A data analysis method for software performance prediction

System-level designers typically rely on white-box detailed descriptions of embedded systems in order to perform their design choices and optimisations. The white-box approach assumes that the complexity of the system can be managed by decomposing the global system into a set of simple and well-described components. This assumption is generally not true when non-linear interactions happen between the different subsystems. This paper explores, as an alternative, a black-box modelling methodology based on intelligent data analysis in order to support system-level designers in characterising the performance of embedded applications. The rationale behind this approach is that nowadays the designer can easily store large amount of data related to the system, and consequently discover relevant information by analysing them in a black-box fashion. As a case study, we address the performance modelling of software applications running on an embedded microprocessor. We introduce a data analysis method which, on the basis of a high-level characterisation of the software functionality and the hardware architecture, is able to predict the number of execution cycles on a embedded processor. We propose the adoption of a local learning technique (lazy learning), which proved already to be effective in previous works, to model the unknown input/output relation between the hardware/software parameters of the application and the number of execution cycles. Experiments with standard computational code (sorting, mathematical computation) and with a specific streaming algorithm for MPEG variable length decoding are presented to support this claim.

Findings from knowledge-building and theory-generating qualitative systematic reviews have the potential to help guide policy formation and practice in many disciplines. Unfortunately, this potential is currently hindered by the fact that rigorous data analysis methods have not been consistently used and/or articulated for purposes of conducting these types of reviews. Content analysis is a flexible data analysis method that can be used to conduct qualitative systematic reviews; however, its application in this context has not been fully explicated. Qualitative systematic reviewers who aim to build knowledge and generate theory are urged to adapt content analysis methods to accommodate data that are, by nature, highly organized and contextualized. In addition, they are encouraged to use reflective memoing and diagramming to ensure valid integration, interpretation, and synthesis of findings across studies. Finally, reviewers are advised to clearly and fully explain their data analysis methods in research reports.

This study aims to know the form, function and meaning of Ternate language expressions in Discourse Texts in Outdoor Media. The theory used to describe this research is semantic theory. The method used in this study is the method of data collection, data analysis methods, and data presentation methods. Data collection methods include the method of observation, documentation, and interviews with the guide book instruments, pens, cameras, and recording devices. The data analysis method is a qualitative description. The data in this study are secondary data and primary data. Primary data were obtained through Ternate language speakers, as well as outdoor media in the form of banners (billboards), billboards, and public facilities signboards whose contents contained the Ternate phrase text. Secondary data obtained through references in the form of journals, websites, and books whose reviews are related to the study of this paper. The results of this paper are found in seven forms of dolabololo expression and two forms of poetic expression (poetry). The meaning contained in the expression is the meaning of denotation and connotation meaning. Its function is as a form of advice, innuendo, appreciation, as well as a means of information for the public.

Bu araştırma, Türkiye’de okul öncesi dönem matematik eğitiminde sayı kavramı ile ilgili yazılmış yüksek lisans ve doktora tezlerini belirli değişkenler (tezin türü, yıl, katılımcı, veri toplama aracı, veri analiz yöntemi, birbirine benzer amaçla yazılmış tezler arasında benzerlikler veya farklılıklar) açısından inceleyerek çıkarımlar yapmak amacıyla yapılmıştır. Araştırma da sistematik literatür tarama deseni tercih edilmiştir. Veri analiz yöntemi olarak ise doküman analiz yöntemi kullanılmıştır. Araştırmaya dâhil edilen lisansüstü tezler en çok 2016-2020 tarihleri arasında ve Gazi Üniversitesinde yazılmıştır. Tezlerin katılımcı grubunu en çok okul öncesi dönem çocukları oluştururken yaş dağılımı 60-71 aydır. İncelenen tezlerde çoğunlukla nicel araştırma yöntemi kullanılırken araştırma deseni olarak korelasyonel desen tercih edilmiştir. Veri toplama aracı olarak en çok tanılayıcı testler kullanılmıştır. Lisansüstü tezlerde çoğunlukla tercih edilen veri analiz yöntemi t-testi olmuştur. Araştırmaya dâhil edilen tezlerden birbirine benzer amaçla yazılan 12 adet tez olduğu görülmüştür.

