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A resource usage prediction-based energy-aware scheduling algorithm for instance-intensive cloud workflows 14th

A resource usage prediction-based energy-aware scheduling al...

14th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2018

作者： Wang, Zhibin Wen, Yiping Zhang, Yu Chen, Jinjun Cao, Buqing School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China Key Laboratory of Knowledge Processing and Networked Manufacturing Hunan University of Science and Technology Xiangtan China Swinburne Data Science Research Institute Swinburne University of Technology Melbourne Australia

ISBN: (纸本)9783030129804

The applications of instance-intensive workflow are widely used in e-commerce, advanced manufacturing, etc. However, existing studies normally do not consider the problem of reducing energy consumption by utilizing the characters of instance-intensive workflow applications. This paper presents a resource usage Prediction-based Energy-Aware scheduling algorithm, named PEA. Technically, this method improves the energy efficiency of instance-intensive cloud workflow by predicting resources utilization and the strategies of batch processing and load balancing. The efficiency and effectiveness of the proposed algorithm are validated by extensive experiments. © 2019, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Scheduling algorithms

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Investigating Information Needs of Elderly People in Chinese Rural Community: Presenting an Information Service Framework

Investigating Information Needs of Elderly People in Chinese...

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International Conference on Management of e-Commerce and e-Government, ICMECG

作者： Ping Zhou School of Information Technology Jiangxi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economics Nanchang China

Investigating information needs of elderly people could find out information required to address needs of the aged in a community to present an information service framework to meet their needs. Using data collected from 600 elderly people through field investigation with a questionnaire in a rural community in central China, the results show that the preferred information format of the vast majority of elderly people is audio and/or visual information product, especially audio product. Most of interviewees stated that they were in need of healthy and medical audio information products. The survey maybe lead to improved and expanded information services for respondents who are short of such services, including Public broadcasting services, extending the audiovisual collection, loaning audiovisuals, religious faith audiovisuals and others providing needed information to them. This paper assembles together views on what the elderly people currently need to be helped by both practitioners and researchers in the elderly people services domain.

关键词： Senior citizens Information services Communities Sociology Statistics Aging Educational institutions

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EdgeNN: Efficient Neural Network Inference for CPU-GPU Integrated Edge Devices

EdgeNN: Efficient Neural Network Inference for CPU-GPU Integ...

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International Conference on data engineering

作者： Chenyang Zhang Feng Zhang Kuangyu Chen Mingjun Chen Bingsheng He Xiaoyong Du Key Laboratory of Data Engineering and Knowledge Engineering (MOE) and School of Information Renmin University of China School of Computing National University of Singapore

With the development of the architectures and the growth of AIoT application requirements, data processing on edge has become popular. Neural network inference is widely employed for data analytics on edge devices. This paper extensively explores neural network inference on integrated edge devices and proposes EdgeNN, the first neural network inference solution on CPU-GPU integrated edge devices. EdgeNN has three novel characteristics. First, EdgeNN can adaptively utilize the unified physical memory and conduct the zero-copy optimization. Second, EdgeNN involves a novel inference-targeted inter- and intra-kernel CPU-GPU hybrid execution approach, which co-runs the CPU with the GPU to fully utilize the edge device’s computing resources. Third, EdgeNN adopts a fine-grained adaptive inference tuning approach, which can divide the complicated inference structure into sub-tasks mapped to the CPU and the GPU. Experiments show that on six popular neural network inference tasks, EdgeNN brings an average of 3.97×, 3.12×, and 8.80× speedups to inference on the CPU of the integrated device, inference on a mobile phone CPU, and inference on an edge CPU device. Additionally, it achieves 22.02% time benefits to the direct execution of the original programs. Specifically, 9.93% comes from better utilization of unified memory, and 10.76% comes from CPU-GPU hybrid execution. Besides, EdgeNN can deliver 29.14× and 5.70× higher energy efficiency than the edge CPU and the discrete GPU, respectively. We have made EdgeNN available at https://***/ChenyangZhang-cs/EdgeNN.

