Search results for "LEARNING"

Emerging Joint Expertise? Multiagency Collaboration Described in Local Integration Programmes in Finland

2016

Multiagency collaboration is seen as an essential way of working to promote the two-way integration of newcomers and a receiving society. The term multiagency collaboration underlines the diversity of actors in cooperation. Cross-sectorial networks are mentioned in higher strategies as well as in the local programmes or plans for action. But how is multiagency work structured at the local level? This article looks at the examples of multiagency collaboration in the written documents of local integration programmes in the Finnish context. The examples are chosen from different areas. It seems that collaboration is widely emphasized as a goal or a working method. Whereas expertise in integrat…

Social Competence and Moderate to Vigorous Physical Activity of School-Aged Children through a Creative Physical Education Intervention

2019

Traditional school physical education focuses on physical skills or strategies with an expectation that learning these skills lead to healthier lifestyle outside physical education classes, while children’s overall moderate to vigorous physical activity (MVPA) is widely decreasing. Creative Physical Education (CPE) understands physical education more holistically, as the central pedagogical element of movement is social learning. The current study examined the development of social competence in school physical education (PE) and total moderate to vigorous physical activity (MVPA) participation through a CPE-based intervention. Participants were 363 (177 intervention, 186 control) children …

Converging Perspectives in the LESLLA Context

2013

There has been a surge in Low Educated Second Language and Literacy Acquisition (LESLLA) learners in adult language programs. In response to the growth of this learner population in language classes, there has been increased interest in the professionalization of the field of adult education specific to work with LESLLA learners. As researcher and practitioner awareness and understanding of the LESLLA context continues to expand, necessary and qualitative transformations of second language (L2) teaching and L2 teacher education are taking place. This article provides a glimpse into a larger ethnographic case study that explores the teaching worlds of two LESLLA teachers working in community…

PREDICTIVE MODELS BASED ON RADIOMICS AND MACHINE LEARNING FOR LUNG CANCER RADIOTHERAPY DATA ANALYSIS

2020

Updating strategies for distance based classification model with recursive least squares

2022

Abstract. The idea is to create a self-learning Minimal Learning Machine (MLM) model that is computationally efficient, easy to implement and performs with high accuracy. The study has two hypotheses. Experiment A examines the possibilities of introducing new classes with Recursive Least Squares (RLS) updates for the pre-trained self learning-MLM model. The idea of experiment B is to simulate the push broom spectral imagers working principles, update and test the model based on a stream of pixel spectrum lines on a continuous scanning process. Experiment B aims to train the model with a significantly small amount of labelled reference points and update it continuously with (RLS) to reach ma…

Investigating Novice Developers’ Code Commenting Trends Using Machine Learning Techniques

2023

Code comments are considered an efficient way to document the functionality of a particular block of code. Code commenting is a common practice among developers to explain the purpose of the code in order to improve code comprehension and readability. Researchers investigated the effect of code comments on software development tasks and demonstrated the use of comments in several ways, including maintenance, reusability, bug detection, etc. Given the importance of code comments, it becomes vital for novice developers to brush up on their code commenting skills. In this study, we initially investigated what types of comments novice students document in their source code and further categoriz…

What makes segmentation good? A case study in boreal forest habitat mapping

2013

Segmentation goodness evaluation is a set of approaches meant for deciding which segmentation is good. In this study, we tested different supervised segmentation evaluation measures and visual interpretation in the case of boreal forest habitat mapping in Southern Finland. The data used were WorldView-2 satellite imagery, a lidar digital elevation model (DEM), and a canopy height model (CHM) in 2 m resolution. The segmentation methods tested were the fractal net evolution approach (FNEA) and IDRISI watershed segmentation. Overall, 252 different segmentation methods, layers, and parameter combinations were tested. We also used eight different habitat delineations as reference polygons agains…

Automatic image‐based identification and biomass estimation of invertebrates

2020

Understanding how biological communities respond to environmental changes is a key challenge in ecology and ecosystem management. The apparent decline of insect populations necessitates more biomonitoring but the time-consuming sorting and expert-based identification of taxa pose strong limitations on how many insect samples can be processed. In turn, this affects the scale of efforts to map and monitor invertebrate diversity altogether. Given recent advances in computer vision, we propose to enhance the standard human expert-based identification approach involving manual sorting and identification with an automatic image-based technology. We describe a robot-enabled image-based identificat…

The Truth is Out There : Focusing on Smaller to Guess Bigger in Image Classification

2023

In Artificial Intelligence (AI) in general and in Machine Learning (ML) in particular, which are important and integral components of modern Industry 4.0, we often deal with uncertainty, e.g., lack of complete information about the objects we are classifying, recognizing, diagnosing, etc. Traditionally, uncertainty is considered to be a problem especially in the responsible use of AI and ML tools in the smart manufacturing domain. However, in this study, we aim not to fight with but rather to benefit from the uncertainty to improve the classification performance in supervised ML. Our objective is a kind of uncertainty-driven technique to improve the performance of Convolutional Neural Netwo…

Semantics of Voids within Data: Ignorance-Aware Machine Learning

2021

Operating with ignorance is an important concern of geographical information science when the objective is to discover knowledge from the imperfect spatial data. Data mining (driven by knowledge discovery tools) is about processing available (observed, known, and understood) samples of data aiming to build a model (e.g., a classifier) to handle data samples that are not yet observed, known, or understood. These tools traditionally take semantically labeled samples of the available data (known facts) as an input for learning. We want to challenge the indispensability of this approach, and we suggest considering the things the other way around. What if the task would be as follows: how to buil…