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Kernel Metadata Declaration — on demand

ISO 26324 asks a DOI Registration Agency to produce a Kernel Metadata Declaration for every DOI it issues. Give any DOI: its public record becomes a record in the Smart Scholars DOI Metadata Format 1.0, checked against every rule, and declared as the Kernel XML.

Reads the DOI's public Crossref record; nothing is stored. Until Smart Scholars is accredited, every Declaration is marked as a draft in the XML itself.

✓ Meets every rule of the format

The record below is complete and every controlled value is a DOI Attribute Value Set 2.3 spelling. The Declaration on the right follows DOIMetadataKernel.xsd element by element.

What the DOI identifies

Triplet Network based Few Shot Outlier DetectionSystem in Robot Task Planning for reduced failures/unwanted Executions

10.46243/jst.2022.v7.i04.pp136-142 · JournalArticle — an article in a journal · Digital · Visual · Language · en

Published 2022-06-30

Part of Journal of Science & Technology · ISSN 2456-5660 · vol. 7 · no. 4 · pp. 136–142

Principal agents

  • MidhunM. S MidhunM. S (author → Author)
  • Longman Publishers (publisher → Publisher)

Also in the record, outside the Kernel: the abstract, the licence, 4 links, 16 references. Source: Crossref (member 25296), registered 2024-02-16, last deposited 2026-09-20.

⬇ Record (JSON) ⬇ Declaration (XML)

System metadata — ISO 26324:2025, Annex B · DOI Handbook 10.1

Each element by the standard's name and the Handbook's (in grey), read off the record.

DOI Name
DOI name
10.46243/jst.2022.v7.i04.pp136-142
Referent Type
referentType
Creation
Referent Sub-Type
referentSubType
JournalArticle — an article in a journal
Referent Name(s)
referentName(s)
Triplet Network based Few Shot Outlier DetectionSystem in Robot Task Planning for reduced failures/unwanted Executions (PrincipalTitle, en)
Basic Metadata
basicMetadata
author: MidhunM. S MidhunM. S
publisher: Longman Publishers
published: 2022-06-30
part of: Journal of Science & Technology · ISSN 2456-5660 · vol. 7 · no. 4 · pp. 136–142
language: en
form: Digital · Visual · Language
Referent Identifier(s)
alternateIdentifier(s)
none besides the DOI
Registration Authority
registrationAuthorityCode
Crossref — issued by Crossref (member 25296); held here as a copy
Created Date
issueDate
2024-02-16
relatedIdentifiersnone needed — the descriptive metadata is in this record

