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arXiv 2025年

作者： Zhu, Fenghao Wang, Xinquan Li, Xinyi Zhang, Maojun Chen, Yixuan Huang, Chongwen Yang, Zhaohui Chen, Xiaoming Zhang, Zhaoyang Jin, Richeng Huang, Yongming Feng, Wei Yang, Tingting Bai, Baoming Gao, Feifei Yang, Kun Liu, Yuanwen Muhaidat, Sami Yuen, Chau Huang, Kaibin Wong, Kai-Kit Niyato, Dusit Debbah, Mérouane College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China School of Automation Southeast University Nanjing210096 China Tsinghua University Beijing100084 China Pengcheng Laboratory Shenzhen518066 China State Key Laboratory of Integrated Service Networks Xidian University Xi’an710071 China State Key Laboratory of Novel Software Technology Nanjing University Nanjing210008 China Suzhou215163 China The University of Hong Kong Hong Kong KU 6G Research Center Computer and Communication Engineering Khalifa University Abu Dhabi127788 United Arab Emirates School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore Department of Electronic and Electrical Engineering University College London LondonWC1E 7JE United Kingdom College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore KU 6G Research Center Department of Computer and Information Engineering Khalifa University Abu Dhabi127788 United Arab Emirates

The emergence of sixth-generation and beyond communication systems is expected to fundamentally transform digital experiences through introducing unparalleled levels of intelligence, efficiency, and connectivity. A promising technology poised to enable this revolutionary vision is the wireless large AI model (WLAM), characterized by its exceptional capabilities in data processing, inference, and decision-making. In light of these remarkable capabilities, this paper provides a comprehensive survey of WLAM, elucidating its fundamental principles, diverse applications, critical challenges, and future research opportunities. We begin by introducing the background of WLAM and analyzing the key synergies with wireless networks, emphasizing the mutual benefits. Subsequently, we explore the foundational characteristics of WLAM, delving into their unique relevance in wireless environments. Then, the role of WLAM in optimizing wireless communication systems across various use cases and the reciprocal benefits are systematically investigated. Furthermore, we discuss the integration of WLAM with emerging technologies, highlighting their potential to enable transformative capabilities and breakthroughs in wireless communication. Finally, we thoroughly examine the high-level challenges hindering the practical implementation of WLAM and discuss pivotal future research directions. Copyright © 2025, The Authors. All rights reserved.

关键词： Sorting

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You Only Align Once: Bidirectional Interaction for Spatial-Temporal Video Super-Resolution

arXiv

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arXiv 2022年

作者： Hu, Mengshun Jiang, Kui Nie, Zhixiang Wang, Zheng National Engineering Research Center for Multimedia Software Hubei Key Laboratory of Multimedia and Network Communication Engineering School of Computer Science Wuhan University China

Spatial-Temporal Video Super-Resolution (ST-VSR) technology generates high-quality videos with higher resolution and higher frame rates. Existing advanced methods accomplish ST-VSR tasks through the association of Spatial and Temporal video super-resolution (S-VSR and T-VSR). These methods require two alignments and fusions in S-VSR and T-VSR, which is obviously redundant and fails to sufficiently explore the information flow of consecutive spatial LR frames. Although bidirectional learning (future-to-past and past-to-future) was introduced to cover all input frames, the direct fusion of final predictions fails to sufficiently exploit intrinsic correlations of bidirectional motion learning and spatial information from all frames. We propose an effective yet efficient recurrent network with bidirectional interaction for ST-VSR, where only one alignment and fusion is needed. Specifically, it first performs backward inference from future to past, and then follows forward inference to super-resolve intermediate frames. The backward and forward inferences are assigned to learn structures and details to simplify the learning task with joint optimizations. Furthermore, a Hybrid Fusion Module (HFM) is designed to aggregate and distill information to refine spatial information and reconstruct high-quality video frames. Extensive experiments on two public datasets demonstrate that our method outperforms state-of-the-art methods in efficiency, and reduces calculation cost by about 22%. Copyright © 2022, The Authors. All rights reserved.

