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"taxonomie"
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Level of student understanding in solving geometry problems based on taxonomy of SOLO (Structure of Observed Learning Outcomes)
This research aimed to determine and classify the level of student understanding in solving geometry problems with taxonomy of SOLO (Structure of Observed Learning Outcomes). The material been examined was the geometry of planes and spaces. This research was a type of qualitative research conducted by the method of think loud. The research results were: 55% of students had an understanding that was at a multistructural level with indicators that have been able to use some concepts that was correct but not yet right in terms of linking concepts and 39% of students have been able to use a correct concept (unistructural level). Then about 5% of students have an understanding at the relational level that have been able to use some correct concepts and connect them, whereas 1% of students have a level of prestructural understanding. Based on the research results, it can be concluded that levels of student understanding in solving geometry problems are various from pre-structural level until relational level.
Journal Article
An algorithm for labels aggregation in taxonomy-based crowd-labeling
Crowdsourcing provides a convenient solution for many information processing problems that are still hard or even intractable by modern AI techniques, but are relatively simple for many people. However, complete crowdsourcing solution cannot go by without a quality control mechanisms, as the results received from participants are not always reliable. The paper considers taxonomy-based crowd-labeling - a form of crowdsourcing, in which participants label objects with tags, and there exists an explicit taxonomy relation on the set of tags. We propose a method and an algorithm for label aggregation, allowing to estimate the likelihood of the true object label from a set of noisy labels received from the crowd, and to estimate the expected crowd members' accuracy. The proposed method and algorithm can be used in a wide range of crowd-labeling applications (e.g., classification of scientific literature collections, software repositories, etc.).
Journal Article
A survey on semi-supervised learning
Semi-supervised learning is the branch of machine learning concerned with using labelled as well as unlabelled data to perform certain learning tasks. Conceptually situated between supervised and unsupervised learning, it permits harnessing the large amounts of unlabelled data available in many use cases in combination with typically smaller sets of labelled data. In recent years, research in this area has followed the general trends observed in machine learning, with much attention directed at neural network-based models and generative learning. The literature on the topic has also expanded in volume and scope, now encompassing a broad spectrum of theory, algorithms and applications. However, no recent surveys exist to collect and organize this knowledge, impeding the ability of researchers and engineers alike to utilize it. Filling this void, we present an up-to-date overview of semi-supervised learning methods, covering earlier work as well as more recent advances. We focus primarily on semi-supervised classification, where the large majority of semi-supervised learning research takes place. Our survey aims to provide researchers and practitioners new to the field as well as more advanced readers with a solid understanding of the main approaches and algorithms developed over the past two decades, with an emphasis on the most prominent and currently relevant work. Furthermore, we propose a new taxonomy of semi-supervised classification algorithms, which sheds light on the different conceptual and methodological approaches for incorporating unlabelled data into the training process. Lastly, we show how the fundamental assumptions underlying most semi-supervised learning algorithms are closely connected to each other, and how they relate to the well-known semi-supervised clustering assumption.
Journal Article
Emergent simplicity in microbial community assembly
Under natural conditions, bacteria form mixed, interacting communities. Understanding how such communities assemble and stabilize is important in a range of contexts, from biotechnological applications to what happens in our guts. Goldford et al. sampled the microbial communities from soil and plants containing hundreds to thousands of sequence variants. The organisms were passaged after culture in low concentrations of single carbon sources and were cross-fed with each other's metabolites; then, the resulting communities were sequenced using 16S ribosomal RNA, and the outcomes were modeled mathematically. The mix of species that survived under steady conditions converged reproducibly to reflect the experimentally imposed conditions rather than the mix of species initially inoculated—although at coarse phylogenetic levels, taxonomic patterns persisted. Science , this issue p. 469 Microbial communities assemble into similar family-level compositions containing divergent genera and species. A major unresolved question in microbiome research is whether the complex taxonomic architectures observed in surveys of natural communities can be explained and predicted by fundamental, quantitative principles. Bridging theory and experiment is hampered by the multiplicity of ecological processes that simultaneously affect community assembly in natural ecosystems. We addressed this challenge by monitoring the assembly of hundreds of soil- and plant-derived microbiomes in well-controlled minimal synthetic media. Both the community-level function and the coarse-grained taxonomy of the resulting communities are highly predictable and governed by nutrient availability, despite substantial species variability. By generalizing classical ecological models to include widespread nonspecific cross-feeding, we show that these features are all emergent properties of the assembly of large microbial communities, explaining their ubiquity in natural microbiomes.
