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Synonyms weighted LDA for product aspects extraction

International Journal of Applied Mathematics and Statistics 2013年第24期51卷 283-291页

作者： Peng, Yun Wan, Changxuan School of Information and Technology Jiangxi University of Finance and Economics Nanchang 330013 Jiangxi China Computer and Information Engineering College Jiangxi Normal University Nanchang 330022 Jiangxi China Jiangxi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economics Nanchang 330013 Jiangxi China

It is important to extract the aspects from the comments of shoppers about certain products. Product aspect descriptions often contain words of same meaning, and discriminating these synonyms effectively can improve the efficiency of aspects extraction. Standard topic model in identifying those words, because of the absent introduction of priori knowledge, will largely ignore the relations of words meaning. An improved aspects extracting model of LDA by adding synonymous words constraint is proposed in this paper. In the process of words clustering analysis, words with high similarity are aggregated in the same cluster. In the stage of aspects extraction, we introduce words clustering and cluster membership weights as priori knowledge to strengthen the original LDA topic model for features clustering, which can improve the accuracy of product aspects extraction. Our experiments show that synonyms weighted LDA proposed in this paper outperforms the original LDA by a large margin. © 2013 by CESER Publications.

关键词： Extraction

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SHLE: Devices Tracking and Depth Filtering for Stereo-based Height Limit Estimation

arXiv

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arXiv 2022年

作者： Fan, Zhaoxin Yang, Kaixing Zhang, Min Song, Zhenbo Liu, Hongyan He, Jun Key Laboratory of Data Engineering and Knowledge Engineering of MOE School of Information Renmin University of China Beijing100872 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Department of Management Science and Engineering Tsinghua University Beijing100084 China

Recently, over-height vehicle strike frequently occurs, causing great economic cost and serious safety problems. Hence, an alert system which can accurately discover any possible height limiting devices in advance is necessary to be employed in modern large or medium sized cars, such as touring cars. Detecting and estimating the height limiting devices act as the key point of a successful height limit alert system. Though there are some works research height limit estimation, existing methods are either too computational expensive or not accurate enough. In this paper, we propose a novel stereo-based pipeline named SHLE for height limit estimation. Our SHLE pipeline consists of two stages. In stage 1, a novel devices detection and tracking scheme is introduced, which accurately locate the height limit devices in the left or right image. Then, in stage 2, the depth is temporally measured, extracted and filtered to calculate the height limit device. To benchmark the height limit estimation task, we build a large-scale dataset named "Disparity Height", where stereo images, pre-computed disparities and ground-truth height limit annotations are provided. We conducted extensive experiments on "Disparity Height" and the results show that SHLE achieves an average error below than 10cm though the car is 70m away from the devices. Our method also outperforms all compared baselines and achieves state-of-the-art performance. Code is available at https://***/Yang-Kaixing/SHLE. Copyright © 2022, The Authors. All rights reserved.

关键词： Pipelines

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N-Docker: A NVM-HDD Hybrid Docker Storage Framework to Improve Docker Performance 16th

N-Docker: A NVM-HDD Hybrid Docker Storage Framework to Impro...

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16th IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2019

作者： Gu, Lin Tang, Qizhi Wu, Song Jin, Hai Zhang, Yingxi Shi, Guoqiang Lin, Tingyu Rao, Jia National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China State Key Laboratory of Intelligent Manufacturing System Technology Beijing100854 China The University of Texas at Arlington ArlingtonTX76019 United States