The record

{
    "format": "smartscholars-doi-metadata/1.0",
    "doi": "10.46243/jst.2022.v7.i04.pp136-142",
    "referent": "Creation",
    "type": "JournalArticle",
    "structural_type": "Digital",
    "modes": [
        "Visual"
    ],
    "characters": [
        "Language"
    ],
    "titles": [
        {
            "value": "Triplet Network based Few Shot Outlier DetectionSystem in Robot Task Planning for reduced failures/unwanted Executions",
            "type": "PrincipalTitle",
            "lang": "en"
        }
    ],
    "identifiers": [
        {
            "type": "DOI",
            "value": "10.46243/jst.2022.v7.i04.pp136-142"
        }
    ],
    "agents": [
        {
            "role": "author",
            "name": {
                "given": "MidhunM. S",
                "family": "MidhunM. S"
            },
            "sequence": "first"
        },
        {
            "role": "publisher",
            "name": {
                "org": "Longman Publishers"
            }
        }
    ],
    "dates": {
        "published": "2022-06-30",
        "date_type": "PublicationDate",
        "online": "2022-06-30"
    },
    "language": "en",
    "container": {
        "type": "Journal",
        "titles": [
            {
                "value": "Journal of Science & Technology",
                "type": "PrincipalTitle"
            }
        ],
        "identifiers": [
            {
                "type": "ISSN",
                "value": "2456-5660",
                "medium": "electronic"
            }
        ],
        "volume": "7",
        "issue": "4",
        "pages": {
            "first": "136",
            "last": "142"
        }
    },
    "links": [
        {
            "url": "https://www.jst.org.in/index.php/pub/article/view/659",
            "return_type": "text/html",
            "primary": true
        },
        {
            "url": "https://www.jst.org.in/index.php/pub/article/download/659/587",
            "purpose": "text-mining",
            "return_type": "application/pdf"
        },
        {
            "url": "https://www.jst.org.in/index.php/pub/article/download/659/2108",
            "purpose": "text-mining",
            "return_type": "application/xml"
        },
        {
            "url": "https://jst.org.in/admin/uploads/JST070434.pdf",
            "purpose": "similarity-checking"
        }
    ],
    "abstract": {
        "value": "Human/systems managing the robot may make errors, resulting in a significant loss.A robotic task outlier identification approach for serial manipulator setups to avoid such outliers. The suggested work generates robot tasks in the first stage by recording the joint valu es utilised as the proposed dataset. Then, the metric learning-based Triplet model with convolutional feature learning layers is presented for few -shot feature learning in robot tasks. Each job is represented by an n -dimensional vector, where n represents the robot's degrees of freedom. The robot work comprises about 1500 characters from the Omniglot dataset; therefore, drawing characters from several languages on a canvas is chosen as a test",
        "lang": "en"
    },
    "license": {
        "url": "https://creativecommons.org/licenses/by/4.0/",
        "start": "2022-06-30",
        "applies_to": "vor"
    },
    "references": [
        {
            "key": "ref1",
            "doi": "10.1109/robot.2007.363693",
            "unstructured": "Jo-Anne Ting, Aaron D’Souza, and Stefan Schaal. Automatic outlier detection: A bayesian approach. In Proceedings 2007 IEEE International Conference on Robotics and Automation, pages 2489–2494. IEEE, 2007"
        },
        {
            "key": "ref2",
            "doi": "10.1109/iros.2014.6943078",
            "unstructured": "Rachel Hornung, HolgerUrbanek, Julian Klodmann, Christian Osendorfer, and Patrick Van Der Smagt. Model-free robot anomaly detection. In 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 3676–3683. IEEE, 2014"
        },
        {
            "key": "ref3",
            "unstructured": "Gregory Koch, Richard Zemel, RuslanSalakhutdinov, et al. Siamese neural networks for one-shot image recognition. In ICML deep learning workshop, volume 2, page 0. Lille, 2015. Page | 141 Published by: Longman Publishers www.jst.org.in"
        },
        {
            "key": "ref4",
            "doi": "10.1109/tai.2021.3105621",
            "unstructured": "Yikai Li, Tong Zhang, and CL Philip Chen. Enhanced broad siamese network for facial emotion recognition in human–robot interaction. IEEE Transactions on Artificial Intelligence, 2(5):413–423, 2021"
        },
        {
            "key": "ref5",
            "doi": "10.1016/j.patrec.2021.01.012",
            "unstructured": "Souvik Ghosh, Spandan Ghosh, Pradeep Kumar, Erik Scheme, and ParthaPratim Roy. A novel spatio-temporal siamese network for 3d signature recognition. Pattern Recognition Letters, 144:13–20, 2021"
        },
        {
            "key": "ref6",
            "doi": "10.1007/978-3-319-24261-3_7",
            "unstructured": "Elad Hoffer and NirAilon. Deep metric learning using triplet network. In International workshop on similarity-based pattern recognition, pages 84–92. Springer, 2015"
        },
        {
            "key": "ref7",
            "doi": "10.1109/jsen.2021.3069452",
            "unstructured": "Haodong Lu, Miao Du, Kai Qian, Xiaoming He, and Kun Wang. Gan-based data augmentation strategy for sensor anomaly detection in industrial robots. IEEE Sensors Journal, 2021"
        },
        {
            "key": "ref8",
            "unstructured": "AnisKoub ˆaa et al. Robot Operating System (ROS)., volume 1. Springer, 2017"
        },
        {
            "key": "ref9",
            "unstructured": "Morgan Quigley, Ken Conley, Brian Gerkey, Josh Faust, Tully Foote, Jeremy Leibs, Rob Wheeler, Andrew Y Ng, et al. Ros: an opensource robot operating system. In ICRA workshop on open source software, volume 3, page 5. Kobe, Japan, 2009"
        },
        {
            "key": "ref10",
            "doi": "10.1109/mra.2011.2181749",
            "unstructured": "Sachin Chitta, IoanSucan, and Steve Cousins. Moveit![ros topics]. IEEE Robotics & Automation Magazine, 19(1):18–19, 2012"
        },
        {
            "key": "ref11",
            "doi": "10.1126/science.aab3050",
            "unstructured": "Brenden M. Lake, RuslanSalakhutdinov, and Joshua B. Tenenbaum. Human-level concept learning through probabilistic program induction. Science, 350(6266):1332–1338, 2015"
        },
        {
            "key": "ref12",
            "doi": "10.4249/scholarpedia.1883",
            "unstructured": "Leif E Peterson. K-nearest neighbor. Scholarpedia, 4(2):1883, 2009"
        },
        {
            "key": "ref13",
            "unstructured": "Gavin Hackeling. Mastering Machine Learning with scikit-learn. Packt Publishing Ltd, 2017"
        },
        {
            "key": "ref14",
            "doi": "10.1007/978-3-319-54413-7_7",
            "unstructured": "Peter Corke. Robot arm kinematics. In Robotics, Vision and Control, pages 193–228. Springer, 2017"
        },
        {
            "key": "ref15",
            "doi": "10.1109/icra48506.2021.9561366",
            "unstructured": "Peter Corke and Jesse Haviland. Not your grandmother’s toolbox–the robotics toolbox reinvented for python. In 2021 IEEE International Conference on Robotics and Automation (ICRA), pages 11357–11363. IEEE, 2021"
        },
        {
            "key": "ref16",
            "doi": "10.1109/autoid.2005.48",
            "unstructured": "Ruud M Bolle, Jonathan H Connell, SharathPankanti, Nalini K Ratha, and Andrew W Senior. The relation between the roc curve and the cmc. In Fourth IEEE workshop on automatic identification advanced technologies (AutoID’05), pages 15–20. IEEE, 2005"
        }
    ],
    "record": {
        "registrant": "Longman Publishers",
        "registered": "2024-02-16",
        "updated": "2026-09-20",
        "issue_number": 1,
        "source": "crossref-api",
        "source_agency": "Crossref (member 25296)"
    }
}

