1848954
Adaptive Indexing of Moving Objects with Highly Variable Update Frequencies
In recent years, management of moving objects has emerged as an active topic of spatial access methods. Various data structures (indexes) have been proposed to handle queries of moving points, for example, the well-known Bx-tree uses a novel mapping mechanism to reduce the index update costs. However, almost all the existing indexes for predictive queries are not applicable in certain circumstances when the update frequencies of moving objects become highly variable and when the system needs to balance the performance of updates and queries. In this paper, we introduce two kinds of novel indexes, named By-tree and αBy-tree. By associating a prediction life period with every moving object, the proposed indexes are applicable in the environments with highly variable update frequencies. In addition, the αBy-tree can balance the performance of updates and queries depending on a balance parameter. Experimental results show that the By-tree and αBy-tree outperform the Bx-tree in various conditions.
8051159
A Web-based Framework For On-line Collaborative Learning
This paper presents a framework for design of online courses and collaborative learning based on Web technologies. It provides the following features: construction of structured learning materials in a distributed environment; personalization of course contents in the run-time that adapts to the progress of individual students; it can supply relevant Web resources to the students by analyzing the learning history dynamically; students in a group could exchange their learning experiences and discover relevant knowledge via the communication channels provided by the framework. The proposed techniques could be deployed to a variety of online computer-assisted modules to improve the effectiveness of Web-based e-learning
116588251
On-line evolutionary exponential family mixture
This paper deals with evolutionary clustering, which refers to the problem of clustering data with distribution drifting along time. Starting from a density estimation view to clustering problems, we propose two general on-line frameworks. In the first framework, i.e., historical data dependent (HDD), current model distribution is designed to approximate both current and historical data distributions. In the second framework, i.e., historical model dependent (HMD), current model distribution is designed to approximate both current data distribution and historical model distribution. Both frameworks are based on the general exponential family mixture (EFM) model. As a result, all conventional clustering algorithms based on EFMs can be extended to evolutionary setting under the two frameworks. Empirical results validate the two frameworks.
8340253
UGE4B: An Universal Grid Environment for Bioinformatics Research
Recent years, grid technology has been used widely in the fields of scientific research. In this paper, we present a universal grid environment for bioinformatics research (UGE4B). Although there have been several similar projects successfully constructed in the world, we need to meet some unique requirements from bioinformatics research in China. UGE4B consists of 6 components, including portal, task management, data management, information server, security and computing node. As a national-wide bioinformatics research platform, UGE4B is built through interconnecting multiple sub grids provided by each bioinformatics institutes. Besides provides a universal portal to end users for bioinformatics computing and data accessing, UGE4B also supported some special bioinformatics applications
26669443
Bottom-up discovery of frequent rooted unordered subtrees
In the past decade, XML has emerged as the standard language for information exchanging over the Internet. Due to its tree-structure paradigm, XML is superior for its capability of storing, querying, and manipulating complex data. Therefore, discovering frequent tree patterns over tree-structured data has become an interesting topic for XML data management. In this paper, we propose a tree mining algorithm, named BUXMiner, for finding a special class of frequent trees, called rooted unordered trees, from a tree-structured database. BUXMiner employs an efficient bottom-up approach to enumerate all candidate trees over a compact global tree guide and computes the frequent trees based on the tree guide. In addition to BUXMiner, we also propose a mining approach called BUMXMiner to discover the maximal frequent rooted unordered trees. We compare BUXMiner with previous tree-structure mining algorithms, namely XQPMinerTID and FastXMiner, which were also proposed to discover rooted unordered trees. The experimental results show that our algorithm outperforms XQPMinerTID and FastXMiner in terms of efficiency. The performance results from real-world applications also indicate the usefulness of our proposed tree mining algorithms in a variety of web applications, such as analysis of web page access patterns and mining frequent XML query patterns for caching.
