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IEEE International Congress on Big Data (BigData Congress)

作者： Yan, Danfeng Hua, Enzheng Hu, Bo Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China

ISBN: (纸本)9781509026227

Microblog is a very popular social platform, as the source of news and popular information dissemination. Detection and tracking of hot topics through Microblog research has arose the domestic and foreign scholar's attention. So, this paper mainly focuses on financial domain topic detection and tracking of Chinese Microblog. In this paper, we propose incremental TF-IWF-IDF of terms part-of-speech and position weight calculation method. This weight calculation method can solve the problem that IDF of TF-IDF is a constant value and can't change with the dataset dynamically. The traditional feature vector doesn't consider the semantic and context of terms. The paper proposes a new feature vector representation method to solve this problem by incorporating IWF into TF-IDF. This text representation method is called Word vector based on an incremental TF-IWF-IDF of terms part-of-speech and position. This paper proposes Two Steps of single-pass based on Multi Topic Centers (MC-TSP) to overcome the shortcomings of the traditional single-pass algorithm. By experimental comparison, the improved algorithm has better performance than the traditional single-pass algorithm. With improved algorithm, financial hot topic detection and tracking model is designed and implemented. The application of this model in financial domain improved the accuracy of topic detection and tracking.

关键词： Incremental TF-IWF-IDF Word2Vec single-pass algorithm topic detection and tracking financial domain

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Online randomized interpolative decomposition with a posteriori error estimator for temporal PDE data reduction

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COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2025年 434卷

作者： Li, Angran Becker, Stephen Doostan, Alireza Univ Colorado Boulder Smead Aerosp Engn Sci Boulder CO 80309 USA Univ Colorado Boulder Appl Math Boulder CO 80309 USA

Traditional low-rank approximation is a powerful tool for compressing large data matrices that arise in simulations of partial differential equations (PDEs), but suffers from high computational cost and requires several passes over the PDE data. The compressed data may also lack interpretability thus making it difficult to identify feature patterns from the original data. To address these issues, we present an online randomized algorithm to compute the interpolative decomposition (ID) of large-scale data matrices in situ. Compared to previous randomized IDs that used the QR decomposition to determine the column basis, we adopt a streaming ridge leverage score-based column subset selection algorithm that dynamically selects proper basis columns from the data and thus avoids an extra pass over the data to compute the coefficient matrix of the ID. In particular, we adopt a single-pass error estimator based on the non- adaptive Hutch++ algorithm to provide real-time error approximation for determining the best coefficients. As a result, our approach only needs a single pass over the original data and thus is suitable for large and high-dimensional matrices stored outside of core memory or generated in PDE simulations. A strategy to improve the accuracy of the reconstructed data gradient, when desired, within the ID framework is also presented. We provide numerical experiments on turbulent channel flow and ignition simulations, and on the NSTX Gas Puff Image dataset, comparing our algorithm with the offline ID algorithm to demonstrate its utility in real-world applications.

关键词： Low-rank approximation single-pass algorithm Interpolative decomposition Column subset selection Randomized algorithm

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A single-pass Triclustering algorithm

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AUTOMATIC DOCUMENTATION AND MATHEMATICAL LINGUISTICS 2015年第1期49卷 27-41页

作者： Gnatyshak, D. V. Natl Res Univ Higher Sch Econ Sci & Educ Lab Intelligent Syst & Struct Anal Moscow Russia

As the popularity of the field of big data continues to rise, the problem of the development of efficient algorithms with low time complexity and that ability to be parallelized is more and more frequently posed. This work is aimed at the development of an efficient single-pass algorithm for the triclustering of binary data that is suitable for use in the field of big data. As a result, a single-pass serial online OAC triclustering algorithm (triclustering of object-attribute-condition) was obtained. This algorithm not only has a very low complexity in time and memory, but it also can be effectively parallelized.

