Data Generation Approach Research Articles

From bedside to benchmarks: A physician-scientist workforce dashboard for biomedical research institutions.

There is growing concern about the declining physician-scientist workforce. NIH recently provided a national dashboard describing the biomedical research workforce, but local strategies are needed. We used curated local and national data to develop a workforce dashboard. Many trends at Oregon Health & Science University (OHSU) were similar to those nationally, such as the increasing percentage of Research Project Grant (RPG)-holding PhDs and the aging RPG population, but differences were also apparent. At OHSU, nearly ¾ of physician-scientist RPGs hold MD-only, compared with nationally, where nearly half are MD/PhD. OHSU also lags in the percentage of RPGs held by women physician-scientists. Our analysis also permitted us to gain a more complete picture of research funding that has been done nationally. We used these data to develop a dashboard that allows our institution to develop policies to increase the numbers of physician-scientists. The data generation approaches and dashboard are likely to be useful at other institutions, as well.

Comparative Study Between Flower Pollination Algorithm and Cuckoo Search Algorithm for t -Way Test Data Generation

T-way testing is a sampling approach for test data generation. Recently, adapting meta-heuristic algorithms for t-way testing is very attractive in order to find a minimum subset of test data that can test a system overall. As a consequence, several meta-heuristic algorithms have been used as the basis of t-way strategies. In order to guide software tester (and engineers in general) to select the best algorithm for the problem at hand, there is a need to evaluate and benchmark the performance of each strategy against common case studies. This paper presents a comparative study between two meta-heuristic strategies for t-way test data generation: Flower Pollination Algorithm (FPA) and Cuckoo Search (CS). Our experiments have performed on a real-world case study. Experimental results demonstrate that FPA appears to produce better results in most of the test cases in term of test suite size and convergence rate owing to its ability for controlling local and global search.

Using Artificial Bee Colony Algorithm for Test Data Generation and Path Testing Coverage

Software testing is a significant stage in software development lifecycle. There are different sorts of' structural software testing methodologies that may be generally utilized and moved forward through enhancing the traverse of all of the conceivable code software paths. The interest for automating data testing is growing; however, manual testing strategies utilization would be expensive and costly. Heuristic measure is being applied to; detect how better the result might be (solution fitness); result development mechanism; and suitableness criteria with stop search mechanism depending on wither a result is found or not. Testing experience could be exploited for finding a solution to the optimization problem by utilizing Meta heuristic procedures. The presented approach might have been tested for five programs to demonstrate the distinctive tests issues. This paper proposes an automatic test data generation approach that use artificial bee colony algorithm for software structural testing, particularly, path testing. This is brought on moving the centralization of data generation testing, as opposed to the automation of the whole testing operation. It executes artificial bee colony algorithm by creating testing data for the criteria of path coverage testing, and then applying the strategy to a group of test programs.

Learning to restore deteriorated line drawing

We propose a fully automatic approach to restore aged old line drawings. We decompose the task into two subtasks: the line extraction subtask, which aims to extract line fragments and remove the paper texture background, and the restoration subtask, which fills in possible gaps and deterioration of the lines to produce a clean line drawing. Our approach is based on a convolutional neural network that consists of two sub-networks corresponding to the two subtasks. They are trained as part of a single framework in an end-to-end fashion. We also introduce a new dataset consisting of manually annotated sketches by Leonardo da Vinci which, in combination with a synthetic data generation approach, allows training the network to restore deteriorated line drawings. We evaluate our method on challenging 500-year-old sketches and compare with existing approaches with a user study, in which it is found that our approach is preferred 72.7% of the time.

