An interactive computer program for measurement of data obtained using the extra-stimulus technique during electrode-catheter studies

Current research in computer vision highlights the potential of using computational methods to analyze and access large datasets of real images and videos, performing tasks such as object detection or finding visual similarities. This essay describes how the application of these computational methods to digital art data transforms information inherent in images into new knowledge. We support this claim by presenting various research examples in the field of digital art history, which utilize computational methods for art analysis. We argue that in order to create new knowledge, we must involve transformative processes — from analog to digital data and digital to computational methods. Traditional methods used in art history to access datasets, link or edit images provide suggestions and validations for current practices, but are not sufficient role models for the processing of digital data because they were developed under varying technological conditions and standards and within a different historical context. Aby Warburg’s (1866-1929) Mnemosyne Atlas, which aimed to visualize visual continuities from antiquity to the Renaissance, is one method often cited in the context of digital humanities. We argue that the characteristics of digital data, for example being reproducible or modifiable, require innovative computer methods that do not have a direct analog counterpart. Our argument is based on the success of current projects and personal experience: the authors have expertise in computer vision and art history. They have created and applied computational methods to analyze art data and are thus able to identify shortcomings of traditional approaches and suggest possible solutions. Eventually this essay presents solutions, which demonstrate the great potential of computational methods for a data analysis: approaches enable an easy and explorative access to digital image collections through visualization techniques or an automatic object search. Computational methods establish links between thousands of images, thereby identifying the adaptation of specific motifs or styles by artists over time, or enable a conspicuous editing of images, thus providing new insights for art history unobtainable with analog methods.

Renal parenchymal transit time measurements are used in the diagnosis of obstructive nephropathy but parenchymal regions are usually drawn by hand with or without the aid of a functional image. This paper describes three computerized methods for derivation of parenchymal regions from a mean time functional image and compares them with the manual method by processing renogram data from 82 normal, 19 obstructed and 11 dilated nonobstructed kidneys using all four techniques. Receiver-operator characteristic analysis demonstrated the computer methods to be superior to manually drawn regions. The optimum computer method had a sensitivity of 95% and specificity 95% for distinguishing obstructed kidneys from dilated nonobstructed kidneys. The computer method could be readily incorporated into routine renogram analysis programs.

Testing the interactive computer method (IM) for producing K indices with the data of the Hurbanovo and Budkov magnetic observatories

Computer methods in biomechanics are computer methods for solving biomechanics problems. Rula, Reba, Niosh, Owas is a method of analyzing work risks due to incorrect posture. Each method uses a worksheet on one posture to analyze risk. For dynamic work, a computer method is needed that can quickly calculate every change in posture. Virtual reality and Kinect as dynamic work analysis methods cannot be carried out preventively. Preventive measures are needed to prevent workers from risks resulting from work. For this reason, computer simulation methods are needed to create work stations and work processes so that they can be carried out preventively. The s-task simulation builder (S-TSB) framework provides a dynamic work analysis solution using simulation. The three processes carried out include surveying, creating simulations and processing posture data. Software validation was tested using the black box analysis method and the results were as expected. The dynamic working model is tested showing results in the form of graphs so it is easy to compare each change. The use of simulation also saves design costs because optimization can simply be done by changing the simulated work station data and/or work process.

With the rapid development of artificial intelligence, it is very important to find the pattern of the data from the observed data and the functional dependency relationship between the data. By finding the existing functional dependencies, we can classify and predict them. At present, cardiovascular disease has become a major disease harmful to human health. As a disease with high mortality, the prediction problem of cardiovascular disease is becoming more and more urgent. However, some computer methods are mainly used for disease detection rather than prediction. If the computer method can be used to predict cardiovascular disease in advance and treat it as early as possible, then the consequences of the disease can be reduced to a certain extent. Diseases can be predicted by mechanical methods. Support vector machine (SVM) has strict mathematical theory support, and can deal with nonlinear classification after using kernel techniques. Therefore, support vector machine can be used to predict cardiovascular disease. On the other hand, we also use logical regression and random forest to predict cardiovascular disease. This paper mainly uses the method of machine learning to predict whether the population is sick or not. First of all, we preprocess the obtained data to improve the quality of the data, and then use svm and logical regression to predict, so as to provide reference for the prevention and treatment of cardiovascular diseases.

