{ "ok": true, "doi": "10.46243/jst.2023.v8.i04.pp53-59", "events": [ { "seq": 1, "kind": "register", "at": "2026-09-29 22:00:28", "by": "Administrator (admin)", "by_kind": "user", "note": "registered at Crossref; record read from api.crossref.org", "changes": [ { "path": "abstract.lang", "from": null, "to": "en" }, { "path": "abstract.value", "from": null, "to": "With the developments of internet technologies, dealing with a mass of law cases urgently and assigning classification cases automatically are the most basic and critical steps. Convolutional Neural Networks (CNNs), has been shown to be effective for text classification. To better apply CNNs into law text classification, this paper presents a new semi-supervised Convolutional Neural Networks (SSC) framework. Our method combines unlabeled data with a small labelled training set to train better models, and then integrates into a supervised CNN. More specifically, for effective use of word order for text categorization, we use the feature of not lowdimensional word vectors but high-dimensional text data, that is, a small text region is learned based on sequences of one-hot vectors. To better improve the prediction accuracy of the scheme, we seek effective use of unlabeled data for text categorization for integration into a supervised CNN. We compare the proposed scheme to state-of-the-art methods by the real datasets. The results demonstrate that the semi-supervised learning model can get best text classification accuracy" }, { "path": "agents.0.name.family", "from": null, "to": "Mr.Ch.Mani Kanta Kalyan" }, { "path": "agents.0.name.given", "from": null, "to": "Mr.Ch.Mani Kanta Kalyan" }, { "path": "agents.0.role", "from": null, "to": "author" }, { "path": "agents.0.sequence", "from": null, "to": "first" }, { "path": "agents.1.name.org", "from": null, "to": "Longman Publishers" }, { "path": "agents.1.role", "from": null, "to": "publisher" }, { "path": "characters.0", "from": null, "to": "Language" }, { "path": "container.identifiers.0.medium", "from": null, "to": "electronic" }, { "path": "container.identifiers.0.type", "from": null, "to": "ISSN" }, { "path": "container.identifiers.0.value", "from": null, "to": "2456-5660" }, { "path": "container.issue", "from": null, "to": "4" }, { "path": "container.pages.first", "from": null, "to": "53" }, { "path": "container.pages.last", "from": null, "to": "59" }, { "path": "container.titles.0.type", "from": null, "to": "PrincipalTitle" }, { "path": "container.titles.0.value", "from": null, "to": "Journal of Science & Technology" }, { "path": "container.type", "from": null, "to": "Journal" }, { "path": "container.volume", "from": null, "to": "8" }, { "path": "dates.date_type", "from": null, "to": "PublicationDate" }, { "path": "dates.online", "from": null, "to": "2023-10-04" }, { "path": "dates.published", "from": null, "to": "2023-10-04" }, { "path": "doi", "from": null, "to": "10.46243/jst.2023.v8.i04.pp53-59" }, { "path": "format", "from": null, "to": "smartscholars-doi-metadata/1.0" }, { "path": "identifiers.0.type", "from": null, "to": "DOI" }, { "path": "identifiers.0.value", "from": null, "to": "10.46243/jst.2023.v8.i04.pp53-59" }, { "path": "language", "from": null, "to": "en" }, { "path": "license.applies_to", "from": null, "to": "vor" }, { "path": "license.start", "from": null, "to": "2023-10-04" }, { "path": "license.url", "from": null, "to": "https://creativecommons.org/licenses/by/4.0/" }, { "path": "links.0.primary", "from": null, "to": true }, { "path": "links.0.return_type", "from": null, "to": "text/html" }, { "path": "links.0.url", "from": null, "to": "https://www.jst.org.in/index.php/pub/article/view/710" }, { "path": "links.1.purpose", "from": null, "to": "text-mining" }, { "path": "links.1.return_type", "from": null, "to": "application/pdf" }, { "path": "links.1.url", "from": null, "to": "https://www.jst.org.in/index.php/pub/article/download/710/635" }, { "path": "links.2.purpose", "from": null, "to": "text-mining" }, { "path": "links.2.return_type", "from": null, "to": "application/xml" }, { "path": "links.2.url", "from": null, "to": "https://www.jst.org.in/index.php/pub/article/download/710/1856" }, { "path": "links.3.purpose", "from": null, "to": "similarity-checking" }, { "path": "links.3.url", "from": null, "to": "https://www.jst.org.in/admin/uploads/B12.pdf" }, { "path": "modes.0", "from": null, "to": "Visual" }, { "path": "record.issue_number", "from": null, "to": 1 }, { "path": "record.registered", "from": null, "to": "2024-02-16" }, { "path": "record.registrant", "from": null, "to": "Longman Publishers" }, { "path": "record.source", "from": null, "to": "crossref-api" }, { "path": "record.source_agency", "from": null, "to": "Crossref (member 25296)" }, { "path": "record.updated", "from": null, "to": "2026-09-17" }, { "path": "references.0.doi", "from": null, "to": "10.1145/846183.846188" }, { "path": "references.0.key", "from": null, "to": "ref1" }, { "path": "references.0.unstructured", "from": null, "to": "J. Srivastava, R. Cooley, M. Deshpande, and P.-N. Tan, ‘Web Usage Mining: Discovery and Applications of Usage Patterns from Web Data’, SIGKDD Explor Newsl, vol. 1, no. 2, pp. 12–23, Jan. 2000" }, { "path": "references.1.doi", "from": null, "to": "10.12691/jcsa-1-3-3" }, { "path": "references.1.key", "from": null, "to": "ref2" }, { "path": "references.1.unstructured", "from": null, "to": "M. Jafari, F. SoleymaniSabzchi, and S. Jamali, ‘Extracting Users’Navigational Behaviour from Web Log Data: a Survey’, J. Comput. Sci. Appl. J. Comput. Sci. Appl., vol. 1, no. 3, pp. 39–45, Jan. 2013" }, { "path": "references.10.key", "from": null, "to": "ref11" }, { "path": "references.10.unstructured", "from": null, "to": "S. Kanoje, S. Girase, and D. Mukhopadhyay, ‘User profiling trends, techniques and applications’, ArXiv Prepr. ArXiv150307474, 2015" }, { "path": "references.11.key", "from": null, "to": "ref12" }, { "path": "references.11.unstructured", "from": null, "to": "H. Kim and P. K. Chan, ‘Implicit indicators for interesting web pages’, 2005" }, { "path": "references.12.doi", "from": null, "to": "10.1109/adc.2001.904471" }, { "path": "references.12.key", "from": null, "to": "ref13" }, { "path": "references.12.unstructured", "from": null, "to": "J. Xiao, Y. Zhang, X. Jia, and T. Li, ‘Measuring similarity of interests for clustering Web-users’, in Proceedings 12th Australasian Database Conference. ADC 2001, 2001, pp. 107–114" }, { "path": "references.2.key", "from": null, "to": "ref3" }, { "path": "references.2.unstructured", "from": null, "to": "N. Soni and P. K. Verma, ‘A Survey On Web Log Mining And Pattern Prediction’, Int. J. Adv. Technol. Eng. Sci.-2348-7550" }, { "path": "references.3.key", "from": null, "to": "ref4" }, { "path": "references.3.unstructured", "from": null, "to": "T. R. Ramesh and C. Kavitha, ‘Web user interest prediction framework based on user behaviour for dynamic websites’, Life Sci. J., vol. 10, no. 2, pp. 1736–1739, 2013" }, { "path": "references.4.doi", "from": null, "to": "10.1145/956750.956785" }, { "path": "references.4.key", "from": null, "to": "ref5" }, { "path": "references.4.unstructured", "from": null, "to": "L. Yi, B. Liu, and X. Li, ‘Eliminating Noisy Information in Web Pages for Data Mining’, in Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, USA, 2003, pp. 296–305" }, { "path": "references.5.doi", "from": null, "to": "10.1109/icacci.2014.6968377" }, { "path": "references.5.key", "from": null, "to": "ref6" }, { "path": "references.5.unstructured", "from": null, "to": "A. Dutta, S. Paria, T. Golui, and