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Priority-Based Optimization of I/O Isolation for Hybrid Deployed Services 15th

Priority-Based Optimization of I/O Isolation for Hybrid Depl...

15th EAI International Conference on Collaborative computing: networking, Applications and Worksharing, CollaborateCom 2019

作者： Zhang, Jiancheng Li, Youhuizi Zhou, Li Ren, Zujie Wan, Jian Wang, Yuan Key Laboratory of Complex Systems Modeling and Simulation Ministry of Education Hangzhou Dianzi University Hangzhou China School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China Zhejiang Lab Hangzhou China School of Information and Electronic Engineering Zhejiang University of Science and Technology Hangzhou China Key Enterprise Research Institute of NetEase Big Data of Zhejiang Province Netease Hangzhou Network Co.Ltd Hangzhou China

ISBN: (纸本)9783030301453

With the increasing of software complexity and user demands, collaborative service is becoming more and more popular. Each service focuses on its own specialty, their cooperation can support complicated task with high efficiency. To improve the resources utilization, virtualization technology like container is used and it enables multiple services running in the same physical machine. However, since the host physical machine is shared by several services, the resource competition is inevitable. Isolation is an effective solution, but the weak isolation mechanisms of container cannot handle such complicated scenarios. In the worst situation, the performance of services cannot meet the requirements and the system may crash. In order to solve this problem, we propose a priority-based optimization mechanism for I/O isolation after analyzing the characteristics of typical service workloads. Based on the real-time performance data, priority is automatically assigned to each service and corresponding optimization methods are applied. We evaluate the optimization effects of the priority-based mechanism in both static and dynamic workload cases, besides, the influence of different priority order is also analyzed. The experimental results show that our approach can indeed improve the system performance and guarantee the requirements of all the running services are satisfied. © 2019, ICST institute for Computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Containers

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Temporal Knowledge Propagation for Image-to-Video Person Re-Identification

Temporal Knowledge Propagation for Image-to-Video Person Re-...

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International Conference on Computer Vision (ICCV)

作者： Xinqian Gu Bingpeng Ma Hong Chang Shiguang Shan Xilin Chen Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China University of Chinese Academy of Sciences Beijing China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai China

ISBN: (数字)9781728148038

ISBN: (纸本)9781728148045

In many scenarios of Person Re-identification (Re-ID), the gallery set consists of lots of surveillance videos and the query is just an image, thus Re-ID has to be conducted between image and videos. Compared with videos, still person images lack temporal information. Besides, the information asymmetry between image and video features increases the difficulty in matching images and videos. To solve this problem, we propose a novel Temporal Knowledge Propagation (TKP) method which propagates the temporal knowledge learned by the video representation network to the image representation network. Specifically, given the input videos, we enforce the image representation network to fit the outputs of video representation network in a shared feature space. With back propagation, temporal knowledge can be transferred to enhance the image features and the information asymmetry problem can be alleviated. With additional classification and integrated triplet losses, our model can learn expressive and discriminative image and video features for image-to-video re-identification. Extensive experiments demonstrate the effectiveness of our method and the overall results on two widely used datasets surpass the state-of-the-art methods by a large margin.

关键词： Videos Image representation Feature extraction Knowledge engineering Visualization Robustness Training

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RhythmNet: End-to-end Heart Rate Estimation from Face via Spatial-temporal Representation

arXiv

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arXiv 2019年

作者： Niu, Xuesong Shan, Shiguang Han, Hu Chen, Xilin Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Institute of Computing Technology CAS Beijing100190 China Peng Cheng Laboratory Shenzhen China Institute of Computing Technology CAS Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China CAS Center for Excellence in Brain Science and Intelligence Technology

Heart rate (HR) is an important physiological signal that reflects the physical and emotional status of a person. Traditional HR measurements usually rely on contact monitors, which may cause inconvenience and discomfort. Recently, some methods have been proposed for remote HR estimation from face videos;however, most of them focus on well-controlled scenarios, their generalization ability into less-constrained scenarios (e.g., with head movement, and bad illumination) are not known. At the same time, lacking large-scale HR databases has limited the use of deep models for remote HR estimation. In this paper, we propose an end-to-end RhythmNet for remote HR estimation from the face. In RyhthmNet, we use a spatial-temporal representation encoding the HR signals from multiple ROI volumes as its input. Then the spatial-temporal representations are fed into a convolutional network for HR estimation. We also take into account the relationship of adjacent HR measurements from a video sequence via Gated Recurrent Unit (GRU) and achieves efficient HR measurement. In addition, we build a large-scale multi-modal HR database (named as VIPL-HR1), which contains 2,378 visible light videos (VIS) and 752 near-infrared (NIR) videos of 107 subjects. Our VIPL-HR database contains various variations such as head movements, illumination variations, and acquisition device changes, replicating a less-constrained scenario for HR estimation. The proposed approach outperforms the state-of-the-art methods on both the public-domain and our VIPL-HR databases. Copyright © 2019, The Authors. All rights reserved.