The assessment of the bone age, or skeletal maturity, is an important task in pediatrics that measures the degree of maturation of children's bones. Nowadays, there is no standard clinical procedure for assessing bone age and the most widely used approaches are the Greulich and Pyle and the Tanner and Whitehouse methods. Computer methods have been proposed to automatize the process; however, there is a lack of exploration about how to combine the features of the different parts of the hand, and how to take advantage of ensemble techniques for this purpose. This paper presents a study where the use of ensemble techniques for improving bone age assessment is evaluated. A new computer method was developed that extracts descriptors for each joint of each finger, which are then combined using different ensemble schemes for obtaining a final bone age value. Three popular ensemble schemes are explored in this study: bagging, stacking and voting. Best results were achieved by bagging with a rule-based regression (M5P), scoring a mean absolute error of 10.16 months. Results show that ensemble techniques improve the prediction performance of most of the evaluated regression algorithms, always achieving best or comparable to best results. Therefore, the success of the ensemble methods allow us to conclude that their use may improve computer-based bone age assessment, offering a scalable option for utilizing multiple regions of interest and combining their output.

To reduce variability of Cobb angle measurement for scoliosis assessment, a computerized method was developed. This method automatically measured the Cobb angle on spinal posteroanterior radiographs after the brightness and the contrast of the image were adjusted, and the top and bottom of the vertebrae were selected. The automated process started with the edge detection of the vertebra by Canny edge detector. After that, the fuzzy Hough transform was used to find line structures in the vertebral edge images. The lines that fitted to the endplates of vertebrae were identified by selecting peaks in Hough space under the vertebral shape constraints. The Cobb angle was then calculated according to the directions of these lines. A total of 76 radiographs were respectively analyzed by an experienced surgeon using the manual measurement method and by two examiners using the proposed method twice. Intraclass correlation coefficients (ICC) showed high agreement between automatic and manual measurements (ICCs > 0.95). The mean absolute differences between automatic and manual measurements were less than 5 degrees . In the interobserver analyses, ICCs were higher than 0.95, and mean absolute differences were less than 5 degrees . In the intraobserver analyses, ICCs were 0.985 and 0.978, respectively, for each examiner, and mean absolute differences were less than 3 degrees . These results demonstrated the validity and reliability of the proposed method.

There is growing need for a systematic presentation of the fundamental concepts of earth-reinforced retaining structure design and the basic steps involved in the computational methods therewith since, unfortunately, no texts suitable for undergraduate instruction with computer methods are presently available. This paper illustrates with the in-house developed software "GEOWALL" the advantage of using a computer to perform a design process, which is otherwise lengthy and tedious, for soil reinforcement method of retaining a soil mass. The earth-reinforced retaining system comprises a relatively large number of closely spaced metallic reinforcing strips or woven geotextiles or continuous sheets of geogrids, segmental facing units and selected granular fill. The software package is an interactive program developed for the design of segmental wall systems to retain soils behind the wall. The program uses recent design practices, computerizes manual methods of design to achieve improved speed as well as reliability of calculations, and runs on IBM-PC or other compatible microcomputer hardware using DOS operating system. The program achieves a high degree of user friendliness through an easily followed format and explicit data prompts.

In order to limit the scope of this review, a working definition of a decision support system is needed. L. Adelman has defined decision support systems (DSSs) as “interactive computer programs that utilize analytical methods, such as decision analysis, optimization algorithms, program scheduling routines, and so on, for developing models to help decision makers formulate alternatives, analyze their impacts, and interpret and select appropriate options for implementation” (Adelman [1992], p. 2). Another definition has been offered by S. J. Andriole, who defined decision support as consisting of “any and all data, information, expertise or activities that contribute to option selection“ (Andriole [1989], p. 3). A common idea explicit in each of these definitions is that DSSs integrate various technologies and aid in option selection. Implicit in each definition is that these are options for solving relatively large, unstructured problems. Thus, the following working definition of a DSS will be used in this review: A DSS is an integrated, interactive computer system, consisting of analytical tools and information management capabilities, designed to aid decision makers in solving relatively large, unstructured problems.

