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IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Yinggen Xu Wei Chen Shaoqi Wang Xiaobo Zhou Changjun Jiang The Key Lab of Embedded System and Service Computing Tongji University Shanghai China Department of Computer Science University of Colorado Colorado Springs Colorado USA

ISBN: (纸本)9781538641293

Modern datacenter schedulers apply a static policy to partition resources among different tasks. The amount of allocated resource won't get changed during a task's lifetime. However, we found that resource usage during a task's runtime demonstrates high dynamics and it only reaches full usage at few moments. Therefore, the static allocation policy doesn't exploit the dynamic nature of resource usage, leading to low system resource utilization. To address this hard problem, a recently proposed task-consolidation approach packs as many tasks as possible on the same node based on real-time resource demands. However, this approach may cause resource over-allocation and harm application performance. In this paper, we propose and develop ECS, an elastic container based scheduler that leverages resource usage variation within the task lifetime to exploit the potential utilization and parallelism. The key idea is to proactively select and shift tasks backward so that the inherent paralleled tasks can be identified without over-allocation. We formulate the scheduling scheme as an online optimization problem and solves it using a resource leveling algorithm. We have implemented ECS in Apache Yarn and performed evaluations with various MapReduce benchmarks in a cluster. Experimental results show that ECS can efficiently utilize resource and achieves up to 29% reduction on average job completion time while increasing CPU utilization by 25%, compared to stock Yarn.

关键词： Task analysis Containers Dynamic scheduling Resource management Benchmark testing Parallel processing Runtime

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A Privacy Preserving Algorithm Based on R-constrained Dummy Trajectory in Mobile Social Network

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Procedia Computer Science 2018年 129卷 420-425页

作者： Lina Ni Yanfeng Yuan Xiao Wang Jiguo Yu Jinquan Zhang College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China School of Information Science and Engineering Qufu Normal University Shandong China

Recently the research of location privacy preserving has become a hot spot. Location privacy preserving involves not only a single location privacy but also the trajectory privacy where the mobile users in different locations publish the consecutive query requests. In this paper, we consider the problem of trajectory privacy preserving in MSN. Particularly, we propose privacy preserving algorithms based on R-constraint dummy trajectory (RcDT). By constraining the generating range R of the dummy positions, the generated dummy positions are within a certain range near the real locations. Furthermore, the dummy trajectories which have higher similarity to the real trajectories are generated via constraining both the single location exposure risk and the trajectory exposure risk.

关键词： Privacy preserving mobile social network trajectory privacy location-based service R-constrained dummy trajectory

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Robust Learning to Rank Based on Portfolio Theory and AMOSA Algorithm

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IEEE Transactions on systems, Man, and Cybernetics: systems 2017年第6期47卷 1007-1018页

作者： Li, Jinzhong Liu, Guanjun Yan, Chungang Jiang, Changjun Key Laboratory of the Ministry of Education for Embedded System and Service Computing Department of Computer Science and Technology Tongji University Shanghai201804 China Key Laboratory of Watershed Ecology and Geographical Environment Monitoring NASG Department of Computer Science and Technology Jinggangshan University Ji'an343009 China

Effectiveness is the most important factor considered in the ranking models yielded by algorithms of learning to rank (LTR). Most of the related ranking models only focus on improving the average effectiveness but ignore robustness. When a ranking model ignores robustness, the effectiveness for many queries is possibly very poor although the average effectiveness for all queries is relatively high. Therefore, Wang et al. first consider robustness in their ranking models. However, the robustness formula defined by Wang et al. cannot characterize those queries whose effectiveness are hurt seriously in comparison with the baseline model. In order to overcome this shortcoming, we propose a novel formula of characterizing robustness based on portfolio theory, and construct a multiobjective optimization model of the robust LTR in which the formula is used. Based on this model, we propose an approach of risk-sensitive and robust LTR, named as R2Rank, which is based on the framework of archived multiobjective simulated annealing algorithm and the idea of preference ranking organization method for enrichment evaluation. The experimental results show that the ranking models produced by our proposed R2Rank approach are better in both effectiveness and robustness than those produced by three state-of-the-art LTR approaches. © 2016 IEEE.