关键词： Optical resolving power

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Fast target-aware learning for few-shot video object segmentation

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Science China(Information Sciences) 2022年第8期65卷 71-86页

作者： Yadang CHEN Chuanyan HAO Zhi-Xin YANG Enhua WU Engineering Research Center of Digital Forensics Ministry of EducationSchool of Computer and SoftwareNanjing University of Information Science and Technology School of Education Science and Technology Nanjing University of Posts and Telecommunications State Key Laboratory of Internet of Things for Smart City Department of Electromechanical EngineeringUniversity of Macau State Key Laboratory of Computer Science Institute of SoftwareUniversity of Chinese Academy of Sciences Faculty of Science and Technology University of Macau

Few-shot video object segmentation(FSVOS) aims to segment a specific object throughout a video sequence when only the first-frame annotation is given. In this study, we develop a fast target-aware learning approach for FSVOS, where the proposed approach adapts to new video sequences from its firstframe annotation through a lightweight procedure. The proposed network comprises two models. First, the meta knowledge model learns the general semantic features for the input video image and up-samples the coarse predicted mask to the original image size. Second, the target model adapts quickly from the limited support set. Concretely, during the online inference for testing the video, we first employ fast optimization techniques to train a powerful target model by minimizing the segmentation error in the first frame and then use it to predict the subsequent frames. During the offline training, we use a bilevel-optimization strategy to mimic the full testing procedure to train the meta knowledge model across multiple video *** proposed method is trained only on an individual public video object segmentation(VOS) benchmark without additional training sets and compared favorably with state-of-the-art methods on DAVIS-2017, with a J &F overall score of 71.6%, and on YouT ubeVOS-2018, with a J &F overall score of 75.4%. Meanwhile,a high inference speed of approximately 0.13 s per frame is maintained.

关键词： video object segmentation few-shot target-aware meta knowledge bilevel-optimization

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Discovering Temporal Patterns from Images using Extended PLSA

Discovering Temporal Patterns from Images using Extended PLS...

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International Conference on Multimedia Technology

作者： Liao, Xiaofeng Wang, Yongji Ding, Liping National Engineering Research Center for Fundamental Software Institute of Software Chinese Academy of Science Beijing 100190 China Laboratory for Internet Software Technologies Institute of Software Chinese Academy of Sciences Beijing 100190 China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing 100190 China Graduate University Chinese Academy of Sciences Beijing 100049 China Information Engineering School Nanchang University Nanchang Jiangxi 330031 China

ISBN: (纸本)9781424478743

This paper considers the problem of modelling the topics in a sequence of images with known time stamp. Detecting and tracking of temporal data is an important task in multiple applications, such as finding hot research point from scientific literature, news article series analysis, email surveillance, search query log mining, etc. In contrast to existing works mainly focusing on text document collections, this paper considers mining temporal topic trends from image data set. An extension of the Probabilistic Latent Semantic Analysis(PLSA) model, which includes an additional variable associated with the time stamp to better model the temporal topics, is presented to track not only topics among images but also how topics change over time. Experiments show the effectiveness of this method. ©2010 IEEE.

关键词： Data mining

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Makespan and Security-Aware Workflow Scheduling for Cloud Service Cost Minimization Using Firefly Optimizer 22nd

Makespan and Security-Aware Workflow Scheduling for Cloud ...