The aim of this research is to examine the influence of the Tri Kaya Parisudha-based discovery learning model on the social attitudes and critical thinking abilities of the science and science subjects of class V students at SD N 4 Banyuasri, both individually and simultaneously. This research is experimental research with a research design, namely One Group Pretest-Posttest Design. The population which is also the sample in this research is the fifth grade students of SD N 4 Banyuasri, totaling 30 students. The independent variable in this research is a discovery learning model based on Tri Kaya Parisudha. Meanwhile, the dependent variables in this research are students' social attitudes and critical thinking abilities. The data collection methods used are observation methods and test methods. The observation method is used to measure social attitudes, while the test method is used to measure students' critical thinking abilities. The data analysis methods used in this research are descriptive data analysis methods and inferential data analysis methods. Descriptive data analysis was carried out to find the mean, median, mode, standard deviation, variance and category of each variable. Meanwhile, the inferential data analysis method uses MANOVA data analysis. Based on the research that has been conducted, it is concluded that there is a significant influence of the Tri Kaya Parisudha-based discovery learning model on the social attitudes and critical thinking abilities of the science and science subjects of class V students at SD N 4 Banyuasri, both individually and simultaneously.

Nonnegative matrix factorization and I-divergence alternating minimization

Comment on “Berkovich nanoindentation of Zr55Cu30Al10Ni5 bulk metallic glass at a constant loading rate”, J. Non-Cryst. Solids, 561 (2021) 120750

Women occupy disadvantaged position despite their immense labor contributions to paid and unpaid activities. This study investigated gender differences pertaining to domestic and agricultural activities. The study employed a cross sectional survey design with a mixed research approach of data collection and analysis. Among 17th kebeles that are found in Yayo district, Witate and Hamuma were selected purposively. By doing so, among the total 2060 husbands and wives who live together, 324 of them were picked randomly by using Raosoft sample size determination formula. Descriptive statistics, independent T-test and chi-square were used as quantitative methods of data analysis were as thematic analysis was used as a qualitative method of data analysis. The study revealed that rural women had higher participation in agricultural activities such as weeding, threshing and storing, marketing and livestock management than men. Chi-square test showed that there were an association between gender and participation in decision making with regard to using agricultural inputs, poultry production and household expenditures. With regard to participation in human capitals such as training on agriculture and rural development, chi-square showed significant gender differences between responding husbands and wives. Therefore, it demands an effective policy intervention and awareness creation to address women’s working conditions.

The aim of this study was to examine the methodological tendencies in the doctorate theses which were prepared in the field of Sports Management in Turkish between 2007 and 2016 and which were open to access in the database of the Council of Higher Education (CHE) National Theses Center. In this context, 111 doctorate theses prepared in the last decade were examined. The study was conducted with the Qualitative Research Method, which is frequently used in social sciences, and the Case Study Method was also made use of in the course of the study. The data obtained in the study were analyzed and interpreted with the Content Analysis Method. According to findings, it was determined that the study method was not mentioned in 92 of the 111 doctorate theses, which were included in the study; the study design was not mentioned in 36 of them; and the sampling method was not mentioned in 46 of them. It was also determined that the Questionnaire Method was used frequently in the doctorate theses that were examined in the context of the study; and the Data analysis methods that were used frequently were the Descriptive and Predictive Analysis Methods. As a result, it is possible to claim that the methodological structures of the doctorate theses are extremely missing and inadequate. In addition, it is clear that the methodological structures of the theses are weak and unclear, and cannot convey the methodological data to the reader in an accurate manner. Right at this point, it may be recommended to consider the doctorate thesis literature on Sports Management again, and certain and meticulous rules are set for future studies in terms of writing styles and methodological structures.

Chemical imaging, such as mass imaging, provides a distribution image of a particular matter and is crucial for analyzing the chemical and physical mechanisms of a sample. However, methods that provide molecular or elemental distribution do not always have sufficiently high spatial resolution to evaluate the nanosized structures in a sample. To address this issue, a multimodal data analysis method was developed by integrating the obtained low spatial resolution chemical images with complementary methods. In this study, the hydrogen distribution of a steel sample was measured using electron stimulated desorption (ESD) and scanning electron microscopy (SEM). ESD provided the time-course images of hydrogen distribution in the steel sample, whereas SEM provided the outline of the steel sample structure. The multimodal images of the same sample were fused, and then all the data were analyzed together to extract detailed physical and chemical information that cannot be observed by only one of the methods. The alignment of the images obtained using different methods was evaluated based on the minimization of each pixel subtraction. Three different data analysis methods, principal component analysis, least absolute shrinkage and selection operator, and autoencoder, are applied to the image fusion dataset of the ESD image and SEM images to help elucidate the hydrogen permeation behavior through the steel structure.

Private universities have devised a strategy to counteract the ongoing competition. Private universities can use the appropriate data analysis method to make higher education management decisions. The goal of this research is to find a new approach to data analysis methods in the form of visualization using the TTM (Temporal Topic Model) method to assist private university management. These findings are the two formulas used to generate time-based visualizations and the Temporal Topic Model per month to visually change news topics related to rankings so that management can decide on marketing strategies and policies that are in relation to public opinion.

