Browsing, searching and retrieving images from large databases based on low level color or texture visual features have been widely studied in recent years but are also often limited in terms of usefulness. In this pa...
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Browsing, searching and retrieving images from large databases based on low level color or texture visual features have been widely studied in recent years but are also often limited in terms of usefulness. In this paper, we propose a new framework that allows users to effectively browse and search in large image database based on their segmentation-based descriptive content and, more precisely, based on the geometrical layout and shapes of the different objects detected and segmented in the scene. This descriptive information, provided at a higher level of abstraction, can be a significant and complementary information which helps the user to browse through the collection in an intuitive and efficient manner. In addition, we study and discuss various ways and tools for efficiently clustering or for retrieving a specific subset or class of images in terms of segmentation-based descriptive content which can also be used to efficiently summarize the content of the image database. Experiments conducted on the Berkeley Segmentation Datasets show that this new framework can be effective in supporting image browsing and retrieval tasks.
Sales forecasting is a critical task for computer retailers endeavoring to maintain favorable sales performance and manage inventories. In this study, a clustering-based forecasting model by combining clustering and m...
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Sales forecasting is a critical task for computer retailers endeavoring to maintain favorable sales performance and manage inventories. In this study, a clustering-based forecasting model by combining clustering and machine-learning methods is proposed for computer retailing sales forecasting. The proposed method first used the clustering technique to divide training data into groups, clustering data with similar features or patterns into a group. Subsequently, machine-learning techniques are used to train the forecasting model of each group. After the cluster with data patterns most similar to the test data was determined, the trained forecasting model of the cluster was adopted for sales forecasting. Since the sales data of computer retailers show similar data patterns or features at different time periods, the accuracy of the forecast can be enhanced by using the proposed clustering-based forecasting model. Three clustering techniques including self-organizing map (SOM), growing hierarchical self-organizing map (GHSOM), and K-means and two machine-learning techniques including support vector regression (SVR) and extreme learning machine (ELM) are used in this study. A total of six clustering-based forecasting models were proposed. Real-life sales data for the personal computers, notebook computers, and liquid crystal displays are used as the empirical examples. The experimental results showed that the model combining the GHSOM and ELM provided superior forecasting performance for all three products compared with the other five forecasting models, as well as the single SVR and single ELM models. It can be effectively used as a clustering-based sales forecasting model for computer retailing.
Salient region detection without prior knowledge is a challenging task, especially for 3D deformable shapes. This paper presents a novel framework that relies on clustering of a data set derived from the scale space o...
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Salient region detection without prior knowledge is a challenging task, especially for 3D deformable shapes. This paper presents a novel framework that relies on clustering of a data set derived from the scale space of the auto diffusion function. It consists of three major techniques: scalar field construction, shape segmentation initialization and salient region detection. We define the scalar field using the auto diffusion function at consecutive time scales to reveal shape features. Initial segmentation of a shape is obtained using persistence-based clustering, which is performed on the scalar field at a large time scale to capture the global shape structure. We propose two measures to assess the clustering both on a global and local level using persistent homology. From these measures, salient regions are detected during the evolution of the scalar field. Experimental results on three popular datasets demonstrate the superior performance of the proposed framework in region detection. (C) 2017 Published by Elsevier Ltd.
Regression testing is essential for assuring the quality of a software product. Because rerunning all test cases in regression testing may be impractical under limited resources, test case prioritization is a feasible...
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Regression testing is essential for assuring the quality of a software product. Because rerunning all test cases in regression testing may be impractical under limited resources, test case prioritization is a feasible solution to optimize regression testing by reordering test cases for the current testing version. In this paper, we propose a novel test case prioritization approach that combines the clustering algorithm and the scheduling algorithm for improving the effectiveness of regression testing. By using the clustering algorithm, test cases with same or similar properties are merged into a cluster, and the scheduling algorithm helps allocate an execution priority for each test case by incorporating fault detection rates with the waiting time of test cases in candidate set. We have conducted several experiments on 12 C programs to validate the effectiveness of our proposed approach. Experimental results show that our approach is more effective than some well studied test case prioritization techniques in terms of average percentage of fault detected (APFD) values.
In synthetic aperture radar (SAR) images, scattering centers (SCs) from the same geometric structure of the man-made target usually have the same scattering type and similar coordinates. Inspired by this observation, ...
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In synthetic aperture radar (SAR) images, scattering centers (SCs) from the same geometric structure of the man-made target usually have the same scattering type and similar coordinates. Inspired by this observation, a novel clustering-based geometrical structure retrieval (C-GSR) method is proposed to estimate the geometrical structure of targets by clustering SCs according to their types and coordinates. The C-GSR method considers each peak in a SAR image as a single SC and extracts both frequency and polarization features for classification. Then, SCs are efficiently clustered using the density-distance-based clustering algorithm. Finally, the geometrical structure corresponding to each canonical scatterer can be retrieved by computing the coordinates of SCs associated with the corresponding cluster. Experimental results have demonstrated the feasibility and accuracy of the proposed C-GSR method.
