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IEEE International Conference on Signal and Image Processing (ICSIP)

作者： Ling Liu Shuai Liu Zhong Qu Dongyang Zhou School of Big Data and Software Engineering College of Mobile Telecommunications of Chongqing University of Posts and Telecommunications Chongqing China College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China College of Mobile Telecommunications of Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9781665446006

The ViBe (Visual Background Extraction) algorithm uses the first frame to initialize the pixel background model for each pixel, which may introduce the ghost regions in the following video frames. Although ViBe can use its background model update mechanism to eliminate the ghost regions, it is time-consuming. Firstly, a ghost elimination algorithm is proposed in this paper in order to reduce the elimination time for the ghost regions based on its properties. Secondly, a fixed global segmentation decision threshold is used to segment pixels in ViBe algorithm. However, because it cannot adapt to the dynamic background, a self-adaptive segmentation decision threshold based on single pixel is used to adjust to the different backgrounds. Finally, a self-adaptive update factor is presented, which makes the background model more reliable and adjusts to the background changes through the self-adaptive processing. The experimental result shows that this improved method can quickly eliminate the ghost regions, and adapt to the different kinds of backgrounds, ensuring more accurate results.

关键词： Visualization Adaptation models Heuristic algorithms Image processing Conferences Lighting Reliability

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A novel hybrid ensemble approach for wind speed forecasting with dual-stage decomposition strategy using optimized GRU and transformer models

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Energy 2025年 329卷

作者： Ullah, Sajid Chen, Xi Han, Han Wu, Junhao Dong, Jinghan Liu, Ruiqing Ding, Weijie Liu, Min Li, Qingli Qi, Honggang Huang, Yonggui Yu, Philip Lh East China Normal University Shanghai200241 China School of Geographic Sciences East China Normal University Shanghai200241 China Software Engineering Institute East China Normal University Shanghai200026 China State Key Laboratory of Estuarine and Coastal Research East China Normal University Shanghai200241 China Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai200241 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China Peking University Chongqing Research Institute of Big Data Chongqing High-tech Zone Science Valley Building 10 China Department of Mathematics and Information Technology The Education University of Hong Kong Hong Kong

Wind energy has attracted global interest owing to its sustainable and environmentally friendly characteristics. Nevertheless, precisely forecasting wind speed can be challenging due to its volatile and unpredictable nature. This paper presents a new hybrid forecasting approach based on dual stage decomposition mechanism, namely TMQGDT for wind speed prediction. At first, a decomposition technique called time-varying filtered based empirical mode decomposition (TVFEMD) is utilized to decompose the original wind speed data into several intrinsic mode functions (IMFs). Afterwards, multi-scale permutation entropy (MPE) is used to assess the complexity of each IMF. Based on the entropy values, the IMFs are further classified into high-frequency and low-frequency IMFs. To address the significant volatility observed in the high-frequency IMFs, discrete wavelet transform (DWT) method is employed to perform secondary decomposition. The low-frequency IMFs are forecasted using gated recurrent unit (GRU) model optimized with quantum particle swarm optimization (QPSO) algorithm, while the high-frequency IMFs are forecasted with the Transformer model. The proposed model is trained and validated using four wind speed time series datasets collected from Germany and China. Five individual models and six hybrid models are compared against the proposed model to validate the forecasting performance of the proposed TMQGDT model. The prediction outcomes reveals that the R2 of the model is 0.973, 0.968, 0.956, and 0.996 on the four dataset test sets, which has improved by 3.39 %, 3.93 %, 5.53 %, and 0.50 %, respectively, compared to the TVFEMD-MPE-QPSO-GRU-DWT-Autoformer model. The excellent accuracy performance of the TMQGDT model indicates that developing a hybrid model based on deep learning techniques using secondary decomposition mechanism and optimization algorithm can enhance the precision of wind speed prediction. © 2025

关键词： Particle swarm optimization (PSO)

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High-Frequency Stereo Matching Network