关键词： Simulated annealing

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Spatial sparse subspace clustering for compressive spectral imaging

arXiv

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arXiv 2019年

作者： Zhu, Jianchen Zhang, Tong Zhao, Shengjie Hinojosa, Carlos Liu, Zengli Arce, Gonzalo R. Key Laboratory of Embedded System and Service Computing Ministry of Education School of Software Engineering Tongji University Shanghai201804 China School of Electronic and Information Engineering Tongji University Shanghai201804 China Department of System Engineering and Informatics Universidad Industrial de Santander Bucaramanga680002 Colombia Institute of information engineering and automation Computer Center Kunming University of Science and Technology Kunming650500 China Department of Electrical and Computer Engineering University of Delaware NewarkDE19716 United States

This paper aims at developing a clustering approach with spectral images directly from CASSI compressive measurements. The proposed clustering method first assumes that compressed measurements lie in the union of multiple low-dimensional subspaces. Therefore, sparse subspace clustering (SSC) is an unsupervised method that assigns compressed measurements to their respective subspaces. In addition, a 3D spatial regularizer is added into the SSC problem, thus taking full advantages of the spatial information contained in spectral images. The performance of the proposed spectral image clustering approach is improved by taking optimal CASSI measurements obtained when optimal coded apertures are used in CASSI system. Simulation with one real dataset illustrates the accuracy of the proposed spectral image clustering approach. Copyright © 2019, The Authors. All rights reserved.

关键词： Spectroscopy

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A Recommender system for Ordering Platform Based on an Improved Collaborative Filtering Algorithm

A Recommender System for Ordering Platform Based on an Impro...

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International Conference on Audio, Language and Image Processing, ICALIP

作者： Chengchao Yu Qingshi Tang Zheng Liu Bin Dong Zhihua Wei Department of Computer Science and Technology Tongji University Shanghai China Shanghai Zhuoji Information Technology ltd. Shanghai China Key Laboratory of Embedded System and Service Computing Tongji University Shanghai China

With the development of ordering platform, an increasing number of people are paying their attention to design a suitable recommender system. Most of the traditional recommender systems are based on the abundant rating information of users. However, Only historical order data can be provided to the recommender system in ordering platform as training data. This paper proposes an improved Collaborative Filtering algorithm based on historical order data of restaurants. The recommender system includes two parts: 1) rule generation module, we define a new method for measuring the similarity between dishes. Furthermore, we incorporate an incremental learning method in this module. 2) recommendation module, we design user interest vector and propose a noise filtering method. Experimental results demonstrate that the proposed algorithm can effectively improve the performance of recommendation in terms of the accuracy and coverage ratio. Moreover, our recommender system has been successfully put into service.

关键词： Recommender systems Collaboration Training Measurement Filtering algorithms Training data Learning systems

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An Event Timeline Extraction Method Based on News Corpus 2

An Event Timeline Extraction Method Based on News Corpus

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2017 IEEE 2nd International Conference on Big Data Analysis（ICBDA 2017）

作者： Yaguang Wu Haichun Sun Chungang Yan Department of Computer Science and Technology Tongji University The Key Laboratory of Embedded System and Service Computing Tongji University College of Police Information Engineering People's Public Security University of China

ISBN: (纸本)9781509036202

Event extraction is an important research point in information extraction area, and news event extraction has a greater practical significance. The existing methods of extracting news event, which starts from the time element, is to identify the date sentences hold by Natural Language Processing and extract the event on the date by Text Clustering. However, they only process the news that holds a time-tag and gives up the news that has no accurate date, which easily leads an extraction deviation of significant events and reduces the accuracy of ordering the significant events. In this paper, improvement for this defect is to calculate the similarities between sentences to put part of the non-time-tag sentences into the right date container, thereby the accuracy of ordering the significant event is *** experiments and compared with the existing methods,the accuracy of ordering the significant events in this paper is improved by 14.6%.

关键词： event extraction text information mining sentence similarity timeline

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An Improved Convolutional Neural Network Algorithm for Multi-Label Classification

An Improved Convolutional Neural Network Algorithm for Multi...

