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arXiv 2024年

作者： Gan, Yuhang Xuan, Wenjie Luo, Zhiming Fang, Lei Wang, Zengmao Liu, Juhua Du, Bo School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Wuhan University Wuhan China Land Satellite Remote Sensing Application Center MNR Beijing China Information Technology Co. Ltd. Ningbo China

When given two similar images, humans identify their differences by comparing the appearance (e.g., color, texture) with the help of semantics (e.g., objects, relations). However, mainstream change detection models adopt a supervised training paradigm, where the annotated binary change map is the main constraint. Thus, these methods primarily emphasize the difference-aware features between bi-temporal images and neglect the semantic understanding of the changed landscapes, which undermines the accuracy in the presence of noise and illumination variations. To this end, this paper explores incorporating semantic priors to improve the ability to detect changes. Firstly, we propose a Semantic-Aware Change Detection network, namely SA-CDNet, which transfers the common knowledge of the visual foundation models (i.e., FastSAM) to change detection. Inspired by the human visual paradigm, a novel dual-stream feature decoder is derived to distinguish changes by combining semantic-aware features and difference-aware features. Secondly, we design a single-temporal semantic pre-training strategy to enhance the semantic understanding of landscapes, which brings further increments. Specifically, we construct pseudo-change detection data from public single-temporal remote sensing segmentation datasets for large-scale pre-training, where an extra branch is also introduced for the proxy semantic segmentation task. Experimental results on five challenging benchmarks demonstrate the superiority of our method over the existing stateof- the-art methods. The code is available at SA-CD. Copyright © 2024, The Authors. All rights reserved.

关键词： Change detection

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Deep Global Distance Estimation Hashing for Image Retrieval

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IEEE Transactions on Big Data 2025年

作者： Xu, Mengfei Luo, Bowen Xu, Jianfeng Ding, Feng The School of Software Nanchang University Jiangxi330047 China Department of Computer Science University of Regina ReginaSKS4S 0A2 Canada Nanchang Key Laboratory for Innovative Application of Information Technology China

Hashing technology has an excellent high dimensional visual information encoding ability. It can map distance information from high dimensional to low dimensional Hamming space, which is widely used in large-scale image nearest neighbor search tasks. Most recent studies, such as pair-wise and triplet-wise, use local positional relations to construct hashing functions. These learning methods, which are only based on local information, will lead to an incoherent feature representation, especially for semantic similarity features. Therefore, we propose a global distance estimation hashing (DEH). The DEH establishes a deep hashing model by constructing global distance relations that can define a distance between every paired category. Thus the distance relations constructed by DEH include much more detailed information. In other words, DEH focuses on global distance relations and makes the division of boundaries have excellent performance, especially for those samples with subtle changes. After the mapping, the model quantization approach effectively decreases the quantization loss caused by transitioning from the real-valued space to the Hamming space. Numerous experiments show that our DEH achieves excellent results on CIFAR-10, NUS-WIDE and ImageNet. These achievements not only validate the effectiveness of DEH in general image hashing retrieval tasks but also demonstrate its significant advantages in handling the large-scale multimedia retrieval systems. © 2015 IEEE. All rights reserved.

关键词： Content based retrieval

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A Novel Approach for User Demand-aware Data Center Construction and Service Consolidation 1

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29th International Conference on Web Services, ICWS 2022 Held as Part of the Services Conference Federation, SCF 2022

作者： Lv, Yifei Guo, Kunyin Xia, Yunni Li, Yin Ma, Yong Li, Fan Zeng, Linchengxi Peng, Qinglan Long, Tingyan College of Computer Science Chongqing University Chongqing400030 China Guangzhou Institute of Software Application Technology Guangzhou510000 China School of Computers and Information Jiangxi Normal University Nanchang330022 China Key Laboratory of Fundamental Synthetic Vision Graphics and Image Science for National Defense Sichuan University Chengdu610000 China Chongqing China School of Artificial Intelligence Henan University Kaifeng475001 China

