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Research on load balancing strategy based on middleware

ICIC Express Letters 2014年第9期8卷 2655-2661页

作者： Gu, Xiaoying Liu, Yongshan Man, Yubao Department of Information Science and Engineering Qinhuangdao 066004 China The Key Lab. for Computer Virtual Tech. and System Integration of Hebei Province Yanshan University No. 438 Hebei Ave Qinhuangdao 066004 China Data Industry Research Lab Qinhuangdao 066004 China

According to the former research achievements about load balancing strategy, considering the load distribution, the load condition of service node and the number of user connections, we propose a load balancing strategy based on middleware. According to the cluster service load and the number of user connections, the requests from user will be reasonably allocated to each service node by using middleware technology, then service node and requested user establish a temporary connection, after all the requests of this user are processed by this node. Experiments show that, this strategy reduces the number of requests for allocation, improves the resource utilization rate of cluster system and load balancing degree, and effectively suppresses the load jitter of system. © 2014 ISSN 1881-803X.

关键词： Middleware

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Dual encoding for video retrieval by text

arXiv

引用

arXiv 2020年

作者： Dong, Jianfeng Li, Xirong Xu, Chaoxi Yang, Xun Yang, Gang Wang, Xun Wang, Meng College of Computer and Information Engineering Zhejiang Gongshang University Hangzhou310035 China Key Lab of Data Engineering and Knowledge Engineering Renmin University of China The AI and Media Computing Lab School of Information Renmin University of China Beijing100872 China School of Computing National University of Singapore Singapore37580 Singapore School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China

This paper attacks the challenging problem of video retrieval by text. In such a retrieval paradigm, an end user searches for unlab.led videos by ad-hoc queries described exclusively in the form of a natural-language sentence, with no visual example provided. Given videos as sequences of frames and queries as sequences of words, an effective sequence-to-sequence cross-modal matching is crucial. To that end, the two modalities need to be first encoded into real-valued vectors and then projected into a common space. In this paper we achieve this by proposing a dual deep encoding network that encodes videos and queries into powerful dense representations of their own. Our novelty is two-fold. First, different from prior art that resorts to a specific single-level encoder, the proposed network performs multi-level encoding that represents the rich content of both modalities in a coarse-to-fine fashion. Second, different from a conventional common space learning algorithm which is either concept based or latent space based, we introduce hybrid space learning which combines the high performance of the latent space and the good interpretability of the concept space. Dual encoding is conceptually simple, practically effective and end-to-end trained with hybrid space learning. Extensive experiments on four challenging video datasets show the viability of the new method. Code is availab.e at https://***/danieljf24/hybrid_space. Copyright © 2020, The Authors. All rights reserved.

关键词： Signal encoding

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A load balancing algorithm for multi-root ONS

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Journal of Computational Information Systems 2014年第7期10卷 2735-2742页

作者： Hao, Fengzhai Liu, Yongshan Chen, Wei College of Information Science and Engineering Yanshan University Qinhuangdao 066004 China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University Qinhuangdao 066004 China Data Industry Research Lab. Qinhuangdao 066004 China

In order to solve the overload problem of root ONS in the EPC network, a load balancing algorithm based on multi-root ONS is proposed. Based on the proposed load balancing ONS (LB ONS) architecture, the ONS Root is defined to have two states, idle or busy. According to CPU utilization and memory utilization of the ONS Root and in combination with threshold controlling to periodically update the ONS Root states, which makes the requests coming from client applications be transferred to an ONS Root which is idle. Experiment results show that the algorithm can reduce query hops and query delays effectively and offer lookup service efficiently. © 2014 Binary Information Press.

关键词： Internet of things

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Indoor Sound Source Localization Algorithm Based on BP Neural Network 21

Indoor Sound Source Localization Algorithm Based on BP Neura...