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The Master-Slave Encoder Model for Improving Patent Text Summarization: A New Approach to Combining Specifications and Claims

arXiv

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arXiv 2024年

作者： Zhou, Shu Wang, Xin Zhou, Zhengda Yi, Haohan Zheng, Xuhui Wang, Hao School of Information Management Nanjing University China Key Laboratory of Data Engineering and Knowledge Services in Jiangsu Provincial Universities Nanjing University China Baidu Inc. Beijing China

In order to solve the problem of insufficient generation quality caused by traditional patent text abstract generation models only originating from patent specifications, the problem of new terminology OOV caused by rapid patent updates, and the problem of information redundancy caused by insufficient consideration of the high professionalism, accuracy, and uniqueness of patent texts, we proposes a patent text abstract generation model (MSEA) based on a master-slave encoder architecture;Firstly, the MSEA model designs a master-slave encoder, which combines the instructions in the patent text with the claims as input, and fully explores the characteristics and details between the two through the master-slave encoder;Then, the model enhances the consideration of new technical terms in the input sequence based on the pointer network, and further enhances the correlation with the input text by re weighing the "remembered" and "for-gotten" parts of the input sequence from the encoder;Finally, an enhanced repetition suppression mechanism for patent text was introduced to ensure accurate and non redundant abstracts generated. On a publicly available patent text dataset, compared to the state-of-the-art model, Improved Multi-Head Attention Mechanism (IMHAM), the MSEA model achieves an improvement of 0.006, 0.005, and 0.005 in Rouge-1, Rouge-2, and Rouge-L scores, respectively. MSEA leverages the characteristics of patent texts to effectively enhance the quality of patent text generation, demonstrating its advancement and effectiveness in the experiments. Copyright © 2024, The Authors. All rights reserved.

关键词： Specifications

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Energy-efficient Robust data-centric Storage in Wireless Sensor Networks

Energy-efficient Robust Data-centric Storage in Wireless Sen...

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第三届IEEE无线通讯、网络技术暨移动计算国际会议

作者： Yongxuan Lai Yufeng Wang Hong Chen School of Information Renmin University of China Beijing P.R.China Key Laboratory of Data Engineering and Knowledge Engineering MOE

In this paper, we propose a new Dynamic datacentric Storage (DDS) mechanism in wireless sensor network. DDS, which is aware of the data distributions of the network, dynamically adjusts the mappings from sensor readings to the storage points to reduce the cost of storing these readings, as well as to balance the storage and workload in the network. Moreover, it takes advantage of the GPSR routing protocol to store multiple copies of readings to improve the robustness of the network with little overhead. Simulation results show that the approach is more energy-efficient and robust than other data-centric schemes.

关键词： energy-efficient robust storage data-centric storage wireless sensor networks

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Achieving efficient and secure range query in two-tiered wireless sensor networks

Achieving efficient and secure range query in two-tiered wir...

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International Workshop on Quality of Service

作者： Xiaoying Zhang Lei Dong Hui Peng Hong Chen Deying Li Cuiping Li Ministry of Education Key Laboratory of Data Engineering and Knowledge Engineering Beijing China School of Information Renmin University of China Beijing China

ISBN: (纸本)9781479950058

Wireless sensor network is an important part of the Internet of Things. Preservation of privacy and integrity in wireless sensor networks is extremely urgent and challenging. To address this problem, we propose in this paper an efficient and secure range query protocol in two-tiered wireless sensor networks introducing master nodes. Our proposal not only prevents adversaries from gaining sensitive information of both queries issued by users and data collected by sensor nodes but also allows the sink to verify whether results are valid. It offers confidentiality of queries and data by constructing a special code, provides integrity verification by the correlation among data and also enables efficient query processing. Finally, theoretical analysis and simulation results confirm the security and efficiency of our proposal.

关键词： Protocols Wireless sensor networks Equations Privacy data privacy Security Quality of service

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An Actor-centric Causality Graph for Asynchronous Temporal Inference in Group Activity

An Actor-centric Causality Graph for Asynchronous Temporal I...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Zhao Xie Tian Gao Kewei Wu Jiao Chang Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology School of Computer Science and Information Engineering Hefei University of Technology