ISBN: (纸本)9783030307080

Docker has been widely adopted in production environment, but unfortunately deployment and cold-start of container are limited by the low speed of disk. The emerging non-volatile memory (NVM) technology, which has high speed and can store data permanently, brings a new chance to accelerate the deployment and cold-start of container. However, it is expensive to replace the whole hard disk driver (HDD) with NVM. To achieve the fastest deployment and cold-start with lowest cost, we conduct in-depth analysis on the Top-134 images in Docker Hub and obtain two main insights as: (1) the storing latency of layered image has become the bottleneck of container deployment;(2) only a few image layers are required for container cold-start. Based on these two findings, we propose a NVM-HDD hybrid docker storage framework as N-Docker. It can effectively accelerate container cold-start by detecting the bottleneck layers as well as cold-start required layers and storing them into NVM for faster container startup with limited NVM capacity. Experimental results show that N-Docker can accelerate the container deployment by 1.21X and cold-start by 2.96X. Compared to NVM-Docker, which stores all images into NVM, N-Docker achieves the same performance improvements while reducing the usage of NVM by 88.22%. © 2019, IFIP International Federation for Information Processing.

关键词： Containers

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Epileptic brain network analysis based on Kendall's improved synchronization algorithm 1

Epileptic brain network analysis based on Kendall's improved...

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1st International Conference on Physics, Mathematics and Statistics, ICPMS 2018

作者： Ding, Chuchu Sun, Ting Zou, Xin Dai, Jiafei Li, Jin Wang, Jun Hou, Fengzhen Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing China Nanjing General Hospital of Nanjing Military Command Nanjing210002 China College of Physics and Information Technology Shaanxi Normal University Xi'an China Key Laboratory of Biomedical Functional Materials China Pharmaceutical University Nanjing210009 China

In this paper, we use a new algorithm, the IRC algorithm, to improve the Kendall algorithm. Research on complex networks has gradually deepened into all areas of social science. The study of brain networks has become a hot topic in the study of brain function. The method of wavelet filtering is used to filter the EEG data to obtain the required α-band (8-16 Hz). Using the improved IRC algorithm, the brain functional network is constructed based on the EEG data, and the related characteristics of the brain network constructed are analyzed. The experimental results show that the method is suitable for distinguishing the network degree indicators of epilepsy and normal brain tissue, and further deepening the study of the neurokinetic behavior of the brain. © Published under licence by IOP Publishing Ltd.

关键词： Complex networks

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Unsupervised vehicle re-identification with progressive adaptation

arXiv

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arXiv 2020年

作者： Peng, Jinjia Wang, Yang Wang, Huibing Zhang, Zhao Fu, Xianping Wang, Meng College of Information and Science Technology Dalian Maritime University LiaoningDalian China Pengcheng Laboratory ShenzhenGuangdong China Key Laboratory of Knowledge Engineering with Big Data Ministry of education Hefei University of Technology China School of Computer Science and Information Engineering Hefei University of Technology China

Vehicle re-identification (reID) aims at identifying vehicles across different non-overlapping cameras views. The existing methods heavily relied on well-labeled datasets for ideal performance, which inevitably causes fateful drop due to the severe domain bias between the training domain and the real-world scenes;worse still, these approaches required full annotations, which is labor-consuming. To tackle these challenges, we propose a novel progressive adaptation learning method for vehicle reID, named PAL, which infers from the abundant data without annotations. For PAL, a data adaptation module is employed for source domain, which generates the images with similar data distribution to unlabeled target domain as "pseudo target samples". These pseudo samples are combined with the unlabeled samples that are selected by a dynamic sampling strategy to make training faster. We further proposed a weighted label smoothing (WLS) loss, which considers the similarity between samples with different clusters to balance the confidence of pseudo labels. Comprehensive experimental results validate the advantages of PAL on both VehicleID and VeRi-776 dataset. Copyright © 2020, The Authors. All rights reserved.