Effective deep learning training for single-image super-resolution in endomicroscopy exploiting video-registration-based reconstruction

PurposeProbe-based confocal laser endomicroscopy (pCLE) is a recent imaging modality that allows performing in vivo optical biopsies. The design of pCLE hardware, and its reliance on an optical fibre bundle, fundamentally limits the image quality with a few tens of thousands fibres, each acting as the equivalent of a single-pixel detector, assembled into a single fibre bundle. Video registration techniques can be used to estimate high-resolution (HR) images by exploiting the temporal information contained in a sequence of low-resolution (LR) images. However, the alignment of LR frames, required for the fusion, is computationally demanding and prone to artefacts.MethodsIn this work, we propose a novel synthetic data generation approach to train exemplar-based Deep Neural Networks (DNNs). HR pCLE images with enhanced quality are recovered by the models trained on pairs of estimated HR images (generated by the video registration algorithm) and realistic synthetic LR images. Performance of three different state-of-the-art DNNs techniques were analysed on a Smart Atlas database of 8806 images from 238 pCLE video sequences. The results were validated through an extensive image quality assessment that takes into account different quality scores, including a Mean Opinion Score (MOS).ResultsResults indicate that the proposed solution produces an effective improvement in the quality of the obtained reconstructed image.ConclusionThe proposed training strategy and associated DNNs allows us to perform convincing super-resolution of pCLE images.

Approach for a Systematic Energy Data Generation and Evaluation

Due to significantly rising energy prices, companies are urged to improve the energy efficiency of their production. The basis for implementing the intended savings are energy data analyses, which are only possible with a detailed and source-specific acquisition and analysis of energy data. However, the need for a detailed and specific analysis is competing with the requirement to keep the examination's time and effort, as well as the costs involved, as low as possible. To resolve this conflict, this paper proposes a method for analyzing energy data of machine tools, which allows for an economical and user-friendly execution of the sought-after analysis.

Artificially generated near-infrared spectral data for classification purposes

Near-Infrared Spectroscopy has became a widely used analytical technique in different research fields due to its non-destructiveness and low-cost. The spectra are rich in information but extremely complex, therefore their analysis necessitates the use of advanced statistical methods. The empirical properties of the statistical methods can be assessed using artificially generated data that resemble real Near-Infrared Spectroscopy.In this paper we propose a new data generation approach (ABS) that takes into account the theoretical knowledge about the near-infrared absorption of the functional groups. The proposed method is compared to real data and to a simpler data generation method, which simulates the data from a multivariate normal distribution whose parameters are estimated from real data (MVNorig). The comparison between real data and the data generation approaches is based on a class-imbalanced classification problem using linear discriminant analysis, classification trees and support vector machines.Both simulation approaches generated spectra with a good resemblance to real data, MVNorig performing slightly better than ABS; using real and simulated data we would have reached similar conclusions about the class-imbalance problem in classification.Both methods can be used to artificially generate near-infrared spectra. The method based on multivariate normal distribution can be used when a large number of real data spectra is available, while the appropriateness of the results of the ABS method depend on the exactness of functional group near-infrared absorption knowledge.

Handling a Small Dataset Problem in Prediction Model by employ Artificial Data Generation Approach: A Review

The emerging era of big data for past few years has led to large and complex data which needed faster and better decision making. However, the small dataset problems still arise in a certain area which causes analysis and decision are hard to make. In order to build a prediction model, a large sample is required as a training sample of the model. Small dataset is insufficient to produce an accurate prediction model. This paper will review an artificial data generation approach as one of the solution to solve the small dataset problem.

Test Data Generation Approaches for Structural Testing and Automatic Programming Assessment: A Systematic Literature Review

Test Data Generation Approaches for Structural Testing and Automatic Programming Assessment: A Systematic Literature Review

Creating sustainable physical sciences learning environments: A case for decolonised and transformative learning

There is an urgent need for transformation and decolonisation of teaching and learning of physical sciences. This need is evidenced by, among other factors, the alarming rate at which learner enrolment in physical sciences and science education, in general, is decreasing. Central to these causes is apparent, persistent below-expectation learner performance in science education, which, in turn, causes scepticism about the quality of teaching and learning, and questions about the quality of support given to teachers to sustain the required level of learner performance. Thus, decolonisation and transformation of teaching and learning should persistently pursue meaningful and functional knowledge creation. To this end, service-learning projects for teaching and learning physical sciences hold promise. The main reason for this consideration resides in the empowering capacity and resultant decolonising and transformative nature of the created knowledge. Thus, using service-learning projects to create knowledge that is meaningful and functional is equivalent to creating sustainable physical sciences learning environments. Bricolage’s principles of multiple perspectives and multiple theories served as a useful lens for scrutinising the diverse knowledge of the participants. Van Dijk’s socio-cognitive critical discourse analysis was pivotal for analysing, interpreting and making sense of participants’ prevalent knowledge and experiences. The principles of participatory action research and free attitude interviews were applied as an approach and technique for data generation. The major finding suggests that using service learning projects to create sustainable (physical sciences) learning environments, contributes substantially to decolonising and transforming teaching and learning.

