Merge branch 'beta' of https://code-repo.d4science.org/D-Net/dnet-hadoop into graph_cleaning
This commit is contained in:
commit
6082d235d3
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@ -3,7 +3,7 @@ package eu.dnetlib.dhp.bulktag.criteria;
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|
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import java.io.Serializable;
|
||||
|
||||
@VerbClass("contains_ignorecase")
|
||||
@VerbClass("contains_caseinsensitive")
|
||||
public class ContainsVerbIgnoreCase implements Selection, Serializable {
|
||||
|
||||
private String param;
|
||||
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@ -3,7 +3,7 @@ package eu.dnetlib.dhp.bulktag.criteria;
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|
||||
import java.io.Serializable;
|
||||
|
||||
@VerbClass("equals_ignorecase")
|
||||
@VerbClass("equals_caseinsensitive")
|
||||
public class EqualVerbIgnoreCase implements Selection, Serializable {
|
||||
|
||||
private String param;
|
||||
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|
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|||
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@ -3,7 +3,7 @@ package eu.dnetlib.dhp.bulktag.criteria;
|
|||
|
||||
import java.io.Serializable;
|
||||
|
||||
@VerbClass("not_contains_ignorecase")
|
||||
@VerbClass("not_contains_caseinsensitive")
|
||||
public class NotContainsVerbIgnoreCase implements Selection, Serializable {
|
||||
|
||||
private String param;
|
||||
|
|
|
|||
|
|
@ -3,7 +3,7 @@ package eu.dnetlib.dhp.bulktag.criteria;
|
|||
|
||||
import java.io.Serializable;
|
||||
|
||||
@VerbClass("not_equals_ignorecase")
|
||||
@VerbClass("not_equals_caseinsensitive")
|
||||
public class NotEqualVerbIgnoreCase implements Selection, Serializable {
|
||||
|
||||
private String param;
|
||||
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|
|||
|
|
@ -1193,7 +1193,7 @@
|
|||
<organizations/>
|
||||
</community>
|
||||
<community id="science-innovation-policy">
|
||||
<advancedConstraints>{"criteria":[{"constraint":[{"verb":"equals_ignorecase","field":"subject","value":"ciencias de la comunicación"},
|
||||
<advancedConstraints>{"criteria":[{"constraint":[{"verb":"equals_caseinsensitive","field":"subject","value":"ciencias de la comunicación"},
|
||||
{"verb":"equals","field":"subject","value":"Miriam"}]},
|
||||
{"constraint":[{"verb":"equals","field":"subject","value":"miriam"}]}]}</advancedConstraints>
|
||||
<subjects>
|
||||
|
|
@ -1317,81 +1317,81 @@
|
|||
<datasources>
|
||||
<datasource>
|
||||
<openaireId>opendoar____::358aee4cc897452c00244351e4d91f69</openaireId>
|
||||
<selcriteria>{"criteria":[{"constraint":[{"verb":"contains_ignorecase","field":"title","value":"COVID-19"}]},
|
||||
{"constraint":[{"verb":"contains_ignorecase","field":"title","value":"SARS-CoV-2"}]},
|
||||
{"constraint":[{"verb":"contains_ignorecase","field":"title","value":"2019-nCoV"}}]}
|
||||
<selcriteria>{"criteria":[{"constraint":[{"verb":"contains_caseinsensitive","field":"title","value":"COVID-19"}]},
|
||||
{"constraint":[{"verb":"contains_caseinsensitive","field":"title","value":"SARS-CoV-2"}]},
|
||||
{"constraint":[{"verb":"contains_caseinsensitive","field":"title","value":"2019-nCoV"}}]}
|
||||
</selcriteria>
|
||||
</datasource>
|
||||
<datasource>
|
||||
<openaireId>re3data_____::7b0ad08687b2c960d5aeef06f811d5e6</openaireId>
|
||||
<selcriteria>{"criteria":[{"constraint":[{"verb":"contains_ignorecase","field":"title","value":"COVID-19"}]},
|
||||
{"constraint":[{"verb":"contains_ignorecase","field":"title","value":"SARS-CoV-2"}]},
|
||||
{"constraint":[{"verb":"contains_ignorecase","field":"title","value":"2019-nCoV"}]}]}
|
||||
<selcriteria>{"criteria":[{"constraint":[{"verb":"contains_caseinsensitive","field":"title","value":"COVID-19"}]},
|
||||
{"constraint":[{"verb":"contains_caseinsensitive","field":"title","value":"SARS-CoV-2"}]},
|
||||
{"constraint":[{"verb":"contains_caseinsensitive","field":"title","value":"2019-nCoV"}]}]}
|
||||
</selcriteria>
|
||||
</datasource>
|
||||
<datasource>
|
||||
<openaireId>driver______::bee53aa31dc2cbb538c10c2b65fa5824</openaireId>
|
||||
