Qualitative data modeling: application of a mechanism for interpreting graphical data

This paper describes a qualitative technique for interpreting graphical data. Given a set of numerical observations regarding the behaviour of a system, its attributes can be determined by plotting the data and qualitatively comparing the shape of the resulting graph with graphs of system behaviour models. Qualitative data modeling incorporates techniques from pattern recognition and qualitative reasoning to characterize observed data, generate hypothetical interpretations, and select models that best fit the shape of the data. Domain-specific knowledge may be used to substantiate or refute the likelihood of hypothesized interpretations. The basic data modeling technique is domain independent and is applicable to a wide range of problems. It is illustrated here in the context of a knowledge-based system for well test interpretation.     

ARDF：一种用于应用迁移的资源描述框架

针对个人用户的应用无缝迁移是普适环境特征之一。此方面现有的工作大多表现为针对某个特定应用的迁移能力,较难满足实际需求。给出了一个具有一定普遍意义的应用资源描述框架,该框架可用于描述与应用相关的系统和用户资源的状态,从而支持需迁移的应用状态的获取和重建。在此基础上,在自行设计和实现的FollowMe普适空间中,实现了一个针对个人Office办公需求的应用迁移示范应用。     

Mapping DNA by stochastic relaxation: a new approach to fragment sizes

Instead of the traditional manipulations with given fixed fragment lengths in the restriction map construction a method of varying the lengths is proposed and realized under the simulated annealing algorithm scheme. The described approach has no upper limit on the number of fragments mapped with even ordinary hardware. A program has been derived from the algorithm combined with the last-squares refinement procedure for both linear and circular maps. The algorithm's ability to pick up missed maps is illustrated and the problem of reducing the number of solutions is discussed.     

DNA微阵列数据特征提取的分类方法研究

常用的排列方法从DNA微数据中选择的基因集合往往会包含相关性较高的基因,而且使用单个基因评价方法也不能真正反映由此得到的特征集合分类能力的优劣。另外,基因数量远多于样本数量是进行疾病诊断面临的又一挑战。为此,提出一种DNA微阵列数据特征提取方法用于组织分类。该方法运用K-means方法对基因进行聚类分析,获取各子类DNA微阵列数据中心,用排列法去除对分类无关的子类,然后利用ICA方法提取剩余子类集合的特征,用SVMs方法构造分类器对组织进行分类。真实的生物学数据实验表明,该方法通过提取一种复合基因,能综合评价基因分类能力,减少特征数,提高分类器的分类准确性。     

GELLAB: A computer system for 2D gel electrophoresis analysis. II. pairing spots

An algorithm and computer program CMPGEL, which is part of the GELLAB 2D electrophoretic gel analysis system, is described for pairing comparable spots in two gels. Pairing of spots between two gels at a time is necessary for the comparison of multiple gel images by the construction of multiple gel data bases. Interrogation of and experimentation with the spot data base may then be performed in order to extract measurements on particular spots for the set of gels. The enormous problem of pairing all spots in one gel with all spots in another gel is reduced to a small number of much simpler problems by partitioning spots in the gels into local regions. These are denoted by proximity to so called “landmark spots” and are termed “landmark regions.” Spots within landmark regions are then compared in order to perform the pairing.     

A computer program for calculating kappa: application to interexaminer agreement in periodontal research

The kappa statistic is a frequently used measure of interobserver agreement when two or more observers are asked to rate the same items or subjects on some criterion. The advantage of the kappa statistic over simple agreement is that it corrects for agreement by chance. In dental research, because multiple examiners may be involved in assessing subject variables such as attachment loss, bleeding on probing, or periodontal pocket depth, some statistical measure of agreement is needed. The program described here was developed for estimating agreement among periodontal examiners, but also could be used in clinical teaching applications. Assumptions and limitations of the kappa statistic are discussed. Examples of outputs illustrate applications of the program.     

NMscatt: a program for calculating inelastic scattering from large biomolecular systems using classical force-field simulations

Computational tools for normal mode analysis, which are widely used in physics and materials science problems, are designed here in a single package called NMscatt (Normal Modes & scattering) that allows arbitrarily large systems to be handled. The package allows inelastic neutron and X-ray scattering observables to be calculated, allowing comparison with experimental data produced at large scale facilities. Various simplification schemes are presented for analyzing displacement vectors, which are otherwise too complicated to understand in very large systems.

Program summary

Title of program:NMscattCatalogue identifier:ADZA_v1_0Program summary URL:http://cpc.cs.qub.ac.uk/summaries/ADZA_v1_0.htmlProgram obtainable from:CPC Program Library, Queen's University of Belfast, N. IrelandLicensing provisions:noNo. of lines in distributed program, including test data, etc.:573 535No. of bytes in distributed program, including test data, etc.:4 516 496Distribution format:tar.gzProgramming language:FORTRAN 77Computer:x86 PCOperating system:GNU/Linux, UNIXRAM:Depends on the system size to be simulatedWord size:32 or 64 bitsClassification:16.3External routines:LAPACKNature of problem: Normal mode analysis, phonons calculation, derivation of incoherent and coherent inelastic scattering spectra.Solution method: Full diagonalization (producing eigen-vectors and eigen-values) of dynamical matrix which is obtained from potential energy function derivation using finite difference method.Running time: About 7 hours per one k-point evaluation in sampling all modes dispersion curves for a system containing 3550 atoms in the unit cell on AMD Athlon 64 X2 Dual Core Processor 4200+.     

