Learning Growth Research Articles

Research hotspots and development trends of model and modelling education research: Bibliometric analysis based on CiteSpace

Model-based learning and teaching are vital for addressing real-world challenges and are gaining research traction. This study, employing CiteSpace, analyses 583 articles, uncovering trends in authors, regions, and highly cited documents. Noteworthy focuses include learning achievements, technical support, and teaching approaches. Keyword analysis emphasises thinking cultivation and interdisciplinary integration. The study discusses current and future developments in modelling and modelling education research, particularly in learning evaluation and teacher professional development. Offering an international perspective, this analysis provides stakeholders with valuable insights. In summary, model-based learning's growth and influence are evident in the identified trends and future directions, guiding the field toward effective teaching strategies and solving complex problems. This research contributes to the broader understanding of modelling education's dynamics, facilitating informed decision-making for educators and policymakers.

Deep Scene Flow Learning: From 2D Images to 3D Point Clouds.

Scene flow describes the 3D motion in a scene. It can be modeled as a single task or as a composite of the auxiliary tasks of depth, camera motion, and optical flow estimation. Deep learning's emergence in recent years has broadened the horizons for new methodologies in estimating these tasks, either as separate tasks or as joint tasks to reconstruct the scene flow. The sequence of images that are either synthesized or captured by a camera is used as input for these methods, which face the challenge of dealing with various situations in images to provide the most accurate motion, such as image quality. Nowadays, images have been superseded by point clouds, which provide 3D information, thereby expediting and enhancing the estimated motion. In this paper, we dig deeply into scene flow estimation in the deep learning era. We provide a comprehensive overview of the important topics regarding both image-based and point-cloud-based methods. In addition, we cover the methodologies for each category, highlighting the network architecture. Furthermore, we provide a comparison between these methods in terms of performance and efficiency. Finally, we conclude this survey with insights and discussions on the open issues and future research directions.

Active learning for data efficient semantic segmentation of canine bones in radiographs.

X-ray bone semantic segmentation is one crucial task in medical imaging. Due to deep learning's emergence, it was possible to build high-precision models. However, these models require a large quantity of annotated data. Furthermore, semantic segmentation requires pixel-wise labeling, thus being a highly time-consuming task. In the case of hip joints, there is still a need for increased anatomic knowledge due to the intrinsic nature of the femur and acetabulum. Active learning aims to maximize the model's performance with the least possible amount of data. In this work, we propose and compare the use of different queries, including uncertainty and diversity-based queries. Our results show that the proposed methods permit state-of-the-art performance using only 81.02% of the data, with time complexity.

How Do Competition and Capabilities Influence Managerial Performance?

The environment in which an organization operates namely, new entrants, substitute products, abundance of competitors with diverse strategies, lack of differentiation, origins and personalities (Porter, 1979), dictates how the company should plan its internal business processes as well as its learning and growth development to satisfy customers and thus achieve enterprise performance. For this study, we collected data until February 2009 by surveying American and Canadian companies using the Dunn and Bradstreet database. We used OLS regression to test hypothesized relation between the variables. We find that there is a positive relationship between Learning & growth (LG) and Technological dynamism (TD); and LG and Environmental competitiveness (EC); but a weak positive relationship between LG and Internal business process (IBP). These results support our argument that firms that confront external environment need to invest in IBP in order to succeed. We found that LG has a strong positive relationship with EC but not significant relationship with TD. While LG has a strong positive influence on Customer satisfaction (CS), IBP shows a very weak positive relationship but Financial Performance (FP) has a strong positive relationship with EC, IBP, and CS, but a relatively weak positive relationship with TD and LG. This means that a competitive environment with fast moving technology influences firms to invest in internal processes and human resources which in turn creates customer satisfaction thus have better operating results. Before the results could be generalized, more research is warranted.