5747087
Efficient Processing of Warping Time Series Join of Motion Capture Data
Discovering non-trivial matching subsequences from two time series is very useful in synthesizing novel time series. This can be applied to applications such as motion synthesis where smooth and natural motion sequences are often required to be generated from existing motion sequences. We first address this problem by defining it as a problem of l-e-join over two time series. Given two time series, the goal of l-e-join is to find those non-trivial matching subsequences by detecting maximal l-connections from the e-matching matrix of the two time series. Given a querying motion sequence, the l-e-join can be applied to retrieve all connectable motion sequences from a database of motion sequences. To support efficient l-e-join of time series, we propose a two-step filter-and-refine algorithm, called Warping Time Series Join (WTSJ) algorithm. The filtering step serves to prune those sparse regions of the e-matching matrix where there are no maximal l-connections without incurring costly computation. The refinement step serves to detect closed l-connections within regions that cannot be pruned by the filtering step. To speed up the computation of e-matching matrix, we propose a block-based time series summarization method, based on which the block-wise e-matching matrix is first computed. Lots of pairwise distance computation of elements can then be avoided by applying the filtering algorithm on the block-wise e-matching matrix.Extensive experiments on l-e-join of motion capture sequences are conducted. The results confirm the efficiency and effectiveness of our proposed algorithm in processing l-e-join of motion capture time series.
8846257
A Noise Insensitive Cluster Validity Measure for Pattern Classification
When trying to discover knowledge on a collection of data, one of the first arising tasks is to identify groups of similar objects, that is, to carry out cluster analysis for obtaining data partitions. Thus, a decision must be taken for choosing the clustering result that produces the best data partition for a given data collection. In order to support such a decision, indexes for measuring the quality of a data partitioning must be constructed. So far, several cluster validity indexes have been formulated in the literatures. Each of those indexes has strengths and drawbacks when compared with the others. In the present study, an alternative cluster validity index is formulated. The proposed validity index not only takes the contribution of each pattern into consideration, but also relies on information of intra-cluster and inter-cluster distance. The main advantage of the presented index is that is insensitive to noise by introducing the Gaussian kernel into the proposed validity index. An experimental design was devised in order to determine the comparative performance of the proposed cluster validity index against DB index previously formulated in the literature. Experimental results show that the proposed index is insensitive to noise and adaptive to produce good clustering solution.
13807032
Coordinating Agents in Shop Floor Environments From a Dynamic Systems Perspective
In this paper, we address agent coordination from a dynamic systems perspective and propose a dynamic coordination model, which is inspired by biological metabolic systems. A new coordination mechanism through dynamic local adjustment (CDLA) is presented, and coordination is achieved when every agent utilizes explicitly the global system dynamics and performs iteratively a dynamic local adjustment procedure. The CDLA mechanism is investigated in an example multiagent shop floor system. The results show that the example manufacturing process is well-coordinated and the coordination approach is practically applicable and effective
42691815
Global stability of a class of Cohen-Grossberg neural networks with delays
This paper is concerned with the Global Asymptotic Stability (GAS) of a general class of Cohen-Grossberg neural networks with both multiple time varying delays and distributed delays. Criteria are established to ensure the GAS of the concerned neural networks, which can be expressed in the form of Linear Matrix Inequality and independent of amplification functions. Furthermore, a sufficient condition guaranteeing the global robust stability is established for the general class of Cohen-Grossberg neural networks with both multiple time varying delays and distributed delays in the case of parameter uncertainties.
8517904
A Method of Video Quality Assessment Based on the Sensitive Region
Zhou Wang proposed the Structural Similarity theory (MSSIM), Guan-Hao Chen proposed a marginal texture distorts theory on it. Jian-Xin Pang proposed a method of image quality assessment based on the energy of structural distortion (ESD). Because the theory isn't applied directly on the video field and it don’t think over HVS. So we propose a method of video quality assessment based on the sensitive region. First of all, we get the energy of structural distortion (ESD) based on the block. Then, we get the weighted factor according to the structure information and the movement information. Finally, we get the final results according to the HVS. From the last results, we know that the weighting method base on the ESD (WESD) is better than the PSNR, MSSIM and ESSIM on the compress losing, smooth losing and noise losing.
14482306
New procedures to identify redundant stuck-at faults and removal of redundant logic
We present new procedures for identifying redundant stuck-at faults including multiple line stuck-at faults on the branches of fan-out stems. The methods proposed include new procedures to identify stuck-at faults that are simultaneously redundant thus allowing simultaneous removal of logic associated with several redundant faults. Experimental results on benchmark as well as industrial circuits are also presented to demonstrate the effectiveness of the proposed methods.