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Content-Centric Networking (CCN) proposals rethink the communication model around named data. In-network caching is a fundamental feature to distinguish the CCN from the current host-centric IP network. In this paper, we have proposed a hybrid caching scheme which combines the on-path one and the off-path one. We leverage the ISOMAP manifold learning algorithm to distinguish the importance of nodes...
Frameworks are popularly used to reduce implementation complexity and improve productivity. Unfortunately, most frameworks are quite complex and not well documented. Hence, correctly and effectively programming with Framework is still a great challenge. One of the significant obstacles for us to smoothly use Framework is the complicated attribute value configuration of XML files. To overcome these...
The blast furnace gas is an important secondary energy for the iron and steel production. Establishing an effective model to describe the state of BFG system is of great significant to maintain the system balance and stability. Considering the strong coupling characteristics of the blast furnace gas system and the high level noises in the industrial data, a simplex unscented Kalman filter-based Wang-Mendel...
Concerning the problem of noise interference in the mechanical equipment fault signal acquisition, a novel mechanical equipment fault diagnosis method of wavelet shrinkage threshold based on Bayesian estimation combined with EMD is proposed. The fault signal denoising characteristics of different scales are considered in the proposed method. A new threshold which is suitable for the situation of noise...
There are some independent cyber security knowledge bases for different aspect now. In the internet, there is also much cyber security related content which exists in the form of text. Fusion of these cyber security related information can be a meaningful work. In this paper, we propose a framework to integrate existing cyber security knowledge base and extract cyber security related information from...
Maximal Clique Enumeration (MCE) is a long standing problem in database community. Though it is extensively studied, almost all researches focus on calculating maximal cliques as a one-time effort. MCE on dynamic graph has been rarely discussed so far, the only work on this topic is to maintain maximal cliques with graph evolving. The key within this problem is to find maximal cliques that contains...
Predicting microblog user retweet behaviors is the basis of building the information diffusion model in microblog social networks. In order to improve the accuracy of predicting user retweet behaviors, under the MRF (Markov Random Field) framework, the paper comprehensively analyzes the effects on user retweet behaviors caused by various features (e.g., user attributes and microblog contents) and...
This work proposes to investigate the question of whether attending conference will breed new scientific collaboration based on the focal closure theory. Through the analysis of conference closure on individual and community level, we show that attending conference can promote new scientific collaborations, and conferences with more attendees and higher field ratings bring more new scientific collaborations.
Mining advisor-advisee relationships can benefit many interesting applications such as advisor recommendation and protege performance analysis. Based on the hypothesis that, advisor-advisee relationships among researchers are hidden in scholarly big data, we propose in this work a deep learning based advisor-advisee relationship identification method which considers the personal properties and network...
The Cu line resistance has been observed to reduce after processing through higher interconnect levels. In this paper, the geometry impact on the reduction of Cu interconnect wire resistance was studied on a large data set using data mining techniques. The data show that the narrower and higher the Cu lines, the larger the line resistance reduction and that Cu line height has much stronger impact...
Extracting opinion words and opinion targets from online reviews is an important task for fine-grained opinion mining. Usually, traditional extraction methods under the pipeline-based framework have higher precision but lower recall, while methods in the propagation-based framework possess greater recall but poorer precision. To achieve better performance both in precision and recall, this paper proposes...
While e-commerce has grown substantially over last several years, more and more people are utilizing this popular channel to purchase products and services. Thus the ability to predict user demographics, including gender, age and location has important applications in advertising, personalization, and recommendation. In this paper, we aim to automatically predict the users' genders based on their...
In order to improve the discriminant power, a new discriminant analysis algorithm is proposed based on Fisher's linear discriminant, called variant fisher discriminant analysis with orthogonal discriminant components (VFDAODC). The basic idea of the proposed VFDAODC is to overcome the problems of the conventional fisher discriminant analysis algorithm. First, a two-step feature extraction procedure...
We present a novel hierarchical MRFs optimization method for dense and deformable motion extraction in dynamic scenes. In particular, this hierarchical MRFs structure consists of two layers, the segmentation and the correspondence layer. Firstly, dynamic RGB-D foreground data is segmented through a pixel-level MRF in the segmentation layer. Subsequently, the extracted foreground data is transformed...
In current days, data tend to become much bigger than before, and the distributed computing system is an prevalent option to deal with them. As one of powerful tools, MapReduce framework provides a cheap and efficient way to write parallel programs to run on distributed computing systems. Chance discovery (CD) is an extension of data mining, where chance refers to rare but important events or situations...
An approach for keyframe extraction using AdaBoost is proposed which is based on foreground detection. The aim of this approach is to extract keyframes from sequences of specific vehicle images of lane vehicle surveillance video. This method utilizes integral channel features and the area feature as the image feature descriptor, combined with training an AdaBoost classifier. The experimental results...
The problem of efficiently finding top-k frequent items has attracted much attention in recent yeras. Storage constraints in the processing node and intrinsic evloving feature of the data streams are two main challenges. In this paper, we propose a method to tackle these two challenges based on space-saving and gossip-based algorithms respectively. Our method is implemented on SAMOA, a scalable advanced...
Extracting main object from photos is prerequisite for image processing and semantic image understanding in many areas especially in multimedia signal processing at internet. So far, either human interaction in single image or sequence image frames are required for the extraction and most of them still rely on hand-crafted features. In contrast, the proposed work cast the human boundary detection...
Big data analytics is the process of examining large amounts of data of a variety of types (big data) to uncover hidden patterns, unknown correlations and other useful information. Its revolutionary potential is now universally recognized. Data complexity, heterogeneity, scale, and timeliness make data analysis a clear bottleneck in many biomedical applications, due to the complexity of the patterns...
Based on data mining, the main impact factors of urban life water consumption are made gray relational analysis with the water consumption. The main driving factors of urban life water consumption are discussed. By the gray forecasting of the development trends of the main impact factors, a grey prediction of GM (0, N) model on urban life water is established. An instance proves to fit the data better...
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