The causality relation modeling remains a challenging task for group activity recognition. The causality relations describe the influence on the centric actor (effect actor) from its correlative actors (cause actors). Most existing graph models focus on learning the actor relation with synchronous temporal features, which is insufficient to deal with the causality relation with asynchronous temporal features. In this paper, we propose an Actor-Centric Causality Graph Model, which learns the asynchronous temporal causality relation with three modules, i.e., an asynchronous temporal causality relation detection module, a causality feature fusion module, and a causality relation graph inference module. First, given a centric actor and its correlative actor, we analyze their influences to detect causality relation. We estimate the self influence of the centric actor with self regression. We estimate the correlative influence from the correlative actor to the centric actor with correlative regression, which uses asynchronous features at different timestamps. Second, we synchronize the two action features by estimating the temporal delay between the cause action and the effect action. The synchronized features are used to enhance the feature of the effect action with a channel-wise fusion. Third, we describe the nodes (actors) with causality features and learn the edges by fusing the causality relation with the appearance relation and distance relation. The causality relation graph inference provides crucial features of effect action, which are complementary to the base model using synchronous relation inference. Experiments show that our method achieves state-of-the-art performance on the Volleyball dataset and Collective Activity dataset.

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Minimizing XML Queries Using a Family of Constraints

Minimizing XML Queries Using a Family of Constraints

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International Conference on Fuzzy Systems and knowledge Discovery (FSKD)

作者： Xiping Liu Changxuan Wan School of Information Technology Jiangxi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economics Nanchang China

An effective way to optimize XML queries is to minimize XML queries. In this paper, we improve redundance elimination in XPath queries greatly by incorporating two novel kinds of constraints: parent constraint and sibling constraint, and by extending the tractable fragment to include descendant-or-self axis. The two novel kinds of constraints, together with child constraint and descendant constraint, form a family of constraints, which complicate the problem but offer possibilities for further minimization. Two techniques, tree augmentation and simulation augmentation, are employed to cope with constraints. We elaborate on the minimizing algorithms and running efficiencies both in the absence and in the presence of various kinds of constraints.

关键词： XML Polynomials Minimization methods Information technology Finance Laboratories knowledge engineering Constraint optimization Fuzzy systems System testing

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Adaptive data-Free Quantization

Adaptive Data-Free Quantization

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Biao Qian Yang Wang Richang Hong Meng Wang Key Laboratory of Knowledge Engineering with Big Data Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology China

data-free quantization (DFQ) recovers the performance of quantized network (Q) without the original data, but generates the fake sample via a generator (G) by learning from full-precision network (P), which, however, is totally independent of Q, overlooking the adaptability of the knowledge from generated samples, i.e., informative or not to the learning process of Q, resulting into the overflow of generalization error. Building on this, several critical questions — how to measure the sample adaptability to Q under varied bit-width scenarios? whether the largest adaptability is the best? how to generate the samples with adaptive adaptability to improve Q's generalization? To answer the above questions, in this paper, we propose an Adaptive data-Free Quantization (AdaDFQ) method, which revisits DFQ from a zero-sum game perspective upon the sample adaptability between two players — a generator and a quantized network. Following this viewpoint, we further define the disagreement and agreement samples to form two boundaries, where the margin between two boundaries is optimized to adaptively regulate the adaptability of generated samples to Q, so as to address the over-and-under fitting issues. Our AdaDFQ reveals: 1) the largest adaptability is NOT the best for sample generation to benefit Q's generalization; 2) the knowledge of the generated sample should not be informative to Q only, but also related to the category and distribution information of the training data for P. The theoretical and empirical analysis validate the advantages of AdaDFQ over the state-of-the-arts. Our code is available at https://***/hfutqian/AdaDFQ.

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DSOSW: A Deleting Strategy in Mining Frequent Itemsets over Sliding Window of Stream

DSOSW: A Deleting Strategy in Mining Frequent Itemsets over ...

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International Symposiums on Information Processing (ISIP)

作者： Haifeng Li Hong Chen Key Laboratory of Data Engineering and Knowledge Engineering MOE Renmin University of China Beijing China Renmin University of China Beijing CN

Most traditional mining approaches of frequent item sets consider mainly on databases and thus can use the second storage and need multiple scans which are not adapted to mining of stream. Some new algorithms over stream's sliding window are presented recently, which perform addition and deletion over stream independently, so the common deleting strategy which removes the earliest transaction is used when the window slides. This paper considers both operations together to reduce the computation cost, consequently, three deleting strategies are proposed to improve the performance with little precision loss. The experimental results show that these strategies over current method are effective and efficient.

关键词： data mining Itemsets Writing Laboratories data engineering knowledge engineering Transaction databases Information processing Computational efficiency Performance loss

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