ISBN: (数字)9783031235795

ISBN: (纸本)9783031235788

The recent intensifying computational demands from multinationals enterprises have motivated the magnification for large complicated cloud data centers (DCs) to handle business, monetary, Internet and commercial applications of different enterprises. A cloud data center encompasses thousands of physical server nodes arranged in racks along with network, storage, and other equipment that entails an extensive amount of power to process different processes and amenities required by business firms. More and more cloud data centers are turning for adapting to dynamics of user demands and reducing operational cost. Therefore, in this paper, we propose a user demand-aware (UDA) method for servers selection and a modified adaptive large neighbourhood search (MALNS) algorithm for dynamic service consolidation. Experiments based on real-world datasets demonstrate our approach outperformed conventional strategies in terms of multiple metrics. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Heuristic algorithms

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Defect Identification, Categorization, and Repair: Better Together

arXiv

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arXiv 2022年

作者： Ni, Chao Yang, Kaiwen Xia, Xin Lo, David Chen, Xiang Yang, Xiaohu School of Software Technology Zhejiang University Zhejiang Hangzhou310007 China College of Computer Science and Technology Zhejiang University Hangzhou China Software Engineering Application Technology Lab Huawei Hangzhou China Singapore Management University Singapore School of Information Science and Technology Nantong University Nantong China Computer Science and Technology Zhejiang University Hangzhou China

Just-In-Time defect prediction (JIT-DP) models can identify defect-inducing commits at check-in time. Even though previous studies have achieved a great progress, these studies still have the following limitations: 1) useful information (e.g., semantic information and structure information) are not fully used;2) existing work can only predict a commit as buggy one or clean one without more information about what type of defect it is;3) a commit may involve changes in many files, which cause difficulty in locating the defect;4) prior studies treat defect identification and defect repair as separate tasks, none aims to handle both tasks simultaneously. In this paper, to handle aforementioned limitations, we propose a comprehensive defect prediction and repair framework named CompDefect, which can identify whether a changed function (a more fine-grained level) is defect-prone, categorize the type of defect, and repair such a defect automatically if it falls into several scenarios, e.g., defects with single statement fixes, or those that match a small set of defect templates. Generally, the first two tasks in CompDefect are treated as a multiclass classification task, while the last one is treated as a sequence generation task. The whole input of CompDefect consists of three parts (exampled with positive functions): the clean version of a function (i.e., the version before defect introduced), the buggy version of a function and the fixed version of a function. In multiclass classification task, CompDefect categorizes the type of defect via multiclass classification with the information in both the clean version and the buggy version. In code sequence generation task, CompDefect repairs the defect once identified or keeps it unchanged. To verify the effectiveness of CompDefect, we first build a large-scale function-level dataset named Function-SStuBs4J, which totally contains 21,047 instances with three versions of modified functions and we then evaluate CompDefect with

关键词： Just in time production

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Rapid diagnosis of osteoarthritis using serum Raman spectroscopy based on MFCC transformation combined with MCNN 4

Rapid diagnosis of osteoarthritis using serum Raman spectros...

引用

4th International Conference on Optics and Image Processing, ICOIP 2024

作者： Xu, Hongze Chen, Chen Tian, Xuecong He, Liang Zuo, Enguang Chen, Xiaomei Wu, Lijun Chen, Cheng Lv, Xiaoyi College of Software Xinjiang University Urumqi830046 China College of Information Science and Engineering Xinjiang University Urumqi830046 China Key Laboratory of signal detection and processing Xinjiang University Urumqi830046 China Xinjiang Cloud Computing Application Laboratory Karamay834099 China Department of Rheumatology and Immunology People's Hospital of Xinjiang Uygur Autonomous Region Urumqi830001 China Xinjiang Clinical Research Center for Rheumatoid Arthritis Urumqi830001 China Department of Electronic Engineering Tsinghua National Laboratory for Information Science and Technology Tsinghua University Beijing China School of Information Science and Engineering Xinjiang University Ürümqi China

ISBN: (纸本)9781510682481

Osteoarthritis (OA) is a common chronic degenerative disease worldwide. Currently, traditional diagnostic methods for OA have disadvantages such as high cost and long time consumption. For this reason, it is urgent to have a method that can diagnose osteoarthritis quickly and accurately. During this study, we suggest a multi-column convolutional neural networks (MCNN) model based on Mel-frequency cepstral coefficient (MFCC). We first compare the effectiveness of Partial Least Squares (PLS) and MFCC in preprocessing the data and comprehensively evaluate the performance of the model by five-fold cross-validation. To further improve the ability of recognizing spectral features, We utilize the Spectral Segmentation Method (SSM) to construct the MFCC feature sequences into 2D spectral feature maps and input them into the MCNN for classification. The final experimental results show that our proposed mfc - ssm - mcnn model has a high classification efficiency in diagnosing osteoarthritis with an accuracy of 95.0%. © 2024 SPIE.