D. K. Kole, ‘Structural analysis and regular expressionsbased noise elimination from web pages for web content mining’, in 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2014, pp. 1445–1451" }, { "path": "references.6.doi", "from": null, "to": "10.6339/jds.2013.11(1).1091" }, { "path": "references.6.key", "from": null, "to": "ref7" }, { "path": "references.6.unstructured", "from": null, "to": "G. D. S. Jayakumar and B. J. Thomas, ‘A new procedure of clustering based on multivariate outlier detection’, J. Data Sci., vol. 11, no. 1, pp. 69–84, 2013" }, { "path": "references.7.doi", "from": null, "to": "10.5121/ijcsa.2014.4209" }, { "path": "references.7.key", "from": null, "to": "ref8" }, { "path": "references.7.unstructured", "from": null, "to": "V. Chitraa and A. S. Thanamani, ‘Web Log Data Analysis by Enhanced Fuzzy C Means Clustering’, Int. J. Comput. Sci. Appl., vol. 4, no. 2, pp. 81–95, Apr. 2014" }, { "path": "references.8.doi", "from": null, "to": "10.5121/ijnsa.2011.3107" }, { "path": "references.8.key", "from": null, "to": "ref9" }, { "path": "references.8.unstructured", "from": null, "to": "L. K. Joshila Grace, V. Maheswari, and D. Nagamalai, ‘Analysis of Web Logs And Web User In Web Mining’, Int. J. Netw. Secur. Its Appl., vol. 3, no. 1, pp. 99–110, Jan. 2011" }, { "path": "references.9.doi", "from": null, "to": "10.1109/wi-iat.2013.62" }, { "path": "references.9.key", "from": null, "to": "ref10" }, { "path": "references.9.unstructured", "from": null, "to": "P. Peñas, R. del Hoyo, J. Vea-Murguía, C. González, and S. Mayo, ‘Collective Knowledge Ontology User Profiling for Twitter–Automatic User Profiling’, in 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT), 2013, vol. 1, pp. 439–444" }, { "path": "referent", "from": null, "to": "Creation" }, { "path": "structural_type", "from": null, "to": "Digital" }, { "path": "titles.0.lang", "from": null, "to": "en" }, { "path": "titles.0.type", "from": null, "to": "PrincipalTitle" }, { "path": "titles.0.value", "from": null, "to": "Text Classification for Newsgroup using Deep Learning" }, { "path": "type", "from": null, "to": "JournalArticle" } ], "record_after": { "format": "smartscholars-doi-metadata/1.0", "doi": "10.46243/jst.2023.v8.i04.pp53-59", "referent": "Creation", "type": "JournalArticle", "structural_type": "Digital", "modes": [ "Visual" ], "characters": [ "Language" ], "titles": [ { "value": "Text Classification for Newsgroup using Deep Learning", "type": "PrincipalTitle", "lang": "en" } ], "identifiers": [ { "type": "DOI", "value": "10.46243/jst.2023.v8.i04.pp53-59" } ], "agents": [ { "role": "author", "name": { "given": "Mr.Ch.Mani Kanta Kalyan", "family": "Mr.Ch.Mani Kanta Kalyan" }, "sequence": "first" }, { "role": "publisher", "name": { "org": "Longman Publishers" } } ], "dates": { "published": "2023-10-04", "date_type": "PublicationDate", "online": "2023-10-04" }, "language": "en", "container": { "type": "Journal", "titles": [ { "value": "Journal of Science & Technology", "type": "PrincipalTitle" } ], "identifiers": [ { "type": "ISSN", "value": "2456-5660", "medium": "electronic" } ], "volume": "8", "issue": "4", "pages": { "first": "53", "last": "59" } }, "links": [ { "url": "https://www.jst.org.in/index.php/pub/article/view/710", "return_type": "text/html", "primary": true }, { "url": "https://www.jst.org.in/index.php/pub/article/download/710/635", "purpose": "text-mining", "return_type": "application/pdf" }, { "url": "https://www.jst.org.in/index.php/pub/article/download/710/1856", "purpose": "text-mining", "return_type": "application/xml" }, { "url": "https://www.jst.org.in/admin/uploads/B12.pdf", "purpose": "similarity-checking" } ], "abstract": { "value": "With