High-Frequency Stereo Matching Network

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Haoliang Zhao Huizhou Zhou Yongjun Zhang Jie Chen Yitong Yang Yong Zhao Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry State Key Laboratory of Public Big Data College of Computer Science and Technology Institute of Artificial Intelligence Guizhou University Guiyang Guizhou China Ghost-Valley AI Technology Shenzhen Guangdong China School of Physics and Optoelectronic Engineering Guangdong University of Technology Guangzhou China The Key Laboratory of Integrated Microsystems Shenzhen Graduate School Peking University China

In the field of binocular stereo matching, remarkable progress has been made by iterative methods like RAFT-Stereo and CREStereo. However, most of these methods lose information during the iterative process, making it difficult to generate more detailed difference maps that take full advantage of high-frequency information. We propose the Decouple module to alleviate the problem of data coupling and allow features containing subtle details to transfer across the iterations which proves to alleviate the problem significantly in the ablations. To further capture high-frequency details, we propose a Normalization Refinement module that unifies the disparities as a proportion of the disparities over the width of the image, which address the problem of module failure in cross-domain scenarios. Further, with the above improvements, the ResNet-like feature extractor that has not been changed for years becomes a bottleneck. Towards this end, we proposed a multi-scale and multi-stage feature extractor that introduces the channel-wise self-attention mechanism which greatly addresses this bottleneck. Our method (DLNR) ranks 1st on the Middlebury leaderboard, significantly outperforming the next best method by 13.04%. Our method also achieves SOTA performance on the KITTI-2015 benchmark for D1-fg. Code and demos are available at: https://***/David-Zhao-1997/High-frequency-Stereo-Matching-Network.

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Correction: DeepDet: YAMNet with BottleNeck Attention Module (BAM) for TTS synthesis detection

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EURASIP Journal on Audio, Speech, and Music Processing 2024年第1期2024卷 1-1页

作者： Mahum, Rabbia Irtaza, Aun Javed, Ali Mahmoud, Haitham A. Hassan, Haseeb Computer Science Department UET Taxila Taxila Pakistan Software Engineering Department UET Taxila Taxila Pakistan Industrial Engineering Department College of Engineering King Saud University Riyadh Saudi Arabia College of Big Data and Internet Shenzhen Technology University (SZTU) Shenzhen China

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Meta-Chain: User-Aware Cross-Layer Space Allocation Strategy for Blockchain Storage Systems: Work-in-Progress

Meta-Chain: User-Aware Cross-Layer Space Allocation Strategy...

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International Conference on Hardware/software Codesign and System Synthesis (CODES)

作者： Jing Liao Zhengda Li Yi Wang College of Computer Science and Software Engineering The National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

ISBN: (数字)9781728191980

ISBN: (纸本)9781728191997

Current LSM-Tree-based blockchain storage system will assign the data from multiple system users to the same disk drive. This will lead to the inefficient usage of physical spaces in disk and cause extra compaction operations for LSM - Tree. This paper presents Meta-Chain , a user-aware cross-layer space allocation strategy for blockchain storage systems. As a cross-layer design, Meta-Chain redesigns the organization of LSM - Tree and utilizes the rich functionalities provided by open-channel SSD. Experimental results show that Meta-Chain can effectively reduce write amplification and extend the lifetime of SSD in comparison with representative schemes.

关键词： Disk drives Blockchain Organizations Compaction Resource management

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Generating High-quality Movie Tags from Social Reviews: A Learning-driven Approach

Generating High-quality Movie Tags from Social Reviews: A Le...

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IEEE/ACM Int'l Conference on & Int'l Conference on Cyber, Physical and Social Computing (CPSCom) Green Computing and Communications (GreenCom)

作者： Zhenxiao Luo Guopin Tang Chen Wang Yipeng Zhou Xi Zheng Jessie Hui Wang Gang Liu Di Wu School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Faculty of Science and Engineering Macquarie University Sydney Australia Institute for Network Sciences and Cyberspace Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9781665417631