<selcriteria>{"criteria":[{"constraint":[{"verb":"contains_ignorecase","field":"title","value":"COVID-19"}]},
|
||||
{"constraint":[{"verb":"contains_ignorecase","field":"title","value":"SARS-CoV-2"}]},
|
||||
{"constraint":[{"verb":"contains_ignorecase","field":"title","value":"2019-nCoV"}]}]}
|
||||
<selcriteria>{"criteria":[{"constraint":[{"verb":"contains_caseinsensitive","field":"title","value":"COVID-19"}]},
|
||||
{"constraint":[{"verb":"contains_caseinsensitive","field":"title","value":"SARS-CoV-2"}]},
|
||||
{"constraint":[{"verb":"contains_caseinsensitive","field":"title","value":"2019-nCoV"}]}]}
|
||||
</selcriteria>
|
||||
</datasource>
|
||||
<datasource>
|
||||
<openaireId>openaire____::437f4b072b1aa198adcbc35910ff3b98</openaireId>
|
||||
<selcriteria>{"criteria":[{"constraint":[{"verb":"contains_ignorecase","field":"title","value":"COVID-19"}]},
|
||||
{"constraint":[{"verb":"contains_ignorecase","field":"title","value":"SARS-CoV-2"}]},
|
||||
{"constraint":[{"verb":"contains_ignorecase","field":"title","value":"2019-nCoV"}]}]}
|
||||
<selcriteria>{"criteria":[{"constraint":[{"verb":"contains_caseinsensitive","field":"title","value":"COVID-19"}]},
|
||||
{"constraint":[{"verb":"contains_caseinsensitive","field":"title","value":"SARS-CoV-2"}]},
|
||||
{"constraint":[{"verb":"contains_caseinsensitive","field":"title","value":"2019-nCoV"}]}]}
|
||||
</selcriteria>
|
||||
</datasource>
|
||||
<datasource>
|
||||
<openaireId>openaire____::081b82f96300b6a6e3d282bad31cb6e2</openaireId>
|
||||
<selcriteria>{"criteria":[{"constraint":[{"verb":"contains_ignorecase","field":"title","value":"COVID-19"}]},
|
||||
{"constraint":[{"verb":"contains_ignorecase","field":"title","value":"SARS-CoV-2"}]},
|
||||
{"constraint":[{"verb":"contains_ignorecase","field":"title","value":"2019-nCoV"}]}]}
|
||||
<selcriteria>{"criteria":[{"constraint":[{"verb":"contains_caseinsensitive","field":"title","value":"COVID-19"}]},
|
||||
{"constraint":[{"verb":"contains_caseinsensitive","field":"title","value":"SARS-CoV-2"}]},
|
||||
{"constraint":[{"verb":"contains_caseinsensitive","field":"title","value":"2019-nCoV"}]}]}
|
||||
</selcriteria>
|
||||
</datasource>
|
||||
<datasource>
|
||||
<openaireId>openaire____::9e3be59865b2c1c335d32dae2fe7b254</openaireId>
|
||||
<selcriteria>{"criteria":[{"constraint":[{"verb":"contains_ignorecase","field":"title","value":"COVID-19"}]},
|
||||
{"constraint":[{"verb":"contains_ignorecase","field":"title","value":"SARS-CoV-2"}]},
|
||||
{"constraint":[{"verb":"contains_ignorecase","field":"title","value":"2019-nCoV"}]}]}
|
||||
<selcriteria>{"criteria":[{"constraint":[{"verb":"contains_caseinsensitive","field":"title","value":"COVID-19"}]},
|
||||
{"constraint":[{"verb":"contains_caseinsensitive","field":"title","value":"SARS-CoV-2"}]},
|
||||
{"constraint":[{"verb":"contains_caseinsensitive","field":"title","value":"2019-nCoV"}]}]}
|
||||
</selcriteria>
|
||||
</datasource>
|
||||
<datasource>
|
||||
<openaireId>opendoar____::8b6dd7db9af49e67306feb59a8bdc52c</openaireId>
|
||||
<selcriteria>{"criteria":[{"constraint":[{"verb":"contains_ignorecase","field":"title","value":"COVID-19"}]},
|
||||
{"constraint":[{"verb":"contains_ignorecase","field":"title","value":"SARS-CoV-2"}]},
|
||||
{"constraint":[{"verb":"contains_ignorecase","field":"title","value":"2019-nCoV"}]}]}
|
||||
<selcriteria>{"criteria":[{"constraint":[{"verb":"contains_caseinsensitive","field":"title","value":"COVID-19"}]},
|
||||
{"constraint":[{"verb":"contains_caseinsensitive","field":"title","value":"SARS-CoV-2"}]},
|
||||
{"constraint":[{"verb":"contains_caseinsensitive","field":"title","value":"2019-nCoV"}]}]}
|
||||
</selcriteria>
|
||||
</datasource>
|
||||
<datasource>
|
||||
<openaireId>share_______::4719356ec8d7d55d3feb384ce879ad6c</openaireId>
|
||||
<selcriteria>{"criteria":[{"constraint":[{"verb":"contains_ignorecase","field":"title","value":"COVID-19"}]},
|
||||
{"constraint":[{"verb":"contains_ignorecase","field":"title","value":"SARS-CoV-2"}]},
|
||||
{"constraint":[{"verb":"contains_ignorecase","field":"title","value":"2019-nCoV"}]}]}
|
||||