the developments of internet technologies, dealing with a mass of law cases urgently and assigning classification cases automatically are the most basic and critical steps. Convolutional Neural Networks (CNNs), has been shown to be effective for text classification. To better apply CNNs into law text classification, this paper presents a new semi-supervised Convolutional Neural Networks (SSC) framework. Our method combines unlabeled data with a small labelled training set to train better models, and then integrates into a supervised CNN. More specifically, for effective use of word order for text categorization, we use the feature of not lowdimensional word vectors but high-dimensional text data, that is, a small text region is learned based on sequences of one-hot vectors. To better improve the prediction accuracy of the scheme, we seek effective use of unlabeled data for text categorization for integration into a supervised CNN. We compare the proposed scheme to state-of-the-art methods by the real datasets. The results demonstrate that the semi-supervised learning model can get best text classification accuracy", "lang": "en" }, "license": { "url": "https://creativecommons.org/licenses/by/4.0/", "start": "2023-10-04", "applies_to": "vor" }, "references": [ { "key": "ref1", "doi": "10.1145/846183.846188", "unstructured": "J. Srivastava, R. Cooley, M. Deshpande, and P.-N. Tan, ‘Web Usage Mining: Discovery and Applications of Usage Patterns from Web Data’, SIGKDD Explor Newsl, vol. 1, no. 2, pp. 12–23, Jan. 2000" }, { "key": "ref2", "doi": "10.12691/jcsa-1-3-3", "unstructured": "M. Jafari, F. SoleymaniSabzchi, and S. Jamali, ‘Extracting Users’Navigational Behaviour from Web Log Data: a Survey’, J. Comput. Sci. Appl. J. Comput. Sci. Appl., vol. 1, no. 3, pp. 39–45, Jan. 2013" }, { "key": "ref3", "unstructured": "N. Soni and P. K. Verma, ‘A Survey On Web Log Mining And Pattern Prediction’, Int. J. Adv. Technol. Eng. Sci.-2348-7550" }, { "key": "ref4", "unstructured": "T. R. Ramesh and C. Kavitha, ‘Web user interest prediction framework based on user behaviour for dynamic websites’, Life Sci. J., vol. 10, no. 2, pp. 1736–1739, 2013" }, { "key": "ref5", "doi": "10.1145/956750.956785", "unstructured": "L. Yi, B. Liu, and X. Li, ‘Eliminating Noisy Information in Web Pages for Data Mining’, in Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, USA, 2003, pp. 296–305" }, { "key": "ref6", "doi": "10.1109/icacci.2014.6968377", "unstructured": "A. Dutta, S. Paria, T. Golui, and D. K. Kole, ‘Structural analysis and regular expressionsbased noise elimination from web pages for web content mining’, in 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2014, pp. 1445–1451" }, { "key": "ref7", "doi": "10.6339/jds.2013.11(1).1091", "unstructured": "G. D. S. Jayakumar and B. J. Thomas, ‘A new procedure of clustering based on multivariate outlier detection’, J. Data Sci., vol. 11, no. 1, pp. 69–84, 2013" }, { "key": "ref8", "doi": "10.5121/ijcsa.2014.4209", "unstructured": "V. Chitraa and A. S. Thanamani, ‘Web Log Data Analysis by Enhanced Fuzzy C Means Clustering’, Int. J. Comput. Sci. Appl., vol. 4, no. 2, pp. 81–95, Apr. 2014" }, { "key": "ref9", "doi": "10.5121/ijnsa.2011.3107", "unstructured": "L. K. Joshila Grace, V. Maheswari, and D. Nagamalai, ‘Analysis of Web Logs And Web User In Web Mining’, Int. J. Netw. Secur. Its Appl., vol. 3, no. 1, pp. 99–110, Jan. 2011" }, { "key": "ref10", "doi": "10.1109/wi-iat.2013.62", "unstructured": "P. Peñas, R. del Hoyo, J. Vea-Murguía, C. González, and S. Mayo, ‘Collective Knowledge Ontology User Profiling for Twitter–Automatic User Profiling’, in 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT), 