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We describe a dynamic graph generator with overlapping communities that is capable of simulating community scale events while at the same time maintaining crucial graph properties. Such a benchmark generator is useful to measure and compare the responsiveness and efficiency of dynamic community detection algorithms. Since the generator allows the user to tune multiple parameters, it can also be used...
This paper takes an unsupervised learning approach for monitoring edge activity within an enterprise computer network. Using NetFlow records, features are gathered across the active connections (edges) in 15-minute time windows. Then, edges are grouped into clusters using the k-means algorithm. This process is repeated over contiguous windows. A series of informative indicators are derived by examining...
In this paper, a new method of saliency-based traffic sign detection is presented. On the basis of the visual attention mechanism model, edge and color information are extracted as early visual features, and each feature is computed and normalized to obtain feature maps, conspicuity maps and the saliency map. Then the candidate regions containing traffic signs are determined with self-organizing map...
Traditional mean shift image segmentation algorithms need selecting fixed bandwidth manually, which leads to under-segmentation and non-global optimum. To overcome these disadvantages, a bandwidth adaptive mean shift algorithm is proposed. In this algorithm, a new bandwidth window function is defined, with the bandwidth is determined automatically based on probability distribution characteristics...
Tuberculosis (TB) is an infectious disease caused by mycobacterium which can be diagnosed by analyzing medical test report of patient that take more than a month to get the result. The aim of this study is to propose a solution which can be used to identify TB's presence and start treating such suspected patients. In this paper, we propose a method to use city block measure for segmenting x-ray image...
A persona in a social network is defined as the person's activities and attributes in a social network as seen by others. And a community in a social network is defined as a group of users in that social network which share common interests and are most likely to interact with each other in the network. For community detection, a user's persona and its connections with the other users in a network,...
Twitter, a well-liked online social networking site, facilitates millions of users on a daily basis to dispatch and orate quick 140-character notes named tweets. Nowadays, twitter is cogitated as the fastest and popular intermediate of communication and is used to follow latest events. Tweets pertaining to a specific event can be effortlessly found using keyword matching, but there are numerous tweets...
A hybrid clustering approach is proposed for processing image-like data such as plots in flow cytometry. Clustering or partitioning data into relatively homogeneous and coherent subpopulations can be an effective pre-processing method to achieve data analysis tasks such as pattern recognition and classification. Our method uses a graph to model the initial manual partition of the dataset. Based on...
Recently, opinion leader discovery has drawn much attention due to its widespread applicability. By identifying the opinion leader, companies or governments can manipulate the selling or guiding public opinion, respectively. However, mining opinion leader is a challenge task because of the complexity of processing social graph and analyzing leadership quality. In this study, a novel method, TCOL-Miner,...
Currently, the understanding of the human mobility is an important challenge that has a large number of applications, especially in the study of a nation's ability to thrive economically and socially. Some works have shown that, it is possible to observe developed and developing countries reviewing their administrative regions borders, in order to reduce costs, or to solve ethnic claims and/or independence...
In this paper, we propose a cluster-driven anisotropic diffusion (CDAD) filter for speckle reduction in ultrasound images. The proposed filter is based on the multiplicative noise model and is driven by K-means clustering algorithm. Instead of choosing homogeneous sample region with manual selection, the proposed algorithm is able to do it automatically (based on the clustering results). In addition,...
Circle detection from digital images is a necessary operation in many robotics and computer vision tasks to facilitate shape and object recognition. We propose and analyze a novel method, based on line segment detection and circle completeness verification, to detect circles in images. The key idea is to use line segments instead of raw edge pixels to get the circle candidates followed by a verification...
Automatic 2D-to-3D conversion aims to reduce the existing gap between the scarce 3D content and the incremental amount of displays that can reproduce this 3D content. Here, we present an automatic 2D-to-3D conversion algorithm that extends the functionality of the most of the existing machine learning based conversion approaches to deal with moving objects in the scene, and not only with static backgrounds...
Social networks play an important role in the dissemination of information and the spread of influence. Identifying the most influential individuals spreading information or infectious diseases can assist or hinder information dissemination, product exposure, and contagious disease detection. The leader or influential members may be even more critical to product diffusion and the formation of widespread...
The detection of obstacles is a fundamental issue in autonomous navigation, as it is the main key for collision prevention. This paper presents a method for the segmentation of general obstacles by stereo vision with no need of dense disparity maps or assumptions about the scenario. A sparse set of points is selected according to a local spatial condition and then clustered in function of its neighborhood,...
2D-to-3D conversion is an important task for reducing the current gap between the number of 3D displays and the available 3D content. Here, we present an automatic 2D-to-3D image conversion approach based on machine learning principles. Stemming from the hypothesis that images with a similar structure have likely a similar 3D structure, the depth of a query color image is estimated using a color plus...
A video summarization technique is proposed in this work using minimal spanning tree (MST) of data points. The data points correspond to image frames of a shot in the video which is to be summarized. Correlation is chosen as a similarity metric for computing the edge weights of the MST. The representative frames for each shot are chosen by computing the density of each data point. A novel method for...
Image segmentation plays a vital role in medical imaging applications. It facilitates the delineation of anatomical structures and other regions of interest. Magnetic resonance imaging (MRI), computed tomography (CT), digital mammography, and other imaging modalities provide an effective means for noninvasively mapping the anatomy of a subject. These technologies have greatly increased knowledge of...
Due to its important applications in data mining, many techniques have been developed for clustering. For today's real-world databases which typically have millions of items with many thousands of fields, resulting in datasets that range in size into terabytes, many traditional clustering techniques have more and more restricted capabilities and novel approaches that are computationally efficient...
Considering the fuzziness and diversity of the capsule foreign matter defect in the image, the BP neural network is applied to discern the capsule foreign matter defect Firstly, the capsule image is separated into three parts by vertical Sobel operator, and every part of image is processed by median filter to clear the noise. Then the histogram features of all the three parts of the image, namely...
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