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QBS-tree: A spatial index with high update efficiency for real-time processing system 21

QBS-tree: A spatial index with high update efficiency for re...

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21st IEEE international conference on High Performance computing and Communications, 17th IEEE international conference on Smart City and 5th IEEE international conference on Data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Zhang, Zonglei Fang, Junhua Chen, Wei Zhang, Detian Liu, An Li, Zhixu School of Computer Science Technology Soochow University Suzhou China

ISBN: (纸本)9781728120584

Along with the popularization of big data applications, real-time data analysis is playing an increasingly important role in data analytic applications. The distributed parallel processing framework is a good choice for real-time big data processing, since it provides an effective solution which facilitates processing of massive data stream with low latency. Furthermore, among the real-time applications, location based service is recognized as the most challenging one, especially under expectation on fast response to distribution change and minimal resource consumption. We find that the fundamental reason for this is that the index performance is a stumbling block. Specifically, the original intention of index design is to speed up retrieval speed;in other words, the index is more useful for operations that have more queries than updates. However, in the distributed parallel processing framework, each incoming tuple is both a query request and an update request. In this paper, we propose a new tree index, quad balanced space tree called QBS-tree, to solve this problem, with practical algorithms to support dynamic index items assignment for distributed parallel processing framework. Specifically, make tree index's tolerability to the index items' unbalanced distribution in space be more resilient through a configurable balance factor. In addition, through the lazy update mechanism, let some updates offset each other and the upper node's update covers the underlying node's. Experiments show that QBS-tree responds to a new tuple 1300 times faster than the traditional tree index. © 2019 IEEE.

关键词： Location based services

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Non-Blocking Commitment Algorithm in distributed Systems with Fail-stop Model 17th

Non-Blocking Commitment Algorithm in Distributed Systems wit...

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17th international conference on Information technology: New Generations, ITNG 2020

作者： Park, Sung-Hoon Seo, Yong-cheol Yoo, Su-Chang Department of Computer Engineering Chungbuk National University CheongjuChungbuk Korea Republic of

ISBN: (纸本)9783030430191

This paper defines the Non-Blocking Atomic Commitment problem in a message-passing asynchronous system and determines a failure detector to solve the problem. This failure detector, which we call the modal failure detectorstar, and which we denote by M*, is strictly weaker than the perfect failure detector P but strictly stronger than the eventually perfect failure detector P. The paper shows that at any environment, the problem is solvable with M*. © Springer Nature Switzerland AG 2020.

关键词： Message passing

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Multi-scale convolutional neural networks for scene recognition 21

Multi-scale convolutional neural networks for scene recognit...

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作者： Yi, Zheng Zhang, HanLing College of Computer Science and Electronic Engineering Hunan University Changsha China

ISBN: (纸本)9781728120584

In recent years, convolutional neural network (CNN) has performed well in a number of image classification tasks, but it hit a bottleneck on scene recognition task, due to the multilevel semantic information in a scene. This paper is dedicated to studying the deep learning methods in scene recognition task, and making contributes to improving the classification performance in the field of scene recognition, and an effective method that captures and fuses multi-level semantic information is proposed. First of all, we compare the differences between object classification task and scene recognition task in order to apply the successful replication of CNN in object classification task to scene recognition task after resolving the differences. Then we use a multi-scale learning method to capture different scale visual features at multiple levels. In addition, on the basis of multi-scale learning, we propose a method of feature fusion at the level of category, aiming to effectively combine different scale features. The experimental results show that the success of the object classification task can be applied to the scene recognition task by our method. © 2019 IEEE.

关键词： intelligent systems

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A study of teaching model construction under big data background-taking 《computer information technology》 for example

A study of teaching model construction under big data backgr...

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1st international conference on Big Data Analytics for Cyber-Physical System in Smart City, BDCPS 2019

作者： Zheng, Jun Network Information Center Baotou Teachers’ College Baotou China

ISBN: (纸本)9789811525674

The advent of the era of big data has driven rapid development of the educational industry, and meanwhile proposed higher requirements for informatization of education and teaching. As for modern education, the concept of big data must be introduced into the daily education and teaching. Teaching objectives, teaching model, and pre-class, in-class and after-class teaching activity models under the big data background were designed in this paper, and a teaching case was designed by taking the Computer Information technology course for example. Teaching data were collected through the big data teaching platform, followed by an intelligent analysis. In the end, the new-type teaching model was constructed and implemented under the big data background. This teaching model has exerted a promoting effect on educational informatization and provided a big data-based classroom teaching model which can be used for reference. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Big data

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Parallel computing for large-scale author name disambiguation in medline 21

Parallel computing for large-scale author name disambiguatio...