关键词： formal concept analysis data analysis clustering triclustering big data linear algorithm single-pass algorithm

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An Object Association Matching Method Based on V2I System

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2023年第1期16卷 132-132页

作者： Jin, Wujie Yan, Lixin Jiang, Junfeng East China Jiaotong Univ Sch Transportat Engn Nanchang Peoples R China State Grid Zhoushan Power Supply Co Zhoushan Zhejiang Peoples R China Southeast Univ Sch Transportat Nanjing 211189 Peoples R China

Vehicle-to-infrastructure (V2I) is one of the effective ways to solve the problem of intelligent connected vehicle perception, and the core is to fuse the information sensed by vehicle sensors with that sensed by infrastructure sensors. However, accurately matching the objects detected by the vehicle with multiple objects detected by the infrastructure remains a challenge. This paper presents an object association matching method to fuse the object information from vehicle sensors and roadside sensors, enabling the matching and fusion of multiple target information. The proposed object association matching algorithm consists of three steps. First, the deployment method for vehicle sensors and roadside sensors is designed. Then, the laser point cloud data from the roadside sensors are processed using the DBSCAN algorithm to extract the object information on the road. Finally, an improved single-pass algorithm for object association matching is proposed to achieve the matched target by setting a change threshold for selection. To validate the effectiveness and feasibility of the proposed method, real-vehicle experiments are conducted. Furthermore, the improved single-pass algorithm is compared with the classical Hungarian algorithm, Kuhn-Munkres (KM) algorithm, and nearest neighbor (NN) algorithm. The experimental results demonstrate that the improved single-pass algorithm achieves a target trajectory matching accuracy of 0.937, which is 6.60%, 1.85%, and 2.07% higher than the above-mentioned algorithms, respectively. In addition, this paper investigates the curvature of the target vehicle trajectory data after fusing vehicle sensing information and roadside sensing information. The curvature mean, curvature variance, and curvature standard deviation are analyzed. The experimental results illustrate that the fused target information is more accurate and effective. The method proposed in this study contributes to the advancement of the theoretical system of V2I coopera

关键词： V2I Object association matching single-pass algorithm DBSCAN Perceptual information fusion

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Improving Online Clustering of Chinese Technology Web News With Bag-of-Near-Synonyms

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IEEE ACCESS 2020年 8卷 94245-94257页

作者： Zhang, Zhe Chen, Le Yin, Fengjing Zhang, Xin Guo, Lixiang Natl Univ Def Technol Coll Syst Engn Sci & Technol Informat Syst Engn Lab Changsha 410073 Peoples R China Acad Mil Sci Inst Syst Engn Beijing 100091 Peoples R China

In the Internet era, online clustering of technology web news can help discover scientific breakthroughs and grasp technology trends. To do that automatically, the news documents to be clustered must be represented appropriately with numerical vectors. However, traditional representations such as Term Frequency-Inverse Document Frequency (TF-IDF) cannot distinguish near-synonyms and may cause "dimension disaster" To overcome these problems, this article proposes the Bag-of-Near-Synonyms (BoNS) model based on the idea to construct near-synonym sets using word embeddings and agglomerative clustering, and then to represent a document with a Set Frequency-Inverse Document Frequency (SF-IDF) vector in which each dimension corresponds to a near-synonym set rather than a single word. To speed up computation, we further propose the hashed version of SF-IDF and name it hSF-IDF, which employs a hash function to map each near-synonym set to a unique number as the key and hence reduces the computation of SF to linear time. In addition, we apply hSF-IDF to online clustering of Chinese technology web news and propose an improved batch-based method. Extensive experiments have been conducted on a real-world dataset. The results show that our model outperforms some strong baselines including TF-IDF, average pooling of word or character embeddings, Latent Dirichlet Allocation (LDA), and bag-of-concepts in terms of both accuracy and efficiency.

