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Intelligent spam filtering for massive short message stream

COMPEL-THE INTERNATIONAL JOURNAL FOR COMPUTATION AND MATHEMATICS IN ELECTRICAL AND ELECTRONIC ENGINEERING 2013年第2期32卷 586-596页

作者： Xia, Hu Fu, Yan Zhou, Junlin Xia, Qi Univ Elect Sci & Technol China Sch Comp Sci & Engn Web Sci Ctr Chengdu 610054 Peoples R China Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 610054 Peoples R China

Purpose - The purpose of this paper is to provide an intelligent spam filtering method to meet the real-time processing requirement of the massive short message stream and reduce manual operation of the system. Design/methodology/approach - An integrated framework based on a series of algorithms is proposed. The framework consists of message filtering module, log analysis module and rules handling module, and dynamically filters the short message spam, while generating the filtering rules. Experiments using Java are used to execute the proposed work. Findings -The experiments are carried out both on the simulation model (off-line) and on the actual plant (on-line). All experiment data are considered in both normal and spam real short messages. The results show that use of the integrated framework leads to a comparable accuracy and meet the real-time filtration requirement. Originality/value - The approach in the design of the filtering system is novel. In addition, implementation of the proposed integrated framework allows the method not only to reduce the computational cost which leads to a high processing speed but also to filter spam messages with a high accuracy.

关键词： Internet Data management Information technology Mobile technology Mobile networks Programming and algorithm theory Spam filtration Massive data processing sampling algorithm

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Generating labeled planar graphs uniformly at random

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THEORETICAL COMPUTER SCIENCE 2007年第3期379卷 377-386页

作者： Bodirsky, Manuel Groepl, Clemens Kang, Mihyun Free Univ Berlin Inst Informat D-14195 Berlin Germany Humboldt Univ Inst Informat Abt Algorithmen & Komplexitat 1 D-10099 Berlin Germany

We present a deterministic polynomial time algorithm to sample a labeled planar graph uniformly at random. Our approach uses recursive formulae for the exact number of labeled planar graphs with n vertices and m edges, based on a decomposition into 1-, 2-, and 3-connected components. We can then use known sampling algorithms and counting formulae for 3-connected planar graphs. (c) 2007 Elsevier B.V. All rights reserved.

关键词： labeled planar graph enumeration decomposition sampling algorithm dynamic programming graph theory

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On the Green's function of the partially diffusion-controlled reversible ABCD reaction for radiation chemistry codes

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JOURNAL OF COMPUTATIONAL PHYSICS 2015年 297卷 515-529页

作者： Plante, Ianik Devroye, Luc Wyle Sci Technol & Engn Houston TX 77058 USA McGill Univ Sch Comp Sci Montreal H3A 0E9 PQ Canada

Several computer codes simulating chemical reactions in particles systems are based on the Green's functions of the diffusion equation (GFDE). Indeed, many types of chemical systems have been simulated using the exact GFDE, which has also become the gold standard for validating other theoretical models. In this work, a simulation algorithm is presented to sample the interparticle distance for partially diffusion-controlled reversible ABCD reaction. This algorithm is considered exact for 2-particles systems, is faster than conventional look-up tables and uses only a few kilobytes of memory. The simulation results obtained with this method are compared with those obtained with the independent reaction times (IRT) method. This work is part of our effort in developing models to understand the role of chemical reactions in the radiation effects on cells and tissues and may eventually be included in event-based models of space radiation risks. However, as many reactions are of this type in biological systems, this algorithm might play a pivotal role in future simulation programs not only in radiation chemistry, but also in the simulation of biochemical networks in time and space as well. (C) 2015 Elsevier Inc. All rights reserved.

关键词： Theory Modeling Computer simulation sampling algorithm Monte Carlo simulations Green's functions of the diffusion equation Chemical reactions Radiation chemistry

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Evaluation of subgraph searching algorithms for detecting network motifs in biological networks

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中国高等学校学术文摘·计算机科学 2009年第3期3卷 412-416页

作者： Jialu HU Lin GAO Guimin QIN School of Computer Science and Technology Xidian University Xi'an 710071 China Software School Xidian University Xi'an 710071 China

Despite several algorithms for searching sub-graphs in motif detection presented in the literature, no ef-fort has been done for characterizing their performance till now. This paper presents a methodology to evaluate the performance of three algorithms: edge sampling algorithm (ESA), enumerate subgraphs (ESU) and randomly enumer-ate subgraphs (RAND-ESU). A series of experiments are performed to test sampling speed and sampling quality. The results show that RAND-ESU is more efficient and has less computational cost than other algorithms for large-size mo-tif detection, and ESU has its own advantage in small-size motif detection.