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作者： Tang, Anyao Wu, Chengkun Liu, Jie Wang, Wei Yang, Xi Xing, Yuting Science and Technology on Parallel and Distributed Processing Laboratory Laboratory of Software Engineering for Complex Systems National University of Defense Technology Changsha410073 China State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha410073 China College of Computer National University of Defense Technology Changsha 410073 China

ISBN: (纸本)9781728120584

Author name disambiguation (AND) is an important task in the field of scientific data mining. It has become a great challenge with the rapid growth of academic digital libraries. The task of AND for a large number of authors is computationally intensive. In particular, an author's name in MEDLINE is represented by full last name and initials, like 'Zhang S', which leads to a lot of identical strings that actually represent different names. In this paper, we proposed an efficient algorithm for parallel AND computation. The proposed algorithm mainly addresses the load balancing issue across many computing nodes. It involves the following strategies:(1) Author-based load balancing, which splits the computation load for each core by author name labels. (2) Matrix-based strategy, which calculates the pairwise similarity between publications and saves them in a matrix globally shared by all processes. Then group them by width-first search. We combine the above two strategies, the second of which is used to calculate authors with a large number of documents, and the other authors apply the first. We constructed a publications database written by Chinese authors from MEDLINE, the biggest public database for biomedical literature (abstracts). For benchmark testing, we experimented our algorithm with a dataset of 1 million publications on the Tianhe-2A supercomputer. Firstly, we trained an AND classifier that can achieve 98.1% of F1. The serial computation time is estimated to be approximately 246 hours, while the parallel execution time is approximately 66 hours in the case of four cores on a single node (with a speedup of 3.7x). Finally, we reduced the total parallel computing time of 1 million documents to about 2 hours and achieved 65.8% of parallelism efficiency using 200 cores on 90 nodes. © 2019 IEEE.

关键词： Matrix algebra

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An Algorithm for computing the True Discrete Fractional Fourier Transform 21st

An Algorithm for Computing the True Discrete Fractional Four...

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21st international Multi-conference on Advanced Computer Systems, ACS 2018

作者： Majorkowska-Mech, Dorota Cariow, Aleksandr Faculty of Computer Science and Information Technology West Pomeranian University of Technology Szczecin ul. Zolnierska 49 Szczecin71-210 Poland

ISBN: (纸本)9783030033132

This paper proposes an algorithm for computing the discrete fractional Fourier transform. This algorithm takes advantages of a special structure of the discrete fractional Fourier transformation matrix. This structure allows to reduce the number of arithmetic operations required to calculate the discrete fractional Fourier transform. © 2019, Springer Nature Switzerland AG.

关键词： Eigenvalues and eigenfunctions

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Data reduction through decision-making based on event-sensitivity in iot-enabled event monitoring 21

Data reduction through decision-making based on event-sensit...

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作者： Bhuiyan, Md Zakirul Alam Wang, Tian Zaman, Aliuz Wang, Guojun School of Computer Science and Cyber Engineering Guangzhou University Guangzhou510006 China Department of Computer Science and Technology Huaqiao University Xiamen Fujian361021 China Department of Computer and Information Sciences Fordham University New YorkNY10458 United States

ISBN: (纸本)9781728120584

The Internet of Things (IoT) are being adopted to improve the efficiency and reliability of diverse monitoring applications, including critical events (such as fire, structural damage, crack) in the smart city's public safety application. In this paper, we present BigReduce, an IoT-enabled scheme to reduce a big quantity of data through decision-making when acquiring data and transferring the acquired data throughout the IoT network, while providing event sensitivity in industrial environments. At first, BigReduce executes decision-making briefly on the ongoing collected data in a discrete-time based on the event sensitivity so that data acquisition regarding the event-insensitivity is reduced. Then, BigReduce executes decisionmaking locally at the IoT devices to decide whether or not transmitting only a part of the gathered data that have a high event-sensitivity so that data with event-insensitivity is reduced. At an upstream IoT device (say cluster head), BigReduce runs a distributed decision-making to verify whether or not the collected data have to be transferred. Outcomes achieved through a proof-of-concept implementation shows that BigReduce is capable of reducing data quantity by 87% and energy cost by at least 82% compared to related schemes. © 2019 IEEE.