It is our great pleasure to welcome you to the 4th ACM SIGCHI Symposium on Engineering Interactive Computing Systems -- EICS'12 held in Copenhagen (25--28 June 2012). EICS is an international conference devoted to all aspects of engineering usable and effective interactive computing systems. Topics of interest include multi-device interactive systems, new and emerging modalities (e.g., gesture), entertaining applications (e.g., mobile and ubiquitous games), safety critical systems (e.g. medical devices), and design and development methods (e.g., extreme programming). EICS focuses on tools, techniques and methods for designing and developing interactive systems. The conference brings together people who study or practice the engineering of interactive systems, drawing from Human-Computer Interaction (HCI), Software Engineering, Requirements Engineering, Computer-Supported Cooperative Work (CSCW), Ubiquitous & Pervasive Systems, Game Development, and Cognitive Engineering communities. The conference is the legatee of a number of conferences and workshops series: EHCI (Engineering Human Computer Interaction), DSV-IS (International Workshop on the Design, Specification and Verification of Interactive Systems), CADUI (International Conference on Computer-Aided Design of User Interfaces) and TAMODIA (International Workshop on Task Models and Diagrams). Since its beginning EICS has witnessed a growing number of submissions. This year the program contains 21 full papers carefully chosen from a total of 95 submissions (22% acceptance rate). There are also 12 late breaking papers (five of which presented as posters) as well as a number of doctoral reports, workshop reports, tutorial abstracts and demonstration descriptions. The published material originates from 15 countries, including New Zealand, North and South America, and Europe. In addition, keynote addresses will be offered by Robert Jacob (Tufts University, USA) and Jakob Bardram (IT University of Copenhagen, Denmark). We believe that for this fourth EICS edition we obtained an exciting and interactive program, which stimulates fruitful discussion in the relevant research fields. Topics range from model-based approaches to the design, analysis and generation of user interfaces, to toolkits supporting their development. A diversity of interaction styles and application areas is covered including multimodal, multidevice or multi-touch interfaces, ubiquitous computing, and health. We hope that you will find this year program interesting and thought provoking. The symposium aims to provide you with a valuable opportunity to share ideas with other researchers and practitioners from institutions around the world. We also wish the best to the next edition, EICS 2013 to be held in London, UK, in June 2013.

Analysis of dietary data: An interactive computer method for storage and retrieval

The use of craniofacial superimposition for disaster victim identification

An interactive computer system’ service is characterized by the random waiting (or response) time perceived by users. This paper presents a novel solution to the problem of efficiently computing the second moment of the waiting time of a class of large interactive systems. The physical system consists of a bank of terminals, each of which asynchronously alternates between “thinking” and waiting for service from a CPU which operates under the processor-sharing discipline. The problem of obtaining higher order waiting time moments is quite different from that of obtaining CPU queue statistics. Only the first moment of the waiting time is obtainable from the CPU queue statistics. The technique for arriving at the second moment of the waiting time consists of developing an asymptotic expansion in inverse powers of the number of terminals. Hence, as the system grows, quite fortuitously, fewer terms of the series require computation to achieve the desired accuracy. Beside its numerical advantages, the results give new insight since the leading terms of the series, which contain most of the information, are obtained explicitly. The novelty also rests on the fact that instead of solving matrix equations, the problem is turned into one of solving a second order differential equation. A simple two-dimensional recursion yields all the terms of the asymptotic expansion.

In the course of a study on the relationship between tissue damage and pain sensation due to thermal stimulation, <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> a computer method was developed for the analysis of the experimental data. Considerable interest in the details of this method has been expressed by various investigators who encounter similar data analysis problems. This report is written, therefore, to state the problem and its solution as an indication of some of the possibilities computer methods offer the biological investigator having little or no previous experience in the use of computers. The rationale, physiological observations, and results of the study itself have been reported elsewhere <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> so that only those facts necessary to an understanding of the computer solution are noted here.

BackgroundDifferential gene expression patterns in cells of the mammalian brain result in the morphological, connectional, and functional diversity of cells. A wide variety of studies have shown that certain genes are expressed only in specific cell-types. Analysis of cell-type-specific gene expression patterns can provide insights into the relationship between genes, connectivity, brain regions, and cell-types. However, automated methods for identifying cell-type-specific genes are lacking to date.ResultsHere, we describe a set of computational methods for identifying cell-type-specific genes in the mouse brain by automated image computing of in situ hybridization (ISH) expression patterns. We applied invariant image feature descriptors to capture local gene expression information from cellular-resolution ISH images. We then built image-level representations by applying vector quantization on the image descriptors. We employed regularized learning methods for classifying genes specifically expressed in different brain cell-types. These methods can also rank image features based on their discriminative power. We used a data set of 2,872 genes from the Allen Brain Atlas in the experiments. Results showed that our methods are predictive of cell-type-specificity of genes. Our classifiers achieved AUC values of approximately 87% when the enrichment level is set to 20. In addition, we showed that the highly-ranked image features captured the relationship between cell-types.ConclusionsOverall, our results showed that automated image computing methods could potentially be used to identify cell-type-specific genes in the mouse brain.