关键词： Raman spectroscopy

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A cross Transformer for image denoising

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Information Fusion 2024年 102卷

作者： Tian, Chunwei Zheng, Menghua Zuo, Wangmeng Zhang, Shichao Zhang, Yanning Lin, Chia-Wen School of Software Northwestern Polytechnical University Xi'anShaanxi710129 China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi'anShaanxi710129 China School of Computer Science and Technology Harbin Institute of Technology Heilongjiang Harbin150001 China Peng Cheng Laboratory Guangdong Shenzhen518055 China School of Computer Science and Engineering Central South University Hunan Changsha410083 China School of Computer Science Northwestern Polytechnical University Shaanxi Xi'an710129 China Department of Electrical Engineering and the Institute of Communications Engineering National Tsing Hua University Hsinchu30013 Taiwan

Deep convolutional neural networks (CNNs) depend on feedforward and feedback ways to obtain good performance in image denoising. However, how to obtain effective structural information via CNNs to efficiently represent given noisy images is key for complex scenes. In this paper, we propose a cross Transformer denoising CNN (CTNet) with a serial block (SB), a parallel block (PB), and a residual block (RB) to obtain clean images for complex scenes. A SB uses an enhanced residual architecture to deeply search structural information for image denoising. To avoid loss of key information, PB uses three heterogeneous networks to implement multiple interactions of multi-level features to broadly search for extra information for improving the adaptability of an obtained denoiser for complex scenes. Also, to improve denoising performance, Transformer mechanisms are embedded into the SB and PB to extract complementary salient features for effectively removing noise in terms of pixel relations. Finally, a RB is applied to acquire clean images. Experiments illustrate that our CTNet is superior to some popular denoising methods in terms of real and synthetic image denoising. It is suitable to mobile digital devices, i.e., phones. Codes can be obtained at https://***/hellloxiaotian/CTNet. © 2023 Elsevier B.V.

关键词： Image denoising

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A self-supervised CNN for image watermark removal

arXiv

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arXiv 2024年

作者： Tian, Chunwei Zheng, Menghua Jiao, Tiancai Zuo, Wangmeng Zhang, Yanning Lin, Chia-Wen the School of Software Northwestern Polytechnical University Shaanxi Xi’an710129 China the National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Xi’an710129 China the School of Computer Science and Technology Harbin Institute of Technology Heilongjiang Harbin150001 China the Peng Cheng Laboratory Laboratory Guangdong Shenzhen518055 China the School of Computer Science Northwestern Polytechnical University the National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Xi’an710129 China the Department of Electrical Engineering the Institute of Communications Engineering National Tsing Hua University Hsinchu300 Taiwan

Popular convolutional neural networks mainly use paired images in a supervised way for image watermark removal. However, watermarked images do not have reference images in the real world, which results in poor robustness of image watermark removal techniques. In this paper, we propose a self-supervised convolutional neural network (CNN) in image watermark removal (SWCNN). SWCNN uses a self-supervised way to construct reference watermarked images rather than given paired training samples, according to watermark distribution. A heterogeneous U-Net architecture is used to extract more complementary structural information via simple components for image watermark removal. Taking into account texture information, a mixed loss is exploited to improve visual effects of image watermark removal. Besides, a watermark dataset is conducted. Experimental results show that the proposed SWCNN is superior to popular CNNs in image watermark removal. Codes can be obtained at https://***/hellloxiaotian/SWCNN. Copyright © 2024, The Authors. All rights reserved.

关键词： Textures

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New Insights on Relieving Task-Recency Bias for Online Class Incremental Learning

arXiv

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arXiv 2023年

作者： Liang, Guoqiang Chen, Zhaojie Chen, Zhaoqiang Ji, Shiyu Zhang, Yanning Northwestern Polytechnical University 710072 China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology China Key Laboratory of Big Data Storage and Management China School of Computer Science China School of Software Engineering China

