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IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Li, Xin Yuan, Kun Pei, Yajing Lu, Yiting Sun, Ming Zhou, Chao Chen, Zhibo Timofte, Radu Sun, Wei Wu, Haoning Zhang, Zicheng Jia, Jun Zhang, Zhichao Cao, Linhan Chen, Qiubo Min, Xiongkuo Lin, Weisi Zhai, Guangtao Sun, Jianhui Wang, Tianyi Li, Lei Kong, Han Wang, Wenxuan Li, Bing Luo, Cheng Wang, Haiqiang Chen, Xiangguang Meng, Wenhui Pan, Xiang Shi, Huiying Zhu, Han Xu, Xiaozhong Sun, Lei Chen, Zhenzhong Liu, Shan Kong, Fangyuan Fan, Haotian Xu, Yifang Xu, Haoran Yang, Mengduo Zhou, Jie Li, Jiaze Wen, Shijie Xu, Mai Li, Da Yao, Shunyu Du, Jiazhi Zuo, Wangmeng Li, Zhibo He, Shuai Ming, Anlong Fu, Huiyuan Ma, Huadong Wu, Yong Xue, Fie Zhao, Guozhi Du, Lina Guo, Jie Zhang, Yu Zheng, Huimin Chen, Junhao Liu, Yue Zhou, Dulan Xu, Kele Xu, Qisheng Sun, Tao Ding, Zhixiang Hu, Yuhang Univ Sci & Technol China Hefei Peoples R China Kuaishou Technol Beijing Peoples R China Univ Wurzburg Wurzburg Germany Shanghai Jiao Tong Univ Shanghai Peoples R China Nanyang Technol Univ Singapore Singapore Xiaohongshu Shanghai Peoples R China WeChat Beijing Peoples R China Tencent Shenzhen Peoples R China Wuhan Univ Wuhan Peoples R China ByteDance Shenzhen Peoples R China Zhejiang Univ Hangzhou Peoples R China Beihang Univ Dept Elect & Informat Engn Beijing Peoples R China Southern Univ Sci & Technol Shenzhen Peoples R China Harbin Inst Technol Harbin Peoples R China Beijing Univ Posts & Telecommun Beijing Peoples R China China Merchants Bank Shenzhen Peoples R China Shandong Jianzhu Univ Jinan Peoples R China Key Lab Parallel & Distributed Proc Changsha Peoples R China Chinese Acad Sci Inst Automat Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9798350365474

This paper reviews the NTIRE 2024 Challenge on Short-form UGC Video Quality Assessment (S-UGC VQA), where various excellent solutions are submitted and evaluated on the collected dataset KVQ from popular short-form video platform, i.e., Kuaishou/Kwai Platform. The KVQ database is divided into three parts, including 2926 videos for training, 420 videos for validation, and 854 videos for testing. The purpose is to build new benchmarks and advance the development of S-UGC VQA. The competition had 200 participants and 13 teams submitted valid solutions for the final testing phase. The proposed solutions achieved state-of-the-art performances for S-UGC VQA. The project can be found at https://***/lixinustc/KVQ-Challenge-CVPR- NTIRE2024.

关键词： HTTP

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Learning to Communicate Efficiently with Group Division in Decentralized Multi-Agent Cooperation 13

Learning to Communicate Efficiently with Group Division in D...

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13th IEEE International Conference on Service-Oriented System Engineering (SOSE) / 10th International Workshop on Joint Cloud Computing (JCC) / IEEE International Workshop on Cloud Computing in Robotic Systems (CCRS)

作者： Liu, Shuqi Geng, Mingyang Xu, Kele Northeastern Univ Natl Key Lab Big Data Management & Anal Shenyang Liaoning Peoples R China Natl Univ Def Technol Natl Key Lab Parallel & Distributed Proc Changsha Hunan Peoples R China

ISBN: (数字)9781728114422

ISBN: (纸本)9781728114422

Recent advances in multi-agent reinforcement learning show that agents can spontaneously learn when and what to communicate with each other to support effective cooperation. However, the existing approaches assume a fully-connected network with unlimited bandwidth, which is impractical in many real-world scenarios. For instance, in many multi-robot applications, robots are connected only through an unstable wireless network with limited bandwidth. Therefore, we must enable the agents to learn communication strategy that takes the consumption of network resources into account. This paper proposes a group division-based attentional communication model (GDAC), which can divide agents into groups according to their "attention" in the learned communication strategy. According to the novel "attention" mechanism, agents can be dynamically grouped according to their task relevance, and the communication only takes places inside the same group. As a result, it avoids a fully-connected communication architecture and can significantly reduce the bandwidth consumption at runtime. This model has been successfully applied to the environmental exploration task with a group of agents. The results show that GDAC could effectively reduce the total amount of communication message and yield improved performance over the existing fully-connected communication architecture.