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22nd International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2022

作者： Zhou, Chengliang Wang, Tian Li, Liying Sun, Jin Zhou, Junlong School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China National Trusted Embedded Software Engineering Technology Research Center East China Normal University Shanghai200062 China

ISBN: (纸本)9783031226762

As efficient commercial information technology, cloud computing has attracted more and more users to submit their requests to the cloud platform and pay for them based on the amount and quality of services. One of the most important concerns of cloud service providers is to minimize the monetary cost for completing users’ requests while meeting constraints on the quality of services such as makespan and security. To solve this concern, this paper proposes a makespan and security-aware workflow scheduling method based on an improved firefly optimizer that consists of a new solution initialization scheme, a firefly position updating scheme, a firefly-to-solution mapping operator, and two task-to-virtual machine assignment schemes. We carry out extensive simulation experiments on real-world workflows to validate our proposed workflow scheduling method. The results reveal that the monetary cost reduction achieved by our method can be up to 54.0% compared to a baseline and two state-of-the-art approaches. © 2023, Springer Nature Switzerland AG.

关键词： Cloud computing

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Data-Driven Active Power Dispatch for Wind Farms 13

Data-Driven Active Power Dispatch for Wind Farms

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13th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2024

作者： Jiao, Xuguo Zhang, Daoyuan Wang, Xin Ma, Zhaoxing Zhang, Zhenyong Liu, Wenfeng School of Information and Control Engineering Qingdao University of Technology Qingdao266520 China College of Control Science and Engineering Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Jinan250014 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250014 China College of Computer Science and Technology Guizhou University State Key Laboratory of Public Big Data Guiyang550025 China School of Civil Engineering Qingdao University of Technology Qingdao266520 China

ISBN: (纸本)9798350361674

Active power dispatch of wind farms plays an important role in power grid scheduling. In this paper, a data-driven active power dispatch strategy for wind farms is proposed, which uses the key point of minimizing the standard deviation of wind turbines' output power series to reduce their fluctuations. First, to build the optimization problem, the historical active power output data of each wind turbine is collected from the Supervisory Control And Data Acquisition (SCADA) system. Then, the objective function is developed based on the idea of minimizing the standard deviation of the output power time series. Meanwhile, considering the constraints of wind turbines' output power and active power balance of the grid, the feasible domain of the cost function is constructed. Further, to solve the proposed nonlinear optimization problem with equality and inequality constraints, the trust region interior point method (TRIPM) is employed to explore optimal solution within the feasible domain. Finally, actual data collected from wind farms is used to test effectiveness of the designed approach and experimental results show that our method can achieve better output power smoothing performance than conventional Proportional Dispatch (PD) method. © 2024 IEEE.

关键词： Cost functions

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Dual-Domain Multi-Model GAN Fingerprint Restoration for Compressed Fake Face Attribution

Dual-Domain Multi-Model GAN Fingerprint Restoration for Comp...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Chengxiang Fan Aohong Shen Zhen Han Cai Tong Zhongyuan Wang Dekang Yi National Engineering Research Center for Multimedia Software and Hubei Key Laboratory of Multimedia and Network Communication Engineering School of Computer Science Wuhan University Wuhan China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Recent advances in GAN fingerprint have shown increasing success in fake face attribution. However, the fake faces are usually compressed during network transmission, which causes the degradation of GAN fingerprint and the decrease of attribution accuracy. To this issue, a dual-domain multi-model GAN fingerprint restoration method for compressed fake face attribution is proposed in this paper. Firstly, considering that image-domain and fingerprint-domain are directly and indirectly affected by compression respectively, we propose a dual-domain parallel restoration architecture that enhances GAN fingerprint using direct image-domain and indirect fingerprint-domain restoration, thereby improving attribution performance by mining the cross-domain complementarity. Secondly, since real and fake GAN-speciffc restoration models can describe GAN fingerprint from different aspects, we first enhance GAN fingerprint by multiple restoration models, and then improve attribution performance by exploiting the cross-model complementarity through the multi-model restoration fusion strategy. Experiments demonstrate the superiority of our method under different compression qualities.

关键词： Degradation Image coding Accuracy Fingerprint recognition Generative adversarial networks Image restoration Faces

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A deduplication algorithm based on data similarity and delta encoding 4th

A deduplication algorithm based on data similarity and delta...