关键词： Heart

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Learning from context： a mutual reinforcement model for Chinese microblog opinion retrieval

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Frontiers of Computer science 2018年第4期12卷 714-724页

作者： Jingjing WEI Xiangwen LIAO Houdong ZHENG Guolong CHEN Xueqi CHENG College of Mathematics and Computer Science Fuzhou University Fuzhou 350108 China College of Electronics and Information Science Fujian Jiangxia University Fuzhou 350108 China Fujian Provincial Key Laboratory of Network Computing and Intelligence Processing Fuzhou University Fuzhou 350108 China Institute of Computing Technology Chinese Academic of Sciences Beijing 100190 China

This study addresses the problem of Chinese microblog opinion retrieval, which aims to retrieve opinionated Chinese microblog posts relevant to a target specified by a user query. Existing studies have shown that lexicon-based approaches employed online public sentiment resources to rank sentiment words relying on the document features. However, this approach could not be effectively applied to mi- croblogs that have typical user-generated content with valu- able contextual information：＂user-user＂ interpersonal interactions and ＂user-post/comment＂ intrapersonal interactions. This contextual information is very helpful in estimating the strength of sentiment words more accurately. In this study, we integrate the social contextual relationships among users, posts/comments, and sentiment words into a mutual reinforcement model and propose a unified three-layer heterogeneous graph, on which a random walk sentiment word weighting algorithm is presented to measure the strength of opinion of the sentiment words. Furthermore, the weights of sentiment words are incorporated into a lexicon-based model for Chinese microblog opinion retrieval. Comparative experiments are conducted on a Chinese microblog corpus, and the results show that our proposed mutual reinforcement model achieves significant improvement over previous methods.

关键词： opinion retrieval sentiment words lexiconweighting mutual reinforcement model

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A novel decision tree for depression recognition in speech

arXiv

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arXiv 2020年

作者： Liu, Zhenyu Wang, Dongyu Zhang, Lan Hu, Bin Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University Lanzhou China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences Shanghai China Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University Institute of Semiconductors Chinese Academy of Sciences Beijing China Lanzhou University Second Hospital Lanzhou China

Depression is a common mental disorder worldwide which causes a range of serious outcomes. The diagnosis of depression relies on patient-reported scales and psychiatrist’s interview which may lead to subjective bias. In recent years, more and more researchers are devoted to depression recognition in speech, which may be an effective and objective indicator. This study proposes a new speech segment fusion method based on decision tree to improve the depression recognition accuracy and conducts a validation on a sample of 52 subjects (23 depressed patients and 29 healthy controls). The recognition accuracy are 75.8% and 68.5% for male and female respectively on gender-dependent models. It can be concluded from the data that the proposed decision tree model can improve the depression classification performance. Copyright © 2020, The Authors. All rights reserved.

关键词： Decision trees

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A quantum-inspired classical algorithm for separable non-negative matrix factorization

arXiv

引用

arXiv 2019年

作者： Chen, Zhihuai Li, Yinan Sun, Xiaoming Yuan, Pei Zhang, Jialin CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Centrum Wiskunde & Informatica and QuSoft Science Park 123 Amsterdam1098XG Netherlands

Non-negative Matrix Factorization (NMF) asks to decompose a (entry-wise) non-negative matrix into the product of two smaller-sized nonnegative matrices, which has been shown intractable in general. In order to overcome this issue, separability assumption is introduced which assumes all data points are in a conical hull. This assumption makes NMF tractable and is widely used in text analysis and image processing, but still impractical for huge-scale datasets. In this paper, inspired by recent development on dequantizing techniques, we propose a new classical algorithm for separable NMF problem. Our new algorithm runs in polynomial time in the rank and logarithmic in the size of input matrices, which achieves an exponential speedup in the low-rank setting. Copyright © 2019, The Authors. All rights reserved.

