In this paper we present a novel algorithm for video anomaly detection. It is based on multiple local cells, which are acquired by splitting entire monitor scene. At each local cell, we group all feature vectors with ...
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ISBN:
(纸本)9789881563958
In this paper we present a novel algorithm for video anomaly detection. It is based on multiple local cells, which are acquired by splitting entire monitor scene. At each local cell, we group all feature vectors with clustering algorithm based on minimum spanning tree, and further model all groups using improved one-class SVM to build ensemble classifiers. For any new features at each local node in incoming video clips, we use the corresponding learned ensemble classifiers to estimate maximum abnormality degree. The proposed approach has been tested on publicly available datasets with frame-level and pixel-level criteria, and outperforms other state-of-the-art approaches.
More and more new cloud users use the personalised cloud service combination strategy (CSCS). Solving the Cold Start problem of the cloud environment becomes intractable. The paper represents a novel method of choosin...
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ISBN:
(纸本)9781538693803
More and more new cloud users use the personalised cloud service combination strategy (CSCS). Solving the Cold Start problem of the cloud environment becomes intractable. The paper represents a novel method of choosing the most optimal combinatorial features based on the attenuation function to cluster, and integrating multi level sampling method to cope with the pure Cold Start for cloud users. By means of every clustering process with different combinatorial features, then using the relatively stable number of clusters for every clustering obtains the optimal combinatorial features, which presents the tendency of the whole society of cloud users who use the CSCS. Meanwhile, we propose the function of periodic attenuation that enhances the degree of recommendation for CSCSs which have been issued recently. We harness the vectors of preference and disfavour to calculate the similarity of cloud users. An improved cluster algorithm of CFSFDP is employed. Moreover, it is worth selecting the most representative features to cluster which demonstrates effectively. In addition, the attenuation function can increase the probability of recommendation of recent CSCS, and the multi level sampling method has been used to heighten the diversity of recommendations. The method of ours can enhance the effectiveness and intelligence of recommendation for the pure Cold Start problem.
clustering is a key technique to improve energy efficiency in wireless sensor networks (WSNs). In continuous monitoring applications, the clusters should be formed dynamically according to the event development for en...
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clustering is a key technique to improve energy efficiency in wireless sensor networks (WSNs). In continuous monitoring applications, the clusters should be formed dynamically according to the event development for energy-efficient data gathering. In this paper, an energy-efficient adaptive overlapping clustering (EEAOC) method is proposed in WSNs for continuous monitoring applications. In EEAOC, a 2-logical-coverage overlapping clustering topology is established such that the adjacent sensors in the event area can be grouped into the same cluster for data fusion and the cluster migration operation can be processed without changing the overlapping structure among clusters. Moreover, to further reduce energy consumption, a hybrid data reporting strategy that switches between time-driven and event-driven schemes is introduced based on the QoS requirements in continuous monitoring applications. Simulation results show that EEAOC achieves a longer network lifetime cycle.
This paper proposes a new algorithm, Slice_OP, which selects the initial cluster centers on high-dimensional data. A set of observation points is allocated to transform the high-dimensional data into one-dimensional d...
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ISBN:
(数字)9783030050900
ISBN:
(纸本)9783030050900;9783030050894
This paper proposes a new algorithm, Slice_OP, which selects the initial cluster centers on high-dimensional data. A set of observation points is allocated to transform the high-dimensional data into one-dimensional distance data. Multiple Gamma models are built on distance data, which are fitted with the expectation-maximization algorithm. The best-fitted model is selected with the second-order Akaike information criterion. We estimate the candidate initial centers from the objects in each component of the best-fitted model. A cluster tree is built based on the distance matrix of candidate initial centers and the cluster tree is divided into K branches. Objects in each branch are analyzed with k-nearest neighbor algorithm to select initial cluster centers. The experimental results show that the Slice OP algorithm outperformed the state-of-the-art Kmeans++ algorithm and random center initialization in the k-means algorithm on synthetic and real-world datasets.
The attacks on industrial control systems ( ICS) have been exemplified by the malwares Stuxnet, Industroyer, and Triton that targeted nuclear facilities of Iran in 2010, power grid of Ukraine in 2016, and Safety Instr...