Journal Article
Practical experience of using LMS Moodle in personnel training for machine-building enterprises
The article describes the experience of conducting classes and monitoring knowledge on the LMS Moodle online platform for full-time students at the university when studying one of the technical disciplines. The conditions for organizing test control of students' knowledge on theoretical topics and practical tasks, which give an objective picture of students' knowledge, are outlined The analysis of the results of the knowledge control for various types of tasks was carried out. As a consequence the conclusion was made about the possibility of conducting certain types of classes in a distance learning format. Assessment of the learning results was carried out according to Bloom's taxonomy. The analysis and conclusions made by the authors will be useful to teachers of secondary and higher educational institutions using distance learning, organizing test control and independent work of students on online platforms with a mixed form of student learning.
Journal Article
Decoupling function and taxonomy in the global ocean microbiome
Microbial metabolism powers biogeochemical cycling in Earth's ecosystems. The taxonomic composition of microbial communities varies substantially between environments, but the ecological causes of this variation remain largely unknown. We analyzed taxonomic and functional community profiles to determine the factors that shape marine bacterial and archaeal communities across the global ocean. By classifying >30,000 marine microorganisms into metabolic functional groups, we were able to disentangle functional from taxonomic community variation. We find that environmental conditions strongly influence the distribution of functional groups in marine microbial communities by shaping metabolic niches, but only weakly influence taxonomic composition within individual functional groups. Hence, functional structure and composition within functional groups constitute complementary and roughly independent \"axes of variation\" shaped by markedly different processes.
Journal Article
Impacts of historical warming on marine fisheries production
Climate change is altering habitats for marine fishes and invertebrates, but the net effect of these changes on potential food production is unknown. We used temperature-dependent population models to measure the influence of warming on the productivity of 235 populations of 124 species in 38 ecoregions. Some populations responded significantly positively (n = 9 populations) and others responded significantly negatively (n = 19 populations) to warming, with the direction and magnitude of the response explained by ecoregion, taxonomy, life history, and exploitation history. Hindcasts indicate that the maximum sustainable yield of the evaluated populations decreased by 4.1% from 1930 to 2010, with five ecoregions experiencing losses of 15 to 35%. Outcomes of fisheries management—including long-term food provisioning—will be improved by accounting for changing productivity in a warmer ocean.
Journal Article
An effort to train the biological computation skill and teach animal phenetic taxonomy to pre-service biology teacher
Computational biology skills for studying phenetic taxonomy is inseparable from learning outcomes of Animal Systematics Course. Pre-service biology teachers are expected to have computational biology skills, which can support further study in bioinformatics. This study was aimed to train computational biology skills and evaluate learning outcomes of phenetic taxonomy material. Phenetic taxonomy practicum was held online and assignments were given as mini-projects. Indicators of biological computation skills were evaluated using ntsys 2.2 software and analysis of resulting dendograms based on synapomorphy, automorphy, and apomorphy. Respondents consisted of three classes contained 84 students who programmed Animal Systematics Course. Computational biology skills were quantified based on self-assessment questionnaire while learning outcomes were evaluated based on mini-project assessment. Data were analyzed using descriptive quantitative method. Results indicated that mastery of computational biology for phenetic taxonomy was very good, as supported by students' ability to use Ntsys software of 86.04%, dendogram analysis of 83.33% or categorized as good. In addition, learning outcomes of phenetic taxonomy were classified as good with average score of 77.7 ± 4.17. Evaluation of qualitative assessment data showed that computational biology skills supports the development of higher-order thinking skills (data synthesis, analysis, and evaluation) of pre-service biology teachers.
Journal Article
Interoperability in Internet of Things: Taxonomies and Open Challenges
In the last few years, many smart objects found in the physical world are interconnected and communicate through the existing internet infrastructure which creates a global network infrastructure called the Internet of Things (IoT). Research has shown a substantial development of solutions for a wide range of devices and IoT platforms over the past 6-7 years. However, each solution provides its own IoT infrastructure, devices, APIs, and data formats leading to interoperability issues. Such interoperability issues are the consequence of many critical issues such as vendor lock-in, impossibility to develop IoT application exposing cross-platform, and/or cross-domain, difficulty in plugging non-interoperable IoT devices into different IoT platforms, and ultimately prevents the emergence of IoT technology at a large-scale. To enable seamless resource sharing between different IoT vendors, efforts by several academia, industry, and standardization bodies have emerged to help IoT interoperability, i.e., the ability for multiple IoT platforms from different vendors to work together. This paper performs a comprehensive survey on the state-of-the-art solutions for facilitating interoperability between different IoT platforms. Also, the key challenges in this topic is presented.
Journal Article