Structured data in structural analysis software

This paper discusses the use of computer data structures in finite-element structural analysis programs. A number of data structure types that have been shown to be useful in such programs are introduced and described. A simple finite-element model is used to demonstrate how the given set of data structure types naturally lend themselves to developing software for the model. Different methods of implementing data structures in the context of a program are discussed.     

Godpp: Programs for the presentation and analysis of structural data

The package of FORTRAN programs described provides the field geologist with a system for processing and analyzing orientation data. The facilities offered include the plotting of stereograms, maps and rose diagrams together with rotations and statistical analysis of orientations. Orientation data may be supplied to the package in a variety of frequently used styles. Package facilities are specified by option statements, which the package interprets, that may be supplied in any sequence thus allowing maximum manipulative flexibility. The package has been successfully used to process and analyse structural data from a metamorphic terrain within the Norwegian Caledonides, but may be applied readily to other types of orientation data. The emphasis in writing this package was placed upon the production of usable graphical output, thus replacing tedious manual plotting and drafting tasks. These programs are not designed to undertake sophisticated interpretative operations on orientation data, these tasks remain with the user who will have hopefully more time in which to accomplish them.     

Hierarchical representation learning for big complex multimedia data

Multimedia Tools and Applications -     

Hierarchical discriminant analysis for image retrieval

A self-organizing framework for object recognition is described. We describe a hierarchical database structure for image retrieval. The self-organizing hierarchical optimal subspace learning and inference framework (SHOSLIF) system uses the theories of optimal linear projection for optimal feature derivation and a hierarchical structure to achieve logarithmic retrieval complexity. A space-tessellation tree is generated using the most expressive features (MEF) and most discriminating features (MDF) at each level of the tree. The major characteristics of the analysis include: (1) avoiding the limitation of global linear features by deriving a recursively better-fitted set of features for each of the recursively subdivided sets of training samples; (2) generating a smaller tree whose cell boundaries separate the samples along the class boundaries better than the principal component analysis, thereby giving a better generalization capability (i.e., better recognition rate in a disjoint test); (3) accelerating the retrieval using a tree structure for data pruning, utilizing a different set of discriminant features at each level of the tree. We allow for perturbations in the size and position of objects in the images through learning. We demonstrate the technique on a large image database of widely varying real-world objects taken in natural settings, and show the applicability of the approach for variability in position, size, and 3D orientation. This paper concentrates on the hierarchical partitioning of the feature spaces     

Hierarchical multiple-SIMD architecture for image analysis

Real-time image analysis requires the use of massively parallel machines. Conventional parallel machines consist of an array of identical processors organized in either single instruction multiple data (SIMD) or multiple instruction multiple data (MIMD) configurations. Machines of this type generally only operate effectively on parts of the image analysis problem. SIMD on the low level processing and MIMD on the high level processing. In this paper we describe the Warwick Pyramid Machine, an architecture consisting of both SIMD and MIMD parts in a multiple-SIMD (MSIMD) organization which can operate effectively at all levels of the image analysis problem.     

Hierarchical cluster algorithm for remote sensing data of earth

A new automatic, hierarchical, multidimensional, histogram-based clusterization algorithm is considered. A method for choosing the clusterization detailedness in different regions of the vector space of spectral features depending on the average separability of clusters is proposed. The algorithm is applied for the automatic classification of multispectral satellite data in recognizing the land cover.     

Hierarchical pose estimation for human gait analysis

Articulated structures like the human body have many degrees of freedom. This makes an evaluation of the configuration's likelihood very challenging. In this work we propose new linked hierarchical graphical models which are able to efficiently evaluate likelihoods of articulated structures by sharing visual primitives. Instead of evaluating all configurations of the human body separately we take advantage of the fact that different configurations of the human body share body parts, and body parts, in turn, share visual primitives. A hierarchical Markov random field is used to integrate the sharing of visual primitives in a probabilistic framework. We propose a scalable hierarchical representation of the human body and show that this representation is especially well suited for human gait analysis from a frontal camera perspective. Furthermore, the results of the evaluation on a gait dataset show that sharing primitives substantially accelerates the evaluation and that our hierarchical probabilistic framework is a robust method for scalable detection of the human body.     

Hierarchical music representation for composition and analysis

In this paper, we present intermediate results of continuing research into the utility of generalised hierarchical structures for the representation of musical information. We build on an abstract data type presented in Wigginset al. (1989), usingconstituents, which are structurally significant groupings of musical events. We suggest that a division into such groupings can be musically meaningful, and that it can be more flexible than similar approaches. We demonstrate our representation system at work in both analysis and composition, with output from computer programs. We conclude that it is possible and useful to represent music in a way independent of the particular style, tonal system, etc., of the music itself.The authors work in the Department of Artificial Intelligence, University of Edinburgh, Scotland.     

