1,720,965 research outputs found
Inductive Graph-based Knowledge Tracing
No full textThe rise of virtual education and increase in distance, partly owing to the spread of COVID-19 pandemic, has made it more difficult for teachers to determine each student’s learning status. In this situation, knowledge tracing (KT), which tracks a student’s mastery of specific knowledge concepts, is receiving increasing attention. KT utilizes a sequence of studentexercise interactive activities to predict the mastery of concepts corresponding to a target problem, recommending appropriate learning resources to students and optimizing learning sequences for adaptive learning. With the development of deep learning, various studies have been proposed, such as sequential models using recurrent neural networks, attention models influenced by transformers, and graph-based models that depict the relationships between knowledge concepts. However, they all have common limitations in that they cannot utilize the learning activities of students other than the target student and can only use a limited form of exercise information. In this study, we have applied the concept of rating prediction to the student exercise knowledge tracing problem and solved the limitations of the existing models. Our proposed Inductive Graph-based Knowledge Tracing (IGKT) designed to integrate structural information and various unrestricted types of additional information into the model through subgraph sampling, has been found superior over the existing models across two different datasets in predicting student performances
Comprehensive Analysis of Traffic Accidents in Seoul: Major Factors and Types Affecting Injury Severity
No full textAccident and fatality rates of traffic accidents worldwide are steadily increasing every year; thus, considerable effort has been made to prevent traffic accidents and prepare countermeasures. This study aims to identify the major factors and types that affect the severity of traffic accidents in Seoul by utilizing the Seoul Metropolitan Government’s traffic accident dataset. To achieve this, we perform a comprehensive analysis by adopting various machine learning techniques—not only supervised learning methods but also unsupervised learning methods. As a result of the experiment, we derived several critical factors that were found to affect the severity of traffic accidents via supervised learning methods (i.e., ensemble-based and regression-based algorithms) and discovered dominant accident types via unsupervised learning methods (i.e., clustering-based algorithms). One of our primary findings is that, in contrast to common sense, environmental factors such as weather, season, and day of the week do not significantly affect the severity of traffic accidents in Seoul. Moreover, all methods highlight the importance of pedestrian-related factors, implying that it is highly necessary to prepare more meticulous institutional measures for pedestrians to reduce the negative influence of serious traffic accidents in Seoul
[Regular Paper] EP-CapsNet: Extending Capsule Network with Inception Module for Electrophoresis Binary Classification
No full textElectrophoresis (EP) test separates protein components based on their density. Patterns exhibited by this test mostly show very close approximation, making it difficult to examine test results within a short amount of time as it has many variations of patterns and requires a significant amount of knowledge to discern them accurately. To help clinical examiners save time and produce consistent results, a new deep-learning model optimized for EP graphic images was developed. Extending recent work on capsule network, which is a state-of-the-art deep learning model, this study was carried out to develop a best-performing model in classifying abnormal and normal electrophoresis patterns. Instead of extracting features from the image, we used the whole slide image as an input to the classifier. This study used 39,484 electrophoresis 2D graph images and utilized capsule network as the foundation of the deep learning architecture to learn the images without data augmentation. The formulated models were compared for a multitude of performance metrics including accuracy, sensitivity, and specificity. Overall, the study results show that our proposed architecture EP-CapsNet, which combines capsule network with Google's inception module, is the best performing model, outperforming the baseline and alternative models in almost all comparisons
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
임상 검사 결과를 질의로 활용하는 새로운 의학 구절 검색 프레임 워크의 제안
No full text학위논문(박사) - 한국과학기술원 : 지식서비스공학대학원, 2019.8,[vi, 89 p. :]Clinical Decision Support (CDS) search is performed to retrieve key medical literature that can assist the practice of medical experts by offering appropriate medical information relevant to the medical case in hand. In this paper, we present a novel CDS search framework designed for passage retrieval from biomedical textbooks in order to support clinical decision making using laboratory test results. The framework utilizes two unique characteristics of the textual reports derived from the test results, which are syntax variation and negation information.
For the first part of this dissertation, we proposed a novel framework that consists of three components: domain ontology, index repository, and query processing engine. We first created a domain ontology to resolve syntax variation by applying the ontology to detect medical concepts from the test results with language translation. We then pre-processed and performed indexing of biomedical textbooks recommended by clinicians for passage retrieval. We finally built the query processing engine tailored for CDS, including translation, concept detection, query expansion, pseudo relevance feedback at the local and global levels, and ranking with differential weighting of negation information. To evaluate the effectiveness of the proposed framework, we followed the standard information retrieval evaluation procedures. An evaluation dataset was created including 28,581 textual reports for 30 laboratory test results and 56,228 passages from widely used biomedical textbooks, recommended by clinicians. A total of 20 assessors manually determined whether or not the top 500 retrieved passages were relevant to each test result. Overall, our proposed passage retrieval framework, GPRF-NEG, outperforms the baseline by 36.2, 100.5, 69.7 percent for MRR, R-precision, and Precision at 5, respectively. It also outperforms the best state-of-the-art approach by 4.3, 37.9, and 14.0 percent for MRR, R-precision, and Precision at 5, respectively. Our study results indicate that the proposed CDS search framework specifically designed for passage retrieval of biomedical literature a practically viable choice for clinicians as it supports their decision making processes by providing relevant passages extracted from the sources that they prefer to refer to, with improved performance. The domain ontology and dataset created in this paper are available at: http://kirc.kaist.ac.kr/dataset/.
For the second part of this dissertation, we further developed the passage retrieval framework by incorporating proximity information. To do so, we use knowledge structure, which graphically visualizes key concepts and their relationships in a specific domain where nodes are concepts with their associative relationships. Our framework investigated two new approaches to build knowledge structure compared to the conventional knowledge structure building approach. First, we used word embedding techniques to build initial knowledge structure. Second, instead of exploiting beg of word approach, we investigated node analysis to determine which of the analysis techniques perform effectively to detect the term importance. To do so, we compared two models with/without edge pruning to capture more latent relationship between terms. Our experiment shown that the embedded based knowledge structure outperformed the previous version of knowledge structure building approach and other proximity-aware state-of-the-art models. The strength of this dissertation lies in a wide variety of clinical decision support search tasks. Especially, one of the most frequency medical speciality, clinical laboratory test, was proposed as query to test practice of the proposed framework as we believe that this work will potentially enhance the quality of CDS search in practice.한국과학기술원 :지식서비스공학대학원
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