<selcriteria>{"criteria":[{"constraint":[{"verb":"contains_caseinsensitive","field":"title","value":"COVID-19"}]},
|
||||
{"constraint":[{"verb":"contains_caseinsensitive","field":"title","value":"SARS-CoV-2"}]},
|
||||
{"constraint":[{"verb":"contains_caseinsensitive","field":"title","value":"2019-nCoV"}]}]}
|
||||
</selcriteria>
|
||||
</datasource>
|
||||
<datasource>
|
||||
<openaireId>share_______::bbd802baad85d1fd440f32a7a3a2c2b1</openaireId>
|
||||
<selcriteria>{"criteria":[{"constraint":[{"verb":"contains_ignorecase","field":"title","value":"COVID-19"}]},
|
||||
{"constraint":[{"verb":"contains_ignorecase","field":"title","value":"SARS-CoV-2"}]},
|
||||
{"constraint":[{"verb":"contains_ignorecase","field":"title","value":"2019-nCoV"}]}]}
|
||||
<selcriteria>{"criteria":[{"constraint":[{"verb":"contains_caseinsensitive","field":"title","value":"COVID-19"}]},
|
||||
{"constraint":[{"verb":"contains_caseinsensitive","field":"title","value":"SARS-CoV-2"}]},
|
||||
{"constraint":[{"verb":"contains_caseinsensitive","field":"title","value":"2019-nCoV"}]}]}
|
||||
</selcriteria>
|
||||
</datasource>
|
||||
<datasource>
|
||||
<openaireId>opendoar____::6f4922f45568161a8cdf4ad2299f6d23</openaireId>
|
||||
<selcriteria>{"criteria":[{"constraint":[{"verb":"contains_ignorecase","field":"title","value":"COVID-19"}]},
|
||||
{"constraint":[{"verb":"contains_ignorecase","field":"title","value":"SARS-CoV-2"}]},
|
||||
{"constraint":[{"verb":"contains_ignorecase","field":"title","value":"2019-nCoV"}]}]}
|
||||
<selcriteria>{"criteria":[{"constraint":[{"verb":"contains_caseinsensitive","field":"title","value":"COVID-19"}]},
|
||||
{"constraint":[{"verb":"contains_caseinsensitive","field":"title","value":"SARS-CoV-2"}]},
|
||||
{"constraint":[{"verb":"contains_caseinsensitive","field":"title","value":"2019-nCoV"}]}]}
|
||||
</selcriteria>
|
||||
</datasource>
|
||||
<datasource>
|
||||
<openaireId>re3data_____::7980778c78fb4cf0fab13ce2159030dc</openaireId>
|
||||
<selcriteria>{"criteria":[{"constraint":[{"verb":"contains_ignorecase","field":"title","value":"SARS-CoV-2"}]},{"constraint":[{"verb":"contains_ignorecase","field":"title","value":"COVID-19"}]},{"constraint":[{"verb":"contains_ignorecase","field":"title","value":"2019-nCov"}]}]}</selcriteria>
|
||||
<selcriteria>{"criteria":[{"constraint":[{"verb":"contains_caseinsensitive","field":"title","value":"SARS-CoV-2"}]},{"constraint":[{"verb":"contains_caseinsensitive","field":"title","value":"COVID-19"}]},{"constraint":[{"verb":"contains_caseinsensitive","field":"title","value":"2019-nCov"}]}]}</selcriteria>
|
||||
</datasource>
|
||||
<datasource>
|
||||
<openaireId>re3data_____::978378def740bbf2bfb420de868c460b</openaireId>
|
||||
<selcriteria>{"criteria":[{"constraint":[{"verb":"contains_ignorecase","field":"title","value":"SARS-CoV-2"}]},{"constraint":[{"verb":"contains_ignorecase","field":"title","value":"COVID-19"}]},{"constraint":[{"verb":"contains_ignorecase","field":"title","value":"2019-nCov"}]}]}</selcriteria>
|
||||
<selcriteria>{"criteria":[{"constraint":[{"verb":"contains_caseinsensitive","field":"title","value":"SARS-CoV-2"}]},{"constraint":[{"verb":"contains_caseinsensitive","field":"title","value":"COVID-19"}]},{"constraint":[{"verb":"contains_caseinsensitive","field":"title","value":"2019-nCov"}]}]}</selcriteria>
|
||||
</datasource>
|
||||
</datasources>
|
||||
<zenodocommunities>
|
||||
|
|
|
|||
|
|
@ -128,6 +128,20 @@ public class IndexRecordTransformerTest {
|
|||
testRecordTransformation(record);
|
||||
}
|
||||
|
||||
@Test
|
||||
public void testForEOSCFutureSoftwareNotebook() throws IOException, TransformerException {
|
||||
final String record = IOUtils
|
||||
.toString(getClass().getResourceAsStream("eosc-future/software-justthink.xml"));
|
||||
testRecordTransformation(record);
|
||||
}
|
||||
|
||||
@Test
|
||||
public void testForEOSCFutureSoftwareNotebookClaim() throws IOException, TransformerException {
|
||||
final String record = IOUtils
|
||||
.toString(getClass().getResourceAsStream("eosc-future/software-justthink-claim.xml"));
|
||||
testRecordTransformation(record);
|
||||
}
|
||||
|
||||
@Test
|
||||
void testDoiUrlNormalization() throws MalformedURLException {
