检索结果-内蒙古大学图书馆

SSRN 2022年

作者： Jin, Shu-Qi Li, Nan Bai, Fan Chen, Yu-Jie Feng, Xiang-You Li, Hao-Wei Gong, Xiao-Ming Tao, Wen-Quan Xi’an Key Laboratory of Energy Saving and Low Carbon Technology of Data Center Key Laboratory of Thermal Fluid Science and Engineering of MOE School of Energy and Power Engineering Xi’an Jiaotong University Shaanxi Xi'An710049 China School of Mechanical Engineering Beijing Key Laboratory of Pipeline Critical Technology and Equipment for Deepwater Oil & Gas Development Beijing Institute of Petrochemical Technology Beijing102617 China

With the development of digital economy, the number of data centers and their capacity have been increasing sharply. CFD is currently widely used to obtain the thermal fields inside data centers while a full simulation needs a lot of time which can not be accepted for real-time operation. In the present study, a design tool called PICT is applied to optimize the simulation conditions and to maximize the amount of useful information obtained with the minimum number of numerical tests. Based on the snapshots, a POD-MARS method is proposed in a real row-level data center of 199 independent variables. Under design conditions, POD-MARS predictions are in good agreement with CFD simulations with the average mean relative error for 20 tested cases being ~0.01%. For rapid temperature field prediction under off-design conditions, another 20 randomized cases are tested. The average mean relative error is 6.40%, the corresponding mean absolute error is 1.88℃ and on average there is 93.03% area of the total three-dimensional temperature field where the relative error doesn't exceed 15%. The POD-MARS computation takes only 30s to obtain a 3D temperature field for the same test case which is ~240 times faster than CFD simulation on the same computer. © 2022, The Authors. All rights reserved.

关键词： Forecasting

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C olumnSGD: A Column-oriented Framework for Distributed Stochastic Gradient Descent

C olumnSGD: A Column-oriented Framework for Distributed Stoc...

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International Conference on data engineering

作者： Zhipeng Zhang Wentao Wu Jiawei Jiang Lele Yu Bin Cui Ce Zhang School of EECS & Key Laboratory of High Confidence Software Technologies (MOE) Peking University Microsoft Research Redmond USA ETH Zurich Switzerland Tencent Inc Center for Data Science Peking University & National Engineering Laboratory for Big Data Analysis and Applications

ISBN: (数字)9781728129037

ISBN: (纸本)9781728129044

Distributed machine learning (ML) has triggered tremendous research interest in recent years. Stochastic gradient descent (SGD) is one of the most popular algorithms for training ML models, and has been implemented in almost all distributed ML systems, such as Spark MLlib, Petuum, MXNet, and TensorFlow. However, current implementations often incur huge communication and memory overheads when it comes to large models. One important reason for this inefficiency is the row-oriented scheme (RowSGD) that existing systems use to partition the training data, which forces them to adopt a centralized model management strategy that leads to vast amount of data exchange over the network. We propose a novel, column-oriented scheme (ColumnSGD) that partitions training data by columns rather than by rows. As a result, ML model can be partitioned by columns as well, leading to a distributed configuration where individual data and model partitions can be collocated on the same machine. Following this locality property, we develop a simple yet powerful computation framework that significantly reduces communication overheads and memory footprints compared to RowSGD, for large-scale ML models such as generalized linear models (GLMs) and factorization machines (FMs). We implement ColumnSGD on top of Apache Spark, and study its performance both analytically and experimentally. Experimental results on both public and real-world datasets show that ColumnSGD is up to 930× faster than MLlib, 63× faster than Petuum, and 14× faster than MXNet.

关键词： Conferences data engineering Radio frequency

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Unveiling Funded Research: Impact and Dual Innovation

SSRN

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SSRN 2023年

作者： Yang, Alex Jie School of Information Management Nanjing University Nanjing210023 China Jiangsu Key Laboratory of Data Engineering and Knowledge Service Nanjing210023 China International Joint Informatics Laboratory Nanjing University Nanjing210023 China

In the relentless pursuit of scientific advancement, comprehending the profound impact and innovation nature inherent in funded research projects assumes paramount significance. To illuminate this matter, I delve into the realm of research supported by the esteemed National Institutes of Health (NIH) and the distinguished National Science Foundation (NSF). Our evaluative framework encompasses a spectrum of metrics, including the 5-year citation, normalized citation, and citation to patents, as markers of research impact. Moreover, I embrace ex-ante innovation (Novelty) and ex-post innovation (Disruption) as dual indispensable yardsticks for evaluating the innovative nature of research projects. Novelty denotes the manifestation of atypical combinations of existing knowledge, while Disruption signifies the extent of paradigm-shifting potential and the ability to exert a disruptive influence on future research endeavors. First, the analysis reveals that funded research projects manifest a conspicuously heightened impact in comparison to their non-funded counterparts. Second, I uncover a noteworthy finding, namely that funded research demonstrates significantly higher levels of ex-ante innovation (Novelty). However, in a surprising twist, the investigation also uncovers that funded research exhibits significantly lower levels of ex-post innovation (Disruption). Additionally, I undertake a meticulous scrutiny of the robustness of the research findings by scrutinizing patterns across disparate years and fields. This comprehensive analysis further unveils inconspicuous heterogeneity, thus substantiating the multifaceted nature of funded research and the vast potential for its innovation across various disciplines and temporal dimensions. © 2023, The Authors. All rights reserved.

关键词： Patents and inventions

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Efficient Processing of Global Top-k Aggregate Queries in Uncertain database

Efficient Processing of Global Top-k Aggregate Queries in Un...