引用

21st IEEE International Conference on Communication Technology, ICCT 2021

作者： Wang, Lan Zhang, Kun Shen, Chong Wang, Chai Fu, Xixi Hainan University State Key Laboratory of Marine Resources Utilization in South China Sea Hainan Haikou570228 China Data Mining and Monitoring and Early Warning Technol. of Isl. Tourism Rsrc. of Key Lab. of the Min. of Culture AndTourism of the People's Rep. of China Hainan Tropical Ocean University Hainan Sanya572022 China College of Information and Communication Engineering Hainan University Hainan Haikou570228 China

ISBN: (纸本)9781665432061

Sound source localization is an important part of the perception of things around. Sound source localization can overcome the shortcomings of visual localization, and can also locate the invisible place. The application of sound source localization in indoor is the latest trend. Starting from the application of deep learning to indoor sound source localization, this paper focuses on the analysis and research of BP neural network applied to indoor sound source localization algorithm. In this paper, an off-line sampling scheme is used to construct the network structure with 7 neurons hidden in the layer, and the BP algorithm of LevenBerg-Marquardt is used as the training function, this algorithm can solve the traditional algorithm through the study of the physical properties of sound, set up the corresponding equation, and then solve, the process is complex, to solve the difficult problem. The simulation results show that the algorithm can be implemented in 100 square meters of the house, through sampling 400 sets of data for machine training, positioning error can be controlled in a few centimeters effect. © 2021 IEEE.

关键词： Deep learning

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Edge collapse mesh simplification algorithm based on detail features preserving

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Journal of Computational Information Systems 2014年第7期10卷 2883-2890页

作者： Jia, Qi Liu, Yongshan Gu, Xiaoying College of Information Science and Engineering Yanshan University Qinhuangdao 066004 China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University Qinhuangdao 066004 China Data Industry Research Lab. Qinhuangdao 066004 China

The existing model simplification algorithm in simplified speed and quality can't reach a better compromise, so we present an improved quadric error metrics edge collapse mesh simplification algorithm. This algorithm makes the local region unevenness of vertex, the local region average area of vertex and edge length as constraint factors add to the edge collapse cost, by changing the order of the edge collapse to retain the important detail features of the original model. New vertex position is calculated on the different types of edge, keeping the boundary characteristics of the mesh model. Experimental results show that the improved algorithm not only runs faster, but also maintains a higher degree on the model feature region. © 2014 Binary Information Press.

关键词： Mesh generation

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Metric based on multi-order spaces for cross-modal retrieval

Metric based on multi-order spaces for cross-modal retrieval

引用

IEEE International Conference on Multimedia and Expo (ICME)

作者： Liang Zhang Bingpeng Ma Guorong Li Qingming Huang Key Laboratory of Big Data Mining and Knowledge Management CAS China School of Computer and Control Engineering University of Chinese Academy of Sciences China Key Lab of Intell. Info. Process. Inst. of Comput. Tech. CAS China

ISBN: (纸本)9781509060689

This paper proposes a novel method for cross-modal retrieval. Different from vector (text)-to-vector (image) framework of the traditional cross-modal methods, we adopt a vector (text)-to-matrix (image) framework. We assume that compared with vectors, matrices can directly represent images and characterize the structure of feature space. Furthermore, we propose a Metric based on Multi-order spaces (MMs). Multi-order statistic features are used to represent images for enriching the semantic information, and metrics among the multi-spaces are jointly learned to measure the similarity between two different modalities. Specifically, there are three steps for MMs. First, we jointly use the bags of visual features (zero-order), mean (first-order) and covariance (second-order) to characterize each image. Second, considering that covariance matrices and vectors lie on a Riemannian manifold and an Euclidean space respectively, we embed multi-order spaces into their corresponding Hilbert spaces to reduce the heterogeneity among the original spaces. Finally, the similarity between two different modalities can be measured by learning multiple transformations from the different Hilbert spaces to a common subspace. The performance of the proposed method over the state-of-the-art has been demonstrated through the experiments on two public datasets.