|
||||
|
||||
|
|
|
|||
|
|
@ -0,0 +1,305 @@
|
|||
<record>
|
||||
<result xmlns:dri="http://www.driver-repository.eu/namespace/dri">
|
||||
<header xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
|
||||
<dri:objIdentifier>od______2659::3801993ea8f970cfc991277160edf277</dri:objIdentifier>
|
||||
<dri:dateOfCollection>2022-08-08T03:06:13Z</dri:dateOfCollection>
|
||||
<status>under curation</status>
|
||||
<counters/>
|
||||
</header>
|
||||
<metadata>
|
||||
<oaf:entity xmlns:oaf="http://namespace.openaire.eu/oaf"
|
||||
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
|
||||
xsi:schemaLocation="http://namespace.openaire.eu/oaf https://www.openaire.eu/schema/1.0/oaf-1.0.xsd">
|
||||
<oaf:result>
|
||||
<title classid="main title" classname="main title"
|
||||
schemeid="dnet:dataCite_title" schemename="dnet:dataCite_title">JUSThink
|
||||
Alignment Analysis</title>
|
||||
<creator rank="1" name="" surname="">Norman, Utku</creator>
|
||||
<creator rank="2" name="" surname="">Dinkar, Tanvi</creator>
|
||||
<creator rank="3" name="" surname="">Bruno, Barbara</creator>
|
||||
<creator rank="4" name="" surname="">Clavel, Chloé</creator>
|
||||
<dateofacceptance/>
|
||||
<resulttype classid="software" classname="software"
|
||||
schemeid="dnet:result_typologies" schemename="dnet:result_typologies"/>
|
||||
<language classid="eng" classname="English" schemeid="dnet:languages"
|
||||
schemename="dnet:languages"/>
|
||||
<description>
|
||||
<p>
|
||||
<strong>1. Description</strong>
|
||||
</p>
|
||||
<p>This repository contains<strong> tools to automatically analyse how
|
||||
participants align their use of task-specific referents in their
|
||||
dialogue and actions for a collaborative learning activity, and how
|
||||
it relates to the task success</strong> (i.e. their learning
|
||||
outcomes and task performance).</p>
|
||||
<p>As a use case, it processes data from a collaborative problem solving
|
||||
activity named JUSThink <a
|
||||
href="https://zenodo.org/record/4675070#references">[1, 2]</a>, i.e.
|
||||
JUSThink Dialogue and Actions Corpus data set that is available from the
|
||||
Zenodo Repository, DOI: <a href="http://doi.org/10.5281/zenodo.4627104"
|
||||
>10.5281/zenodo.4627104</a>, and reproduces the results and figures
|
||||
in <a href="https://zenodo.org/record/4675070#references">[3]</a>.</p>
|
||||
<p>In brief: </p>
|
||||
<ol>
|
||||
<li><strong>JUSThink Dialogue and Actions Corpus</strong> contains
|
||||
transcripts, event logs, and test responses of children aged 9
|
||||
through 12, as they participate in the JUSThink activity <a
|
||||
href="https://zenodo.org/record/4675070#references">[1, 2]</a>
|
||||
in pairs of two, to solve a problem on graphs together. </li>
|
||||
<li><strong>The JUSThink activity and its study</strong> is first
|
||||
described in <a href="https://zenodo.org/record/4675070#references"
|
||||
>[1]</a>, and elaborated with findings concerning the link
|
||||
between children's learning, performance in the activity, and
|
||||
perception of self, the other and the robot in <a
|
||||
href="https://zenodo.org/record/4675070#references">[2]</a>. </li>
|
||||
<li><strong>Alignment analysis in our work <a
|
||||
href="https://zenodo.org/record/4675070#references"
|
||||
>[3]</a></strong> studies the participants' use of
|
||||
expressions that are related to the task at hand, their follow up
|
||||
actions of these expressions, and how it links to task success.</li>
|
||||
</ol>
|
||||
<p>
|
||||
<strong>2. Publications</strong>
|
||||
</p>
|
||||
<p>If you use this work in an academic context, please cite the following
|
||||
publications:</p>
|
||||
<ul>
|
||||
<li>
|
||||
<p>Norman*, U., Dinkar*, T., Bruno, B., & Clavel, C. (2022).
|
||||
Studying Alignment in a Collaborative Learning Activity via
|
||||
Automatic Methods: The Link Between What We Say and Do. Dialogue
|
||||
& Discourse, 13(2), 1 - ;48. *Contributed equally to this
|
||||
work. <a href="https://doi.org/10.5210/dad.2022.201"
|
||||
>https://doi.org/10.5210/dad.2022.201</a></p>
|
||||
</li>
|
||||
<li>
|
||||
<p>Norman, U., Dinkar, T., Bruno, B., & Clavel, C. (2021).
