS (Quality of Service) provided. Therefore, it is necessary to consider both energy saving and SLA violations in the process of VMC. In this paper, we propose an energy and SLA-aware VMC algorithm that considers both energy saving and SLA violation optimization in data centers. The proposed algorithm uses the local regression method to predict the utilization of virtual machines, based on which it determines whether the host will be overloaded, so as to make the decision of virtual machine migrations. The proposed VMC algorithm is evaluated with CloudSim toolkit. Simulation results demonstrate that our algorithm has good energy saving performance and can effectively reduce SLA violation in cloud data center. Considering energy consumption and SLA violation, the ESV can be reduced by 7.57% on average and 16.36% at most compared with other algorithms.

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Ciphertext-policy attribute-based proxy re-encryption via constrained PRFs

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Science China(information Sciences) 2021年第6期64卷 242-243页

作者： Zengpeng LI Vishal SHARMA Chunguang MA Chunpeng GE Willy SUSILO College of Computer Science and Technology Qingdao University Guizhou Provincial Key Laboratory of Public Big Data Guizhou University ISTD Pillar Singapore University of Technology and Design (SUTD) College of Computer Science and Engineering Shandong University of Science and Technology College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics School of Computing and Information Technology University of Wollongong

Dear editor,To maintain the confidentiality of the sensitive data, users tend to encrypt their data under an associated access policy(or attributes) before outsourcing them to the *** the traditional access control model via cryptographic approaches, e.g., ciphertext-policy attribute-based encryption(or CP-ABE), users are allowed to formulate access policies and let the encrypted data associated with access policies. Only authenticated users, whose own attributes matching their formulated access policies, have the privilege to recover the ciphertext. However, once the access policies are formulated, the authorized user cannot update it unless the user downloads and re-encrypts the file under the new access policy. Hence, to achieve dynamic access control, the model of ciphertext-policy attribute-based proxy re-encryption(or CP-AB-PRE) is adopted by integrating the proxy re-encryption(or PRE).

关键词： Ciphertext-policy attribute-based proxy re-encryption via constrained PRFs

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Optical image processing using acousto-optic modulators as programmable volume holograms: a review [Invited]

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Chinese Optics Letters 2022年第2期20卷 24-33页

作者： Yaping Zhang Houxin Fan Ting-Chung Poon Yunnan Provincial Key Laboratory of Modern Information Optics Kunming University of Science and TechnologyKunming 650500China The Bradley Department of Electrical and Computer Engineering Virginia TechBlacksburgVirginia 24061USA

Bragg processing using a volume hologram offers an alternative in optical image processing in contrast to Fourier-plane processing. By placing a volume hologram near the object in an optical imaging setup, we achieve Bragg processing. In this review, we discuss various image processing methods achievable with acousto-optic modulators as dynamic and programmable volume holograms. In particular, we concentrate on the discussion of various differentiation operations leading to edge extraction capabilities.

关键词： optical image processing edge extraction acousto-optics volume gratings

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FSD-YOLO: An Improved Method for Steel Surface Defect Detection Based on YOLOv5

FSD-YOLO: An Improved Method for Steel Surface Defect Detect...

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International Conference on computer Supported Cooperative Work in Design

作者： Yuechen Zhang Aimin Li Xiaotong Kong Wenqiang Li Zhiyao Li Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Steel surface defect detection is very important in industrial quality inspection, and there have been many studies on Steel surface defect detection in recent years. Existing steel surface defect detection algorithms suffer from low detection accuracy and high model complexity. To improve the above problems, this paper proposes an optimized target detection algorithm based on YOLOv5. Firstly, the original spatial pyramid pooling (SPP) module is replaced by the SPPFCSPC module to better capture the target and scene information at different scales, and to improve the sensory field and feature expression ability of the model. Secondly, we propose the C2F-Faster module based on FasterNet and C2F module to replace the C3 module of the backbone network, which perfectly integrates the ideas of PConv and ELAN and ensures the detection accuracy while effectively reducing the model size. Finally, the Dynamic Head detection head is fused in the head of the model to improve the detection capability of the target detection head using the separated attention mechanism. We conducted experimental validation on the widely used NEU-DET dataset. The experimental results show that the improved model improves the original YOLOv5 model over the original YOLOv5 model by 3.6% in mAP @50 , 9.9% and 2.8% in AP and AR, respectively, and the amount of model parameters is reduced by 13.2%. The improved model also outperforms SSD, FasterRCNN, YOLOv5, YOLOv6, YOLOv7, YOLOv8 and other mainstream target detection models, which better meets the requirements of actual industrial production on the accuracy and speed of steel surface defects detection model.