关键词： Hilbert space Manifolds Correlation Covariance matrices Feature extraction Linear programming

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LadRa-Net: Locally-aware dynamic re-read attention net for sentence semantic matching

arXiv

引用

arXiv 2021年

作者： Zhang, Kun Lv, Guangyi Wu, Le Chen, Enhong Liu, Qi Wang, Meng Key Laboratory of Knowledge Engineering with Big Data School of Computer and Information Hefei University of Technology Anhui Hefei230029 China AI Lab at Lenovo Research Beijing100094 China University of Science and Technology of China Hefei230026 China

Sentence semantic matching requires an agent to determine the semantic relation between two sentences, which is widely used in various natural language tasks, such as Natural Language Inference (NLI), Paraphrase Identification (PI), and so on. Much recent progress has been made in this area, especially attention-based methods and pre-trained language model based methods. However, most of these methods focus on all the important parts in sentences in a static way and only emphasize how important the words are to the query, inhibiting the ability of attention mechanism. In order to overcome this problem and boost the performance of attention mechanism, we propose a novel dynamic re-read attention, which can pay close attention to one small region of sentences at each step and re-read the important parts for better sentence representations. Based on this attention variation, we develop a novel Dynamic Re-read Network (DRr-Net) for sentence semantic matching. Moreover, selecting one small region in dynamic re-read attention seems insufficient for sentence semantics, and employing pre-trained language models as input encoders will introduce incomplete and fragile representation problems. To this end, we extend DRr-Net to Locally-Aware Dynamic Re-read Attention Net (LadRa-Net), in which local structure of sentences is employed to alleviate the shortcoming of Byte-Pair Encoding (BPE) in pre-trained language models and boost the performance of dynamic reread attention. Extensive experiments on two popular sentence semantic matching tasks demonstrate that DRr-Net can significantly improve the performance of sentence semantic matching. Meanwhile, LadRa-Net is able to achieve better performance by considering the local structures of sentences. In addition, it is exceedingly interesting that some discoveries in our experiments are consistent with some findings of psychological research. © 2021, CC BY.

关键词： Computational linguistics

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SHREC'16 Track: Partial Shape Queries for 3D object retrieval 9

SHREC'16 Track: Partial Shape Queries for 3D object retrieva...

引用

9th Eurographics Workshop on 3D Object Retrieval, 3DOR 2016

作者： Pratikakis, I. Savelonas, M.A. Arnaoutoglou, F. Ioannakis, G. Koutsoudis, A. Theoharis, T. Tran, M.-T. Nguyen, V.-T. Pham, V.-K. Nguyen, H.-D. Le, H.-A. Tran, B.-H. To, Q.H. Truong, M.-B. Phan, T.V. Nguyen, M.-D. Than, T.-A. Mac, K.-N.C. Do, M.N. Duong, A.-D. Furuya, T. Ohbuchi, R. Aono, M. Tashiro, S. Pickup, D. Sun, X. Rosin, P.L. Martin, R.R. Democritus University of Thrace Department of Electrical and Computer Engineering Greece ATHENA Research Center Institute for Language and Speech Processing Greece Norwegian University of Science and Technology Department of Computer and Information Science Norway University of Science VNU-HCM Viet Nam University of Science University of Information Technology VNU-HCM Viet Nam University of Science John von Neumann Institute VNU-HCM Viet Nam University of Illinois Urbana-Champaign United States University of Information Technology VNU-HCM Viet Nam Graduate School of Medicine and Engineering University of Yamanashi Japan Knowledge Data Engineering Lab. Toyohashi University of Technology Japan School of Computer Science and Informatics Cardiff University United Kingdom

ISBN: (纸本)9783038680048

Despite numerous recent efforts, 3D object retrieval based on partial shape queries remains a challenging problem, far from being solved. The problem can be defined as: given a partial view of a shape as query, retrieve all partially similar 3D models from a repository. The objective of this track is to evaluate the performance of partial 3D object retrieval methods, for partial shape queries of various scan qualities and degrees of partiality. The retrieval problem is often found in cultural heritage applications, for which partial scans of objects query a dataset of geometrically distinct classes. © 2016 The Eurographics Association.