MWIR microsphere-lens-enhanced detectors have exhibited an increase in detector sensitivity. One factor affecting microsphere-lens-enhanced MWIR detectors sensitivity is the microsphere lens misalignment as we show in this work. Microsphere lens misalignment can cause reduced spectral responses. Modeling of all physical factors, including detector environment sources causing non-desired artifacts, is tedious and complex (not complete). There is a need for better empirical methods/signal processing/data analysis to try to accurately estimate and extract microsphere misalignment. This paper presents a novel signal processing and data analysis method based on a custom software filtering algorithm that involves a step based process consisting of FTIR boundary computation, wavelet de-noising, sensitivity normalization and quantitative computation to estimate microsphere misalignment directly from microsphere enhanced-lens detector FTIR signal responses. Multiple SLS detector sizes, microsphere-lens materials and microsphere sizes are analyzed. Results show microsphere misalignment, based on the data considered, generally increases with the diameter of the sphere, and decreases with respect to the size of the detector. This calls for new signal processing methods and data analysis methods to automatically detect and correct microsphere misalignment.

Recently, there has been an increasing interest in the development of an approach to characterize the as-built heat loss coefficient (HLC) of buildings based on a combination of on-board monitoring (OBM) and data-driven modeling. OBM is hereby defined as the monitoring of the energy consumption and interior climate of in-use buildings via non-intrusive sensors. The main challenge faced by researchers is the identification of the required input data and the appropriate data analysis techniques to assess the HLC of specific building types, with a certain degree of accuracy and/or within a budget constraint. A wide range of characterization techniques can be imagined, going from simplified steady-state models applied to smart energy meter data, to advanced dynamic analysis models identified on full OBM data sets that are further enriched with geometric info, survey results, or on-site inspections. This paper evaluates the extent to which these techniques result in different HLC estimates. To this end, it performs a sensitivity analysis of the characterization outcome for a case study dwelling. Thirty-five unique input data packages are defined using a tree structure. Subsequently, four different data analysis methods are applied on these sets: the steady-state average, Linear Regression and Energy Signature method, and the dynamic AutoRegressive with eXogenous input model (ARX). In addition to the sensitivity analysis, the paper compares the HLC values determined via OBM characterization to the theoretically calculated value, and explores the factors contributing to the observed discrepancies. The results demonstrate that deviations up to 26.9% can occur on the characterized as-built HLC, depending on the amount of monitoring data and prior information used to establish the interior temperature of the dwelling. The approach used to represent the internal and solar heat gains also proves to have a significant influence on the HLC estimate. The impact of the selected input data is higher than that of the applied data analysis method.

Data analysis methods have been extensively used in active thermography for defect identification. Among them, principal component thermography (PCT) is popular for dimensionality reduction and feature extraction. PCT summarizes the thermal images with a small number of empirical orthogonal functions that better reflect the information of defects. However, PCT does not induce sparsity, which limits the interpretation of PCT results. Recently, sparse PCT (SPCT) has been proposed to provide more interpretable analysis results. However, SPCT does not consider the spatial connectivity between pixels, omitting the fact that a defective region is usually spatially connected. In this article, a novel thermographic data analysis method is proposed to overcome the shortcomings of the existing methods. The proposed method imposes both spatial connectivity and sparsity constraints in PCT. Finally, one case study on an ancient marquetry sample and another on a carbon fiber-reinforced polymer composite illustrate the feasibility of the proposed method.

Researchers conducting longitudinal data analysis in psychology and the behavioral sciences have several statistical methods to choose from, most of which either require specialized software to conduct or advanced knowledge of statistical methods to inform the selection of the correct model options (e.g., correlation structure). One simple alternative to conventional longitudinal data analysis methods is to calculate the area under the curve (AUC) from repeated measures and then use this new variable in one’s model. The present study assessed the relative efficacy of two AUC measures: the AUC with respect to the ground (AUC-g) and the AUC with respect to the increase (AUC-i) in comparison to latent growth curve modeling (LGCM), a popular repeated measures data analysis method. Using data from the ongoing Panel Study of Income Dynamics (PSID), we assessed the effects of four predictor variables on repeated measures of social anxiety, using both the AUC and LGCM. We used the full information maximum likelihood (FIML) method to account for missing data in LGCM and multiple imputation to account for missing data in the calculation of both AUC measures. Extracting parameter estimates from these models, we next conducted Monte Carlo simulations to assess the parameter bias and power (two estimates of performance) of both methods in the same models, with sample sizes ranging from 741 to 50. The results using both AUC measures in the initial models paralleled those of LGCM, particularly with respect to the LGCM baseline. With respect to the simulations, both AUC measures preformed as well or even better than LGCM in all sample sizes assessed. These results suggest that the AUC may be a viable alternative to LGCM, especially for researchers with less access to the specialized software necessary to conduct LGCM.