As a kind of keywords to describe video contents, tags are extremely beneficial for viewers to locate videos when they search the site. Additionally, tags also help video platform operators to better organize and recommend videos to platform users. Previous approaches are mainly based on manually tagging or tag propagation via video content analysis, which is time-consuming and resource-consuming. Especially, given the high volume of fresh movies generated per year, it is difficult to accurately tag all the movies manually. Moreover, video content analysis is also not easy considering typical features (e.g., long duration, complicated scenarios) of commercial movies. In this paper, we propose an automatic tagging algorithm called TagRec that exploits crowdsourced user reviews to generate accurate movie tags. We observe that user reviews contain rich information about movies (e.g., quality, actors) which can be learned to generate high-quality movie tags. Inspired by the above observation, we choose to transform the movie video tagging problem into a tag recommendation problem, in which tags are recommended to different movies by extracting knowledge from crowdsourced movie reviews. We take latent topics, tag co-occurrence probability and tag semantics into account, and formulate the problem as a recommendation optimization problem. We evaluate the performance of our pro-posed TagRec algorithm with a large-scale real-world dataset. Extensive experiments demonstrate that TagRec achieves 7.1 % and 9.6% improvement compared with other state-of-the-art methods in terms of Hit Ratio and Normalized Discounted Cumulative Gain respectively.

关键词： Social computing Filtering Semantics Transforms Tagging Motion pictures Optimization

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Enhancing WMMSE With EVM Feedback Through Bayesian Optimization

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IEEE Transactions on Vehicular Technology 2025年

作者： Zhao, Zhuobing Guan, Xin Xu, Fan Shi, Qingjiang Tongji University School of Computer Science and Technology Shanghai201804 China Tongji University College of Electronic and Information Engineering Shanghai201804 China Shenzhen Research Institute of Big Data Shenzhen518172 China

This paper presents an enhancement to the classical weighted minimum mean square error (WMMSE) approach which assumes ideal Gaussian-distributed transmitted symbols, addressing its performance decline for multi-user precoding in practice. By incorporating scaling coefficients that bridge the gap between theoretical communication rates and actual spectral efficiency based on the modulation and coding schemes (MCS) of each user, the proposed method maximizes real communication rates by optimizing precoding vectors and estimating scaling coefficients jointly. With fixed scaling coefficients, we first design an efficient algorithm for optimal precoders, which is surprisingly almost the same as the pure WMMSE. Due to the implicitness of the scaling coefficients, we then estimate these coefficients by minimizing the error vector magnitude (EVM) of all users, which is solved by Bayesian optimization due to the black box nature of the mapping between these coefficients and EVM. Simulation results demonstrate the superiority of the proposed method over existing benchmarks. © 1967-2012 IEEE.

关键词： Mean square error

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SODU2-NET: a novel deep learning-based approach for salient object detection utilizing U-NET

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PeerJ computer science 2025年 11卷 e2623-e2623页

作者： Abbas, Hyder Ren, Shen Bing Asim, Muhammad Hassan, Syeda Iqra El-Latif, Ahmed A. Abd State Key Laboratory of Public Big Data College of Computer Science and Technology Institute for Artificial Intelligence Guizhou University Guizhou Guiyang China School of Computer Science and Engineering Central South University Changsha China EIAS Data Science and Blockchain Laboratory College of Computer and Information Sciences Prince Sultan University Riyadh Saudi Arabia School of Computer Science and Technology Guangdong University of Technology Guangzhou China Department of Electrical and Electronic Engineering British Malaysian Institute Universiti of Kuala Lumpur Kuala Lumpur Malaysia Software Engineering Department Sir Syed University of Engineering and Technology Karachi Pakistan Department of Mathematics and Computer Science Faculty of Science Menoufia University Shebin El-Koom Egypt