2013, vol. 1, pp. 439–444" }, { "key": "ref11", "unstructured": "S. Kanoje, S. Girase, and D. Mukhopadhyay, ‘User profiling trends, techniques and applications’, ArXiv Prepr. ArXiv150307474, 2015" }, { "key": "ref12", "unstructured": "H. Kim and P. K. Chan, ‘Implicit indicators for interesting web pages’, 2005" }, { "key": "ref13", "doi": "10.1109/adc.2001.904471", "unstructured": "J. Xiao, Y. Zhang, X. Jia, and T. Li, ‘Measuring similarity of interests for clustering Web-users’, in Proceedings 12th Australasian Database Conference. ADC 2001, 2001, pp. 107–114" } ], "record": { "registrant": "Longman Publishers", "registered": "2024-02-16", "updated": "2026-09-17", "issue_number": 1, "source": "crossref-api", "source_agency": "Crossref (member 25296)" } }, "url_after": "https://www.jst.org.in/index.php/pub/article/view/710", "sha256_before": null, "sha256_after": "ca6bc59c77be6f4cce5a4fcb2eedaedbbb357eace0d350ded09b330fbb9d0c67" }, { "seq": 2, "kind": "update", "at": "2026-09-29 23:59:46", "by": "Administrator (admin)", "by_kind": "user", "note": "record re-read from api.crossref.org", "changes": [ { "path": "container.titles.0.value", "from": "Journal of Science & Technology", "to": "Journal of Science & Technology" } ], "record_after": { "format": "smartscholars-doi-metadata/1.0", "doi": "10.46243/jst.2023.v8.i04.pp53-59", "referent": "Creation", "type": "JournalArticle", "structural_type": "Digital", "modes": [ "Visual" ], "characters": [ "Language" ], "titles": [ { "value": "Text Classification for Newsgroup using Deep Learning", "type": "PrincipalTitle", "lang": "en" } ], "identifiers": [ { "type": "DOI", "value": "10.46243/jst.2023.v8.i04.pp53-59" } ], "agents": [ { "role": "author", "name": { "given": "Mr.Ch.Mani Kanta Kalyan", "family": "Mr.Ch.Mani Kanta Kalyan" }, "sequence": "first" }, { "role": "publisher", "name": { "org": "Longman Publishers" } } ], "dates": { "published": "2023-10-04", "date_type": "PublicationDate", "online": "2023-10-04" }, "language": "en", "container": { "type": "Journal", "titles": [ { "value": "Journal of Science & Technology", "type": "PrincipalTitle" } ], "identifiers": [ { "type": "ISSN", "value": "2456-5660", "medium": "electronic" } ], "volume": "8", "issue": "4", "pages": { "first": "53", "last": "59" } }, "links": [ { "url": "https://www.jst.org.in/index.php/pub/article/view/710", "return_type": "text/html", "primary": true }, { "url": "https://www.jst.org.in/index.php/pub/article/download/710/635", "purpose": "text-mining", "return_type": "application/pdf" }, { "url": "https://www.jst.org.in/index.php/pub/article/download/710/1856", "purpose": "text-mining", "return_type": "application/xml" }, { "url": "https://www.jst.org.in/admin/uploads/B12.pdf", "purpose": "similarity-checking" } ], "abstract": { "value": "With the developments of internet technologies, dealing with a mass of law cases urgently and assigning classification cases automatically are the most basic and critical steps. Convolutional Neural Networks (CNNs), has been shown to be effective for text classification. To better apply CNNs into law text classification, this paper presents a new semi-supervised Convolutional Neural Networks (SSC) framework. Our method combines unlabeled data with a small labelled training set to train better models, and then integrates into a supervised CNN. More specifically, for effective use of word order for text categorization, we use the feature of not lowdimensional word vectors but high-dimensional text data, that is, a small text region is learned based on sequences of one-hot vectors. To better improve the prediction accuracy of the scheme, we seek effective use of unlabeled data for text categorization for integration into a supervised CNN. We compare the proposed scheme to state-of-the-art methods by the real datasets. The results demonstrate that the semi-supervised learning model can get best text classification accuracy", "lang": "en" }, "license": { "url": "https://creativecommons.org/licenses/by/4.0/", "start": "2023-10-04", "applies_to": "vor" }, "references": [ { "key": "ref1", "doi": "10.1145/846183.846188", "unstructured": "J. Srivastava, R. Cooley, M. Deshpande, and P.