|
||||
JUSThink Alignment Analysis. In Dialogue & Discourse
|
||||
(v1.0.0, Vol. 13, Number 2, pp. 1 - ;48). Zenodo. <a
|
||||
href="https://doi.org/10.5281/zenodo.4675070"
|
||||
>https://doi.org/10.5281/zenodo.4675070</a></p>
|
||||
</li>
|
||||
</ul>
|
||||
<p>
|
||||
<strong>3. Content</strong>
|
||||
</p>
|
||||
<p>The tools provided in this repository consists of 7 Jupyter Notebooks
|
||||
written in Python 3, and two additional external tools utilised by the
|
||||
notebooks.</p>
|
||||
<p>
|
||||
<strong>3.1. Jupyter Notebooks</strong>
|
||||
</p>
|
||||
<p>We highlight that the notebooks up until the last (i.e. to test the
|
||||
hypotheses (tools/7_test_the_hypotheses.ipynb)) present a general
|
||||
pipeline to process event logs, test responses and transcripts to
|
||||
extract measures of task performance, learning outcomes, and measures of
|
||||
alignment.</p>
|
||||
<ol>
|
||||
<li><strong>Extract task performance (and other features) from the logs
|
||||
</strong>(tools/1_extract_performance_and_other_features_from_logs.ipynb):
|
||||
Extracts various measures of task behaviour from the logs, at
|
||||
varying granularities of the activity (i.e. the whole corpus, task,
|
||||
attempt, and turn levels). In later notebooks, we focus on one of
|
||||
the features to estimate the task performance of a team: (minimum)
|
||||
error.</li>
|
||||
<li><strong>Extract learning outcomes from the test responses</strong>
|
||||
(tools/2_extract_learning_gain_from_test_responses.ipynb): Extracts
|
||||
measures of learning outcomes from the responses to the pre-test and
|
||||
the post-test. In later notebooks, we focus on one of the features
|
||||
to estimate the learning outcome of a team: relative learning gain
|
||||
<a href="https://sandbox.zenodo.org/record/742549#references"
|
||||
>[4]</a></li>
|
||||
<li><strong>Select and visualise a subset of teams for
|
||||
transcription</strong>
|
||||
(tools/3_visualise_transcribed_teams.ipynb): Visualises the
|
||||
transcribed teams among the other teams in the feature space spanned
|
||||
by task performance and learning outcome, as well as the
|
||||
distribution of their number of attempts and turns.</li>
|
||||
<li><strong>Extract routines from transcripts</strong>
|
||||
(tools/4_extract_routines_from_transcripts.ipynb) (uses <a
|
||||
href="https://github.com/GuillaumeDD/dialign">dialign</a> to
|
||||
extract routines): Extracts routines of referring expressions that
|
||||
are "fixed", i.e. become shared or established amongst
|
||||
interlocutors.</li>
|
||||
<li><strong>Combine transcripts with logs</strong>
|
||||
(tools/5_construct_the_corpus_by_combining_transcripts_with_logs.ipynb):
|
||||
Merges transcripts with event logs to have a combined dialogue and
|
||||
actions corpus, to be processed e.g. to detect follow-up
|
||||
actions.</li>
|
||||
<li><strong>Recognise instructions and detect follow-up actions</strong>
|
||||
(tools/6_recognise_instructions_detect_follow-up_actions.ipynb):
|
||||
Extracts verbalised instruction such as "connect Mount Basel to
|
||||
Montreux", and pairs them with the follow-up action that may
|
||||
<em>match</em> (e.g. if the other connects Basel to Montreux) or
|
||||
<em>mismatch</em> (e.g. if the other connects Basel to
|
||||
Neuchatel) with the instruction.</li>
|
||||
<li><strong>Test the hypotheses </strong>in <a
|
||||
href="https://sandbox.zenodo.org/record/742549#references"
|
||||
>[3]</a> (tools/7_test_the_hypotheses.ipynb) (uses
|
||||
<strong>effsize</strong> to estimate effect size, specifically
|
||||
Cliff's Delta): Considers each research questions and hypotheses
|
||||
studied in <a
|
||||
href="https://sandbox.zenodo.org/record/742549#references"
|
||||
>[3]</a> and generates the results in <a
|
||||
href="https://sandbox.zenodo.org/record/742549#references"
|
||||
>[3]</a>.</li>
|
||||
</ol>
|
||||
<p>
|
||||
<strong>3.2. External Tools</strong>
|
||||
</p>
|
||||
<ol>
|
||||
<li><strong><a href="https://github.com/GuillaumeDD/dialign">dialign</a>
|
||||
tool</strong> to extract routines, specifically <a
|
||||
href="https://github.com/GuillaumeDD/dialign/releases/tag/v1.0"
|
||||
>Release 1.0</a> from <a
|
||||
href="https://github.com/GuillaumeDD/dialign/releases/download/v1.0/dialign-1.0.zip"
|
||||
>dialign-1.0.zip</a>:\n It extracts routine expressions that are
|
||||
"shared" among the participants from transcripts. \n It is
|
||||
used as an external module (in accordance with its CeCILL-B License,
|
||||
see <strong>License</strong>).</li>
|
||||
<li><strong>effsize tool</strong> to compute estimators of effect
|
||||
size.\n We specifically use it to compute Cliff's Delta, which
|
||||
quantifies the amount difference between two groups of observations,
|
||||
by computing the Cliff's Delta statistic.\n It is taken from
|
||||
project <a
|
||||
href="https://acclab.github.io/DABEST-python-docs/index.html"
|
||||
>DABEST</a> (see <strong>License</strong>).</li>
|
||||
</ol>
|
||||
<p>
|
||||
<strong>4. Research Questions and Hypotheses in <a
|
||||
href="https://sandbox.zenodo.org/record/742549#references"
|
||||
>[3]</a></strong>
|
||||
</p>
|
||||
<ul>
|
||||
<li><strong>RQ1 Lexical alignment</strong>: How do the interlocutors
|
||||
<em>use</em> expressions related to the task? Is this associated
|
||||
with task success? <ul>
|
||||
<li><strong>H1.1</strong>: Task-specific referents become
|
||||
routine early for more successful teams.</li>
|
||||
<li><strong>H1.2</strong>: Hesitation phenomena are more likely
|
||||
to occur in the vicinity of priming and establishment of
|
||||
task-specific referents for more successful teams.</li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><strong>RQ2 Behavioural alignment</strong>: How do the interlocutors
|
||||
<em>follow up</em> these expressions with actions? Is this
|
||||
associated with task success? <ul>
|
||||
<li><strong>H2.1</strong>: Instructions are more likely to be
|
||||
followed by a corresponding action early in the dialogue for
|
||||
more successful teams.</li>
|
||||
<li><strong>H2.2</strong>: When instructions are followed by a
|
||||
corresponding or a different action, the action is more
|
||||
likely to be in the vicinity of information management
|
||||
phenomena for more successful teams.</li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
<p>The RQs and Hs are addressed in the notebook for testing the hypotheses
|
||||
(i.e. tools/7_test_the_hypotheses.ipynb).</p>
|
||||
<p>
|
||||
<strong>Acknowledgements</strong>
|
||||
</p>
|
||||
<p>This project has received funding from the European Union's Horizon
|
||||
2020 research and innovation programme under grant agreement No 765955.
