with the rapid development of the Internet, the problem of cyber security has become increasingly serious. Trojan viruses and botnet programs play an important role in the Internet black industry chain. How to deal wi...
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ISBN:
(纸本)9781728140346
with the rapid development of the Internet, the problem of cyber security has become increasingly serious. Trojan viruses and botnet programs play an important role in the Internet black industry chain. How to deal with cyber security incidents quickly and efficiently is a problem that needs to be solved. It has been found that the malware infected users are widely distributed and have diverse types, which cause great difficulties for cybersecurity experts to handle the threats. Based on practical cybersecurity incident handling experience, this paper proposes a User-Event Threat Matrix Model to define the infected users, and implement a classification method based on machine learning algorithm. So that we could find out the high-risk infected users and pay more attention to them.
Protein and protein-water hydrogen bonds shape the conformational energy landscape of G Protein-Coupled Receptors, GPCRs. As numerous static structures of GPCRs have been solved, the important question arises whether ...
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Protein and protein-water hydrogen bonds shape the conformational energy landscape of G Protein-Coupled Receptors, GPCRs. As numerous static structures of GPCRs have been solved, the important question arises whether GPCR structures and GPCR conformational dynamics could be described in terms of conserved hydrogen-bond networks, and alterations of these hydrogen-bond networks along the reaction coordinate of the GPCR. To enable efficient analyses of the hydrogen-bond networks of GPCRs we implemented graph-based algorithms, and applied these algorithms to static GPCR structures from structural biology, and from molecular dynamics simulations of two opioid receptors. We find that static GPCR structures tend to have a conserved, core hydrogen-bond network which, when protein and water dynamics are included with simulations, extends to comprise most of the interior of an inactive receptor. In an active receptor, the dynamic protein-water hydrogen-bond network spans the entire receptor, bridging all functional motifs. Such an extensive, dynamic hydrogen-bond network might contribute to the activation mechanism of the GPCR.
In this paper, an ensemble of clustering trees (ECTs) is adopted to improve the performance of the Fuzzy Min-Max (FMM) network with individual clustering trees. The key advantage of combining FMM and ECT together is t...
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In this paper, an ensemble of clustering trees (ECTs) is adopted to improve the performance of the Fuzzy Min-Max (FMM) network with individual clustering trees. The key advantage of combining FMM and ECT together is to formulate an accurate and useful learning model that is able to perform online clustering and to explain its predictions. The online clustering capability is inherited from the FMM hyperboxes, while the explanatory capability arises from the underlying decision trees of ECT. Four different mean measures, namely harmonic, geometric, arithmetic, and root mean square, are incorporated into FMM for computing its hyperbox centroids. A series of benchmark and real-world data sets are used for evaluating the FMM-ECT performance. The results are analyzed and compared with those from other models. The outcomes indicate that FMM-ECT is able to achieve comparable clustering performances, with the advantage of providing explanations of its predictions using a decision tree. (c) 2017 Elsevier B.V. All rights reserved.
In order to solve the problem that the sorting threshold of traditional frequency-hopping signal needs to be manually adjusted, Faster-RCNN and clustering algorithm is proposed. In this paper, the Faster-RCNN is first...
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ISBN:
(纸本)9781538692981
In order to solve the problem that the sorting threshold of traditional frequency-hopping signal needs to be manually adjusted, Faster-RCNN and clustering algorithm is proposed. In this paper, the Faster-RCNN is firstly used to identify and locate all frequency-hopping points in the time-frequency spectrum diagram, and then AlexNet is used to obtain the number of frequency-hopping signal. Experimental results show that the Faster-RCNN can be effectively used for automatic signal sorting when the number of frequencyhopping signal is small.
Poor understanding and low clustering efficiency of massive data is a problem under the context of big data. To solve this problem, MapReduce programming model is adopted to combine Canopy and K-means clustering algor...
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Poor understanding and low clustering efficiency of massive data is a problem under the context of big data. To solve this problem, MapReduce programming model is adopted to combine Canopy and K-means clustering algorithms within cloud computing environment, so as to fully make use of the computing and storing capacity of Hadoop clustering. Large quantities of buyers on taobao are taken as application context to do case study through Hadoop platform's data mining set Mahout. General procedure for miming with Mahout is also given.
In order to solve the shortcomings of traditional industrial control network intrusion detection schemes, such as insensitivity to detection samples and inaccurate judgment of internal anomalies. An industrial control...
