Proceedings of QG2010: The Third Workshop on Question Generation

These are the peer-reviewed proceedings of "QG2010, The Third Workshop on Question Generation". The workshop included a special track for "QGSTEC2010: The First Question Generation Shared Task and Evaluation Challenge". QG2010 was held as part of The Tenth International Conference on Intelligent Tutoring Systems (ITS2010)

A Comprehensive Review on Machine Learning Based Models for Healthcare Applications

At present, there has been significant progress concerning AI and machine learning, specifically in medical sector. Artificial intelligence refers to computing programmes that replicate and simulate human intelligence, such as an individual's problem-solving capabilities or their capacity for learning. Moreover, machine learning can be considered as a subfield within the broader domain of artificial intelligence. The process automatically identifies and analyses patterns within unprocessed data. The objective of this work is to facilitate researchers in acquiring an extensive knowledge of machine learning and its utilisation within the healthcare domain. This research commences by providing a categorization of machine learning-based methodologies concerning healthcare. In accordance with the taxonomy, we have put forth, machine learning approaches in the healthcare domain are classified according to various factors. These factors include the methods employed for the process of preparing data for analysis, which includes activities such as data cleansing and data compression techniques. Additionally, the strategies for learning are utilised, such as reinforcement learning, semi-supervised learning, supervised learning, and unsupervised learning. are considered. Also, the evaluation approaches employed encompass simulation-based evaluation as well as evaluation of actual use in everyday situations. Lastly, the applications of these ML-based methods in medicine pertain towards diagnosis and treatment. Based on the classification we have put forward; we proceed to examine a selection of research that have been presented in the framework of machine learning applications within the healthcare domain. This review paper serves as a valuable resource for researchers seeking to gain familiarity with the latest research on ML applications concerning medicine. It aids towards the recognition for obstacles and limitations associated with ML in this domain, while also facilitating the identification of potential future research directions

Metalearning-Informed Competence in Children: Implications for Responsible Brain-Inspired Artificial Intelligence

This paper offers a novel conceptual framework comprising four essential cognitive mechanisms that operate concurrently and collaboratively to enable metalearning (knowledge and regulation of learning) strategy implementation in young children. A roadmap incorporating the core mechanisms and the associated strategies is presented as an explanation of the developing brain's remarkable cross-context learning competence. The tetrad of fundamental complementary processes is chosen to collectively represent the bare-bones metalearning architecture that can be extended to artificial intelligence (AI) systems emulating brain-like learning and problem-solving skills. Utilizing the metalearning-enabled young mind as a model for brain-inspired computing, this work further discusses important implications for morally grounded AI.Comment: 27 pages, 3 figure

Proceedings

Proceedings of the Workshop on Annotation and Exploitation of Parallel Corpora AEPC 2010. Editors: Lars Ahrenberg, Jörg Tiedemann and Martin Volk. NEALT Proceedings Series, Vol. 10 (2010), 98 pages. © 2010 The editors and contributors. Published by Northern European Association for Language Technology (NEALT) http://omilia.uio.no/nealt . Electronically published at Tartu University Library (Estonia) http://hdl.handle.net/10062/15893

Creating a flexible learning environment to support the student's self-regulation

https://www.ester.ee/record=b5258092*es

Genetic architecture and predictability of seedling root traits in maize (Zea mays L.)

The maize (Zea mays L.) root system is important for proper growth and productivity of the plant. There is substantial genetic and phenotypic variation for root architecture, which gives opportunity for selection. Root traits, however, have not been used as selection criterion mainly due to the difficulty in measuring, as well as their quantitative mode of inheritance. Studying seedling roots offer an opportunity to study multiple individuals and to enable repeated measurements per year as compared to adult root phenotyping. Here we have evaluated phenotypic and genotypic variation within seedling root traits for two panels of inbred lines. Constructed maize association mapping panels were used within both a candidate gene based and genome-wide based association study to connect putative QTL with seedling root traits. Also, to collect seedling root phenotypes a software program ARIA (Automatic Root Image Analysis) was developed in order to allow for larger scale quantitative studies. A Genome-wide association mapping panel was also used as a training population to predict the performance in relation to total root length at the seedling stage of a large set of maize inbred lines. Candidate gene association analyses revealed several polymorphisms within the Rtcl, Rth3, Rum1, and Rul1 genes associated with seedling root traits. Several nucleotide polymorphisms in Rtcl, Rth3, Rum1, and Rul1 were significantly (P\u3c0.05) associated with seedling root traits in maize suggesting that all four tested genes are involved in the maize root development. We developed a new software framework to capture various traits from a single image of seedling roots based on the mathematical notion of converting images of roots into an equivalent graph. This allows automated querying of multiple traits simply as graph operations. This framework is furthermore extendable to 3D tomography image data. Within a Genome-wide association analysis utilizing both a general linear model and mixed linear model, a GWAS study was conducted identifying 268 marker trait associations (p ≤ 5.3x10-7). Analysis of significant SNP markers for multiple traits showed that several were located within gene models with some SNP markers localized within regions with previously identified root quantitative trait loci. Gene model GRMZM2G153722 located on chromosome 4 contained nine significant markers. This predicted gene is expressed in roots and shoots. Finally a Genomic prediction study successfully predicted extreme groups with regard to TRL were significantly different (p=0.0001). The difference of predicted means for TRL between groups was 145.1 cm, and 118.7 cm for observed means, which were significantly different (p=0.001). The accuracy of predicting the rank 1-200 of the validation population based on TRL, longest to shortest was determined using a Spearman correlation to be ρ=0.55. In conclusion this work exemplifies the vast amount of diversity seen within root architecture even at the seedling stage and lays ground work for future studies two build upon moving forward studying roots

Unifying context with labeled property graph: A pipeline-based system for comprehensive text representation in NLP

Extracting valuable insights from vast amounts of unstructured digital text presents significant challenges across diverse domains. This research addresses this challenge by proposing a novel pipeline-based system that generates domain-agnostic and task-agnostic text representations. The proposed approach leverages labeled property graphs (LPG) to encode contextual information, facilitating the integration of diverse linguistic elements into a unified representation. The proposed system enables efficient graph-based querying and manipulation by addressing the crucial aspect of comprehensive context modeling and fine-grained semantics. The effectiveness of the proposed system is demonstrated through the implementation of NLP components that operate on LPG-based representations. Additionally, the proposed approach introduces specialized patterns and algorithms to enhance specific NLP tasks, including nominal mention detection, named entity disambiguation, event enrichments, event participant detection, and temporal link detection. The evaluation of the proposed approach, using the MEANTIME corpus comprising manually annotated documents, provides encouraging results and valuable insights into the system\u27s strengths. The proposed pipeline-based framework serves as a solid foundation for future research, aiming to refine and optimize LPG-based graph structures to generate comprehensive and semantically rich text representations, addressing the challenges associated with efficient information extraction and analysis in NLP