关键词： Internet of things

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Social Skills Training System in Social Settings using Multiple Animated Characters

Social Skills Training System in Social Settings using Multi...

引用

international Symposium on Soft computing and intelligent Systems (SCIS)

作者： Masahide Yuasa Shonan Institute of Technology Fujisawa Japan

ISBN: (数字)9781728197326

ISBN: (纸本)9781728197333

In recent years, the development of information technology and the decrease in opportunities for direct communication among young adults have weakened “social skills,” including the ability to verbally communicate with others, interpersonal skills, and stress responses. Social skills training systems using an animated character have been developed. However, the existing systems only involve one-on-one conversation; conflicts or pressure situations are not considered. Therefore, we propose a training system that reproduces various types of communication scenarios using multiple animated characters. Through role-playing and feedback, the user can experience various interactions among people and acquire additional social skills.

关键词： Training Turning Clocks Videos Tools stress Proposals

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Whale: Efficient One-to-Many Data Partitioning in RDMA-Assisted distributed stream Processing Systems

Whale: Efficient One-to-Many Data Partitioning in RDMA-Assis...

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Supercomputing conference

作者： Jie Tan Hanhua Chen Yonghui Wang Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computing Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9781450384421

ISBN: (纸本)9781665483902

To process large-scale real-time data streams, existing distributed stream processing systems (DSPSs) leverage different stream partitioning strategies. The one-to-many data partitioning strategy plays an important role in various applications. With one-to-many data partitioning, an upstream processing instance sends a generated tuple to a potentially large number of downstream processing instances. Existing DSPSs leverage an instance-oriented communication mechanism, where an upstream instance transmits a tuple to different downstream instances separately. However, in one-to-many data partitioning, multiple downstream instances typically run on the same machine to exploit multi-core resources. Therefore, a DSPS actually sends a data item to a machine multiple times, raising significant unnecessary costs for serialization and communication. We show that such a mechanism can lead to serious performance bottleneck due to CPU overload. To address the problem, we design and implement Whale, an efficient RDMA (Remote Direct Memory Access) assisted distributed stream processing system. Two factors contribute to the efficiency of this design. First, we propose a novel RDMA-assisted stream multicast scheme with a self-adjusting non-blocking tree structure to alleviate the CPU workloads of an upstream instance during one-to-many data partitioning. Second, we re-design the communication mechanism in existing DSPSs by replacing the instance-oriented communication with a new worker-oriented communication scheme, which saves significant costs for redundant serialization and communication. We implement Whale on top of Apache storm and conduct comprehensive experiments to evaluate its performance with large-scale real world datasets. The results show that Whale achieves 56.6x improvement of system throughput and 97% reduction of processing latency compared to existing designs.

关键词： Costs storms High performance computing Memory management Whales distributed databases Throughput

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An Empirical study on Pair Performance and Perception in distributed Pair Programming 21st

An Empirical Study on Pair Performance and Perception in Dis...

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21st international conference on Interactive Collaborative Learning (ICL) / 47th IGIP international conference on Engineering Pedagogy - Teaching and Learning in a Digital World

作者： Tsompanoudi, Despina Satratzemi, Maya Xinogalos, stelios Karamitopoulos, Leonidas Univ Macedonia Dept Appl Informat Thessaloniki Greece Alexander TEI Thessaloniki Thessaloniki Greece

ISBN: (纸本)9783030119355;9783030119348

This paper reports students' perceptions and experiences attending an object-oriented programming course in which they developed software using the distributed Pair Programming (DPP) technique. Pair programming (PP) is typically performed on one computer, involving two programmers working collaboratively on the same code or algorithm. DPP on the other hand is performed remotely allowing programmers to collaborate from separate locations. PP started in the software industry as a powerful way to train programmers and to improve software quality. Research has shown that PP (and DPP) is also a successful approach to teach programming in academic programming courses. The main focus of PP and DPP research was PP's effectiveness with respect to student performance and code quality, the investigation of best team formation strategies and studies of students' attitudes. There are still limited studies concerning relationships between performance, attitudes and other critical factors. We have selected some of the most common factors which can be found in the literature: academic performance, programming experience, student confidence, "feel-good" factor, partner compatibility and implementation time. The main goal of this study was to investigate correlations between these attributes, while DPP was used as the main programming technique.

关键词： Pair programming distributed pair programming

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