The Kernel Metadata Declaration

DOIMetadataKernel.xsd · namespace http://www.doi.org/2010/DOISchema · Attribute Value Sets 2.3 · draft — the agency's DOI name is filled in on accreditation

<?xml version="1.0" encoding="UTF-8"?>
<!-- DRAFT declaration: Smart Scholars is not yet a DOI Registration Agency. registrationAgencyDoiName is a marker (10.0/ is no RA's prefix) and is filled in on accreditation. -->
<kernelMetadata xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.doi.org/2010/DOISchema https://www.doi.org/doi_schemas/DOIMetadataKernel.xsd" xmlns="http://www.doi.org/2010/DOISchema">
  <referentDoiName>10.46243/jst.2022.v7.i04.pp136-142</referentDoiName>
  <primaryReferentType>Creation</primaryReferentType>
  <registrationAgencyDoiName>10.0/smart-scholars-draft</registrationAgencyDoiName>
  <issueDate>2026-10-04</issueDate>
  <issueNumber>1</issueNumber>
  <referentCreation>
    <name primaryLanguage="en">
      <value>Triplet Network based Few Shot Outlier DetectionSystem in Robot Task Planning for reduced failures/unwanted Executions</value>
      <type>PrincipalTitle</type>
    </name>
    <identifier>
      <nonUriValue>10.46243/jst.2022.v7.i04.pp136-142</nonUriValue>
      <uri returnType="text/html" doesContentNegotiation="true">https://doi.org/10.46243/jst.2022.v7.i04.pp136-142</uri>
      <type>DOI</type>
    </identifier>
    <identifier>
      <uri returnType="text/html">https://www.jst.org.in/index.php/pub/article/view/659</uri>
      <type>URI</type>
    </identifier>
    <identifier>
      <uri>https://www.jst.org.in/index.php/pub/article/download/659/587</uri>
      <type>URI</type>
    </identifier>
    <identifier>
      <uri returnType="application/xml">https://www.jst.org.in/index.php/pub/article/download/659/2108</uri>
      <type>URI</type>
    </identifier>
    <identifier>
      <uri>https://jst.org.in/admin/uploads/JST070434.pdf</uri>
      <type>URI</type>
    </identifier>
    <structuralType>Digital</structuralType>
    <mode>Visual</mode>
    <character>Language</character>
    <type>JournalArticle</type>
    <principalAgent>
      <name>
        <value>MidhunM. S MidhunM. S</value>
        <type>PrincipalName</type>
      </name>
      <role>Author</role>
    </principalAgent>
    <principalAgent>
      <name>
        <value>Longman Publishers</value>
        <type>PrincipalName</type>
      </name>
      <role>Publisher</role>
    </principalAgent>
    <linkedCreation>
      <name>
        <value>Journal of Science &amp; Technology</value>
        <type>PrincipalTitle</type>
      </name>
      <identifier>
        <nonUriValue>2456-5660</nonUriValue>
        <type>ISSN</type>
      </identifier>
      <referentCreationRole>Part</referentCreationRole>
      <referentCreationSequenceIdentifier>
        <value>7</value>
        <type>VolumeNumber</type>
      </referentCreationSequenceIdentifier>
      <referentCreationSequenceIdentifier>
        <value>4</value>
        <type>IssueNumber</type>
      </referentCreationSequenceIdentifier>
      <referentCreationSequenceIdentifier>
        <value>136-142</value>
        <type>PageNumber</type>
      </referentCreationSequenceIdentifier>
    </linkedCreation>
    <language>en</language>
    <languageOfReferentContent>
      <language>en</language>
      <languageOfReferentContentType>Original</languageOfReferentContentType>
    </languageOfReferentContent>
    <creationDate>
      <date>2022-06-30</date>
      <creationDateType>PublicationDate</creationDateType>
    </creationDate>
  </referentCreation>
</kernelMetadata>
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