14819927
Gene functional classification by semi-supervised learning from heterogeneous data
Gene function discovery is an important and interesting problem in computational analysis of microarray data. In this paper, we investigate the use of a semi-supervised learning algorithm for inferring gene functional classifications from heterogeneous data set consisting of DNA microarray expression measurements and phylogenetic profiles from whole-genome sequence compassions. The semisupervised learning approach aims at minimizing the disagreement between individual models built from each separate information source by employing a co-updating method and making use of both labeled and unlabeled data. Our results suggest that the semisupervised approach could be used for gene functional classification. The data sets and the program code used for the experiments can be accessed from our webpage.
12908496
Spatial interest pixels (SIPs): useful low-level features of visual media data
Visual media data such as an image is the raw data representation for many important applications. Reducing the dimensionality of raw visual media data is desirable since high dimensionality degrades not only the effectiveness but also the efficiency of visual recognition algorithms. We present a comparative study on spatial interest pixels (SIPs), including eight-way (a novel SIP detector), Harris, and Lucas‐Kanade, whose extraction is considered as an important step in reducing the dimensionality of visual media data. With extensive case studies, we have shown the usefulness of SIPs as low-level features of visual media data. A class-preserving dimension reduction algorithm (using GSVD) is applied to further reduce the dimension of feature vectors based on SIPs. The experiments showed its superiority over PCA.
17702470
A framework of individually-focused teleconferencing (IFT) via an efficient 3D reprojection technique
In this paper, we propose a framework on individually-focused teleconferencing (IFT) which is supported by an efficient 3D reprojection technique. The goal of designing IFT is to provide sufficient 3D pose information to each teleconference participant in order to establish lively communication in a teleconference on low-bandwidth internet. A novel IFT deployment and an efficient 3D reprojection technique are two major contributions of this paper. Our 3D reprojection technique uses a mirror reference view in three-view epipolar geometry. It overcomes the inefficiency in previous techniques and provides visually good recovery of pose information of teleconference participants even though it is theoretically an approximation scheme.
1550828
Two-Dimensional Linear Discriminant Analysis
Linear Discriminant Analysis (LDA) is a well-known scheme for feature extraction and dimension reduction. It has been used widely in many applications involving high-dimensional data, such as face recognition and image retrieval. An intrinsic limitation of classical LDA is the so-called singularity problem, that is, it fails when all scatter matrices are singular. A well-known approach to deal with the singularity problem is to apply an intermediate dimension reduction stage using Principal Component Analysis (PCA) before LDA. The algorithm, called PCA+LDA, is used widely in face recognition. However, PCA+LDA has high costs in time and space, due to the need for an eigen-decomposition involving the scatter matrices. 
 
In this paper, we propose a novel LDA algorithm, namely 2DLDA, which stands for 2-Dimensional Linear Discriminant Analysis. 2DLDA overcomes the singularity problem implicitly, while achieving efficiency. The key difference between 2DLDA and classical LDA lies in the model for data representation. Classical LDA works with vectorized representations of data, while the 2DLDA algorithm works with data in matrix representation. To further reduce the dimension by 2DLDA, the combination of 2DLDA and classical LDA, namely 2DLDA+LDA, is studied, where LDA is preceded by 2DLDA. The proposed algorithms are applied on face recognition and compared with PCA+LDA. Experiments show that 2DLDA and 2DLDA+LDA achieve competitive recognition accuracy, while being much more efficient.
1016063
A comparative study on content-based music genre classification
Content-based music genre classification is a fundamental component of music information retrieval systems and has been gaining importance and enjoying a growing amount of attention with the emergence of digital music on the Internet. Currently little work has been done on automatic music genre classification, and in addition, the reported classification accuracies are relatively low. This paper proposes a new feature extraction method for music genre classification, DWCHs. DWCHs stands for Daubechies Wavelet Coefficient Histograms. DWCHs capture the local and global information of music signals simultaneously by computing histograms on their Daubechies wavelet coefficients. Effectiveness of this new feature and of previously studied features are compared using various machine learning classification algorithms, including Support Vector Machines and Linear Discriminant Analysis. It is demonstrated that the use of DWCHs significantly improves the accuracy of music genre classification.