Multimedia data warehouses: a multiversion model and a medical application

Histogram feature representation is important in many classification applications for characterization of the statistical distribution of different pattern attributes, such as the color and edge orientation distribution in images. While the construction of these feature representations is simple, this very simplicity may compromise the classification accuracy in those cases where the original histogram does not provide adequate discriminative information for making a reliable classification. In view of this, we propose an optimization approach based on evolutionary computation (Back, Evolutionary algorithms in theory and practice, Oxford University Press, New York, 1996; Fogel, Evolutionary computation: toward a new philosophy of machine intelligence, 2nd edn. IEEE, Piscataway, NJ 1998) to identify a suitable transformation on the histogram feature representation, such that the resulting classification performance based on these features is maximally improved while the original simplicity of the representation is retained. To facilitate this optimization process, we propose a hierarchical classifier structure to demarcate the set of categories in such a way that the pair of category subsets with the highest level of dissimilarities is identified at each stage for partition. In this way, the evolutionary search process for the required transformation can be considerably simplified due to the reduced level of complexities in classification for two widely separated category subsets. The proposed approach is applied to two problems in multimedia data classification, namely the categorization of 3D computer graphics models and image classification in the JPEG compressed domain. Experimental results indicate that the evolutionary optimization approach, facilitated by the hierarchical classification process, is capable of significantly improving the classification performance for both applications based on the transformed histogram representations.

CEA: A Cyclic Expansion Algorithm for data migration in parallel video servers

Parallel video servers can achieve highly storage-saving and granularly load-balancing, but they suffer from a system expansion problem. As the number of users continuously increases, the system inevitably needs to expand the number of video servers. However, the expansion of a parallel video server system is not as simple as that of a replicated video server system. Hence, this work develops an efficient expansion algorithm, called the Cyclic Expansion Algorithm (CEA), for parallel video servers. The proposed CEA algorithm has several good features. First, the data layout of each video content exhibits periodicity. Consequently, the meta-data size of each video and the complexity of the CEA algorithm are reduced. Second, the number of required data movements during a system expansion is optimized. Third, the total number of required XOR recomputations for updating parity blocks during an expansion is also minimized. Additionally, the new CEA can be applied to a variety of distributed storage systems, such as the cloud-based storage systems using striping and parity check techniques.     

An application of a fuzzy classifier extracted from data for collision avoidance support in road vehicles

Road traffic collisions are an outstanding problem in current developed societies. This paper presents a solution to support collision avoidance based on the timely detection of the vehicle maneuvers. Since the longitudinal interaction among vehicles, with the commonly known car-following behavior, is one of the most important causes of crashes, it was decided to focus on longitudinal maneuvers, identifying the maneuvering states of cruise, accelerating or decelerating and stop. The classification is carried out by means of fuzzy rules extracted from navigational data. Therefore, in our proposal no extra sensors are needed apart from two commonly installed for navigation purposes: the odometry of the vehicle and an accelerometer. The system was tested with low-cost sensors showing good results when compared to the literature of the field.     

Optimization models for computer data storage design: An application

In this paper we discuss a model being used to optimize the system design of the Computer Centre of one of the most important Italian banking groups. Data and transactions, processed by the system, are grouped respectively in data sets and by type, so it is possible to deal with the large dimensions of the corresponding optimization models. The transactions' arrivals are considered as stochastic variables and their probability values are estimated on the base of theoretical considerations. The solutions for two optimization problems, constructed and solved for different scenarios, are discussed in detail.     

GELLAB: A computer system for 2D gel electrophoresis analysis I. Segmentation of spots and system preliminaries

GELLAB is a computerized analysis system for two-dimensional gel electrophoresis images. It is particularly concerned with tools for the automation of comparisons among multiple gels rather than simple gel-pair analyses. Interrogation of and experimentation with the spot data base may then be performed in order to extract measurements on particular spots within the set of gels. In the case of gels partitioned into multiple classes, it is possible to search for spots in the spot data base having statistically significant differences between classes. This first paper deals with: (1) some gel properties which bear on image acquisition and image analysis, (2) the broad image acquisition environment, beyond mere scanning, such as auxiliary data, standarization and calibration, and (3) segmentation as a means of spot extraction and characterization. Our segmentation algorithm is based on problem domain heuristics and particularly on properties of the second derivative. It is therefore largely shape and density independent. The segmenter, as the other GELLAB programs, is written in SAIL and runs on either a DECSYSTEM-10 or -20.     

MBA:A market-based approach to data allocation and dynamic migration for cloud database

With the coming shift to cloud computing,cloud database is emerging to provide database service over the Internet.In the cloud-based environment,data are distributed at Internet scale and the system needs to handle a huge number of user queries simultaneously without delay.How data are distributed among the servers has a crucial impact on the query load distribution and the system response time.In this paper,we propose a market-based control method,called MBA,to achieve query load balance via reasonable data distribution.In MBA,database nodes are treated as traders in a market,and certain market rules are used to intelligently decide data allocation and migration.We built a prototype system and conducted extensive experiments.Experimental results show that the MBA method significantly improves system performance in terms of average query response time and fairness.     

A sequential Kriging method assisted by trust region strategy for proxy cache size optimization of the streaming media video data due to fragment popularity distribution

Multimedia Tools and Applications - The Kriging method based on machine learning is an attractive tool. In this work, a sequential Kriging method assisted by trust region strategy (SKM-TRS) is...