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Learning Two-Stream CNN for Multi-Modal Age-related Macular Degeneration Categorization

arXiv

引用

arXiv 2020年

作者： Wang, Weisen Li, Xirong Xu, Zhiyan Yu, Weihong Zhao, Jianchun Ding, Dayong Chen, Youxin The MoE Key Lab of Data Engineering and Knowledge Engineering Renmin University of China Beijing100872 China Aimc Lab School of Information Renmin University of China Beijing100872 China Vistel Ai Lab Visionary Intelligence Ltd. Beijing100872 China The Key Lab of Ocular Fundus Disease Chinese Academy of Medical Sciences Dept. of Ophthalmology Peking Union Medical College Hospital Beijing100730 China

This paper tackles automated categorization of Agerelated Macular Degeneration (AMD), a common macular disease among people over 50. Previous research efforts mainly focus on AMD categorization with a single-modal input, let it be a color fundus photograph (CFP) or an OCT B-scan image. By contrast, we consider AMD categorization given a multi-modal input, a direction that is clinically meaningful yet mostly unexplored. Contrary to the prior art that takes a traditional approach of feature extraction plus classifier training that cannot be jointly optimized, we opt for end-to-end multi-modal Convolutional Neural Networks (MM-CNN). Our MM-CNN is instantiated by a two-stream CNN, with spatially-invariant fusion to combine information from the CFP and OCT streams. In order to visually interpret the contribution of the individual modalities to the final prediction, we extend the class activation mapping (CAM) technique to the multi-modal scenario. For effective training of MM-CNN, we develop two data augmentation methods. One is GAN-based CFP/OCT image synthesis, with our novel use of CAMs as conditional input of a high-resolution image-to-image translation GAN. The other method is Loose Pairing, which pairs a CFP image and an OCT image on the basis of their classes instead of eye identities. Experiments on a clinical dataset consisting of 1,094 CFP images and 1,289 OCT images acquired from 1,093 distinct eyes show that the proposed solution obtains better F1 and Accuracy than multiple baselines for multimodal AMD categorization. Code and data are availab.e at https://***/li-xirong/mmc-amd. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Routing over Best Links is not necessarily Better in Wireless Multi-hop Networks

Routing over Best Links is not necessarily Better in Wireles...

引用

International Workshop on Quality of Service

作者： Shutao Lu Wuyungerile Li Yintu Bao Alvin C. Valera Winston K.G. Seah Baoqi Huang Inner Mongolia Key Lab. of Wireless Networking & Mobile Computing Engineering Research Center of Ecological Big Data Ministry of Education School of Computer Science Inner Mongolia University Hohhot China School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand

ISBN: (数字)9798350350128

ISBN: (纸本)9798350350135

The conventional approach of choosing the best route to carry network traffic in wireless multi-hop networks does not maximize the overall network throughput and can lead to short-term instabilities in network state with dire consequences. To date, wireless network route selection considers mainly network or link metrics, always picking the best links, thus channeling all packets through a subset of all availab.e links. This leaves weaker links under-utilized although such links can in fact be used to carry smaller packets or packets with less stringent requirements and free up bandwidth on the better links for larger packets or traffic with higher service requirements. As network traffic volume and heterogeneity increase in future networks, we need to maximize the usage of availab.e network bandwidth and distribute the network traffic load. We combine network link metrics and packet attributes to determine the successful packet transmission probability, and then use this outcome to pick suitable links to forward the packet, which is not necessarily the link with the best metric. To validate the efficacy of our proposed approach in routing performance and energy efficiency, we applied it in routing for wireless multi-hop networks. More importantly, we are able to spread the traffic across nodes in the network, thus achieving better network load-balancing and higher network resource utilization.

关键词： Measurement Wireless networks Spread spectrum communication Telecommunication traffic Bandwidth Routing Throughput

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