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Network Anomaly Detection plays an importantpart in network security. Among the state-of-the-art approaches, unsupervised anomaly detection is effective when dealing with unlabelled data. However, these approaches also suffer from high false positive rate. We observed that different methods have their own defects and advantages. Inspired by this observation, we provide a new ensemble clustering(NEC)...
Virtual machine live migration technology allows a running VM migrates from one physical host to another with no impact on users. As the scale of distributed computing gets larger and larger, hybrid cloud, which is integrated cloud service utilizing both private and public clouds to perform distinct functions within the same organization becomes a hotspot for both academia and industry. Thus, it will...
In this paper, we propose an efficient and robust gross outlier removal method, called the Conceptual Space based Gross Outlier Removal (CSGOR) method, to remove gross outliers for geometric model fitting. In the proposed method, each data point is mapped to a conceptual space by computing the preference of "good" model hypotheses. In the conceptual space, the distributions of inliers and...
Icing on transmission line is a major risk factor for the safety of the power system. Therefore, it is necessary to research on online monitoring system and prediction model of icing. At present, there are many kinds of the icing prediction models are based on micro meteorological conditions, but the errors of the prediction are caused by the time-varying characteristics of the meteorological conditions...
With the popularity of the Internet and the development of social media, the Online Word of Mouth (OWOM) has brought up growing influences on movie box office, while the development of big data technology provides a new approach and method for the research of this issue. Based on the major OWOM reputation index of influential movie box office, having 218 movies related data obtained from Mtime, going...
The research on regulatory networks is one of the most important themes of bioinformatics. Different kinds of modeling make it possible to analyze the dynamics of system. Nevertheless initial model is probably incomplete, which may have great influence on the dynamics. Therefore, it is important to develop completion approaches for different models. In this paper, two models are presented: Thomas'...
Cheap ubiquitous computing enables the collection of massive amounts of personal data in a wide variety of domains. Many organizations aimto share such data while obscuring features that could discloseidentities or other sensitive information. Much of the data now collected exhibits weak structure (e.g., natural language text) and machine learning approaches have been developed to identify andremove...
Data sparseness is one of the most challenging problems in collaborative filtering(CF) based recommendation systems. Exploiting social tag information is becoming a popular way to alleviate the problem and improve the performance. To this end, in recent recommendation methods the relationships between users/items and tags are often taken into consideration, however, the correlations among tags from...
Google File System (GFS) is a distributed file system developed by Google for massive data-intensive applications. Its high aggregate performance of delivering massive data to many clients but the inexpensiveness of commodity hardware facilitate GFS to successfully meet the massive storage needs and be widely used in industries. In this paper, we first present a formal model of Google File System...
The use of agent based social simulation in computational analysis of public emergency management is becoming a prominent approach in social science research. Due to the complexity of rumor spreading and the radical emotion infecting processes, the results of the group emotion evolution analysis often lose effectiveness, which will affect the control and strategy establishment in emergency management...
Graph data is the default data organization mechanism used in large-scale Social Network Service (SNS) applications. Traditional graph data computing models are used to dig out useful hidden information inside the data. However, the ever growing data volume is adding more and more pressures. To retrieve and discover the information, the system has to introduce a larger number of data iterations. This...
This paper presents a new similarity measure and matching scheme for content-based image retrieval (CBIR), based on modeling positive and negative hypotheses and testing a query image against these two hypotheses. The paper proposes to calculate first a universal image model (UIM), which is built based on a large set of images. The derived UIM is then used as a reference for the calculation of adapted...
With the increasing energy cost in data centers, an energy efficient approach to provide data intensive services in the cloud is highly in demand. This paper solves the energy cost reduction problem of data centers by formulating an energy-aware replica selection problem in order to guide the distribution of workload among data centers. The current popular centralized replica selection approaches...
Research on 3D GIS simulation system of pit mining surface subsidence is not merely simple extension of 2D geographic information system, but from spatial model analysis to spatial database structure until visualization of 3D data. The research and application of this system have important and positive significance on evaluation of mining surface subsidence damage, control of mine geological disaster,...
Data-intensive applications are becoming increasingly common in Grid environments. These applications require enormous volume of data for the computation. Most conventional meta-scheduling approaches are aimed at computation intensive application and they do not take data requirement of the applications into account, thus leading to poor performance. Efficient scheduling of data-intensive applications...
Uncertainty pervades many application domains such as pattern recognition, sensor networks and mobile object tracking. However, in those applications, uncertain data often arrives at high speed and need to be processed in a streaming fashion. Frequent itemset mining is one of the most common problems when analyzing those uncertain transactions in streaming data. In this paper, we propose an efficient...
This issue combined the theory about history of auto style design, configurations and the semantic of product, through the investigation and study among the people, found out the most impressive distinguish among all kinds of cars; next, analyzing the different semantic relations among the different surfaces which are pieced together into the whole configuration of a car, simulating the surface with...
A unified FinFET reliability model including high K stack dynamic threshold (HKSDT), hot carrier injection (HCI), and negative bias temperature instability (NBTI) has been developed and verified by experimental data. The FinFET-based circuit performances are simulated and compared under these reliability issues by HSPICE simulator after the inclusion of the presented model.
Cell broadband engine (Cell BE) is a state-of-the-art heterogeneous multi-core processor. It is an accelerator-based multi-core architecture, which contains a powerful 64-bit dual-threaded PowerPC core and eight high efficient single-instruction-multiple-data (SIMD) cores. Apart from traditional parallel systems, the users must explicitly manage the communication, scheduling and load-balancing to...
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