—To imitate the ability of keeping learning of human, continual learning which can learn from a never-ending data stream has attracted more interests recently. In all settings, the online class incremental learning (OCIL), where incoming samples from data stream can be used only once, is more challenging and can be encountered more frequently in real world. Actually, all continual learning models face a stability-plasticity dilemma, where the stability means the ability to preserve old knowledge while the plasticity denotes the ability to incorporate new knowledge. Although replay-based methods have shown exceptional promise, most of them concentrate on the strategy for updating and retrieving memory to keep stability at the expense of plasticity. To strike a preferable trade-off between stability and plasticity, we propose an Adaptive Focus Shifting algorithm (AFS), which dynamically adjusts focus to ambiguous samples and non-target logits in model learning. Through a deep analysis of the task-recency bias caused by class imbalance, we propose a revised focal loss to mainly keep stability. By utilizing a new weight function, the revised focal loss will pay more attention to current ambiguous samples, which are the potentially valuable samples to make model progress quickly. To promote plasticity, we introduce a virtual knowledge distillation. By designing a virtual teacher, it assigns more attention to non-target classes, which can surmount overconfidence and encourage model to focus on inter-class information. Extensive experiments on three popular datasets for OCIL have shown the effectiveness of AFS. The code will be available at https://***/czjghost/AFS. Copyright © 2023, The Authors. All rights reserved.

关键词： Distillation

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A two-stage learning approach for wind power prediction using dynamic graph convolution network hybrid model

A two-stage learning approach for wind power prediction usin...

引用

IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Hailong Li Yongsheng Wang Shijie Guan Xuehui Wang Daorina Bao Xiaojun Liu Haotian Chen College of Data Science and Application Inner Mongolia University of Technology Huhhot China College of Information and Electrical Engineering China Agricultural University Beijing China College of Energy and Power Engineering Inner Mongolia University of Technology Hohhot China Inner Mongolia Wuchuan County Water Affairs Bureau Hohhot China School of International Information and Software Dalian University of Technology Dalian China

ISBN: (数字)9798331523527

ISBN: (纸本)9798331523534

This paper proposes an effective two-stage learning method to improve the accuracy of wind power prediction. The complex time dependency relationship between meteorological data variables is modelled using a dynamic graph convolutional network, and the wind power prediction is completed using the XGBoost model. Experimental results show that the two-stage learning method can effectively extract key time series features and has good prediction accuracy.

关键词： Learning systems Accuracy Graph convolutional networks Predictive models Wind power generation Feature extraction Data models Hybrid power systems Numerical models Wind forecasting

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Joint Admission Control and Resource Allocation of Virtual Network Embedding via Hierarchical Deep Reinforcement Learning

arXiv

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arXiv 2024年

作者： Wang, Tianfu Shen, Li Fan, Qilin Xu, Tong Liu, Tongliang Xiong, Hui Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science University of Science and Technology of China Hefei230027 China The School of Big Data and Software Engineering Chongqing University ChinaScience Chongqing401331 China JD Explore Academy The UBTECH Sydney Artificial Intelligence Centre The School of Information Technologies Faculty of Engineering and Information Technologies The University of Sydney DarlingtonNSW2008 Australia Thrust of Artificial Intelligence Nansha Guangdong Guangzhou511400 China The Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong

As an essential resource management problem in network virtualization, virtual network embedding (VNE) aims to allocate the finite resources of physical network to sequentially arriving virtual network requests (VNRs) with different resource demands. Since this is an NP-hard combinatorial optimization problem, many efforts have been made to provide viable solutions. However, most existing approaches have either ignored the admission control of VNRs, which has a potential impact on long-term performances, or not fully exploited the temporal and topological features of the physical network and VNRs. In this paper, we propose a deep Hierarchical Reinforcement Learning approach to learn a joint Admission Control and Resource Allocation policy for VNE, named HRL-ACRA. Specifically, the whole VNE process is decomposed into an upper-level policy for deciding whether to admit the arriving VNR or not and a lower-level policy for allocating resources of the physical network to meet the requirement of VNR through the HRL approach. Considering the proximal policy optimization as the basic training algorithm, we also adopt the average reward method to address the infinite horizon problem of the upper-level agent and design a customized multi-objective intrinsic reward to alleviate the sparse reward issue of the lower-level agent. Moreover, we develop a deep feature-aware graph neural network to capture the features of VNR and physical network and exploit a sequence-to-sequence model to generate embedding actions iteratively. Finally, extensive experiments are conducted in various settings, and show that HRL-ACRA outperforms state-of-the-art baselines in terms of both the acceptance ratio and long-term average revenue. Our code is available at https://***/GeminiLight/hrl-acra. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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