关键词： multi-agent cooperation attention mechanism deep reinforcement learning

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Real Network Traffic Collection and Deep Learning for Mobile App Identification

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WIRELESS COMMUNICATIONS & MOBILE COMPUTING 2020年第1期2020卷

作者： Wang, Xin Chen, Shuhui Su, Jinshu Natl Univ Def Technol Sch Comp Changsha 410073 Hunan Peoples R China Natl Univ Def Technol Natl Key Lab Parallel & Distributed Proc PDL Changsha 410073 Hunan Peoples R China

The proliferation of mobile devices over recent years has led to a dramatic increase in mobile traffic. Demand for enabling accurate mobile app identification is coming as it is an essential step to improve a multitude of network services: accounting, security monitoring, traffic forecasting, and quality-of-service. However, traditional traffic classification techniques do not work well for mobile traffic. Besides, multiple machine learning solutions developed in this field are severely restricted by their handcrafted features as well as unreliable datasets. In this paper, we propose a framework for real network traffic collection and labeling in a scalable way. A dedicated Android traffic capture tool is developed to build datasets with perfect ground truth. Using our established dataset, we make an empirical exploration on deep learning methods for the task of mobile app identification, which can automate the feature engineering process in an end-to-end fashion. We introduce three of the most representative deep learning models and design and evaluate our dedicated classifiers, namely, a SDAE, a 1D CNN, and a bidirectional LSTM network, respectively. In comparison with two other baseline solutions, our CNN and RNN models with raw traffic inputs are capable of achieving state-of-the-art results regardless of TLS encryption. Specifically, the 1D CNN classifier obtains the best performance with an accuracy of 91.8% and macroaverage F-measure of 90.1%. To further understand the trained model, sample-specific interpretations are performed, showing how it can automatically learn important and advanced features from the uppermost bytes of an app's raw flows.

关键词： Cryptography

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Filtering Inconsistent Failure in Robot Collective Decision with Blockchain

Filtering Inconsistent Failure in Robot Collective Decision ...

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IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)

作者： Luo, Jie Ding, Bo Xu, Jie Natl Univ Def Technol Natl Key Lab Parallel & Distributed Proc Coll Comp Changsha Peoples R China Univ Leeds Sch Comp Leeds W Yorkshire England

ISBN: (纸本)9781728124933

Robotic swarm is one of typical and significant distributed systems. Fault tolerance is an important and essential ability for such systems because individual robot may malfunction and even maliciously attack, thereby interfering with reaching consensus on a decision at the swarm level. One kind of fault which is difficult to deal with is called inconsistent failure (ie. Byzantine failure). It is shown as that a robot exhibits different behavior at different times to other members of the swarm. Some previous work have proved that its feasible to exclude the interference of consistent failures via blockchain technology. We expanded the work to solve the problem in situation with inconsistent failures. In this paper, we proposed a method to filter inconsistent failures with blockchain in a collective decision task of robot swarm. The basic idea is to use the blockchain to record the behaviors of the robots, and then automatically audit these behaviors through the rules in the smart contract to achieve the purpose of detecting the robot with inconsistent failures. The feasibility and effectiveness of our approach is validated by a series of experiments in a voting scenario.

关键词： Robot Swarm distributed Consensus Blockchain Inconsistent Failure

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Correlation Self-Expression Shrunk for Subspace Clustering

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IEEE ACCESS 2020年 8卷 16595-16605页

作者： Wang, Tuo Zhang, Xiang Lan, Long Liao, Qing Xu, Chuanfu Luo, Zhigang Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Changsha 410073 Peoples R China Natl Univ Def Technol Coll Comp Changsha 410073 Peoples R China Natl Univ Def Technol Inst Quantum Informat Changsha 410073 Peoples R China Natl Univ Def Technol State Key Lab High Performance Comp Changsha 410073 Peoples R China Harbin Inst Technol Shenzhen Dept Comp Sci & Technol Shenzhen 518057 Peoples R China

In subspace clustering, low-rank representation (LRR) is a nice recipe for learning a good affinity matrix of a set of unlabeled data by representing themselves under the low-rank constraint in order to subsequently couple with clustering methods to divide data into several groups. However, the underlying geometric structure within data is still insufficiently explored in LRR. For this reason, we propose a correlation self-expression shrunk (CSS) to refresh LRR with two novel insights: 1) the compound schatten p-norm instead of the nuclear norm which is used to induce adaptive affinity matrix by addressing the issues of both sparse and dense representation;2) to model compact group structure and relieve side effect of the outliers, CSS designs a robust self-expression shrunk regularization to reduce the deviation of similar samples. Thus, CSS can reap the benefits of both insights to boost clustering performance. In addition, we optimize CSS in the frame of alternating direction multiplier method (ADMM) where each sub-problem has a closed-form solution. Experiments of image clustering on four datasets verify the efficacy of CSS against several baseline variants of LRR.