MicroRNA and Transcription Factor Gene Regulatory Network Analysis Reveals Key Regulatory Elements Associated with Prostate Cancer Progression.

Technological and methodological advances in multi-omics data generation and integration approaches help elucidate genetic features of complex biological traits and diseases such as prostate cancer. Due to its heterogeneity, the identification of key functional components involved in the regulation and progression of prostate cancer is a methodological challenge. In this study, we identified key regulatory interactions responsible for primary to metastasis transitions in prostate cancer using network inference approaches by integrating patient derived transcriptomic and miRomics data into gene/miRNA/transcription factor regulatory networks. One such network was derived for each of the clinical states of prostate cancer based on differentially expressed and significantly correlated gene, miRNA and TF pairs from the patient data. We identified key elements of each network using a network analysis approach and validated our results using patient survival analysis. We observed that HOXD10, BCL2 and PGR are the most important factors affected in primary prostate samples, whereas, in the metastatic state, STAT3, JUN and JUNB are playing a central role. Benefiting integrative networks our analysis suggests that some of these molecules were targeted by several overexpressed miRNAs which may have a major effect on the dysregulation of these molecules. For example, in the metastatic tumors five miRNAs (miR-671-5p, miR-665, miR-663, miR-512-3p and miR-371-5p) are mainly responsible for the dysregulation of STAT3 and hence can provide an opportunity for early detection of metastasis and development of alternative therapeutic approaches. Our findings deliver new details on key functional components in prostate cancer progression and provide opportunities for the development of alternative therapeutic approaches.

Piloting, evaluating and modifying ‘purposeful rounding’ an evidenced based approach to nursing care

Background: Purposeful rounding’ or ‘intentional rounding’ is a nursing led systematic evidenced-based nursing intervention designed to anticipate and address hospitalised patient needs (Fabry, 2015; Forde-Johnston, 2014; Harrington et al., 2013). ‘Purposeful rounding’ is reported to enhance patient safety by reducing risk, particularly risk related to ‘falls’, increase authentic regular interaction between nurses and patients and improve patient satisfaction with nursing care (Harrington et al., 2013; Hicks, 2015; McLeod &Telzlaff, 2015). This approach to care involves the judicious use of tools that assist nurses monitor patient wellbeing and limit preventable risk (Fabry, 2015). Objective: The introduction of ‘purposeful rounding’, as a technique to enhance health care outcomes was implemented at a St John of God acute care hospital in 2014–15 on a single ward as a pilot study and subsequently at a second St John of God acute care hospital in 2015–16 in all wards. Methods: A qualitative evaluation of the single ward pilot was completed following organisational human ethics approval that used interviews with nurses as the primary data collection technique. A mixed methods study design was conceptualised and approved by the organisations human ethics committee to evaluate the second hospitals adoption of ‘purposeful rounding’. Data generation approaches included non-participant observation, interviews and focus groups with nursing staff, audits of documentation and patient satisfaction and staff surveys. The decision to use a broader range of data collection techniques was based on insights gained from the pilot that highlighted environmental, models of care, nursing skillmix, documentation and time management issues as critical factors influencing nurses’ capacity to comply with the ‘purposeful rounding’ protocol. Results: There is no doubt that ‘purposeful rounding’ is useful. Our experience demonstrates patients feel safer when they see nursing staff regularly and that ‘falls rates’ reduce. Discussion/Conclusion: While the findings of our study reflect outcomes reported in the literature the contribution our research makes is the recognition that modifications to standardised practice protocols must be made for local conditions and these can’t be realised until pilot studies are undertaken to isolate changes that are required to ensure ‘best fit’. Translating evidence to practice must always be considerate of the local environment and resources.