关键词： network motifs subgraphs searching sub-graphs enumeration sampling algorithm

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Real-world image denoising via efficient diffusion model with controllable noise generation

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JOURNAL OF ELECTRONIC IMAGING 2024年第4期33卷 043003-043003页

作者： Yang, Cheng Wang, Cong Liang, Lijing Su, Zhixun Dalian Univ Technol Sch Math Sci Dalian Peoples R China Hong Kong Polytech Univ Hong Kong Peoples R China

Real-world image denoising is a critical task in image processing, aiming to restore clean images from their noisy counterparts captured in natural environments. While diffusion models have demonstrated remarkable success in image generation, surpassing traditional generative models, their application to image denoising has been limited due to challenges in controlling noise generation effectively. We present a general denoising method inspired by diffusion models. Specifically, our approach employs a diffusion process with linear interpolation, enabling control of noise generation. By interpolating the intermediate noisy image between the original clean image and the corresponding real-world noisy one, our model is able to achieve controllable noise generation. Moreover, we introduce two sampling algorithms for this diffusion model: a straightforward procedure aligned with the diffusion process and an enhanced version that addresses the shortcomings of the former. Experimental results demonstrate that our proposed method, utilizing simple convolutional neural networks such as UNet, achieves denoising performance comparable to that of the transformer architecture. (c) 2024 SPIE and IS&T

关键词： real-world image denoising diffusion model sampling algorithm

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Dielectrophoretic characterization of antibiotic-treated Mycobacterium tuberculosis complex cells

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ANALYTICAL AND BIOANALYTICAL CHEMISTRY 2015年第25期407卷 7673-7680页

作者： Inoue, Shinnosuke Lee, Hyun-Boo Becker, Annie L. Weigel, Kris M. Kim, Jong-Hoon Lee, Kyong-Hoon Cangelosi, Gerard A. Chung, Jae-Hyun Univ Washington Dept Mech Engn Seattle WA 98195 USA Univ Washington Dept Environm & Occupat Hlth Sci Seattle WA 98195 USA NanoFacture Inc Bellevue WA 98015 USA Washington State Univ Sch Engn & Comp Sci Vancouver WA 98686 USA

Multi-drug resistant tuberculosis (MDR-TB) has become a serious concern for proper treatment of patients. As a phenotypic method, dielectrophoresis can be useful but is yet to be attempted to evaluate Mycobacterium tuberculosis complex cells. This paper investigates the dielectrophoretic behavior of Mycobacterium bovis (Bacillus Calmette-Gu,rin, BCG) cells that are treated with heat or antibiotics rifampin (RIF) or isoniazid (INH). The experimental parameters are designed on the basis of our sensitivity analysis. The medium conductivity (sigma (m)) and the frequency (f) for a crossover frequency (f (xo1)) test are decided to detect the change of sigma (m)-f (xo1) in conjunction with the drug mechanism. Statistical modeling is conducted to estimate the distributions of viable and nonviable cells from the discrete measurement of f (xo1). Finally, the parameters of the electrophysiology of BCG cells, C (envelope) and sigma (cyto), are extracted through a sampling algorithm. This is the first evaluation of the dielectrophoresis (DEP) approach as a means to assess the effects of antimicrobial drugs on M. tuberculosis complex cells.

关键词： Dielectrophoresis Mycobacterium tuberculosis Drug resistance sampling algorithm Sensitivity analysis

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Improving the Calculation Efficiency by Sample Selection Using the Alignment

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JOURNAL OF COMPUTATIONAL AND THEORETICAL NANOSCIENCE 2013年第12期10卷 2884-2889页

作者： Tan, Zhiying Xu, Kejia Jia, Ke Song, Xiaobo Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 610054 Peoples R China Chinese Acad Sci Chengdu Inst Comp Applicat Chengdu 610041 Peoples R China Chinese Acad Sci Changzhou Inst Adv Mfg Changzhou 213164 Peoples R China South West JiaoTong Univ Sch Elect Chengdu 610031 Peoples R China

Kernel matrix plays an important role in kernel methods. The kernel matrix contains all of the topology information included in the training sample set, which inspired us to select the significant samples out of a large data set by the kernel matrix. Giving a certain kernel function, the learning efficiency almost depends on the size of the training sample set. This means that once we shrink the scale of training samples, the learning process can be remarkably sped up. In the paper, a sampling algorithm based on the alignment is proposed to improve the calculation efficiency, where kernel alignment is an approach to measure the similarity between different kernel functions and matrices. More specifically, our algorithm can be utilized to pick out the significant samples from any large data set in unsupervised learning problems. The learning efficiency can be greatly improved adopting the small-scaled selected sample set. Another advantage of our method is that the novel sampling process can be completed online, because the algorithm is ready for the update of the training set. We applied the algorithm to detect the defects on tickets' surface. The experimental results indicate that our sampling algorithm not only reduces the mistake rate but also shortens the detection time.