|
||||
Namely, the <a href="https://www.animatas.eu/">ANIMATAS Project</a>.</p>
|
||||
<p>
|
||||
<strong>License</strong>
|
||||
</p>
|
||||
<p>The whole package is under MIT License, see the <strong>LICENSE</strong>
|
||||
file.</p>
|
||||
<p>Classes under the <strong>tools/effsize</strong> package were taken from
|
||||
project <a href="https://acclab.github.io/DABEST-python-docs/index.html"
|
||||
><strong>DABEST</strong></a>, Copyright 2016-2020 Joses W. Ho.
|
||||
These classes are licensed under the BSD 3-Clause Clear License. See
|
||||
<strong>tools/effsize/LICENSE</strong> file for additional
|
||||
details.</p>
|
||||
<p>Classes under the <strong>tools/dialign-1.0</strong> package were taken
|
||||
from project <strong><a href="https://github.com/GuillaumeDD/dialign"
|
||||
>dialign</a></strong>. These classes are licensed under the
|
||||
CeCILL-B License. This package is used as an "external
|
||||
module", see<strong> tools/dialign-1.0/LICENSE.txt</strong> for
|
||||
additional details.</p>
|
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<measure id="influence" score="4.916186E-9" class="C5"/>
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<measure id="popularity_alt" score="0.0" class="C5"/>
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provenanceaction="sysimport:crosswalk:repository" trust="0.9">JUSThink Alignment
|
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Analysis</title>
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<bestaccessright classid="OPEN" classname="Open Access" schemeid="dnet:access_modes"
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schemename="dnet:access_modes"/>
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<creator rank="1" name="Utku" surname="Norman" orcid_pending="0000-0002-6802-1444"
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<creator rank="2" name="Tanvi" surname="Dinkar">Dinkar, Tanvi</creator>
|
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<creator rank="3" name="Barbara" surname="Bruno" orcid_pending="0000-0003-0953-7173"
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>Bruno, Barbara</creator>
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>Clavel, Chloé</creator>
|
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<dateofacceptance>2022-08-08</dateofacceptance>
|
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<description>&lt;strong>1. Description&lt;/strong> This repository
|
||||
contains&lt;strong> tools to automatically analyse how participants align
|
||||
their use of task-specific referents in their dialogue and actions for a
|
||||
collaborative learning activity, and how it relates to the task
|
||||
success&lt;/strong> (i.e. their learning outcomes and task performance). As
|
||||
a use case, it processes data from a collaborative problem solving activity
|
||||
named JUSThink [1, 2], i.e. JUSThink Dialogue and Actions Corpus data set that
|
||||
is available from the Zenodo Repository, DOI: 10.5281/zenodo.4627104, and
|
||||
reproduces the results and figures in [3]. In brief: &lt;strong>JUSThink
|
||||
Dialogue and Actions Corpus&lt;/strong> contains transcripts, event logs,
|
||||
and test responses of children aged 9 through 12, as they participate in the
|
||||
JUSThink activity [1, 2] in pairs of two, to solve a problem on graphs together.
|
||||
&lt;strong>The JUSThink activity and its study&lt;/strong> is first
|
||||
described in [1], and elaborated with findings concerning the link between
|
||||
children's learning, performance in the activity, and perception of self, the
|
||||
other and the robot in [2]. &lt;strong>Alignment analysis in our work
|
||||
[3]&lt;/strong> studies the participants' use of expressions that are
|
||||
related to the task at hand, their follow up actions of these expressions, and
|
||||
how it links to task success. &lt;strong>Changes in Release
|
||||
v1.1.0:&lt;/strong> updated with the publication information, finalized
|
||||
paper structure, research questions and hypotheses as in the published article:
|
||||
U. Norman*&lt;em>, &lt;/em>T. Dinkar*, B. Bruno, and C. Clavel,
|
||||
"Studying Alignment in a Collaborative Learning Activity via Automatic Methods:
|
||||
The Link Between What We Say and Do," Dialogue &amp;amp; Discourse, 13(2),
|
||||
1–48. *Contributed equally to this work. 10.5210/dad.2022.201.