关键词： Non-negative matrix factorization

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Graph based Tensor Recovery for Accurate Internet Anomaly Detection

Graph based Tensor Recovery for Accurate Internet Anomaly De...

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2018 IEEE Conference on Computer Communications, INFOCOM 2018

作者： Xie, Kun Li, Xiaocan Wang, Xin Xie, Gaogang Wen, Jigang Zhang, Dafang College of Computer Science and Electronics Engineering Hunan University China CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China Department of Electrical and Computer Engineering State University of New York at Stony Brook United States Institute of Computing Technology Chinese Academy of Sciences China

ISBN: (纸本)9781538641286

Detecting anomalous traffic is a crucial task of managing networks. Many anomaly detection algorithms have been proposed recently. However, constrained by their matrix-based traffic data model, existing algorithms often suffer from low detection accuracy. To fully utilize the multi-dimensional information hidden in the traffic data, this paper takes an initiative to investigate the potential and methodologies of performing tensor factorization for more accurate Internet anomaly detection. Only considering the low-rank linearity features hidden in the data, current tensor factorization techniques would result in low anomaly detection accuracy. We propose a novel Graph-based Tensor Recovery model (Graph-TR) to well explore both low rank linearity features as well as the non-linear proximity information hidden in the traffic data for better anomaly detection. We encode the non-linear proximity information of the traffic data by constructing nearest neighbor graphs and incorporate this information into the tensor factorization using the graph Laplacian. Moreover, to facilitate the quick building of neighbor graph, we propose a nearest neighbor searching algorithm with the simple locality-sensitive hashing (LSH). We have conducted extensive experiments using Internet traffic trace data Abilene and GEANT. Compared with the state of art algorithms on matrix-based anomaly detection and tensor recovery approach, our Graph-Trcan achieve significantly lower False Positive Rate and higher True Positive Rate. © 2018 IEEE.

关键词： Anomaly detection

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Overview of AI and Communication for 6G network: Fundamentals, Challenges, and Future Research Opportunities

arXiv

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arXiv 2024年

作者： Cui, Qimei You, Xiaohu Ni, Wei Nan, Guoshun Zhang, Xuefei Zhang, Jianhua Lyu, Xinchen Ai, Ming Tao, Xiaofeng Feng, Zhiyong Zhang, Ping Wu, Qingqing Tao, Meixia Huang, Yongming Huang, Chongwen Liu, Guangyi Peng, Chenghui Pan, Zhiwen Sun, Tao Niyato, Dusit Chen, Tao Khan, Muhammad Khurram Jamalipour, Abbas Guizani, Mohsen Yuen, Chau National Engineering Research Center of Mobile Network Technologies Beijing University of Posts and Telecommunications Beijing100876 China Department of Broadband Communication Peng Cheng Laboratory Shenzhen518055 China National Mobile Communications Research Laboratory Southeast University Nanjing210096 China Fudan University Shanghai200433 China School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China CICT Mobile Communication Technology Co. Ltd. Beijing100020 China Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China Shanghai Jiao Tong University Shanghai200240 China College of Information Science and Electronic Engineering Zhejiang University Hangzhou310058 China China Mobile Research Institute Beijing100053 China Huawei Technologies Shanghai201206 China College of Computing and Data Science Nanyang Technological University Singapore117583 Singapore VTT Technical Research Centre of Finland Ltd. EspooFI-02044 VTT Finland Center of Excellence in Information Assurance King Saud University Riyadh11362 Saudi Arabia School of Electrical and Information Engineering University of Sydney NSW2006 Australia Abu Dhabi United Arab Emirates School of Electrical and Electronics Engineering Nanyang Technological University Singapore639798 Singapore