关键词： Adaptive loss low-rank representation schatten p-norm shrunk pattern

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Crustal structure and deformation in southeastern China revealed by receiver functions

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JOURNAL OF ASIAN EARTH SCIENCES 2021年 221卷 104937-104937页

作者： Shahzad, Syed Muzyan Liu, Jianxin Sun, Ya Li, Chuan Cent South Univ Sch Geosci & Infophys Changsha 410083 Peoples R China Cent South Univ Key Lab Metallogen Predict Nonferrous Met Minist Educ Changsha 410083 Peoples R China Sci & Technol Parallel & Distributed Proc Lab PDL Changsha Hunan Peoples R China Guilin Univ Technol Coll Earth Sci Guilin 541006 Peoples R China

The subduction of the Paleo-Pacific and Philippine plates generates large-scale magmatism and crustal thinning in southeastern (SE) China. However, the dynamic process for crustal thinning in this region is still not well understood. In this study, a large number of receiver function data were used to investigate the crustal thickness and Vp/Vs ratio beneath SE China by the H-kappa stacking technique. We also apply a comprehensive analysis method on the Moho Ps converted wave in radial and transverse receiver functions to calculate crustal seismic anisotropy with fast polarization direction and splitting time. The measured results show that the high Vp/Vs ratio (-1.85) was mainly spread around two areas, forming a "V-shaped" zone. The western branch of the "Vshaped" zone was NS-oriented with a thin crustal thickness of -29.1 km beneath the Nanxiang-Jianghan basin. The eastern branch extended along the Tanlu fault with a thin crustal thickness of -28.8 km. Moreover, the measured crustal seismic anisotropy also shows that most stations located near the "V-shaped" zone exhibit significant crustal anisotropy with splitting times of 0.20-0.72 s. The fast directions of these stations are parallel to the direction of the V-shaped extension. Combining the geological data, we propose that the "hot fingers" may grow from mantle corner flow in the southern part of the V-shaped zone associated with the collision of the Pacific and Philippine plates. We deduce that the V-shaped crustal thinning and Moho uplift in SE China might be attributed to this mantle corner flow.

关键词： Receiver functions Vp/Vs ratio Seismic anisotropy V-shaped crustal thinning SE China

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Practical privacy-preserving deep packet inspection outsourcing

Practical privacy-preserving deep packet inspection outsourc...

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9th International Symposium on Cyberspace Safety and Security (CSS)

作者： Li, Jie Su, Jinshu Chen, Rongmao Wang, Xiaofeng Chen, Shuhui Natl Univ Def Technol Sch Comp Changsha Hunan Peoples R China Natl Univ Def Technol Natl Key Lab Parallel & Distributed Proc Changsha Hunan Peoples R China

Hardware-based middleboxes are ubiquitous in computer networks, which usually incur high deployment and management expenses. A recently arising trend aims to address those problems by outsourcing the functions of traditional hardware-based middleboxes to high volume servers in a cloud. This technology is promising but still faces a few challenges from different aspects, including privacy concerns, middlebox functionality, and performance. In this paper, we propose two practical approaches to implementing a cloud-based DPI middlebox. The outsourced DPI middlebox performs payload inspection over encrypted traffic while preserving the privacy of both communication data and inspection rules. Our first approach employs a modified reversible sketch structure, which is used for efficient error-free membership testing, and our second approach extends the famous AC pattern matching algorithm to the cipher text domain. We utilize unkeyed one-way hash functions instead of complex cryptographic protocols to achieve the privacy preservation requirements. Our system supports a wide range of real-world inspection rules. We conduct evaluations on the ClamAV rule set, and the experiment results demonstrate the effectiveness of our proposals.