Aheterogeneous ring domain communication topology with equal area in each ring is presented in this paper in an effort to solve the energy balance problem in original IPv6 routing protocol for low power and lossy netw...
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Aheterogeneous ring domain communication topology with equal area in each ring is presented in this paper in an effort to solve the energy balance problem in original IPv6 routing protocol for low power and lossy networks (RPL). A new clustering algorithm and event- driven cluster head rotation mechanism are also proposed based on this topology. The clustering information announcement message and clustering acknowledgment message were designed according to RFC and original RPL message structure. An energyefficient heterogeneous ring clustering (E2HRC) routing protocol for wireless sensor networks is then proposed and the corresponding routing algorithms and maintenance methods are established. Related messages are analyzed in detail. Experimental results show that in comparison against the original RPL, the E2HRC routing protocol more effectively balances wireless sensor network energy consumption, thus decreasing both node energy consumption and the number of control messages.
The Internet plays an important role in people's lives nowadays. However, Internet security is a major concern. Among the various threats facing the Internet and Internet users are so-called botnet attacks. A typi...
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ISBN:
(纸本)9781538622902
The Internet plays an important role in people's lives nowadays. However, Internet security is a major concern. Among the various threats facing the Internet and Internet users are so-called botnet attacks. A typical botnet is composed of a botmaster, a Command and Control (C&C) server and many compromised devices called bots. A botmaster can control these bots via the C&C server to launch various attacks, such as DDOS attacks, phishing, spam distribution, and so on. Among all botnets, Domain Generation algorithm (DGA) botnets are particularly resilient to traditional detection by associating the C&C server to one of the generated domains in each bot. Accordingly, this study presents a robust approach for detecting DGA botnets based on an inspection of the DNS traffic in a system. In the proposed approach, the DNS records are filtered to remove known benign or malicious domains and are then clustered using a modified Chinese Whispers algorithm. The nature of each group (i.e., malicious or benign) is then identified by means of a Sequence Similarity Module and a Query Sequence Similarity Module. It is shown that the proposed method successfully detects various types of botnet in a real-world, large scale network.
A framework of automatic clustering and tracking algorithm is proposed for the multipath components (MPCs) in time-variant radio channels. The algorithm is based on the channel dynamics in time domain and is able to r...
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A framework of automatic clustering and tracking algorithm is proposed for the multipath components (MPCs) in time-variant radio channels. The algorithm is based on the channel dynamics in time domain and is able to reflect the birth and death behaviors of MPCs naturally. The proposed algorithm is validated by a ray-tracer and the spatial channel model extension simulations. Compared with other existing clustering algorithms, the proposed framework is able to cluster the timevarying MPCs and track the clusters with high accuracy and low complexity.
Wireless Sensor Networks (WSNs) for reducing energy consumption and increasing sensors lifetime can use the clustering algorithms. We propose a new energy-efficient hierarchical clustering algorithm based on soft thre...
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Wireless Sensor Networks (WSNs) for reducing energy consumption and increasing sensors lifetime can use the clustering algorithms. We propose a new energy-efficient hierarchical clustering algorithm based on soft threshold cluster-head election and cluster member bounds for WSNs which called HCABS. Our simulation studies suggest that HCABS achieves longer lifespan and reduce energy consumption in WSNs as well as low latency and moderate overhead across the network.
Vehicle clustering is an efficient approach to improve the scalability of networking protocols in vehicular ad-hoc networks (VANETs). However, some characteristics, like highly dynamic topology and intermittent connec...
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Vehicle clustering is an efficient approach to improve the scalability of networking protocols in vehicular ad-hoc networks (VANETs). However, some characteristics, like highly dynamic topology and intermittent connections, may affect the performance of the clustering. Establishing and maintaining stable clusters is becoming one of big challenging issues in VANETs. Recent years' researches prove that mobility metric based clustering schemes show better performance in improving cluster stability. Mobility metrics, including moving direction, vehicle density, relative velocity and relative distance, etc., are more suitable for VANETs instead of the received radio strength (RSS) and identifier number metrics, which are applied for MANETs clustering. In this paper, a new dynamic mobility-based and stability-based clustering scheme is introduced for urban city scenario. The proposed scheme applies vehicle's moving direction, relative position and link lifetime estimation. We compared the performance of our scheme with Lowest-ID and the most recent and the most cited clustering algorithm VMaSC in terms of cluster head duration, cluster member duration, number of clusters, cluster head change rate and number of state changes. The extensive simulation results showed that our proposed scheme shows a better stability performance. (C) 2017 Elsevier Inc. All rights reserved.
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