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4th International Conference on Geo-Informatics in Resource Management and Sustainable Ecosystem, GRMSE 2016

作者： Song, Bin Xiao, Limin Qin, Guangjun Ruan, Li Qiu, Shida State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing100191 China National Engineering Research Center for Science and Technology Resources Sharing Service Beijing100191 China Space Star Technology Co. Ltd. Beijing100086 China

ISBN: (纸本)9789811039683

Satellite applications such as remote sensing application are overwhelmed with vast quantities of data. Nevertheless, the storage resources in the satellite are so limited that it should be used more efficient. The similarity between the remote sensing data is high, but the dissimilar parts of the data distribute irregularly. When using the traditional deduplication algorithm to split the file into chunks, a large amount of chunks are exactly similar but not the same, which results in the bad effect of data deduplication. We propose a deduplication algorithm based on data similarity and delta encoding to reduce the usage of storage resources. The data similarity analysis can find out the similar data. The delta encoding technology can reduce the usage of storage resources. Through experiments on remote sensing application data, we have achieved deduplication ratios up to 30:1, and analyzed how the chunksize affect the experiment results. © Springer Nature Singapore Pte Ltd. 2017.

关键词： Satellites

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An Auto-Parallel Method for Deep Learning Models Based on Genetic Algorithm 29

An Auto-Parallel Method for Deep Learning Models Based on Ge...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Zeng, Yan Huang, Chengchuang Ni, Yijie Zhou, Chunbao Zhang, Jilin Wang, Jue Zhou, Mingyao Xue, Meiting Zhang, Yunquan Hangzhou Dianzi University School of Computer Science and Technology Hangzhou310018 China Ministry of Education Key Laboratory for Modeling and Simulation of Complex Systems Hangzhou310018 China Data Security Governance Zhejiang Engineering Research Center Hangzhou310018 China Hangzhou Dianzi University School of ITMO Joint Institute Hangzhou310018 China Institute of Computer Network Information Center of the Chinese Academy of Sciences Beijing100086 China HuaWei China Institute of Computing Technology of the Chinese Academy of Sciences State Key Laboratory of Computer Architecture Beijing100086 China

ISBN: (纸本)9798350330717

As the size of datasets and neural network models increases, automatic parallelization methods for models have become a research hotspot in recent years. The existing auto-parallel methods based on machine learning or graph algorithms still have issues with search efficiency and applicability. This paper proposes an automatic parallel method based on a dual-population genetic algorithm, TGA, which transforms model partitioning and placement into an integer linear programming problem and constructs a cost model to evaluate the solution. The solution space is built using the neural network's dataflow graph and device cluster's topology, and the dual-population genetic algorithm is used to search for the optimal model parallel strategy. Experiments with various models show that the proposed method can improve single-step execution time by up to 42% compared to the Baechi method and up to 37.7% compared to the Hierarchical method. © 2023 IEEE.

关键词： Auto-Parallel Distributed machine learning Genetic algorithm Integer linear programming Model parallelism

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A cooperative OA system based on SaaS supporting multi-tenant business process customization

A cooperative OA system based on SaaS supporting multi-tenan...

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2014 International Conference on Education,Management and Computing Technology

作者： Hua Huang Yi-lai Zhang Zaiwen Feng Jiangxi Ceramic Enterprise Information Technology Engineering Research Center Jingdezhen Ceramic Institute State Key Laboratory of Software Engineering School of Computer

The cooperative OA system is a subsystem of the ceramic cloud technology service integration platform, which is a multi-tenant application software based on SaaS. With the use of database accessing middleware and the technology of single sign-on(SSO) and role-based rights management, it integrates and shares the resources of other subsystems in this platform. A framework for multi-tenant business process customization is pulled into this system, through the component of business process visualization self-definition;this system implements multi-tenant business process customization for meeting individual business requirements of different company tenant's online office.

关键词： SaaS Multi-tenant management Business process customization cooperative OA.

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