Defining data literacy: An empirical study of data literacy dimensions

Data literacy has become a core component in higher education as it encompasses a range of data skills and the knowledge necessary to deal with data, which are critical in our social and work lives in the advent of big data. Multiple perspectives to define data literacy have emerged from multiple disciplines, including information science, computer science, business, and education. Along with this, there have been efforts to develop a data literacy competency model to enhance our understanding of the required skills for data literacy. But each model has a different focus, context, and target audience – for instance, some efforts are intended to address the data literacy needs of citizens in today’s society because they see data literacy as a life skill, whereas others are intended to define data literacy as one of the essential skills required to perform tasks in a specific career. Although the importance of data literacy is increasingly recognized, there is no consensus about the definition of data literacy. Further, the constituent dimensions of data literacy remain disputed. As such, this presentation will illustrate the preliminary results of a bibliometric analysis of data literacy literatures in recent ten years. Through citation analysis and topic analysis, this study aims to identify the central dimensions of data literacy and develop an integrated model for data literacy

Artificial intelligence approach for tomato detection and mass estimation in precision agriculture

Funding: This study was carried out with the support of “Research Program for Agricultural Science & Technology Development” (Project No: PJ013891012020), National Institute of Agricultural Sciences, Rural Development Administration, Republic of Korea.Application of computer vision and robotics in agriculture requires sufficient knowledge and understanding of the physical properties of the object of interest. Yield monitoring is an example where these properties affect the quantified estimation of yield mass. In this study, we propose an image-processing and artificial intelligence-based system using multi-class detection with instance-wise segmentation of fruits in an image that can further estimate dimensions and mass. We analyze a tomato image dataset with mass and dimension values collected using a calibrated vision system and accurate measuring devices. After successful detection and instance-wise segmentation, we extract the real-world dimensions of the fruit. Our characterization results exhibited a significantly high correlation between dimensions and mass, indicating that artificial intelligence algorithms can effectively capture this complex physical relation to estimate the final mass. We also compare different artificial intelligence algorithms to show that the computed mass agrees well with the actual mass. Detection and segmentation results show an average mask intersection over union of 96.05%, mean average precision of 92.28%, detection accuracy of 99.02%, and precision of 99.7%. The mean absolute percentage error for mass estimation was 7.09 for 77 test samples using a bagged ensemble tree regressor. This approach could be applied to other computer vision and robotic applications such as sizing and packaging systems and automated harvesting or to other measuring instruments.Publisher PDFPeer reviewe

Defining Data Literacy: An Empirical Study of Data Literacy Dimension

Poster on an analysis of publications from 2002-2021 on data literacy to identify relevant topics and trends. This is a part of preliminary work done to support a proposal for an Institute of Museum and Library Services (IMLS) grant. It was presented at the 2021 Association for Library and Information Science Education (ALISE) Annual Conference held virtually September 20-24, 2021

All-dielectric metasurface imaging platform applicable to laser scanning microscopy with enhanced axial resolution and wavelength selection

Metasurfaces composed of artificially fabricated nano-sized structures have shown extraordinary potential for the precise control of light. Here, we demonstrate for the first time, a metasurface application to reduce the axial size of the point spread function in laser scanning microscopy. The all-dielectric metasurface has wavelength selectivity over the whole visible range, and confinement of the excitation point spread function of the electromagnetic field. These two unique features allow the metasurface to be applied to laser scanning microscope systems. Numerical and experimental demonstrations of the proposed all-dielectric metasurface are reported, showing sharp implicit spectral filtering in the visible range and enhanced axial confinement by observing actin filaments in NIH3T3 cells. We believe that our approach can provide a useful insight on the practicality of using metasurfaces as an imaging platform. (C) 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement11Ysciescopu

Hyperprogressive Disease during Anti-PD-1 (PDCD1) / PD-L1 (CD274) Therapy: A Systematic Review and Meta-Analysis

Hyperprogressive disease (HPD) is a recently acknowledged pattern of rapid tumor progression after the initiation of immune checkpoint inhibitors. HPD has been observed across various types of tumors and has been associated with poor survival. We performed a meta-analysis to identify baseline (i.e., prior to programmed cell death 1 [PD-1, PDCD1] / programmed cell death 1 ligand 1 [PD-L1, CD274] inhibitor therapy) patient factors associated with risks of developing HPD during PD-1/PD-L1 inhibitor therapy. We searched eight databases until 6 June 2019. We calculated the summary odds ratio (OR) and its 95% confidence interval (CI) using the random-effects model and explored between-study heterogeneity and small-study effects. A total of nine articles was eligible (217 HPD cases, 1519 cancer patients) for meta-analysis. There was no standard definition of HPD, and the incidence of HPD ranged from 1 to 30%. We identified twenty-three baseline patient factors, of which five factors were statistically significantly associated with HPD. These were serum lactate dehydrogenase (LDH) above the upper normal limit (OR = 1.89, 95% CI = 1.02-3.49, p = 0.043), more than two metastatic sites (OR = 1.86, 1.34-2.57, p < 0.001), liver metastases (OR = 3.33, 2.07-5.34, p < 0.001), Royal Marsden Hospital prognostic score of 2 or above (OR = 3.33, 1.96-5.66, p < 0.001), and positive PD-L1 expression status that was inversely correlated with HPD (OR = 0.60, 0.36-0.99, p = 0.044). Between-study heterogeneity was low. Evidence of small-study effect was found in one association (PD-L1 expression). Subset analyses of patients with non-small cell lung cancer showed similar results. Future studies are warranted to identify underlying molecular mechanisms and to test their roles as predictive biomarkers of HPD

Efficient Pedestrian Detection at Nighttime Using a Thermal Camera

Most of the commercial nighttime pedestrian detection (PD) methods reported previously utilized the histogram of oriented gradient (HOG) or the local binary pattern (LBP) as the feature and the support vector machine (SVM) as the classifier using thermal camera images. In this paper, we propose a new feature called the thermal-position-intensity-histogram of oriented gradient (TPIHOG or T π HOG) and developed a new combination of the T π HOG and the additive kernel SVM (AKSVM) for efficient nighttime pedestrian detection. The proposed T π HOG includes detailed information on gradient location; therefore, it has more distinctive power than the HOG. The AKSVM performs better than the linear SVM in terms of detection performance, while it is much faster than other kernel SVMs. The combined T π HOG-AKSVM showed effective nighttime PD performance with fast computational time. The proposed method was experimentally tested with the KAIST pedestrian dataset and showed better performance compared with other conventional methods

The functional and cosmetic aspects of alar batten graft

Nasal valve collapse due to lateral nasal wall incompetence hinders the quality of life of patients. It can also cause cosmetic problems, leading patients to seek surgical treatment. Nasal valve collapse is caused by an interruption of the systematic relationships between the septal cartilage, inferior turbinate, and upper and lower lateral cartilage. In particular, when solving problems with the upper and lower later cartilage, the alar batten graft is considered an important surgical method. Herein, we present an example of patients with lateral nasal wall incompetence and the detailed surgical method of the applied alar batten graft to solve this problem. There were no significant adverse effects, and the operator and the patients were satisfied with the functional and cosmetic results