关键词： Vehicles

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SALI: A Scalable Adaptive Learned Index Framework based on Probability Models

arXiv

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arXiv 2023年

作者： Ge, Jiake Zhang, Huanchen Shi, Boyu Luo, Yuanhui Guo, Yunda Chai, Yunpeng Chen, Yuxing Pan, Anqun Key Laboratory of Data Engineering and Knowledge Engineering MOE China Engineering Research Center of Database and Business Intelligence MOE China School of Information Renmin University of China China China and Shanghai Qi Zhi Institute China Tsinghua University China Tencent Inc China

The growth in data storage capacity and the increasing demands for high performance have created several challenges for concurrent indexing structures. One promising solution is learned indexes, which use a learning-based approach to fit the distribution of stored data and predictively locate target keys, significantly improving lookup performance. Despite their advantages, prevailing learned indexes exhibit constraints and encounter issues of scalability on multi-core data storage. This paper introduces SALI, the Scalable Adaptive Learned Index framework, which incorporates two strategies aimed at achieving high scalability, improving efficiency, and enhancing the robustness of the learned index. Firstly, a set of node-evolving strategies is defined to enable the learned index to adapt to various workload skews and enhance its concurrency performance in such scenarios. Secondly, a lightweight strategy is proposed to maintain statistical information within the learned index, with the goal of further improving the scalability of the index. Furthermore, to validate their effectiveness, SALI applied the two strategies mentioned above to the learned index structure that utilizes fine-grained write locks, known as LIPP. The experimental results have demonstrated that SALI significantly enhances the insertion throughput with 64 threads by an average of 2.04× compared to the second-best learned index. Furthermore, SALI accomplishes a lookup throughput similar to that of LIPP+. © 2023, CC BY-NC-ND.

关键词： Scalability

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Finedge: A Dynamic Cost-Efficient Edge Resource Management Platform for NFV Network

Finedge: A Dynamic Cost-Efficient Edge Resource Management P...

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International Workshop on Quality of Service

作者： Miao Li Qixia Zhang Fangming Liu National Engineering Research Center for Big Data Technology and System Key Laboratory of Services Computing Technology and System Ministry of Education School of Computer Science and Technology Huazhong University of Science and Technology China

ISBN: (数字)9781728168876

ISBN: (纸本)9781728168883

With the evolution of network function virtualization (NFV) and edge computing, software-based network functions (NFs) can be deployed on closer-to-end-user edge servers to support a broad range of new services with high bandwidth and low latency. However, due to the resource limitation, strict QoS requirements and real-time flow fluctuations in edge network, existing cloud-based resource management strategy in NFV platforms is inefficient to be applied to the edge. Thus, we propose Finedge, a dynamic, fine-grained and cost-efficient edge resource management platform for NFV network. First, we conduct empirical experiments to find out the effect of NFs' resource allocation and their flow-level characteristics on performance. Then, by jointly considering these factors and QoS requirements (e.g., latency and packet loss rate), Finedge can automatically assign the most suitable CPU core and tune the most cost-efficient CPU quota to each NF. Finedge is also implemented with some key strategies including real-time flow monitoring, elastic resource scaling up and down, and also flexible NF migration among cores. Through extensive evaluations, we validate that Finedge can efficiently handle heterogeneous flows with the lowest CPU quota and the highest SLA satisfaction rate as compared with the default OS scheduler and other state-of-the-art resource management schemes.

关键词： Resource management Noise measurement Packet loss Servers Throughput Quality of service

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Semi-supervised classification on data streams with recurring concept drift and concept evolution

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knowledge-Based Systems 2021年 215卷 106749-106749页

作者： Zheng, Xiulin Li, Peipei Hu, Xuegang Yu, Kui Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education China School of Computer Science and Information Engineering Hefei University of Technology Hefei 230601 China Anhui Province Key Laboratory of Industry Safety and Emergency Technology Hefei 230601 Anhui China

Mining non-stationary stream is a challenging task due to its unique property of infinite length and dynamic characteristics let alone the issues of concept drift, concept evolution and limited labeled data. Although more attention has been attracted on the issues of concept drift and evolution in data streams, however, most of existing methods are supervised in nature, which probably result in a worse classification performance and lower efficiency in the case with scarcity of labeled data. Thus, in this paper, we proposed a semi-supervised framework with recurring concept drift and novel class detection called ESCR, which aims to detect recurring concept drift and concept evolution in data streams with partially labeled data. It is firstly built on an ensemble model consisted of several clustering-based classifiers. In terms of this framework, we adopt Jensen–Shannon divergence based change detection technique on classifier confidence score instead of classification error rate to detect recurring concept drifts. Meanwhile, we take concept evolution into consideration by monitoring the outliers with strong cohesion. Moreover, we further improve the execution efficiency of our framework by exploiting the recursive function and dynamic programming. Finally, extensive experiments conducted on both benchmark and synthetic data sets demonstrate the effectiveness and efficiency of our proposed semi-supervised framework in the handling of data streams with recurring concept drifts and concept evolution, as compared to several well-known semi-supervised data stream classification methods. © 2021 Elsevier B.V.