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International Conference on Information engineering and Computer Science (ICIECS)

作者： Xianli Xing Dexi Liu Principal's Office Jiangxi University of Finance and Economics Nanchang China School of Information Technology Jiangxi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economics Nanchang China

ISBN: (纸本)9781424449934

Top-k queries in uncertain databases are quite popular and useful due to its wide application usage. However, compared to top-k in traditional databases, queries over uncertain database are more complicated because of the existence of exponential possible worlds. A Top-k aggregate query ranks groups of tuples by their aggregate values, sum or average for example, and returns k groups with the highest aggregate values. As a powerful semantic of top-k, global top-k, returns A highest-ranked tuples according to their probabilities of being in the top-k anTopswers in possible worlds. We propose a dynamic programming based method to process global top-k aggregate queries in uncertain database, where the number of retrieved tuples and group states generated on these tuples are minimized. Experiment results show that our algorithm is effective.

关键词： Aggregates databases Finance Power generation economics Dynamic programming Information technology Laboratories knowledge engineering Information retrieval Performance analysis

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Meta-Path Based Graph Contrastive Learning for Micro-Video Recommendation

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SSRN 2022年

作者： He, Ying Wu, Gongqing Cai, Desheng Hu, Xuegang Key Laboratory of Knowledge Engineering with Big Data The Ministry of Education of China Hefei University of Technology China School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei230009 China

Nowadays, micro-video sharing platforms have become popular tools for people creating and viewing micro-videos in daily life. Micro-video recommendation task has attracted significant attention from researchers, recently. The key to a high-quality micro-video recommendation system is learning meaningful user and micro-video representations. Recently, many graph neural networks (GNNs) are proposed to learn node representations in graphs, which could be useful for recommendation tasks. However, we argue that directly utilizing existing GNNs in micro-video recommendations is ill-posed. The reasons are: (1) most previous GNNs fail to capture the heterogeneity of heterogeneous graphs since they are designed for homogeneous graphs;(2) they overemphasize node proximity and could hurt the robustness of node embeddings. In contrast to previous work, we propose a meta-path-based graph contrastive learning network (MPGCL) that learns more meaningful user and video embeddings for recommendation. Specifically, we yield homogeneous graphs for user type and video type to better capture heterogeneity based on a well-designed meta-path-based random walk strategy. Furthermore, to learn more robust node embeddings that are less sensitive to noise, we propose a graph contrastive learning network on different types of homogeneous graphs, which maximizes the consistency between graph representations with different views. We do extensive experiments on three real-world datasets and the results show that our model can learn more meaningful embeddings for user and micro-videos and outperforms the strong baselines. © 2022, The Authors. All rights reserved.

关键词： Graph neural networks

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Analyzing Information Needs of Elderly People: A Survey in Chinese Rural Community

Analyzing Information Needs of Elderly People: A Survey in C...

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The 8th International Conference on Management and Service Science（MASS 2014）

作者： Chunfang Zou Ping Zhou School of business administration Jiangxi University of Finance and Economics School of Information Technology Jiangxi University of Finance and Economics Jiangxi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economics

A survey about the information needs of elderly people could find out the information required to address the needs of the aged in a community. Analyzing 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 majority of aged people is audio and/or visual information product, especially audio product. Most of the aged people stated that they were in need of healthy and medical non-educational 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. In summary, this paper assembles 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： Information Needs,Elderly People,Chinese Rural Community

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Analyzing Information Needs of Elderly People: A Survey in Chinese Rural Community

Analyzing Information Needs of Elderly People: A Survey in C...

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2014年管理与服务科学国际学术会议

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Conceptual Modeling for Advanced Application Domains 1

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丛书名： Lecture Notes in Computer Science

1000年

作者： Shan Wang Katsumi Tanaka Shuigeng Zhou Tok-Wang Ling Jihong Guan Dong-qing Yang Fabio Grandi Eleni E. Mangina Il-Yeol Song Heinrich C. Mayr

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NEW BOOLEAN SATISFIABILITY PROBLEM HEURISTIC STRATEGY: MINIMAL POSITIVE NEGATIVE PRODUCT STRATEGY

arXiv

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arXiv 2023年

作者： Qun, Zhao Xintao, Wang Menghui, Yang School of Information Resource Management Renmin University of China Beijing100872 China Institute of Economics Chinese Academy of Social Sciences Beijing100872 China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education Beijing100872 China

This study presents a novel heuristic algorithm called the "Minimal Positive Negative Product Strategy" to guide the CDCL algorithm in solving the Boolean satisfiability problem. It provides a mathematical explanation for the superiority of this algorithm over widely used heuristics such as the Dynamic Largest Individual Sum (DLIS) and the Variable State Independent Decaying Sum (VSIDS). Experimental results further confirm the effectiveness of this heuristic strategy in *** Codes 03 Copyright © 2023, The Authors. All rights reserved.

关键词： Heuristic algorithms

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Web page malicious code detection based on embedded fingerprints

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Qinghua Daxue Xuebao/Journal of Tsinghua University 2009年第SUPPL. 2期49卷 2208-2214页

作者： Huang, Jianjun Liang, Bin Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education School of Information Renmin University of China Beijing 100872 China

Web sites have become the main targets of many attackers. Signature-based detection needs to maintain a large signature database and Honeypot based methods are not efficient. Since attackers always make the malicious codes in Web pages difficult to detect by the browser users, their methods can be classified into various fingerprints. Various malicious codes were analyzed to identify 6 types of fingerprints. The system utilizes a spider integrated with script interpretation to fetch target Web pages and extract specific tags for detection by HTML parsing for matching with the fingerprints to detect malicious codes. This method needs fewer fingerprints than traditional detection methods and is more efficient. Results for 60 websites show that the system has a false negative rate of 2.63% and a false positive rate of 1.99%.

关键词： Websites

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