3866113
Latent linkage semantic kernels for collective classification of link data
Generally, links among objects demonstrate certain patterns and contain rich semantic clues. These important clues can be used to improve classification accuracy. However, many real-world link data may exhibit more complex regularity. For example, there may be some noisy links that carry no human editorial endorsement about semantic relationships. To effectively capture such regularity, this paper proposes latent linkage semantic kernels (LLSKs) by first introducing the linkage kernels to model the local and global dependency structure of a link graph and then applying the singular value decomposition (SVD) in the kernel-induced space. For the computational efficiency on large datasets, we also develop a block-based algorithm for LLSKs. A kernel-based contextual dependency network (KCDN) model is then presented to exploit the dependencies in a network of objects for collective classification. We provide experimental results demonstrating that the KCDN model, together with LLSKs, demonstrates relatively high robustness on the datasets with the complex link regularity, and the block-based computation method can scale well with varying sizes of the problem.
41595049
Robust speech recognition method based on discriminative environment feature extraction
It is an effective approach to learn the influence of environmental parameters, such as additive noise and channel distortions, from training data for robust speech recognition. Most of the previous methods are based on maximum likelihood estimation criterion. However, these methods do not lead to a minimum error rate result. In this paper, a novel discriminative learning method of environmental parameters, which is based on Minimum Classification Error (MCE) criterion, is proposed. In the method, a simple classifier and the Generalized Probabilistic Descent (GPD) algorithm are adopted to iteratively learn the envirnnmental parameters. Consequently, the clean speech features are estimated from the noisy speech features with the estimated environmental parameters, and then the estimations of clean speech features are utilized in the back-end HMM classifier. Experiments show that the best error rate reduction of 32.1% is obtained, tested on a task of 18 isolated confusion Korean words, relative to aconventional HMM system.
11924885
Distributed video coding using wavelet
This paper proposes a distributed video coding scheme based on the zero tree entropy (ZTE) coding. Wyner-Ziv theory on source coding with side information is taken as the basic coding principle, which makes independent encoding and joint decoding possible. In this scheme, wavelet transform is used to exploit the spatial correlation of a Wyner-Ziv frame. The quantized wavelet coefficients are reorganized in terms of the zero tree structure so as to identify the significant and insignificant coefficients. The significance map is intra-codec and transmitted. In particular, the significant coefficients are independently encoded with turbo coder, and only the parity bits are transmitted. At the decoder, a predictive frame generated through motion-compensated prediction is used as the side information, with which the Wyner-Ziv frame can be conditionally decoded. Experimental results show that, compared to the traditional intra-frame coding and pixel-domain Wnyer-Ziv video coding, the proposed scheme can achieve a better coding performance, especially at low bit rates
58983783
Review the strength of Gabor features for face recognition from the angle of its robustness to mis-alignment
Gabor feature has been widely recognized as better representation for face recognition in terms of rank-1 recognition rate. In this paper, we review the strength of Gabor feature for face recognition from the new angle of its robustness to mis-alignment using a novel quantificational evaluation method combining both the alignment precision and the recognition accuracy. Our experiments show that, compared with the gray-level intensity, Gabor feature is much more robust to image variation caused by the imprecision of facial feature localization, which further support the feasibility of Gabor representation.
7272862
Linear transform based motion compensated prediction for luminance intensity changes
A linear transform based motion compensated prediction (LT-MCP) scheme is proposed in this paper to improve the efficiency of inter-frame prediction when the intensity of luminance changes abruptly across frames. Either uniform or non-uniform intensity changes can be well processed as the proposed scheme is applied at macroblock (MB) level. LT-MCP is implemented based on the H.264 reference software to evaluate its performance. For those sequences with significant luminance changes across frames, such as Crew, we can achieve about 1.5dB improvement over the conventional MCP with intra prediction disabled and about 0.8dB gain with intra prediction enabled. For those sequences without obvious luminance changes, the proposed scheme also works better.
426930
An admission control scheme for end-to-end statistical QoS provision in IP networks
This paper presents a distributed and scalable admission control scheme to provide end-to-end statistical QoS guarantees in IP network. The basic idea of the scheme is that the ingress routers make admission control decisions according to the network status information obtained by sending probing packets along the selected routing path. Each router passively monitors the arriving traffic and marks the probing packets with its network status. The performance of the presented scheme is evaluated with a variety of traffic models, QoS metrics and network topologies. The simulation results show that the proposed scheme can accurately control the admissible region and effectively improve the utilization of network resource.
17317140
A Reconfigurable High Availability Infrastructure in Cluster for Grid
The paper presents the implementation and analysis of a service-based reconfigurable High Availability infrastructure of cluster system for Grid. Based on service notion, the High Availability infrastructure is constructed for mission critical applications on Grid. The application high availability service is responsible for registered applications’ high availability. The Service Manager is in charge of monitoring all services via heartbeat and automatically recovering from failure to assure service high availability. To improve flexibility and dependability, a dynamic reconfiguration mechanism is designed so that the configurations can be changed without interrupting application’s running. A dependability modeling and simulation tool is deployed in the reconfiguration procedure. Dependability parameters can be inputted into the simulation tool. With the objects and sub-model library in the tool, corresponding global model will be constructed and statistic result for different recovery strategies is given. The configuration of application can be adjusted based on these statistic results.
36162111
Reconstruction problems in 3D for viral cryo electron microscopy
Cryo electron microscopy of viruses provides 2D projections of the scattering intensity of the viral particle but the orientation of the projections is not known. We describe an approach to reconstructing the 3D scattering intensity in spite of the unknown projection orientations using nonlinear least squares ideas where the reconstruction is guaranteed to have the icosahedral symmetry known to be present in the viral particle.
8940979
Signature Based Spectrum Sensing Algorithms for IEEE 802.22 WRAN
Spectral sensing for the presence of ATSC DTV signals in VHF/UHF TV bands under a very low SNR environment is one of the core technologies in IEEE 802.22 WRAN. There are multiple signatures embedded in the ATSC DTV signals and these signatures can be utilized to perform spectrum sensing. Signature-based spectrum sensing algorithms have an advantage in that when the signals other than noise are detected, we are almost sure that they are signals we want to detect. Thus, in this paper, several signature based spectrum sensing algorithms are presented. The false alarm rate for detectors employing different algorithms is analytically derived. The operating characteristic curves for different detectors are determined from computer simulations using ATSC A/74 DTV signal captures as a test database.
5747221
Application Research of Design Patterns in Virtual Trade Platform
Shanxi Coal Transportation and Marketing Group Limited has abundant coal and infrastructure network resources, maturity the highway system of coal transportation. The construction of virtual trade platform can share resources, accelerate the circulation speed of resources, reduce business costs, and improve business efficiency. The paper applies design pattern to virtual trade platform. The system has the advantage of strong scalability and maintainability, easy to develop and support multiple types of user interface.
46501259
A proactive fault-detection mechanism in large-scale cluster systems