654543
A survey on wavelet applications in data mining
Recently there has been significant development in the use of wavelet methods in various data mining processes. However, there has been written no comprehensive survey available on the topic. The goal of this is paper to fill the void. First, the paper presents a high-level data-mining framework that reduces the overall process into smaller components. Then applications of wavelets for each component are reviewd. The paper concludes by discussing the impact of wavelets on data mining research and outlining potential future research directions and applications.
8351252
Using object deputy model to prepare data for data warehousing
Providing integrated access to multiple, distributed, heterogeneous databases and other information sources has become one of the leading issues in database research and the industry. One of the most effective approaches is to extract and integrate information of interest from each source in advance and store them in a centralized repository (known as a data warehouse). When a query is posed, it is evaluated directly at the warehouse without accessing the original information sources. One of the techniques that this approach uses to improve the efficiency of query processing is materialized view(s). Essentially, materialized views are used for data warehouses, and various methods for relational databases have been developed. In this paper, we first discuss an object deputy approach to realize materialized object views for data warehouses which can also incorporate object-oriented databases. A framework has been developed using Smalltalk to prepare data for data warehousing, in which an object deputy model and database connecting tools have been implemented. The object deputy model can provide an easy-to-use way to resolve inconsistency and conflicts while preparing data for data warehousing, as evidenced by our empirical study.
8409782
Super edge connectivity properties of connected edge symmetric graphs
1659842
Partition Based Path Join Algorithms for XML Data
Path expression is an important component in querying XML data. The extended preorder numbering scheme enables us to quickly determine the ancestor-descendant relationship between elements in the hierarchy of XML data. Using the numbering scheme, a path expression can be evaluated by join operations to avoid potentially high cost of tree traversals. In this paper, we first formulate XML path queries as range-point join queries. Then we discuss the partition based algorithms that can utilize the range containment property to efficiently process the range-point join queries. Under the partition based framework, we propose three algorithms, namely Descendant partition join, Segment-tree partition join and Ancestor Link partition join, which can be chosen by a query optimizer for different input data characteristics. The experimental results show that the partition based algorithms can make better use of the buffer memory than sort-merge algorithms, and the proposed Ancestor Link join algorithm yields the best performance by using small in-memory data structures and by taking advantage of unevenly sized inputs.
62417564
Global Clock Synchronization in Sensor Networks
Global synchronization is important for many sensor network applications that require precise mapping of collected sensor data with the time of the events, for example, in tracking and surveillance. It also plays an important role in energy conservation in MAC layer protocols. This paper describes four methods to achieve global synchronization in a sensor network: a node-based approach, a hierarchical cluster-based method, a diffusion-based method, and a fault-tolerant diffusion-based method. The diffusion-based protocol is fully localized. We present two implementations of the diffusion-based protocol for synchronous and asynchronous systems and prove its convergence. Finally, we show that, by imposing some constraints on the sensor network, global clock synchronization can be achieved in the presence of malicious nodes that exhibit Byzantine failures.
2297694
Distributed cooperative Apache web server
Given explosive data traAEc in the world-wide web (WWW), it is crucial to achieve the scalable performance of web servers. The overall performance and resource utilization can be improved by spreading document requests among a group of web servers. This leads to the design and implementation of Distributed Cooperative Apache (DC-Apache) web server. In this paper, we describe the unique features of the DC-Apache system (1) to migrate and replicate documents among cooperating servers, (2) using dynamic hyperlink generation to distribute requests for documents to balance the load, and (3) to maintain replicated copies in a consistent state. We also address the issue of storage management for more e ective document replication under limited capacity. In the experiments, the DC-Apache system demonstrated its ability to achieve high performance and scalability by e ectively distributing load among a group of cooperating Apache servers and by eliminating hot spots and performance bottleneck with replicated documents. The DC-Apache system is an e ective and practical solution to provide high performance and scalability to cope with ever increasing demands from clients all over the web. keywords: WWW, Scalable Web server, Apache, DCApache, Distributed Web server, Replication, Load balancing