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ISBN:
(纸本)9781538684740
The attacks on industrial control systems ( ICS) have been exemplified by the malwares Stuxnet, Industroyer, and Triton that targeted nuclear facilities of Iran in 2010, power grid of Ukraine in 2016, and Safety Instrumented System ( SIS) controllers of a Middle East country in 2017, respectively. As a result, the issues concerning Critical Infrastructure Information Protection ( CIIP) have drawn much attention among academia, industry, and government in many countries. In this paper, we propose an anomaly detection method for ICS networks. The main idea of the proposed method is to model the normal behavior patterns of TCP and UDP payloads as frequent patterns and non-frequent pattern clusters. The normal behavior payloads are first processed by sequential pattern mining algorithm to extract frequent patterns, and then the payloads are projected against frequent patterns. After projection, the projected payloads are clustered using hierarchical agglomerative clustering algorithm to find representative variations in normal behaviors. The experimental results show that the proposed method has very good performance in terms of the metrics such as accuracy, recall, precision, false alarm, and false dismissal for the ICS networks that use Modbus/ TCP or BACnet protocols. The proposed system model can also leverage honeypots deployed in ICS networks to generate attack signatures, which can be helpful in filtering out known attacks.
In this paper we propose a method of movie series recommender system development. Our recommender system is content-based, and movie series are represented by their scripts. We experiment with several semantic similar...
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ISBN:
(数字)9783030014377
ISBN:
(纸本)9783030014377;9783030014360
In this paper we propose a method of movie series recommender system development. Our recommender system is content-based, and movie series are represented by their scripts. We experiment with several semantic similarity measures, lexico-morphological metrics, keywords and vector space models to extract similar movie series. Evaluation is conducted in the experiment with informants. The best results are achieved by distributional semantic approach (i.e., using word2vec technology).
Condition-based maintenance (CBM) should be derived carefully to reduce maintenance costs along with useless maintenance shifts and to predict ideal time to do the maintenance. In this paper, a new method is proposed ...
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Condition-based maintenance (CBM) should be derived carefully to reduce maintenance costs along with useless maintenance shifts and to predict ideal time to do the maintenance. In this paper, a new method is proposed by the combination of data mining techniques and time series models to schedule maintenance activities. Considering a real database which contains failures and values of factors degrading the pump in the time of failure, a clustering algorithm is used to categorize failures based on the similarity in types of maintenance activities. Then, rules are extracted for characterizing the clusters and presenting a range for each factor by applying a proper association rule algorithm. Subsequently, time series models are applied to predict the time period that a factor may meet its rule's range. Thus, a novel method is presented for a relative comparison between rules and predicted factor's values and a prognostic scheduling is designed with respect to the effects of previous maintenance activities. The results of numerical experiments reveal that the proposed method can effectively determine when and which maintenance activities should be performed.
The application of Wireless Charging Vehicle (WCV) based wireless energy transfer technology to recharge the wireless sensor network nodes is attracted a great deal of attention recently. In this paper, a novel multi-...
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ISBN:
(纸本)9781538685426
The application of Wireless Charging Vehicle (WCV) based wireless energy transfer technology to recharge the wireless sensor network nodes is attracted a great deal of attention recently. In this paper, a novel multi-node energy supplemental policy based on a circular cell for the wireless rechargeable sensor networks is proposed. In this policy, the WCV is used to charge the sensor node by traversal the center of the circular cells. For achieving the optimal WCV traversal path, a position distribution strategy of lower complexity for circular cells based on k-means algorithm to minimize the cluster quantity of circular cell and smallest enclosing circle algorithm to minimize the radius of each cell are given. Finally, the simulation results show that the position distribution strategy proposed in this paper can help to significantly reduce the WCV traversal time of each charge cycle.
With the rapid development of computer vision technology and image processing technology,image retrieval has also developed from simple text information query to complex content-based image retrieval,which is a proces...
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With the rapid development of computer vision technology and image processing technology,image retrieval has also developed from simple text information query to complex content-based image retrieval,which is a process from low-level to high-level *** paper mainly focuses on the content-based image retrieval method,to analyze the application of an optimized PAM algorithm based on fireworks particle swarm optimization in image retrieval.
The evaluation of region development of network has been a widespread concern in recent years. However, network development of a region, due to its specific characteristics and application scenario, should have a tail...
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ISBN:
(纸本)9789811075216;9789811075209
The evaluation of region development of network has been a widespread concern in recent years. However, network development of a region, due to its specific characteristics and application scenario, should have a tailor-made evaluation system. In this study, taking into account various factors in multiple fields, a multiple-index evaluation system is established. Then, a principal component analysis-based K-means clustering approach is proposed to address the analyzing problem with an acceptable complexity. A simulation experiment is implemented to verify the algorithm. The results can be used to compare the different areas telecommunication networks, and provide rational and effective suggestions for network planning and construction.
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