关键词： Alignment Kernel Principal Component Analysis Maximum Variance Unfolding Unsupervised Learning sampling algorithm

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Motion Planning of Planar Closed Chains Based on Structural Sets

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IEEE ACCESS 2020年 8卷 117203-117217页

作者： Liu, Guanfeng Trinkle, Jeff Yang, Yong Luo, Shaoming Guangdong Polytech Normal Univ Sch Mechatron Engn Guangzhou 510665 Peoples R China Lehigh Univ Dept Comp Sci Bethlehem PA 18015 USA

This paper investigates the motion planning problem of planar m-link (m >= 4) closed chains among point obstacles with extension to arbitrary convex 2-D obstacles. The configuration space (C-space) of closed chains is embedded into two copies of m-3 dimensional tori. Two structural sets, the C-boundaries and the C-obstacles, are analyzed based upon the C-spaces of recursively constructed lower-dimensional closed chains. They contain essential structural information about the connectivity of the collision-free portion (C-free) of the C-space. By approximating each workspace obstacle by a set of points on the boundary after dilation, its corresponding C-obstacle is guaranteed to be covered by the C-obstacle of the convex hull of the point set. This permits a resolution-complete roadmap algorithm that puts specific bias for sampling the structural sets. Several benchmark examples are presented that compare the performance between our algorithm and the traditional algorithms. Animation videos and source codes are also provided which demonstrate the effectiveness of our method for closed chains of up to 20 links.

关键词： Path planning closed chains boundary variety bifurcation narrow passages sampling algorithm

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A deep learning-based surrogate model for trans-dimensional inversion of discrete fracture networks

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JOURNAL OF HYDROLOGY 2024年 638卷

作者： Feng, Runhai Nasser, Saleh Aramco Asia Aramco Res Ctr Beijing Beijing Peoples R China Saudi Aramco Geophys Technol EXPEC Adv Res Ctr Dhahran Saudi Arabia

Fractures and their geometrical patterns are usually required to analyze the mechanical and flow properties of porous media in the subsurface. Fracture characterization is therefore regarded of crucial importance for optimizing production management or achieving maximum storage capacity. In this research, we propose to invert the fracture networks under the Bayesian framework for the uncertainty quantification. In particular, the number of fractures in the modelling system is treated as unknown, leading to a trans-dimensional inverse problem, and the reversible jump Markov chain Monte Carlo algorithm is applied to sample the model space with possible model moves proposed in the sampling process. A deep learning network is further applied as a surrogate model in the sampling process for increasing the computational efficiency, instead of using the physical forward simulator. We apply the proposed methodology to estimate the spatial distribution of fracture networks based on the head measurements from the steady-state flow simulation. The prior distributions of fracture parameters such as position, orientation and length are described using the discrete fracture networks approach that is deeply rooted in stochastic modelling. Due to the high non-uniqueness, the correct spatial distribution of fracture patterns cannot be successfully recovered in this case study, even a good match between observed and simulated head data is reached. More analysis could be performed in the future with the production historical data or more informative priors.

关键词： Fracture networks Deep learning sampling algorithm Uncertainty estimation

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An Efficient Global Trajectory Planner for Highly Dynamical Nonholonomic Autonomous Vehicles on 3-D Terrains

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IEEE TRANSACTIONS ON ROBOTICS 2024年 40卷 1309-1326页

作者： Shen, Congkai Yu, Siyuan Epureanu, Bogdan I. Ersal, Tulga Univ Michigan Dept Mech Engn Ann Arbor MI 48109 USA

A novel hierarchical global trajectory planner is presented to allow highly dynamical nonholonomic off-road autonomous vehicles to achieve high mobility on 3-D terrains. On complex terrains with uneven topology, designing safe and feasible vehicle trajectories often demands an understanding of the vehicle's dynamical and nonholonomic constraints. Prior research, however, treats the global planning problem as a path planning problem without effectively accounting for topology or dynamical constraints. To address this gap, this article presents a three-phase trajectory planning algorithm composed of an A*, a rapidly exploring random tree (RRT), and a local trajectory refining (LTR) phase to incorporate dynamical and nonholonomic constraints on uneven terrain. The algorithm is tested in scenarios with randomized terrain fields and obstacles to demonstrate the necessity for all three phases. The algorithm is shown to have lower cost, higher success rate, and higher computational efficiency compared to state-of-the-art methods. The algorithm is then tested by controlling a simulated MRZR vehicle on a 3-D terrain along with a local controller, with comparisons to state-of-the-art algorithms. It is demonstrated that the new algorithm is capable of planning dynamically feasible trajectories with lower cost where the state-of-the-art algorithms fail to perform due to neglecting dynamical vehicle limitations.

关键词： Hierarchical planning architecture motion planning navigation and control obstacle avoidance off-road sampling algorithm trajectory planning

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