Matrix model for web page community

Discovering intrinsic relationships/structures among concerned web information objects such as web pages is important for effectively processing and managing web information. In this work, a set of web pages that has its own intrinsic structure is called a web page community. This paper proposes a matrix model to describe relationships among concerned web pages. Based on this model, intrinsic relationships among pages could be revealed, and in turn a web page community could be constructed. The issues that are related to this model and its applications are investigated and studied. Some applications based on this model are presented, which demonstrate the potential of this matrix model in different kinds of web page community construction and information processing. <br /

Data pre-processing for more effective gene clustering

The high-throughput experimental data from the new gene microarray technology has spurred numerous efforts to find effective ways of processing microarray data for revealing real biological relationships among genes. This work proposes an innovative data pre-processing approach to identify noise data in the data sets and eliminate or reduce the impact of the noise data on gene clustering, With the proposed algorithm, the pre-processed data sets make the clustering results stable across clustering algorithms with different similarity metrics, the important information of genes and features is kept, and the clustering quality is improved. The primary evaluation on real microarray data sets has shown the effectiveness of the proposed algorithm.<br /

RRHGE: a novel approach to classify the estrogen receptor based breast cancer subtypes

Breast cancer is the most common type of cancer among females with a high mortality rate. It is essential to classify the estrogen receptor based breast cancer subtypes into correct subclasses, so that the right treatments can be applied to lower the mortality rate. Using gene signatures derived from gene interaction networks to classify breast cancers has proven to be more reproducible and can achieve higher classification performance. However, the interactions in the gene interaction network usually contain many false-positive interactions that do not have any biological meanings. Therefore, it is a challenge to incorporate the reliability assessment of interactions when deriving gene signatures from gene interaction networks. How to effectively extract gene signatures from available resources is critical to the success of cancer classification

A neural network based human identification framework using ear images

This paper presents a framework that uses ear images for human identification. The framework makes use of Principal Component Analysis (PCA) for ear image feature extraction and Multilayer Feed Forward Neural Network for classification. Framework are proposed to improve recognition accuracy of human identification. The framework was tested on an ear image database to evaluate its reliability and recognition accuracy. The experimental results showed that our framework achieved higher stable recognition accuracy and over-performed other existing methods. The recognition accuracy stability and computation time with respect to different image sizes and factors were investigated thoroughly as well in the experiments.<br /

Breast cancer prognosis risk estimation using integrated gene expression and clinical data

Novel prognostic markers are needed so newly diagnosed breast cancer patients do not undergo any unnecessary therapy. Various microarray gene expression datasets based studies have generated gene signatures to predict the prognosis outcomes, while ignoring the large amount of information contained in established clinical markers. Nevertheless, small sample sizes in individual microarray datasets remain a bottleneck in generating robust gene signatures that show limited predictive power. The aim of this study is to achieve high classification accuracy for the good prognosis group and then achieve high classification accuracy for the poor prognosis group

Stability and slow-fast oscillation in fractional-order Belousov-Zhabotinsky reaction with two time scales

The fractional-order Belousov-Zhabotinsky (BZ) reaction with different time scales is investigated in this paper. Based on the stability theory of fractional-order differential equation, the critical condition of Hopf bifurcation with two parameters in fractional-order BZ reaction is discussed. By comparison of the fractional-order and integer-order systems, it is found that they will behave in different stabilities under some parameter intervals, and the parameter intervals may become larger with the variation of fractional order. Furthermore, slow-fast effect is firstly studied in fractional-order BZ reaction with two time scales coupled, and the Fold/Fold type slow-fast oscillation with jumping behavior is found, whose generation mechanism is explained by using the slow-fast dynamical analysis method. The influences of different fractional orders on the slow-fast oscillation behavior as well as the internal mechanism are both analyzed

Content exploration for e-learning using XML Web services

Obtaining high quality content in an e-learning portal is critical to maximise the learning experience. For e-learning portals the content specialists are the publishers. Typically, publishers are nominated by portal administrators to make their content available to instructors. Instructors subsequently customise the portal by selecting content according to their requirements. The choice of content is limited to that provided by the publishers. This is a rigid system owing to the fact that instructors do not have access to an exhaustive range of content. We propose a system based on XML Web services which can be adopted by publishers in adherence with a number of emerging Web standards including SOAP and UDDI to disseminate their content. This system can be leveraged by portals to preview and subsequently acquire content which best suits the requirements as determined by instructors. <br /

DDS3D: Dense Pseudo-Labels with Dynamic Threshold for Semi-Supervised 3D Object Detection

In this paper, we present a simple yet effective semi-supervised 3D object detector named DDS3D. Our main contributions have two-fold. On the one hand, different from previous works using Non-Maximal Suppression (NMS) or its variants for obtaining the sparse pseudo labels, we propose a dense pseudo-label generation strategy to get dense pseudo-labels, which can retain more potential supervision information for the student network. On the other hand, instead of traditional fixed thresholds, we propose a dynamic threshold manner to generate pseudo-labels, which can guarantee the quality and quantity of pseudo-labels during the whole training process. Benefiting from these two components, our DDS3D outperforms the state-of-the-art semi-supervised 3d object detection with mAP of 3.1% on the pedestrian and 2.1% on the cyclist under the same configuration of 1% samples. Extensive ablation studies on the KITTI dataset demonstrate the effectiveness of our DDS3D. The code and models will be made publicly available at https://github.com/hust-jy/DDS3DComment: Accepted for publication in 2023 IEEE International Conference on Robotics and Automation (ICRA

Matrix model for web page community construction

The World Wide Web is now a huge information source with its own characteristics. In most cases, traditional database-based technologies are no longer suitable for web information processing and management. For effectively processing and managing web. information, it is necessary to reveal intrinsic relationships/structures among concerned web information objects such as web pages. In this work, a set of web pages that has its own intrinsic structure is called a web page community. This paper proposes a matrix model to describe relationships among concerned web pages. Based on this model, intrinsic relationships among pages could be revealed, and in turn a web page community could be constructed. The issues that are related to this model in its application are deeply investigated and studied. Some applications based on this model are presented, which demonstrate the potential of this matrix model in different kinds of web page community construction and information processing.<br /