-N. Tan, ‘Web Usage Mining: Discovery and Applications of Usage Patterns from Web Data’, SIGKDD Explor Newsl, vol. 1, no. 2, pp. 12–23, Jan. 2000" }, { "key": "ref2", "doi": "10.12691/jcsa-1-3-3", "unstructured": "M. Jafari, F. SoleymaniSabzchi, and S. Jamali, ‘Extracting Users’Navigational Behaviour from Web Log Data: a Survey’, J. Comput. Sci. Appl. J. Comput. Sci. Appl., vol. 1, no. 3, pp. 39–45, Jan. 2013" }, { "key": "ref3", "unstructured": "N. Soni and P. K. Verma, ‘A Survey On Web Log Mining And Pattern Prediction’, Int. J. Adv. Technol. Eng. Sci.-2348-7550" }, { "key": "ref4", "unstructured": "T. R. Ramesh and C. Kavitha, ‘Web user interest prediction framework based on user behaviour for dynamic websites’, Life Sci. J., vol. 10, no. 2, pp. 1736–1739, 2013" }, { "key": "ref5", "doi": "10.1145/956750.956785", "unstructured": "L. Yi, B. Liu, and X. Li, ‘Eliminating Noisy Information in Web Pages for Data Mining’, in Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, USA, 2003, pp. 296–305" }, { "key": "ref6", "doi": "10.1109/icacci.2014.6968377", "unstructured": "A. Dutta, S. Paria, T. Golui, and D. K. Kole, ‘Structural analysis and regular expressionsbased noise elimination from web pages for web content mining’, in 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2014, pp. 1445–1451" }, { "key": "ref7", "doi": "10.6339/jds.2013.11(1).1091", "unstructured": "G. D. S. Jayakumar and B. J. Thomas, ‘A new procedure of clustering based on multivariate outlier detection’, J. Data Sci., vol. 11, no. 1, pp. 69–84, 2013" }, { "key": "ref8", "doi": "10.5121/ijcsa.2014.4209", "unstructured": "V. Chitraa and A. S. Thanamani, ‘Web Log Data Analysis by Enhanced Fuzzy C Means Clustering’, Int. J. Comput. Sci. Appl., vol. 4, no. 2, pp. 81–95, Apr. 2014" }, { "key": "ref9", "doi": "10.5121/ijnsa.2011.3107", "unstructured": "L. K. Joshila Grace, V. Maheswari, and D. Nagamalai, ‘Analysis of Web Logs And Web User In Web Mining’, Int. J. Netw. Secur. Its Appl., vol. 3, no. 1, pp. 99–110, Jan. 2011" }, { "key": "ref10", "doi": "10.1109/wi-iat.2013.62", "unstructured": "P. Peñas, R. del Hoyo, J. Vea-Murguía, C. González, and S. Mayo, ‘Collective Knowledge Ontology User Profiling for Twitter–Automatic User Profiling’, in 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT), 2013, vol. 1, pp. 439–444" }, { "key": "ref11", "unstructured": "S. Kanoje, S. Girase, and D. Mukhopadhyay, ‘User profiling trends, techniques and applications’, ArXiv Prepr. ArXiv150307474, 2015" }, { "key": "ref12", "unstructured": "H. Kim and P. K. Chan, ‘Implicit indicators for interesting web pages’, 2005" }, { "key": "ref13", "doi": "10.1109/adc.2001.904471", "unstructured": "J. Xiao, Y. Zhang, X. Jia, and T. Li, ‘Measuring similarity of interests for clustering Web-users’, in Proceedings 12th Australasian Database Conference. ADC 2001, 2001, pp. 107–114" } ], "record": { "registrant": "Longman Publishers", "registered": "2024-02-16", "updated": "2026-09-17", "issue_number": 1, "source": "crossref-api", "source_agency": "Crossref (member 25296)" } }, "url_after": "https://www.jst.org.in/index.php/pub/article/view/710", "sha256_before": "ca6bc59c77be6f4cce5a4fcb2eedaedbbb357eace0d350ded09b330fbb9d0c67", "sha256_after": "b3f7da202208e2bb15bb0b77bba272c56142f696d9893ba63792ae787ab96f78" } ] }