关键词： Concept evolution data stream classification Recurring concept drift

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Heat to Power: Thermal Energy Harvesting and Recycling for Warm Water-Cooled datacenters

Heat to Power: Thermal Energy Harvesting and Recycling for W...

引用

Annual International Symposium on Computer Architecture, ISCA

作者： Xinhui Zhu Weixiang Jiang Fangming Liu Qixia Zhang Li Pan Qiong Chen Ziyang Jia National Engineering Research Center for Big Data Technology and System Key Laboratory of Services Computing Technology and System Ministry of Education School of Computer Science and Technology Huazhong University of Science and Technology China

ISBN: (数字)9781728146614

ISBN: (纸本)9781728146621

Warm water cooling has been regarded as a promising method to improve the energy efficiency of water-cooled datacenters. In warm water-cooling systems, hot spots occur as a common problem where the hybrid cooling architecture integrating thermoelectric coolers (TECs) emerges as a new remedy. Equipped with this architecture, the inlet water temperature can be raised higher, which provides more opportunities for heat recycling. However, currently, the heat absorbed from the server components is ejected directly into the water without being recycled, which leads to energy wasting. In order to further improve the energy efficiency, we propose Heat to Power (H2P), an economical and energy-recycling warm water cooling architecture, where thermoelectric generators (TEGs) harvest thermal energy from the “used” warm water and generate electricity for reusing in datacenters. Specifically, we propose some efficient optimization methods, including an economical water circulation design, fine-grained adjustments of the cooling setting and dynamic workload scheduling for increasing the power generated by TEGs. We evaluate H2P based on a real hardware prototype and cluster traces from Google and Alibaba. Experiment results show that TEGs equipped with our optimization methods can averagely generate 4.349 W, 4.203 W, and 3.979 W (4.177 W averagely) electricity on one CPU under the drastic, irregular and common workload traces, respectively. The power reusing efficiency (PRE) can reach 12.8%~16.2% (14.23% averagely) and the total cost of ownership (TCO) of datacenters can be reduced by up to 0.57%.

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HUSS: A Heuristic Method for Understanding the Semantic Structure of Spreadsheets

HUSS: A Heuristic Method for Understanding the Semantic Stru...

引用

IEEE International Conference on Big knowledge (ICBK)

作者： Xindong Wu Hao Chen Chenyang Bu Shengwei Ji Zan Zhang Victor S. Sheng 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 Hefei China Department of Computer Science Texas Tech University Lubbock TX USA

Spreadsheets contain a lot of valuable data and have many practical applications. The key technology of these practical applications is how to make machines understand the semantic structure of spreadsheets, e.g., identifying cell function types and discovering relationships between cell pairs. Most existing methods for understanding the semantic structure of spreadsheets do not make use of the semantic information of cells. A few studies do, but they ignore the layout structure information of spreadsheets, which affects the performance of cell function classification and the discovery of different relationship types of cell pairs. In this paper, we propose a Heuristic algorithm for Understanding the Semantic Structure of spreadsheets (HUSS). Specifically, based on the existing cell function classification model [11], we propose five types of heuristic rules to extract four different types of cell pairs, based on the cell style and spatial location information. Our experimental results on two real-world datasets demonstrate that the proposed method HUSS can effectively understand the semantic structure of spreadsheets and outperforms corresponding baselines.

关键词： Analytical models Heuristic algorithms Semantics Layout Feature extraction Classification algorithms data mining

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