关键词： YOLO Accuracy Attention mechanisms Computational modeling Heuristic algorithms Inspection Data models

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Image Segmentation via Fischer-Burmeister Total Variation and Thresholding

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Advances in Applied Mathematics and Mechanics 2022年第4期14卷 960-988页

作者： TingtingWu Yichen Zhao Zhihui Mao Li Shi Zhi Li Yonghua Zeng School of Science Nanjing University of Posts and TelecommunicationsNanjingJiangsu 210023China The Department of Computer Science and Technology Shanghai Key Laboratory of Multidimensional Information ProcessingEast China Normal UniversityShanghai 200241China College of Field Engineering PLA Army Engineering UniversityNanjingJianhsu 210007China

Image segmentation is a significant problem in image *** this paper,we propose a new two-stage scheme for segmentation based on the Fischer-Burmeister total variation(FBTV).The first stage of our method is to calculate a smooth solution from the FBTV Mumford-Shah ***,we design a new difference of convex algorithm(DCA)with the semi-proximal alternating direction method of multipliers(sPADMM)*** the second stage,we make use of the smooth solution and the K-means method to obtain the segmentation *** simulate images more accurately,a useful operator is introduced,which enables the proposed model to segment not only the noisy or blurry images but the images with missing pixels *** demonstrate the proposed method produces more preferable results comparing with some state-of-the-art methods,especially on the images with missing pixels.

关键词： Image segmentation Fischer-Burmeister total variation difference of convex algorithm sPADMM K-means method.

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Symbol-level interleaving to mitigate error propagation of TCM-16QAM in nonlinear fiber transmissions

Symbol-level interleaving to mitigate error propagation of T...

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OptoElectronics and Communications Conference, OECC

作者： Yifan Chen Xingwen Yi Hao Deng Mingzhu Yin Jian Zhao Yi Cai Fan Li School of Electronics and Information Technology Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems Sun Yat-Sen University Guangzhou China School of Electronic and Information Engineering South China University of Technology Guangzhou China School of Electronic and Information Engineering Soochow University Suzhou China

ISBN: (数字)9798350308396

ISBN: (纸本)9798350308402

Decoding of trellis-coded modulation 16-ary quadrature amplitude (TCM-16QAM) after nonlinear fiber transmissions suffers from error propagation. To mitigate the effect, we propose a simple method of symbol-level interleaving based on a transpose operation. In a transmission experiment of 32-GBaud TCM-16QAM over 3×80-km standard single-mode fibers, at a bit error ratio (BER) of 1E−3, the launch power of the interleaved TCM signal can be increased by 0.7 and 1.0 dB in the fiber-nonlinear region, compared to 8PSK and TCM-16QAM without interleaving, respectively.

关键词： Viterbi algorithm Modulation Symbols Optical fiber communication Decoding Standards

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HSD-YOLO: A Lightweight and Accurate Method for PCB Defect Detection

HSD-YOLO: A Lightweight and Accurate Method for PCB Defect D...

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International Joint Conference on Neural Networks (IJCNN)

作者： Zhiyao Li Aimin Li Wenqiang Li Xiaotong Kong Yuechen Zhang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

PCB defect detection is a typical small objects detection task, as with other objects, there is a small object size, the detection process is susceptible to the problem of background interference. In practical industrial production, it is difficult for existing object detection models to realize the balance between accuracy and real-time performance. Therefore, we propose a new and lightweight object detection model. And we named it Hsd-YOLO. Firstly, HGNetv2, the backbone of the new paradigm RT-DETR for object detection, is chosen as the backbone of our model, which makes the model more lightweight and reduces the number of parameters and computation while guaranteeing the accuracy of the model’s detection. Secondly, we use the more lightweight convolutional GSConv, which is introduced into the neck to make the model balance between accuracy and speed. Finally, a unified dynamic head framework, DyHead (Dynamic Head), is introduced to make the model improve the representation of the object detection head without increasing the computational overhead. We perform comparison experiments as well as ablation experiments on a publicly available PCB defect datasets to fully illustrate the effectiveness of ours model. We conduct ablation experiments on the current state-of-the-art single-stage model YOLOv8, and our model improves AP, AR, mAP@0.5 and mAP@0.5-0.9 by 3.8%, 0.2%, 0.6% and 2.2%, respectively, and reduces the number of parameters in 385312 while ensuring accuracy. Comparing with major object detection models, our model performs the best in accuracy.

关键词： Accuracy Computational modeling Neural networks Object detection Quality control Production Real-time systems

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