关键词： Computational modeling Semantics Vocabulary Clustering algorithms Facebook Licenses Artificial intelligence Information retrieval single-pass algorithm document representation word embeddings agglomerative clustering

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Non-localised contact between beams with circular and elliptical cross-sections

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COMPUTATIONAL MECHANICS 2020年第5期65卷 1247-1266页

作者： Magliulo, Marco Lengiewicz, Jakub Zilian, Andreas Beex, Lars A. A. Univ Luxembourg Fac Sci Technol & Commun Inst Computat Engn Maison Nombre 6Ave Fonte L-4364 Esch Sur Alzette Luxembourg Polish Acad Sci IPPT PAN Inst Fundamental Technol Res Ul Pawinskiego 5B PL-02106 Warsaw Poland

The key novelty of this contribution is a dedicated technique to efficiently determine the distance (gap) function between parallel or almost parallel beams with circular and elliptical cross-sections. The technique consists of parametrizing the surfaces of the two beams in contact, fixing a point on the centroid line of one of the beams and searching for a constrained minimum distance between the surfaces (two variants are investigated). The resulting unilateral (frictionless) contact condition is then enforced with the Penalty method, which introduces compliance to the, otherwise rigid, beams' cross-sections. Two contact integration schemes are considered: the conventional slave-master approach (which is biased as the contact virtual work is only integrated over the slave surface) and the so-called two-half-pass approach (which is unbiased as the contact virtual work is integrated over the two contacting surfaces). Details of the finite element formulation, which is suitably implemented using Automatic Differentiation techniques, are presented. A set of numerical experiments shows the overall performance of the framework and allows a quantitative comparison of the investigated variants.

关键词： Beams Contact Circular and elliptical cross-sections Rigid cross-sections single-pass algorithm Two-half-pass algorithm

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RANDOMIZED SKETCHING algorithmS FOR LOW-MEMORY DYNAMIC OPTIMIZATION

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SIAM JOURNAL ON OPTIMIZATION 2021年第2期31卷 1242-1275页

作者： Muthukumar, Ramchandran Kouri, Drew P. Udell, Madeleine Cornell Univ Dept Operat Res & Informat Engn Ithaca NY 14853 USA Sandia Natl Labs Optimizat & Uncertainty Quantificat POB 5800 Albuquerque NM 87185 USA

This paper develops a novel limited-memory method to solve dynamic optimization problems. The memory requirements for such problems often present a major obstacle, particularly for problems with PDE constraints such as optimal flow control, full waveform inversion, and optical tomography. In these problems, PDE constraints uniquely determine the state of a physical system for a given control;the goal is to find the value of the control that minimizes an objective. While the control is often low dimensional, the state is typically more expensive to store. This paper suggests using randomized matrix approximation to compress the state as it is generated and shows how to use the compressed state to reliably solve the original dynamic optimization problem. Concretely, the compressed state is used to compute approximate gradients and to apply the Hessian to vectors. The approximation error in these quantities is controlled by the target rank of the sketch. This approximate first- and second-order information can readily be used in any optimization algorithm. As an example, we develop a sketched trust-region method that adaptively chooses the target rank using a posteriori error information and provably converges to a stationary point of the original problem. Numerical experiments with the sketched trust-region method show promising performance on challenging problems such as the optimal control of an advection-reaction-diffusion equation and the optimal control of fluid flow past a cylinder.

关键词： PDE-constrained optimization matrix approximation randomized algorithm single-pass algorithm sketching adaptivity trust-region method flow control Navier-Stokes equations adjoint equation

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pass-efficient methods for compression of high-dimensional turbulent flow data

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JOURNAL OF COMPUTATIONAL PHYSICS 2020年 423卷 109704-109704页

作者： Dunton, Alec M. Jofre, Lluis Iaccarino, Gianluca Doostan, Alireza Univ Colorado Appl Math Boulder CO 80309 USA Stanford Univ Ctr Turbulence Res Stanford CA 94305 USA Tech Univ Catalonia BarcelonaTech Dept Fluid Mech Barcelona 08019 Spain Univ Colorado Smead Aerosp Engn Sci Boulder CO 80309 USA