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International Conference on Audio, Language and Image Processing, ICALIP

作者： Xinsheng Wang Lijun Sun Zhihua Wei Department of Computer Science and Technology Tongji University Shanghai China Research Center of Big Data and Network Security Tongji University Shanghai China Key Laboratory of Embedded System and Service Computing Tongji University Shanghai China

Recent years conventional neural network(CNN) has been applied to different natural language processing(NLP) tasks such as sentence classification, sentence modeling, etc. Some researchers use CNN to do multi-label classification but their work mainly focus on image rather than text. In this paper, we propose an improved CNN via hierarchical dirichlet process(HDP) model to deal with the multi-label classification problem in NLP. We first apply an HDP model to discard some words which are less important semantically. Then we use word embedding methods to transform words to vectors. Finally, we train CNN based on word vectors. Experimental results demonstrate that our method is superior to most traditional multi-label classification methods and TextCNN in terms of performance.

关键词： Feature extraction Training Convolutional neural networks Natural language processing Vocabulary Transforms

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Vision-based Semantic Mapping and Localization for Autonomous Indoor Parking

arXiv

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arXiv 2018年

作者： Huang, Yewei Zhao, Junqiao He, Xudong Zhang, Shaoming Feng, Tiantian Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai Department of Computer Science and Technology School of Electronics and Information Engineering Tongji University Shanghai School of Surveying and Geo-Informatics Tongji University Shanghai China

In this paper, we proposed a novel and practical solution for the real-time indoor localization of autonomous driving in parking lots. High-level landmarks, the parking slots, are extracted and enriched with labels to avoid the aliasing of low-level visual features. We then proposed a robust method for detecting incorrect data associations between parking slots and further extended the optimization framework by dynamically eliminating suboptimal data associations. Visual fiducial markers are introduced to improve the overall precision. As a result, a semantic map of the parking lot can be established fully automatically and robustly. We experimented the performance of real-time localization based on the map using our autonomous driving platform TiEV, and the average accuracy of 0.3m track tracing can be achieved at a speed of 10kph. Copyright © 2018, The Authors. All rights reserved.

关键词： Indoor positioning systems

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Implicit cooperative caching based on information popularity for vehicular networks 12

Implicit cooperative caching based on information popularity...

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12th Workshop on Challenged Networks, CHANTS 2017

作者： Li, Yuhong Wang, Ji Lindgren, Anders Su, Xiang Riekki, Jukka State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China RISE SICS Sweden Luleå University of Technology Sweden Centre for Ubiquitous Computing University of Oulu Finland

ISBN: (纸本)9781450351447

Information-centric networking (ICN) technology is becoming a popular research topic in vehicular networks due to the connectionless and lightweight characteristics of this networking paradigm. Caching plays an essential role in information-centric networks, but current caching techniques for ICN are not ideal for the dynamic and wireless vehicular networks. This paper presents a caching approach for ICN-based vehicular networks that takes into account both the dynamicity of vehicular networks and the popularity of the information being distributed. We introduce an interval metric for selective caching. With this metric and estimates of information popularity and vehicle density, cooperative caching can be realized without exchanging cache management information among the vehicular nodes. Simulation results show that the proposed approach can increase the storage space utilization and has low data response time for vehicular networks. © 2017 Association for computing Machinery.

关键词： Digital storage

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MapReduce-based parallel GEP algorithm for efficient function mining in big data applications

MapReduce-based parallel GEP algorithm for efficient functio...

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作者： Liu, Yang Ma, Chenxiao Xu, Lixiong Shen, Xiaodong Li, Maozhen Li, Pengcheng School of Electrical Engineering and Information Sichuan University Chengdu610065 China Department of Electronic and Computer Engineering Brunel University London UxbridgeUB8 3PH United Kingdom Key Laboratory of Embedded Systems and Service Computing Ministry of Education Tongji University Shanghai200092 China State Grid Shanxi Electric Power Company Xi'an710048 China

Gene expression programming (GEP) algorithm is one of the most effective function mining algorithms in enabling the mathematical equation fitting for the input dataset. However, GEP algorithm encounters low efficiency issue in big data processing due to large overhead in its evolution when it handles the large-scale data. In order to solve the issue, this paper presents two parallelized GEP algorithms using MapReduce. Based on data separation, the first algorithm aims at speeding up the large-scale classification. However, it is lack of ability to output the mined equation explicitly. Therefore, based on the further improvements of the first algorithm, the second parallelized GEP algorithm aims at mining the equation efficiently and also outputs the equation explicitly and directly. The experimental results show that both algorithms are effective for processing large volume of data. © 2017 John Wiley & Sons, Ltd.

关键词： MapReduce

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