A multi-objective test data generation approach for mutation testing of feature models

Mutation approaches have been recently applied for feature testing of Software Product Lines (SPLs). The idea is to select products, associated to mutation operators that describe possible faults in the Feature Model (FM). In this way, the operators and mutation score can be used to evaluate and generate a test set, that is a set of SPL products to be tested. However, the generation of test sets to kill all the mutants with a reduced, possible minimum, number of products is a complex task. To help in this task, in a previous work, we introduced a multi-objective approach that includes a representation to the problem, search operators, and two objectives related to the number of test cases and dead mutants. The proposed approach was implemented and evaluated with three representative multi-objective and evolutionary algorithms: NSGA-II, SPEA2 and IBEA, and obtained promising results. Now in the present paper we extend such an approach to include a third objective: the pairwise coverage. The goal 4 is to reveal other kind of faults not revealed by mutation testing and to improve the efficacy of the generated test sets. Results of new studies are reported, showing that both criteria can be satisfied with a reduced number of products. The approach produces diverse good solutions and different sets of impacting factors can be considered. At the end, the tester can either prioritize one objective, by choosing solutions in the extreme points of the fronts or choose solutions with smaller ED values, according to the testing goals and resources.

An Efficient Test Data Generation Approach for Unit Testing

To ensure the delivery of high-quality software, software testing plays the vital role. One of the major time-consuming and expensive activities in software testing is the generation of test data. Test data generation activity has a strong impact on the effectiveness and efficiency of the whole testing process. In order to reduce the cost and time involved in the process of test data generation, researchers and practitioners have tried to automate it. In literature, many such techniques have been developed and the most commonly used are; Random testing, Symbolic execution and evolutionary testing. In this work, an enhanced and efficient Random test data generation approach is proposed and investigated on a suite of programs and its efficiency is compared with the Genetic algorithm which is an evolutionary approach. The inconsistency of random approach is that it is not capable of generating a specific set or combination of test cases for the program input variables and in search of these effective test cases multiple populations needs to be created that will increase the burden of size . So, in order to remove these inconsistencies from the test suite, it is seeded with a more effective set of test cases through our proposed approach in order to make test suite more granular and limit its size by not generating more populations in search of these effective test cases. In addition to the proposed approach, the classification of test adequacy criteria and issues with random, symbolic execution and genetic algorithm based test data

Automatic test data generation based on reduced adaptive particle swarm optimization algorithm

Software testing aims to search a set of test data in the entire search space to satisfy a certain standard of coverage. Therefore, finding an effective approach for automatic test data generation is a key issue of software testing. This paper proposes a new approach of reduced adaptive particle swarm optimization for generating the test data automatically. First, the approach reduces the particle swarm evolution equations and gets an evolution equation without velocity. Then, the approach makes an adaptive adjustment scheme based on inertia weight for the reduced evolution equation, which is different from the methods that directly act on the particle velocity in the past. The approach directly impacts on the particle position, namely actual problem solution. Next, according to the particle fitness and the particle aggregation degree, the population will be divided into three parts and inertia weight of each part will be designed accordingly. This can balance the search capabilities of algorithm between global and local. Finally, the approach is applied to automatic test data generation. The experiments results show that our approach can enhance convergence speed of algorithm and solve the problems that particle swarm algorithm easily falls into the local optimal solution and has low search accuracy. The experiments results also turn out that our approach can improve the efficiency of generating test data automatically.

Enhancing Teachers’ Professional Curriculum Practice in Sex Education in a Grade Ten Life Orientation Class

This article documents the research processes followed by a coordinating team for the subject of life orientation in a school. The aim is to enhance life orientation teachers’ professional curriculum practice in pedagogy. The focus is on sex education and cultural influences on the behaviour of four hundred and forty school community members in Motheo district. Critical emancipatory research was used as the theoretical framework to guide the research process. Participatory action research was used as an approach for data generation. Critical discourse analysis was applied for the interpretation of, and to arrive at the following findings: the impact of gender roles, age and experience of teachers has a bearing on teaching sex education; cultural beliefs are a barrier to teaching sex education; and the legacy of colonialism and apartheid education has had a profound impact on education in South Africa. DOI: 10.5901/jesr.2014.v4n6p287

An approach for test data generation using program slicing and particle swarm optimization

Heuristic search-based test data generation has a potential higher efficiency in software testing with path covering. However, these approaches are suffered in covering the long and complex path. In this paper, we propose a method for generating test data based on program slicing and particle swarm optimization. With the interest points selected from a target path, we perform a program slicing to remove the statements which are irrelevant to the interest points. Our method simplifies the target path and the actual path to get a better fitness value. After program slices obtained, the population is evolved using particle swarm optimization to improve the efficiency of test data generation.

