19385120
DJ DreamFactory
DJ DreamFactory is a web-based integrated platform for interactive broadcasting over the Internet. In DJ DreamFactory, users are able to set up Internet-based broadcasting stations with minimal effort. Audience not only can receive broadcasting programs from multiple stations through this unified platform, but can also "talk" and "write" to the broadcasters as well as to other audience via real-time text and/or audio interactions.
7822301
A web-based aggregated platform for user-contributed interactive media broadcasting
In this paper, we present a web-based aggregated platform, DJ DreamFactory, which enables average users to effortlessly participate in and contribute to interactive media broadcasting over the Internet. The platform overcomes several shortcomings of existing Internet-based broadcasting systems, such as inconvenience in channel surfing and content browsing due to the scattering and isolating of broadcasting stations, difficulties in setting up a broadcasting station, lack of communications between broadcasters and audience, and little support for personalized experience. The proposed platform facilitates users' media access by seamlessly aggregating sporadic broadcasting stations run by individual hosts, and enables a virtual community where grassroots users can contribute to media broadcasting, sharing, organizing and annotating through social networking. In addition, it supports real-time multimodal interaction between audience and hosts, provides customized services for both broadcasters and audience, supports personalized media experiences by mining and managing audience's preferences, and facilitates the organization of unstructured media data collections as well as collective human intelligence on the Web.
17592710
Full-Space LDA With Evolutionary Selection for Face Recognition
Linear discriminant analysis (LDA) is a popular feature extraction technique for face recognition. However, it often suffers from the small sample size problem when dealing with the high dimensional face data. Some approaches have been proposed to overcome this problem, but they usually utilize all eigenvectors of null or range subspaces of within-class scatter matrix (Sw). However, experimental results testified that not all the eigenvectors in the full space of S w are positive to the classification performance, some of which might be negative. As far as we know, there have been no effective ways to determine which eigenvectors should be adopted. This paper proposes a new method EDA+Full-space LDA, which takes full advantage of the discriminative information of the null and range subspaces of Sw by selecting an optimal subset of eigenvectors. An estimation of distribution algorithm (EDA) is used to pursuit a subset of eigenvectors with significant discriminative information in full space of Sw middot EDA+Full-space LDA is tested on ORL face image database. Experimental results show that our method outperforms other LDA methods
10205775
Kernel-based Global Folley-Sammon Discriminate Analysis
Kernel trick is a useful method dealing with the nonlinearity of patterns ' distribution, and widely used for non-linear pattern classification problem. Recently, many kernel-based Fisher discriminate methods were proposed, the features extracted by those methods are statistically uncorrelated, usually ineffectively when used in so called "small sample problem "(SSS), which exists in most FR tasks. To overcome this shortcoming, in this paper we present Kernel-based Global Folley-Sammon Discriminate Analysis (KGFS), it can be realized in three steps: firstly by combing kernel function, map data onto an implicit high-dimensional feature space, secondly construct orthogonal feature space there, finally calculate iterative global orthogonal discriminate vectors. Experiments have been done on ORL dataset. In terms of classification error rate performance, proposed kernel based method KGFS has a better performance than other three commonly used kernel-based methods, such as GDA, MGDAandKDDA.
31996271
A Method of General Stiffness Modeling for Multi-axis Machine Tool
In this paper, a new method for computing general stiffness model at tool tip for multi-axis machine tool is presented. The formula of general stiffness model at tool tip is derived from virtual-work principle and point transformation matrix method. Point transformation matrix method indicates the transformation relationship of elastic displacement on both ends of flexible axes as well as the transformation relationship of force. By multiplying the point transformation matrix, the final stiffness matrix at tool tip will be obtained efficiently. In this modeling method, the final stiffness matrix is composed of local compliance matrixes according to their contribution to the final stiffness matrix.
30151415
Genetic Algorithm Based-On the Quantum Probability Representation
A genetic algorithm based on the quantum probability representation (GAQPR) is proposed, in which each individual evolves independently; a new crossover operator is designed to integrate searching processes of multiple individuals into a more efficient global searching process; a new mutation operator is also proposed and analyzed. Optimization capability of GAQPR is studied via experiments on function optimization, results of experiments show that, for multi-peak optimization problem, GAQPR is more efficient than GQA[4].
5644766
Transfer learning for collaborative filtering via a rating-matrix generative model
Cross-domain collaborative filtering solves the sparsity problem by transferring rating knowledge across multiple domains. In this paper, we propose a rating-matrix generative model (RMGM) for effective cross-domain collaborative filtering. We first show that the relatedness across multiple rating matrices can be established by finding a shared implicit cluster-level rating matrix, which is next extended to a cluster-level rating model. Consequently, a rating matrix of any related task can be viewed as drawing a set of users and items from a user-item joint mixture model as well as drawing the corresponding ratings from the cluster-level rating model. The combination of these two models gives the RMGM, which can be used to fill the missing ratings for both existing and new users. A major advantage of RMGM is that it can share the knowledge by pooling the rating data from multiple tasks even when the users and items of these tasks do not overlap. We evaluate the RMGM empirically on three real-world collaborative filtering data sets to show that RMGM can outperform the individual models trained separately.