With the growing demand for seamless connectivity and intelligent communication, the integration of artificial intelligence (AI) and sixth-generation (6G) communication networks has emerged as a transformative paradigm. By embedding AI capabilities across various network layers, this integration enables optimized resource allocation, improved efficiency, and enhanced system robust performance, particularly in intricate and dynamic environments. This paper presents a comprehensive overview of AI and communication for 6G networks, with a focus on emphasizing their foundational principles, inherent challenges, and future research opportunities. We first review the integration of AI and communications in the context of 6G, exploring the driving factors behind incorporating AI into wireless communications, as well as the vision for the convergence of AI and 6G. The discourse then transitions to a detailed exposition of the envisioned integration of AI within 6G networks, delineated across three progressive developmental stages. The first stage, AI for network, focuses on employing AI to augment network performance, optimize efficiency, and enhance user service experiences. The second stage, network for AI, highlights the role of the network in facilitating and buttressing AI operations and presents key enabling technologies, such as digital twins for AI and semantic communication. The final stage, AI as a Service, it is anticipated that future 6G networks will innately provide AI functions as services, supporting application scenarios like immersive communication and intelligent industrial robots. Specifically, we have defined the quality of AI service, which refers to a framework for measuring AI services within the network. We further summarize the standardization process of AI for wireless networks, highlighting key milestones and ongoing efforts. In addition, we conduct an in-depth analysis of the critical challenges faced by the integration of AI and communications

关键词： Resource allocation

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iSplit LBI: Individualized partial ranking with ties via split LBI

arXiv

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arXiv 2019年

作者： Xu, Qianqian Sun, Xinwei Yang, Zhiyong Cao, Xiaochun Huang, Qingming Yao, Yuan Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS Microsoft Research Asia State Key Laboratory of Information Security Institute of Information Engineering CAS School of Cyber Security University of Chinese Academy of Sciences School of Computer Science and Tech. University of Chinese Academy of Sciences Key Laboratory of Big Data Mining and Knowledge Management CAS Peng Cheng Laboratory Department of Mathematics Hong Kong University of Science and Technology Hong Kong

Due to the inherent uncertainty of data, the problem of predicting partial ranking from pairwise comparison data with ties has attracted increasing interest in recent years. However, in real-world scenarios, different individuals often hold distinct preferences. It might be misleading to merely look at a global partial ranking while ignoring personal diversity. In this paper, instead of learning a global ranking which is agreed with the consensus, we pursue the tie-aware partial ranking from an individualized perspective. Particularly, we formulate a unified framework which not only can be used for individualized partial ranking prediction, but also be helpful for abnormal user selection. This is realized by a variable splitting-based algorithm called iSplitLBI. Specifically, our algorithm generates a sequence of estimations with a regularization path, where both the hyperparameters and model parameters are updated. At each step of the path, the parameters can be decomposed into three orthogonal parts, namely, abnormal signals, personalized signals and random noise. The abnormal signals can serve the purpose of abnormal user selection, while the abnormal signals and personalized signals together are mainly responsible for individual partial ranking prediction. Extensive experiments on simulated and real-world datasets demonstrate that our new approach significantly outperforms state-of-the-art alternatives. The code is now availiable at https://***/qianqianxu010/ NeurIPS2019-iSplitLBI. Copyright © 2019, The Authors. All rights reserved.

关键词： Forecasting

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A Hole Repairing Method Based on Slicing 12th

A Hole Repairing Method Based on Slicing

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12th International Conference on e-Learning and Games, Edutainment 2018

作者： Zhao, Yanni Wang, Yinghui Wang, Ningna Ning, Xiaojuan Shi, Zhenghao Zhao, Minghua Lv, Ke Huang, Liangyi Institute of Computer Science and Engineering Xi’an University of Technology Xi’an China Shaanxi Key Laboratory of Network Computing and Security Technology Xi’an China Department of Computer Science Shannxi Vocational and Technical College Xi’an China Booking B.V. Herengracht 597 Amsterdam1017 CE Netherlands Institute of Automation Chinese Academy of Sciences Beijing China School of Engineering and Applied Science George Washington University Washington D.C. United States

ISBN: (纸本)9783030237110

The repairing of 3D point cloud holes has an important meaning to ensure the integrity of cloud data. We present a slice-based repairing method for 3D point cloud in this paper. Firstly, the model is horizontal sliced and each slice is projected on a two-dimensional plane, then the band-shaped points obtained during projection are clustered to select boundary points of the hole. Combined with optimal fitting points, the hole repairing point sets in the projection layer are re-sampled based on the cubic B-spline curve to fit boundary points. Finally, all hole repairing point sets in the projection layer are combined in 3D space to finish the entire hole recovery. The experimental results show that the proposed method can effectively repair complex holes for various point cloud models. © 2019, Springer Nature Switzerland AG.

关键词： Curve fitting

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