Second-order data obtained by beta-cyclodextrin complexes: A novel approach for multicomponent analysis with three-way multivariate calibration methods

This research reports the first application of β-cyclodextrin (β-CD) complexes as a new method for generation of three way data, combined with second-order calibration methods for quantification of a binary mixture of caffeic (CA) and vanillic (VA) acids, as model compounds in fruit juices samples. At first, the basic experimental parameters affecting the formation of inclusion complexes between target analytes and β-CD were investigated and optimized. Then under the optimum conditions, parallel factor analysis (PARAFAC) and bilinear least squares/residual bilinearization (BLLS/RBL) were applied for deconvolution of trilinear data to get spectral and concentration profiles of CA and VA as a function of β-CD concentrations. Due to severe concentration profile overlapping between CA and VA in β-CD concentration dimension, PARAFAC could not be successfully applied to the studied samples. So, BLLS/RBL performed better than PARAFAC. The resolution of the model compounds was possible due to differences in the spectral absorbance changes of the β-CD complexes signals of the investigated analytes, opening a new approach for second-order data generation. The proposed method was validated by comparison with a reference method based on high-performance liquid chromatography photodiode array detection (HPLC-PDA), and no significant differences were found between the reference values and the ones obtained with the proposed method. Such a chemometrics-based protocol may be a very promising tool for more analytical applications in real samples monitoring, due to its advantages of simplicity, rapidity, accuracy, sufficient spectral resolution and concentration prediction even in the presence of unknown interferents.

Development of a novel alternating quadrilinear decomposition algorithm for the kinetic analysis of four-way room-temperature phosphorescence data

Four-way room-temperature phosphorescence (RTP) data recorded by following the kinetic evolution of excitation–emission phosphorescence matrices (EEPMs) have been analyzed for the first time by third-order calibration based on parallel factor analysis (PARAFAC), alternating weighted residue constraint quadrilinear decomposition (AWRCQLD) and alternating quadrilinear decomposition (AQLD) algorithms. The AQLD constructed in pseudo-fully stretched matrix forms of quadrilinear model was a new third-order calibration algorithm, which was developed as a direct extension of alternating trilinear decomposition for quadrilinear data. These methodologies were applied to investigate the hydrolysis kinetic of carbaryl even in the presence of an uncalibrated phosphorescence background and comparisons among them were done subsequently. Spectral background drift produced in measured dataset was overcome by means of modeling the drift as an additional component as well as the analyte of interest in the mathematical model. Satisfactory results were obtained for determination of carbaryl in spiked tap water samples. The spectral and kinetic time profiles resolved by these methodologies were in good agreement with experimental observations. The present work successfully faced the difficulty in investigating the hydrolysis kinetic of analyte with the RTP techniques, opening a new approach for third-order data generation and subsequent third-order calibration.

A progressive approach for cross-browser web data generation

A layout engine combines markup language HTML, etc. with formatting information CSS, etc., and displays the combined contents on the screen. It is commonly used for many existing web development applications and web browsers. Especially, web development applications use layout engine to confirm the impact on the layout of web editing results to achieve what you see is what you get WYSIWYG. At present, different layout engines are adopted by various web development applications and web browsers. As there is not a uniform algorithm adopted by layout engine, the results displayed by web development applications are not necessarily consistent with the display on the web browsers. In addition, the results displayed by web development applications depend on layout engines. Hence, it is difficult to edit the common web contents since it cannot objectively reflect the result on web browsers. In this paper, we propose a progressive WYSIWYG solution, by completely separating web layout design from web data editing to generate cross-browser web data. Our proposed approach does not use layout engine, and thus can avoid the disadvantages mentioned above.