|
||||
&lt;strong>Full Changelog:&lt;/strong>
|
||||
https://github.com/chili-epfl/justhink-alignment-analysis/compare/v1.0.0...v1.1.0
|
||||
&lt;strong>2. Publications&lt;/strong> If you use this work in an
|
||||
academic context, please cite the following publications: Norman*, U., Dinkar*,
|
||||
T., Bruno, B., &amp;amp; Clavel, C. (2022). Studying Alignment in a
|
||||
Collaborative Learning Activity via Automatic Methods: The Link Between What We
|
||||
Say and Do. Dialogue &amp;amp; Discourse, 13(2), 1–48. *Contributed equally
|
||||
to this work. https://doi.org/10.5210/dad.2022.201 Norman, U., Dinkar, T.,
|
||||
Bruno, B., &amp;amp; Clavel, C. (2021). JUSThink Alignment Analysis. In
|
||||
Dialogue &amp;amp; Discourse (v1.1.0, Vol. 13, Number 2, pp. 1–48). Zenodo.
|
||||
https://doi.org/10.5281/zenodo.6974562 &lt;strong>3. Content&lt;/strong>
|
||||
The tools provided in this repository consists of 7 Jupyter Notebooks written in
|
||||
Python 3, and two additional external tools utilised by the notebooks.
|
||||
&lt;strong>3.1. Jupyter Notebooks&lt;/strong> We highlight that the
|
||||
notebooks up until the last (i.e. to test the hypotheses
|
||||
(tools/7_test_the_hypotheses.ipynb)) present a general pipeline to process event
|
||||
logs, test responses and transcripts to extract measures of task performance,
|
||||
learning outcomes, and measures of alignment. &lt;strong>Extract task
|
||||
performance (and other features) from the logs
|
||||
&lt;/strong>(tools/1_extract_performance_and_other_features_from_logs.ipynb):
|
||||
Extracts various measures of task behaviour from the logs, at varying
|
||||
granularities of the activity (i.e. the whole corpus, task, attempt, and turn
|
||||
levels). In later notebooks, we focus on one of the features to estimate the
|
||||
task performance of a team: (minimum) error. &lt;strong>Extract learning
|
||||
outcomes from the test responses&lt;/strong>
|
||||
(tools/2_extract_learning_gain_from_test_responses.ipynb): Extracts measures of
|
||||
learning outcomes from the responses to the pre-test and the post-test. In later
|
||||
notebooks, we focus on one of the features to estimate the learning outcome of a
|
||||
team: relative learning gain [4] &lt;strong>Select and visualise a subset of
|
||||
teams for transcription&lt;/strong>
|
||||
(tools/3_visualise_transcribed_teams.ipynb): Visualises the transcribed teams
|
||||
among the other teams in the feature space spanned by task performance and
|
||||
learning outcome, as well as the distribution of their number of attempts and
|
||||
turns. &lt;strong>Extract routines from transcripts&lt;/strong>
|
||||
(tools/4_extract_routines_from_transcripts.ipynb) (uses dialign to extract
|
||||
routines): Extracts routines of referring expressions that are "fixed", i.e.
|
||||
become shared or established amongst interlocutors. &lt;strong>Combine
|
||||
transcripts with logs&lt;/strong>
|
||||
(tools/5_construct_the_corpus_by_combining_transcripts_with_logs.ipynb): Merges
|
||||
transcripts with event logs to have a combined dialogue and actions corpus, to
|
||||
be processed e.g. to detect follow-up actions. &lt;strong>Recognise
|
||||
instructions and detect follow-up actions&lt;/strong>
|
||||
(tools/6_recognise_instructions_detect_follow-up_actions.ipynb): Extracts
|
||||
verbalised instruction such as "connect Mount Basel to Montreux", and pairs them
|
||||
with the follow-up action that may &lt;em>match&lt;/em> (e.g. if the
|
||||
other connects Basel to Montreux) or &lt;em>mismatch&lt;/em> (e.g. if
|
||||
the other connects Basel to Neuchatel) with the instruction. &lt;strong>Test
|
||||
the hypotheses &lt;/strong>in [3] (tools/7_test_the_hypotheses.ipynb) (uses
|
||||
&lt;strong>effsize&lt;/strong> to estimate effect size, specifically
|
||||
Cliff's Delta): Considers each research questions and hypotheses studied in [3]
|
||||
and generates the results in [3]. &lt;strong>3.2. External
|
||||
Tools&lt;/strong> &lt;strong>dialign tool&lt;/strong> to extract
|
||||
routines, specifically Release 1.0 from dialign-1.0.zip:&lt;br> It extracts
|
||||
routine expressions that are "shared" among the participants from transcripts.
|
||||
&lt;br> It is used as an external module (in accordance with its CeCILL-B
|
||||
License, see &lt;strong>License&lt;/strong>). &lt;strong>effsize
|
||||
tool&lt;/strong> to compute estimators of effect size.&lt;br> We
|
||||
specifically use it to compute Cliff's Delta, which quantifies the amount
|
||||
difference between two groups of observations, by computing the Cliff's Delta
|
||||
statistic.&lt;br> It is taken from project DABEST (see
|
||||
&lt;strong>License&lt;/strong>). &lt;strong>4. Research Questions
|
||||
and Hypotheses in [3]&lt;/strong> &lt;strong>RQ1 Lexical
|
||||
alignment&lt;/strong>: How do the interlocutors &lt;em>use&lt;/em>
|
||||
expressions related to the task? Is this associated with task success?