The future of high-performance computing, specifically on future Exascale computers, will presumably see memory capacity and bandwidth fail to keep pace with data generated, for instance, from massively parallel partial differential equation (PDE) systems. Current strategies proposed to address this bottleneck entail the omission of large fractions of data, as well as the incorporation of in situ compression algorithms to avoid overuse of memory. To ensure that post-processing operations are successful, this must be done in a way that a sufficiently accurate representation of the solution is stored. Moreover, in situations where the input/output system becomes a bottleneck in analysis, visualization, etc., or the execution of the PDE solver is expensive, the number of passes made over the data must be minimized. In the interest of addressing this problem, this work focuses on the utility of pass-efficient, parallelizable, low-rank, matrix decomposition methods in compressing high-dimensional simulation data from turbulent flows. A particular emphasis is placed on using coarse representation of the data - compatible with the PDE discretization grid - to accelerate the construction of the low-rank factorization. This includes the presentation of a novel single-pass matrix decomposition algorithm for computing the so-called interpolative decomposition. The methods are described extensively and numerical experiments on two turbulent channel flow data are performed. In the first (unladen) channel flow case, compression factors exceeding 400 are achieved while maintaining accuracy with respect to first- and second-order flow statistics. In the particle-laden case, compression factors of 100 are achieved and the compressed data is used to recover particle velocities. These results show that these compression methods can enable efficient computation of various quantities of interest in both the carrier and disperse phases. (C) 2020 Elsevier Inc. All rights reserved.

关键词： Big-data compression Interpolative decomposition Low-rank approximation Particle-laden turbulence Randomized algorithm single-pass algorithm

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Real-Time Monitoring of Smart Campus and Construction of Weibo Public Opinion Platform

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IEEE ACCESS 2018年 6卷 76502-76515页

作者： Nan, Feng Suo, Yina Jia, Xueyong Wu, Yuying Shan, Shuaijie North China Univ Sci & Technol Coll Elect Engn Tangshan 063000 Peoples R China North China Univ Sci & Technol Informat Engn Inst Tangshan 063000 Peoples R China North China Univ Sci & Technol Key Lab Engn Calculat Tangshan City Tangshan 063000 Peoples R China

With the development and penetration of information technology, the "fourth media"-the network media is coming, and the "grievance crisis" is increasingly happening. The effective monitoring of online public opinion information becomes a problem. For each university, how to understand the sensation of teachers and students in real time in the era of informationization entering the intelligent campus has become an urgent problem for all colleges and universities. This paper first analyzes the characteristics of Weibo's communication and the process of microblogging's public opinion formation, the mechanism of communication, and so on. For the acquisition of this information, the reptile method was used to obtain the required data, which was beneficial to the real-time sensation in the smart campus. Then, based on the improved single-pass algorithm to analyze the new characteristics of microblog propagation information and the public opinion sent by college students, and compared the improved single-pass algorithm with the effect of single-pass algorithm, the improved algorithm had the recall rate was high, the false detection rate was low, and the running time was short. Finally, taking the lyrics of college teachers and students as the research object, unified modeling language modeling was used to analyze the public opinion monitoring platform of the smart campus, and the campus public opinion of several major events was monitored and analyzed. This provided a reference for the effective, intelligent, and real-time detection of Weibo public opinion in colleges and universities.

关键词： Public opinion monitoring single-pass algorithm UML smart campus Weibo

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A Storm-Based Parallel Clustering algorithm of Streaming Data 25th

A Storm-Based Parallel Clustering Algorithm of Streaming Dat...

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25th International Conference on Neural Information Processing (ICONIP)

作者： Xu, Fang-Zhu Jiang, Zhi-Ying He, Yan-Lin Wang, Ya-Jie Zhu, Qun-Xiong Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing 100029 Peoples R China Minist Educ China Engn Res Ctr Intelligent PSE Beijing 100029 Peoples R China Guizhou Food Safety Testing Engn Technol Res Ctr Guiyang Guizhou Peoples R China

ISBN: (纸本)9783030042127;9783030042110

Aiming at solving the shortcomings of traditional single-pass clustering algorithms, such as low accuracy and large amount of computation, a novel Storm-based parallel single-pass clustering algorithm is proposed to discovery of hot events in the food field. In order to solve the problem of data inconsistency in parallel computing, a method of dynamically acquiring cluster increments and random delays is adopted to improve the single-pass algorithm. In order to validate the performance of the proposed method, a case study of news events classification is carried out. Simulation results show that the proposed algorithm can effectively improve the cluster repetition in clustering results and greatly improve the accuracy and efficiency of clustering compared with the traditional single-pass algorithm.

关键词： Parallel clustering Streaming data single-pass algorithm

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