scholarly journals Evaluation Dataset (DT-Grade) and Word Weighting Approach towards Constructed Short Answers Assessment in Tutorial Dialogue Context

2016 ◽  
Author(s):  
Rajendra Banjade ◽  
Nabin Maharjan ◽  
Nobal Bikram Niraula ◽  
Dipesh Gautam ◽  
Borhan Samei ◽  
...  
Keyword(s):  
Tutorial Dialogue ◽  
Evaluation Dataset

scholarly journals A DICOM dataset for evaluation of medical image de-identification

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Michael Rutherford ◽  
Seong K. Mun ◽  
Betty Levine ◽  
William Bennett ◽  
Kirk Smith ◽  
...  
Keyword(s):  
Health Information ◽  
National Cancer Institute ◽  
Medical Image ◽  
Cancer Imaging ◽  
Protected Health Information ◽  
Dicom Standard ◽  
Clinical Imaging ◽  
X Ray ◽  
Evaluation Dataset ◽  
Data Elements

AbstractWe developed a DICOM dataset that can be used to evaluate the performance of de-identification algorithms. DICOM objects (a total of 1,693 CT, MRI, PET, and digital X-ray images) were selected from datasets published in the Cancer Imaging Archive (TCIA). Synthetic Protected Health Information (PHI) was generated and inserted into selected DICOM Attributes to mimic typical clinical imaging exams. The DICOM Standard and TCIA curation audit logs guided the insertion of synthetic PHI into standard and non-standard DICOM data elements. A TCIA curation team tested the utility of the evaluation dataset. With this publication, the evaluation dataset (containing synthetic PHI) and de-identified evaluation dataset (the result of TCIA curation) are released on TCIA in advance of a competition, sponsored by the National Cancer Institute (NCI), for algorithmic de-identification of medical image datasets. The competition will use a much larger evaluation dataset constructed in the same manner. This paper describes the creation of the evaluation datasets and guidelines for their use.

scholarly journals HOSTED—England’s Household Transmission Evaluation Dataset: preliminary findings from a novel passive surveillance system of COVID-19

2021 ◽  
Author(s):  
J A Hall ◽  
R J Harris ◽  
A Zaidi ◽  
S C Woodhall ◽  
G Dabrera ◽  
...  
Keyword(s):  
Surveillance System ◽  
Household Composition ◽  
Passive Surveillance ◽  
Household Contacts ◽  
Household Transmission ◽  
The North ◽  
Passive Surveillance System ◽  
Evaluation Dataset ◽  
Households With Children ◽  
Lower Transmission

Abstract Background Household transmission of SARS-CoV-2 is an important component of the community spread of the pandemic. Little is known about the factors associated with household transmission, at the level of the case, contact or household, or how these have varied over the course of the pandemic. Methods The Household Transmission Evaluation Dataset (HOSTED) is a passive surveillance system linking laboratory-confirmed COVID-19 cases to individuals living in the same household in England. We explored the risk of household transmission according to: age of case and contact, sex, region, deprivation, month and household composition between April and September 2020, building a multivariate model. Results In the period studied, on average, 5.5% of household contacts in England were diagnosed as cases. Household transmission was most common between adult cases and contacts of a similar age. There was some evidence of lower transmission rates to under-16s [adjusted odds ratios (aOR) 0.70, 95% confidence interval (CI) 0.66–0.74). There were clear regional differences, with higher rates of household transmission in the north of England and the Midlands. Less deprived areas had a lower risk of household transmission. After controlling for region, there was no effect of deprivation, but houses of multiple occupancy had lower rates of household transmission [aOR 0.74 (0.66–0.83)]. Conclusions Children are less likely to acquire SARS-CoV-2 via household transmission, and consequently there was no difference in the risk of transmission in households with children. Households in which cases could isolate effectively, such as houses of multiple occupancy, had lower rates of household transmission. Policies to support the effective isolation of cases from their household contacts could lower the level of household transmission.

scholarly journals Associating Natural Language Comment and Source Code Entities

2020 ◽  
Vol 34 (05) ◽  
pp. 8592-8599
Author(s):  
Sheena Panthaplackel ◽  
Milos Gligoric ◽  
Raymond J. Mooney ◽  
Junyi Jessy Li
Keyword(s):  
Software Development ◽  
Natural Language ◽  
Open Source ◽  
Source Code ◽  
Initial Step ◽  
Binary Classifier ◽  
Sequence Labeling ◽  
Evaluation Dataset ◽  
Revision Histories

Comments are an integral part of software development; they are natural language descriptions associated with source code elements. Understanding explicit associations can be useful in improving code comprehensibility and maintaining the consistency between code and comments. As an initial step towards this larger goal, we address the task of associating entities in Javadoc comments with elements in Java source code. We propose an approach for automatically extracting supervised data using revision histories of open source projects and present a manually annotated evaluation dataset for this task. We develop a binary classifier and a sequence labeling model by crafting a rich feature set which encompasses various aspects of code, comments, and the relationships between them. Experiments show that our systems outperform several baselines learning from the proposed supervision.

scholarly journals Shapelet Discovery by Lazy Time Series Classification

2020 ◽  
Vol 2020 ◽  
pp. 1-19
Author(s):  
Wei Zhang ◽  
Zhihai Wang ◽  
Jidong Yuan ◽  
Shilei Hao
Keyword(s):  
Time Series ◽  
Training Dataset ◽  
Class Label ◽  
Considerable Research ◽  
Evaluation Dataset ◽  
Differential Ability ◽  
Global And Local ◽  
Insight Into ◽  
Specific Evaluation ◽  
Feature Frequency

As a representation of discriminative features, the time series shapelet has recently received considerable research interest. However, most shapelet-based classification models evaluate the differential ability of the shapelet on the whole training dataset, neglecting characteristic information contained in each instance to be classified and the classwise feature frequency information. Hence, the computational complexity of feature extraction is high, and the interpretability is inadequate. To this end, the efficiency of shapelet discovery is improved through a lazy strategy fusing global and local similarities. In the prediction process, the strategy learns a specific evaluation dataset for each instance, and then the captured characteristics are directly used to progressively reduce the uncertainty of the predicted class label. Moreover, a shapelet coverage score is defined to calculate the discriminability of each time stamp for different classes. The experimental results show that the proposed method is competitive with the benchmark methods and provides insight into the discriminative features of each time series and each type in the data.

scholarly journals Evaluating the Effectiveness of a Tutorial Dialogue System for Self-Explanation

2004 ◽  
pp. 443-454 ◽  
Author(s):  
Vincent Aleven ◽  
Amy Ogan ◽  
Octav Popescu ◽  
Cristen Torrey ◽  
Kenneth Koedinger
Keyword(s):  
Dialogue System ◽  
Tutorial Dialogue
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