Crowdsourcing for data management

The discovery of informative itemsets is a fundamental building block in data analytics and information retrieval. While the problem has been widely studied, only few solutions scale. This is particularly the case when (1) the data set is massive, calling for large-scale distribution, and/or (2) the length k of the informative itemset to be discovered is high. In this paper, we address the problem of parallel mining of maximally informative k-itemsets (miki) based on joint entropy. We propose PHIKS (Parallel Highly Informative \(\underline{K}\)-ItemSet), a highly scalable, parallel miki mining algorithm. PHIKS renders the mining process of large-scale databases (up to terabytes of data) succinct and effective. Its mining process is made up of only two efficient parallel jobs. With PHIKS, we provide a set of significant optimizations for calculating the joint entropies of miki having different sizes, which drastically reduces the execution time, the communication cost and the energy consumption, in a distributed computational platform. PHIKS has been extensively evaluated using massive real-world data sets. Our experimental results confirm the effectiveness of our proposal by the significant scale-up obtained with high itemsets length and over very large databases.     

Pattern analysis in daily physical activity data for personal health management

Purpose: sedentary lifestyles have resulted in an increasing number of people who are at increased risk of various conditions and diseases, including overweight, obesity, and metabolic syndromes. Our objective was to systematically record the daily life journal on a platform to increase the self-awareness and improve the sedentary lifestyle and to assist clinicians in understanding and facilitating patients’ daily physical activity.Method: we developed a portable activity pattern recognition system designed to automatically recognize the daily activity habits of users, and provide visualized life logs on the wellness self-management platform for patients and clinicians. Based on the participants’ and the clinician’s comments, appropriate modifications were made.Results: persuading people to improve their activities during non-working hours can enhance the general physical activity. Since users’ smartphones automatically monitor their energy expenditure, healthcare professionals can use these data to assist their patients in addressing health problems stemming from the obesity or metabolic syndromes, thus empowering users to avert or delay the progression of diabetes, cardiovascular disease and other complications.Discussion and conclusions: the clinical pilot study showed the feasibility of applying this persuasive technology to improve the physical activity of overweight people. The limitation of the study is the need for Wi-Fi and 3G environments and a smartphone.     

云数据管理的新数据模型

为解决云计算中海量数据的存储管理问题,分析了关系数据模型和NoSQL数据模型各自的特点,提出了一种新的数据模型。该模型根据数据本身的特点将数据横向切分为一组实体的集合,不同的数据实体负责处理不同的数据应用,结合了关系数据模型的可用性与NoSQL数据模型的可扩展性。通过详细定义该模型的数据结构、约束条件以及数据操作,保证了数据模型的完整性。通过一个原型系统运行实例,验证了该模型的有效性,为云数据管理提供了可行的解决途径。     

Hierarchical multiresolution approaches for dense point-level breast cancer treatment data

The analysis of point-level (geostatistical) data has historically been plagued by computational difficulties, owing to the high dimension of the nondiagonal spatial covariance matrices that need to be inverted. This problem is greatly compounded in hierarchical Bayesian settings, since these inversions need to take place at every iteration of the associated Markov chain Monte Carlo (MCMC) algorithm. This paper offers an approach for modeling the spatial correlation at two separate scales. This reduces the computational problem to a collection of lower-dimensional inversions that remain feasible within the MCMC framework. The approach yields full posterior inference for the model parameters of interest, as well as the fitted spatial response surface itself. We illustrate the importance and applicability of our methods using a collection of dense point-referenced breast cancer data collected over the mostly rural northern part of the state of Minnesota. Substantively, we wish to discover whether women who live more than a 60-mile drive from the nearest radiation treatment facility tend to opt for mastectomy over breast conserving surgery (BCS, or “lumpectomy”), which is less disfiguring but requires 6 weeks of follow-up radiation therapy. Our hierarchical multiresolution approach resolves this question while still properly accounting for all sources of spatial association in the data.     

Enabling user-driven rule management in event data analysis

Event data analysis is becoming increasingly of interest to academic researchers looking for patterns in the data. Unlike domain experts working in large companies who have access to IT staff and expensive software infrastructures, researchers find it harder to efficiently manage their event data analysis by themselves. Particularly, user-driven rule management is a challenge especially when analysis rules increase in size and complexity over time. In this paper, we propose an event data analysis platform called EP-RDR intended for non-IT experts that facilitates the evolution of event processing rules according to changing requirements. This platform integrates a rule learning framework called Ripple-Down Rules (RDR) operating in conjunction with an event pattern detection component invoked as a service (EPDaaS). We have built a prototype to demonstrate this solution on real-life scenario involving financial data analysis.     

Hierarchical visualization of network intrusion detection data

A technique for visualizing intrusion-detection system log files using hierarchical data based on IP addresses represents the number of incidents for thousands of computers in one display space. Our technique applies a hierarchical data visualization technique that represents leaf nodes as black square icons and branch nodes as rectangular borders enclosing the icons. This representation style visualizes thousands of hierarchical data leaf nodes equally in one display space. We applied the technique to bioactive chemical visualization and job distribution in parallel-computing environments.     

Hierarchical splatting of scattered 4D data

To let scientists interactively view these data sets at high resolution on desktop workstations or PCs, we want to visualize the scattered data directly without resampling them to a volume density. We propose accelerating the visualization of scattered point data with a hierarchical data structure based on a principal component analysis (PCA) clustering procedure. By traversing this structure from each frame, we can trade off rendering speed verses image quality and use lower hierarchy levels during interaction. Our scheme also lets us interpolate the given point positions using cubic splines.