关键词： DPI outsourcing network function virtualization privacy preservation

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Edge cloud resource expansion and shrinkage based on workload for minimizing the cost

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2019年 101卷 327-340页

作者： Li Chunlin Sun, Hezhi Yi, Chen Luo, Youlong Wuhan Univ Technol Sch Comp Sci & Technol Wuhan 430063 Hubei Peoples R China Natl Univ Def Technol Coll Comp Natl Key Lab Parallel & Distributed Proc Changsha Hunan Peoples R China Beijing Technol & Business Univ Beijing Key Lab Big Data Technol Food Safety Beijing Peoples R China

The joint use of cloud and edge has been paid more and more attention by enterprises. Enterprises use the cloud's utility computing and elasticity to placement resources flexibly under the adaptive load. However, there are still many challenges in how to minimize edge cloud costs and ensure data integrity in the resource expansion and shrinkage of edge cloud. This paper studies the resource management problem based on load balance in edge and cloud environments. The novelty is that we consider the resource granularity of the cloud service provider and the data loss problem that resource shrinkage may bring. Firstly, the resource expansion and shrinkage model based on service cost is proposed to reduce the cost of edge cloud clusters under the condition of satisfying the cluster load. Secondly, due to the problem of unbalanced cluster load caused by resource expansion and the data reliability caused by resource shrinkage, a data migration model is proposed aiming at the data loss problem that resource shrinkage may bring. Finally, the proposed algorithms are evaluated with extensive comparisons among the benchmarks and sensitivity tests to various environment factors. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Edge cloud architecture Resource expansion and shrinkage Data migration

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Dynamic multi-objective optimized replica placement and migration strategies for SaaS applications in edge cloud

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2019年 100卷 921-937页

作者： Li Chunlin Wang YaPing Tang Hengliang Luo Youlong Wuhan Univ Technol Dept Comp Sci Wuhan 430063 Hubei Peoples R China Natl Univ Def Technol Coll Comp Natl Key Lab Parallel & Distributed Proc Changsha Hunan Peoples R China Beijing Wuzi Univ Sch Informat Beijing 101149 Peoples R China

The multi-replica strategy widely used in the traditional cloud makes storage costly and the replica cannot be accurately located. Due to many heterogeneous nodes in different locations of the edge cloud system, inefficient replica strategy will greatly affect the load balancing of the system. Therefore, the dynamic multi-objective optimized replica placement and migration strategies for SaaS applications in edge cloud are proposed in this paper. The multi-objective optimized replica placement problem is solved according to the fast non-dominated sorting genetic algorithm. In the experiment, the algorithm of replica placement will minimize the effective network utilization, reduce the response time and guarantee the load balancing of data nodes. Moreover, the replica migration model for access hotspots is proposed to obtain the pairing migration relationship from source node to target node. In the experiment, the replica migration algorithm will effectively reduce the migration time, minimize the response time and improve network resource utilization. Besides, the applicability of the proposed replica methods is shown in different scenarios such as the social media, intelligent agriculture, military field and transportation field. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Edge cloud SaaS applications Replica migration

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26th IEEE International Conference on parallel and distributed Systems (IEEE ICPADS)

作者： Long, Saiqin Li, Zhetao Liu, Zihao Deng, Qingyong Oh, Sangyoon Komuro, Nobuyoshi Xiangtan Univ Sch Comp Sci Key Lab Hunan Prov Internet Things & Informat Sec Hunan Int Sci & Technol Cooperat Base Intelligent Xiangtan Peoples R China Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab PDL Changsha Peoples R China Ajou Univ Dept Comp & Informat Engn Suwon South Korea Chiba Univ Grad Sch Adv Integrat Sci Chiba Japan

ISBN: (纸本)9781728190747

Deduplication is a data redundancy elimination technique, designed to save system storage resources by reducing redundant data in cloud storage systems. With the development of cloud computing technology, deduplication has been increasingly applied to cloud data centers. However, traditional technologies face great challenges in big data deduplication to properly weigh the two conflicting goals of deduplication throughput and high duplicate elimination ratio. This paper proposes a similarity clustering-based deduplication strategy (named SCDS), which aims to delete more duplicate data without significantly increasing system overhead. The main idea of SCDS is to narrow the query range of fingerprint index by data partitioning and similarity clustering algorithms. In the data preprocessing stage, SCDS uses data partitioning algorithm to classify similar data together. In the data deletion stage, the similarity clustering algorithm is used to divide the similar data fingerprint superblock into the same cluster. Repetitive fingerprints are detected in the same cluster to speed up the retrieval of duplicate fingerprints. Experiments show that the deduplication ratio of SCDS is better than some existing similarity deduplication algorithms, but the overhead is only slightly higher than some high throughput but low deduplication ratio methods.

关键词： deduplication cloud storage system similarity clustering data partitioning block fingerprint

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