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ISBN:
(纸本)9781728121659
In order to solve the shortcomings of traditional industrial control network intrusion detection schemes, such as insensitivity to detection samples and inaccurate judgment of internal anomalies. An industrial control network intrusion detection scheme based on FCM algorithm and supervised Kohonen is proposed. FCM algorithm, FCM-GRNN network algorithm, FCM-BP network algorithm, FCM-Kohonen network algorithm and FCM-S_ Kohonen network algorithm are built on MATLAB software platform to test DARPA data samples. The accuracy of clustering results of different types of intrusion is counted according to five algorithms. The scheme can detect NORMAL, U2R, R2L, DoS and PRB network attacks more accurately, and the overall average classification accuracy rate is more than 95%.
Wireless sensor networks (WSN) are considered as a special type of ad hoc networks, that represent an emerging technology that is having an increasing success in the scientific, logistical, and military areas. It not ...
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Wireless sensor networks (WSN) are considered as a special type of ad hoc networks, that represent an emerging technology that is having an increasing success in the scientific, logistical, and military areas. It not only realizes benefits for the customer in a technologically sophisticated way, but in addition provides this with high flexibility. However, the size of the sensors is an important limitation, mainly in terms of energy autonomy and lifetime because the battery must be very small. For this reason, many studies are currently focusing on managing the energy consumed by the sensors in the network. With this in mind, we have proposed an algorithm that improves the quality of service based on a clustering approach. In order to confirm the improvements provided by our algorithm, a simulation is done using MATLAB, in which the performance of our algorithm is evaluated and compared with available clustering protocols (LEACH and SEP). (C) 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the Conference Program Chairs.
The consistency of the power battery is a key issue affecting battery performance and battery life. In order to improve the inconsistency of the power battery, this paper proposes a reasonable static sorting method. F...
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ISBN:
(纸本)9781728140940
The consistency of the power battery is a key issue affecting battery performance and battery life. In order to improve the inconsistency of the power battery, this paper proposes a reasonable static sorting method. Firstly, make unqualified recheck operation of battery core parameters. Secondly, based on the ISODATA clustering algorithm, a new method for calculating the distance between data is defined, and in order to carry out consistent sorting of the battery. The improved ISODATA algorithm provides a way to solve cell consistency problems. It does not rely on the number of manually defined clusters, and adaptively defines the number. According to our redefined distance standard, it can reasonably represent the influence relationship between the parameters of the cell data. Finally, by comparing with k-means and ISODATA experiments, the results show that this sorting method can effectively sort the given cell parameters, and the effect is better.
Deep neural network algorithms have shown promising performance for many tasks in computer vision field. Several neural network-based methods have been proposed to recognize group activities from video sequences. Howe...
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ISBN:
(纸本)9781450365734
Deep neural network algorithms have shown promising performance for many tasks in computer vision field. Several neural network-based methods have been proposed to recognize group activities from video sequences. However, there are still several challenges that are related to multiple groups with different activities within a scene. The strong correlation that exists among individual motion, groups and activities can be utilized to detect groups and recognize their concurrent activities. Motivated by these observations, we propose a unified deep learning framework for detecting multiple groups and recognizing their corresponding collective activity based on Long Short-Term Memory (LSTM) network. In this framework, we use a pre-trained convolutional neural network (CNN) to extract features from the frames and appearances of persons. An objective function has been proposed to learn the amount of pairwise interaction between persons. The obtained individual features are passed to a clustering algorithm to detect groups in the scene. Then, an LSTM based model is used to recognize group activities. Together with this, a scene level CNN followed by LSTM is used to extract and learn scene level feature. Finally, the activities from the group level and the scene context level are integrated to infer the collective activity. The proposed method is evaluated on the benchmark collective activity dataset and compared with several baselines. The experimental results show its competitive performance for the collective activity recognition task.
Aiming at the shortcomings of the K-Means algorithm in the traditional K-Means algorithm, the DBSCAN algorithm is used to divide the order set according to the density, and obtain the batch number K value and the init...
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ISBN:
(纸本)9783030374297;9783030374280
Aiming at the shortcomings of the K-Means algorithm in the traditional K-Means algorithm, the DBSCAN algorithm is used to divide the order set according to the density, and obtain the batch number K value and the initial cluster center point. Based on this, the improved K-Means algorithm is used for optimization. Based on the real environment and instance data, the established batch assignment batch model is simulated. The experimental results show that the density-based K-Means clustering algorithm can effectively shorten the picking time and improve the warehouse logistics operation.
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