Literatura científica selecionada sobre o tema "Semi-Structured documents (SSDs)"
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Artigos de revistas sobre o assunto "Semi-Structured documents (SSDs)"
Mandal, Soumya K., G. Revadi, Darshan Parida, Sembagamuthu Sembiah e Anindo Majumdar. "A cross-sectional study on awareness and perceptions regarding taxation and health warnings and factors influencing decreased consumption of sugar sweetened beverages among medical students of Bhopal, India with respect to future implementation of such policies". International Journal Of Community Medicine And Public Health 8, n.º 5 (27 de abril de 2021): 2431. http://dx.doi.org/10.18203/2394-6040.ijcmph20211769.
Texto completo da fontePitiporntapin, Sasithep, Naruemon Yutakom, Troy D. Sadler e Lisa Hines. "Enhancing Pre-service Science Teachers’ Understanding and Practices of SocioScientific Issues (SSIs)-Based Teaching via an Online Mentoring Program". Asian Social Science 14, n.º 5 (19 de abril de 2018): 1. http://dx.doi.org/10.5539/ass.v14n5p1.
Texto completo da fonteAyaya, Onesmus. "Using practitioners’ voices in developing a business rescue practitioner expert profile". Journal of Management and Business Education 7, n.º 2 (24 de abril de 2024). http://dx.doi.org/10.35564/jmbe.2024.0016.
Texto completo da fonteSonnenberg, Jake, Ariana Metchick, Caitlin Schille, Prashati Bhatnagar, Lisa Kessler, Deborah Perry, Vicki Girard, Belinda Taylor e Erin Hall. "Integration of Medical Legal Services into a Hospital-Based Violence Intervention Program: A Survey and Interview-Based Provider Needs Assessment". Journal of Trauma and Acute Care Surgery, 14 de março de 2024. http://dx.doi.org/10.1097/ta.0000000000004302.
Texto completo da fonteTeses / dissertações sobre o assunto "Semi-Structured documents (SSDs)"
Belhadj, Djedjiga. "Multi-GAT semi-supervisé pour l’extraction d’informations et son adaptation au chiffrement homomorphe". Electronic Thesis or Diss., Université de Lorraine, 2024. http://www.theses.fr/2024LORR0023.
Texto completo da fonteThis thesis is being carried out as part of the BPI DeepTech project, in collaboration with the company Fair&Smart, primarily looking after the protection of personal data in accordance with the General Data Protection Regulation (RGPD). In this context, we have proposed a deep neural model for extracting information in semi-structured administrative documents (SSDs). Due to the lack of public training datasets, we have proposed an artificial generator of SSDs that can generate several classes of documents with a wide variation in content and layout. Documents are generated using random variables to manage content and layout, while respecting constraints aimed at ensuring their similarity to real documents. Metrics were introduced to evaluate the content and layout diversity of the generated SSDs. The results of the evaluation have shown that the generated datasets for three SSD types (payslips, receipts and invoices) present a high diversity level, thus avoiding overfitting when training the information extraction systems. Based on the specific format of SSDs, consisting specifically of word pairs (keywords-information) located in spatially close neighborhoods, the document is modeled as a graph where nodes represent words and edges, neighborhood connections. The graph is fed into a multi-layer graph attention network (Multi-GAT). The latter applies the multi-head attention mechanism to learn the importance of each word's neighbors in order to better classify it. A first version of this model was used in supervised mode and obtained an F1 score of 96% on two generated invoice and payslip datasets, and 89% on a real receipt dataset (SROIE). We then enriched the multi-GAT with multimodal embedding of word-level information (textual, visual and positional), and combined it with a variational graph auto-encoder (VGAE). This model operates in semi-supervised mode, being able to learn on both labeled and unlabeled data simultaneously. To further optimize the graph node classification, we have proposed a semi-VGAE whose encoder shares its first layers with the multi-GAT classifier. This is also reinforced by the proposal of a VGAE loss function managed by the classification loss. Using a small unlabeled dataset, we were able to improve the F1 score obtained on a generated invoice dataset by over 3%. Intended to operate in a protected environment, we have adapted the architecture of the model to suit its homomorphic encryption. We studied a method of dimensionality reduction of the Multi-GAT model. We then proposed a polynomial approximation approach for the non-linear functions in the model. To reduce the dimensionality of the model, we proposed a multimodal feature fusion method that requires few additional parameters and reduces the dimensions of the model while improving its performance. For the encryption adaptation, we studied low-degree polynomial approximations of nonlinear functions, using knowledge distillation and fine-tuning techniques to better adapt the model to the new approximations. We were able to minimize the approximation loss by around 3% on two invoice datasets as well as one payslip dataset and by 5% on SROIE
Trabalhos de conferências sobre o assunto "Semi-Structured documents (SSDs)"
Li, Shuangyin, Rong Pan e Jun Yan. "Self-paced Compensatory Deep Boltzmann Machine for Semi-Structured Document Embedding". In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/304.
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