|
||||
&lt;strong>H1.1&lt;/strong>: Task-specific referents become routine
|
||||
early for more successful teams. &lt;strong>H1.2&lt;/strong>: Hesitation
|
||||
phenomena are more likely to occur in the vicinity of priming and establishment
|
||||
of task-specific referents for more successful teams. &lt;strong>RQ2
|
||||
Behavioural alignment&lt;/strong>: How do the interlocutors
|
||||
&lt;em>follow up&lt;/em> these expressions with actions? Is this
|
||||
associated with task success? &lt;strong>H2.1&lt;/strong>: Instructions
|
||||
are more likely to be followed by a corresponding action early in the dialogue
|
||||
for more successful teams. &lt;strong>H2.2&lt;/strong>: When
|
||||
instructions are followed by a corresponding or a different action, the action
|
||||
is more likely to be in the vicinity of information management phenomena for
|
||||
more successful teams. The RQs and Hs are addressed in the notebook for testing
|
||||
the hypotheses (i.e. tools/7_test_the_hypotheses.ipynb).
|
||||
&lt;strong>Acknowledgements&lt;/strong> This project has received
|
||||
funding from the European Union's Horizon 2020 research and innovation programme
|
||||
under grant agreement No 765955. Namely, the ANIMATAS Project.
|
||||
&lt;strong>License&lt;/strong> The whole package is under MIT License,
|
||||
see the &lt;strong>LICENSE&lt;/strong> file. Classes under the
|
||||
&lt;strong>tools/effsize&lt;/strong> package were taken from project
|
||||
&lt;strong>DABEST&lt;/strong>, Copyright 2016-2020 Joses W. Ho. These
|
||||
classes are licensed under the BSD 3-Clause Clear License. See
|
||||
&lt;strong>tools/effsize/LICENSE&lt;/strong> file for additional
|
||||
details. Classes under the &lt;strong>tools/dialign-1.0&lt;/strong>
|
||||
package were taken from project &lt;strong>dialign&lt;/strong>. These
|
||||
classes are licensed under the CeCILL-B License. This package is used as an
|
||||
"external module", see&lt;strong>
|
||||
tools/dialign-1.0/LICENSE.txt&lt;/strong> for additional
|
||||
details.</description>
|
||||
<description>{"references": ["[1] J. Nasir, U. Norman, B. Bruno, and P. Dillenbourg,
|
||||
\"You Tell, I Do, and We Swap until we Connect All the Gold Mines!,\" ERCIM
|
||||
News, vol. 2020, no. 120, 2020, [Online]. Available:
|
||||
https://ercim-news.ercim.eu/en120/special/you-tell-i-do-and-we-swap-until-we-connect-all-the-gold-mines",
|
||||
"[2] J. Nasir*, U. Norman*, B. Bruno, and P. Dillenbourg, \"When Positive
|
||||
Perception of the Robot Has No Effect on Learning,\" in 2020 29th IEEE
|
||||
International Conference on Robot and Human Interactive Communication (RO-MAN),
|
||||
Aug. 2020, pp. 313\u2013320, doi: 10.1109/RO-MAN47096.2020.9223343", "[3] U.
|
||||
Norman*, T. Dinkar*, B. Bruno, and C. Clavel, \"Studying Alignment in a
|
||||
Collaborative Learning Activity via Automatic Methods: The Link Between What We
|
||||
Say and Do,\" Dialogue &amp;amp; Discourse, vol. 13, no. 2, pp. 1\u201348,
|
||||
Aug. 2022, doi: 10.5210/dad.2022.201.", "[4] M. Sangin, G. Molinari, M.-A.
|
||||
N\u00fcssli, and P. Dillenbourg, \"Facilitating peer knowledge modeling: Effects
|
||||
of a knowledge awareness tool on collaborative learning outcomes and
|
||||
processes,\"\" Computers in Human Behavior, vol. 27, no. 3, pp. 1059\u20131067,
|
||||
May 2011, doi: 10.1016/j.chb.2010.05.032."]}</description>
|
||||
<subject classid="keyword" classname="keyword"
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||||
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||||
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||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
provenanceaction="sysimport:crosswalk:repository" trust="0.9">spontaneous
|
||||
speech</subject>
|
||||
<subject classid="keyword" classname="keyword"
|
||||
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|
||||
schemename="dnet:subject_classification_typologies" inferred="false"
|
||||
provenanceaction="sysimport:crosswalk:repository" trust="0.9"
|
||||
>disfluency</subject>
|
||||
<subject classid="keyword" classname="keyword"
|
||||
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|
||||
schemename="dnet:subject_classification_typologies" inferred="false"
|
||||
provenanceaction="sysimport:crosswalk:repository" trust="0.9">mutual
|
||||
understanding</subject>
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
<datainfo>
|
||||
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||||
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||||
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||||
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||||
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||||
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||||
Alignment in a Collaborative Learning Activity via Automatic Methods:
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
</funding>
|
||||
<acronym>ANIMATAS</acronym>
|
||||
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|
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|
||||
<dateofacceptance>2021-03-22</dateofacceptance>
|
||||
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|
||||
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||||
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|
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||||
trust="0.9">JUSThink Alignment Analysis</title>
|
||||
<dateofacceptance>2021-04-09</dateofacceptance>
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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</record>
|
||||
Loading…
Reference in New Issue