Detecting and segmenting salient objects from natural scenes, often referred to as salient object detection, has attracted great interest in computer vision. To address this challenge posed by complex backgrounds in salient object detection is crucial for advancing the field. This article proposes a novel deep learning-based architecture called SODU2-NET (Salient object detection U2-Net) for salient object detection that utilizes the U-NET base structure. This model addresses a gap in previous work that focused primarily on complex backgrounds by employing a densely supervised encoder-decoder network. The proposed SODU2-NET employs sophisticated background subtraction techniques and utilizes advanced deep learning architectures that can discern relevant foreground information when dealing with complex backgrounds. Firstly, an enriched encoder block with full feature fusion (FFF) with atrous spatial pyramid pooling (ASPP) varying dilation rates to efficiently capture multi-scale contextual information, improving salient object detection in complex backgrounds and reducing the loss of information during down-sampling. Secondly the block includes an attention module that refines the decoder, is constructed to enhances the detection of salient objects in complex backgrounds by selectively focusing attention on relevant features. This allows the model to reconstruct detailed and contextually relevant information, which is essential to determining salient objects accurately. Finally, the architecture has been improved by adding a residual block at the encoder end, which is responsible for both saliency prediction and map refinement. The proposed network is designed to learn the transformation between input images and ground truth, enabling accurate segmentation of salient object regions with clear borders and accurate prediction of fine structures. SODU2-NET is demonstrated to have superior performance in five public datasets, including DUTS, SOD, DUT OMRON, HKU-IS, PASCA

关键词： Object detection

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Value Function Dynamic Estimation in Reinforcement Learning based on data Adequacy 20

Value Function Dynamic Estimation in Reinforcement Learning ...

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4th High Performance Computing and Cluster Technologies Conference, HPCCT 2020 and the 3rd International Conference on big data and Artificial Intelligence, BDAI 2020

作者： Gao, Huifan Pan, Yinghui Tang, Jing Zeng, Yifeng Chai, Peihua Cao, Langcai Xiamen Key Laboratory of Big Data Intelligent Analysis and Decision Department of Automation Xiamen University Xiamen China College of Computer Science and Software Engineering Shenzhen University China Newcastle Business School Northumbria University United Kingdom Department of Computer and Information Sciences Northumbria University United Kingdom

ISBN: (纸本)9781450375603

In recent years, reinforcement learning has played an important role in the study of decision problem in computer games. To solve the problem of how to better estimate the value function with limited computational resources, this paper proposes a dynamic estimation method of value function based on data adequacy. In consideration of the varying complexity of each state in the MDP model, we propose a dynamic value function estimation method which is different from the fixed value function estimation method in traditional methods. Based on the PigChase challenge of the Malmo project launched by Microsoft in 2017, we compare the new method with the existing techniques. Experimental results show that the performance of the proposed algorithm is better than traditional algorithms. © 2020 ACM.

关键词： Dynamic programming

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PPFPL: Cross-silo Privacy-preserving Federated Prototype Learning Against data Poisoning Attacks on Non-IID data

arXiv

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arXiv 2025年

作者： Zhang, Hongliang Yu, Jiguo Xu, Fenghua Hu, Chunqiang Zhang, Yongzhao Wang, Xiaofen Yu, Zhongyuan Zhang, Xiaosong School of Computer Science and Technology Qilu University of Technology Shandong Jinan250353 China School of Information and Software Engineering University of Electronic Science and Technology of China Sichuan Chengdu610054 China Cyber Security Institute University of Science and Technology of China Anhui Hefei230026 China School of Big Data and Software Engineering Chongqing University Chongqing400044 China College of Computer Science and Technology China University of Petroleum Shandong Qingdao266580 China

Privacy-Preserving Federated Learning (PPFL) allows multiple clients to collaboratively train a deep learning model by submitting hidden model updates. Nonetheless, PPFL is vulnerable to data poisoning attacks due to the distributed training nature of clients. Existing solutions have struggled to improve the performance of cross-silo PPFL in poisoned Non-IID data. To address the issues, this paper proposes a privacy-preserving federated prototype learning framework, named PPFPL, which enhances the cross-silo FL performance in poisoned Non-IID data while effectively resisting data poisoning attacks. Specifically, we adopt prototypes as client-submitted model updates to eliminate the impact of tampered data distribution on federated learning. Moreover, we utilize two servers to achieve Byzantine-robust aggregation by secure aggregation protocol, which greatly reduces the impact of malicious clients. Theoretical analyses confirm the convergence of PPFPL, and experimental results on publicly available datasets show that PPFPL is effective for resisting data poisoning attacks with Non-IID conditions. © 2025, CC BY.

关键词： Deep learning

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