Добірка наукової літератури з теми "Semi-Structured documents (SSDs)"

Background: Consumption of excessive sugar sweetened beverages (SSBs) has been linked to non-communicable diseases. It is imperative to understand the perceptions regarding taxation and health warnings amongst the medical students, since they are important stakeholders and as there is some evidence that the government may introduce these soon. Objective was to document the awareness and perceptions about taxation and health warnings on SSBs and the predictors of decreasing consumption of SSBs with regards to their future implementation.Methods: This cross-sectional study was conducted among all the undergraduate medical students of a premier teaching hospital of central India during October and November 2019. A web-based self-administered semi-structured questionnaire was used for data collection using Kobo toolbox. Data were analysed using the SPSS software version 24 (IBM SPSS).Results: About three fourths of the study participants were not aware of any taxes on SSBs and had never seen any health warning on SSB packaging. Multivariable logistic regression analysis showed that those aged ≥ 20 were not in favour of decreasing SSB consumption if health warning is present. Participants who were females, whose fathers were professionals, had consumed SSB in the previous seven days, were aware of taxes and those with inadequate sleep were not in favour of decreasing SSB consumption if taxes are increased.Conclusions: There is a need to include health education regarding the harmful effects of consumption of SSBs and to make aware of the benefits of the taxes and warning labels beginning from school days and continued during medical schools.

Science education reformists in Thailand promote the use of socioscientific issues (SSIs)-based teaching to enrich scientific literacy for global citizenship. To achieve this goal, Thai pre-service science teachers (PSTs) must know how to effectively integrate SSIs into their science teaching practices. The purpose of this study was to enhance PSTs’ understanding and practices of SSIs-based teaching via the online mentoring (OM) program. Three PSTs were selected as case studies, and data were collected from online observations, semi-structured interviews, online discussions, and online document reviews. The analytical methods included within-case and cross-case analysis. This study found that the OM program was effective in enhancing PSTs’ understanding and practices of SSIs-based teaching. As a result, their teaching practices evolved from conveying content knowledge to promoting higher-order cognitive practices. In addition, the PSTs demonstrated a deeper appreciation for OM programs as a means to enhance teaching practices. This research demonstrates how the implementation of OM programs has the potential to be powerful tool for professional development of science educators, which is essential for transforming science educational practices.

The study explored how business rescue (BR) practices can inform the development of an occupation profile needed to lay a foundation for developing business rescue practitioners’ occupation-specific qualifications. There are 11 professional organisations whose members qualify to serve as business rescue experts. The relevant occupation cannot have more than one occupation expert profile. The study employed qualitative approaches that entailed semi-structured interviews with 20 informants (12 business rescue practitioners, four short skills development programme (SSDP) managers, and 4 SSDP facilitators) and qualitative document content analysis of 30 court cases settled on the BR. The 12 business rescue practitioners (BRP) were randomly selected. However, other informants were accessed through a purposeful sample selection process. The field findings show that the BRP occupation in South Africa is a regulated practice area without an occupation expert profile. The BR practices have not been incorporated in a qualification framework registered on the Qualification Framework (NQF) governed by the South African Qualifications Authority (SAQA). Development of pipeline talent is limited, and the monitoring of SSDPs encounters limitations without an occupation expert profile and an occupation-specific qualification. The BR practices can be categorised into 11 tasks linked to practitioner training disciplines. The existing SSDPs provide an important mechanism for continuing professional development. However, the contents should be linked to BR practices and an occupation profile embracing the BRP role as an interim managing director in a business rescue process. The uniqueness of this article resides in its documentation of BR practices generated from multiple data sources that should be used to develop an occupation expert profile that reflects BRP’s capability. The setting of the BR process requires experts to operate in the capability realm. Methods: The study employed qualitative approaches that entailed semi-structured interviews with 20 informants (12 business rescue practitioners, four short skills development programme (SSDP) managers, and 4 SSDP facilitators) and qualitative document content analysis of 30 court cases settled on the BR. The 12 business rescue practitioners (BRP) were randomly selected. However, other informants were accessed through a purposeful sample selection process. Results: The field findings show that the BRP occupation in South Africa is a regulated practice area without an occupation expert profile. The BR practices have not been incorporated in a qualification framework registered on the Qualification Framework (NQF) governed by the South African Qualifications Authority(SAQA). Development of pipeline talent is limited, and the monitoring of SSDPs encounters limitations without an occupation expert profile and an occupation-specific qualification. The BR practices can be categorised into 11 tasks linked to practitioner training disciplines. Practical implications: The existing SSDPs provide an important mechanism for continuing professional development. However, the contents should be linked to BR practices and an occupation profile embracing the BRP role as an interim managing director in a business rescue process. Originality value: The uniqueness of this article resides in its documentation of BR practices generated from multiple data sources that should be used to develop an occupation expert profile that reflects BRP’s capability. The setting of BR process requires experts to operate in the capability realm.Keywords: business, expert, occupation, practitioner, practice, profile, rescue, voices.

ABSTRACT Background Violent injury among trauma surgery patients is strongly associated with exposure to harmful social determinants of health and negative long-term health outcomes. Medical-legal partnerships in other settings successfully provide patients with legal services to address similar health-harming legal needs and may offer a promising model for the care of violently-injured patients. Study Design An electronic survey tool was distributed to clinicians and staff affiliated with the hospital-based violence intervention program at a single urban level one trauma center. Semi-structured follow up interviews were conducted with participants, and interviews were coded using thematic analysis and grounded theory. Results Participants reported many health-harming legal needs among their violently injured patients. The most commonly-identified needs were: health insurance denials (62.5%); difficulty accessing crime victims compensation funds (56.3%); trouble accessing official documents (50%); and problems with non-SSDI public benefits (50%). Participants reported inconsistent methods for learning about and responding to patients’ health-harming legal needs. The most common barriers to addressing these needs included: lack of awareness that a lawyer could help with the issue (68.8%); prioritization of other needs (68.8%); previous negative legal experiences (62.5%); and cost (62.5%). Identified needs encompass issues traditionally-addressed by MLPs as well as more novel challenges faced by violent injury survivors. Conclusion This survey and interview-based study identifies complex health-harming legal needs present among violently-injured trauma surgery patients. Medical-legal partnerships specially-designed for the setting of violent injury appear well-suited to meet these needs, potentially reducing risk of violent re-injury, long-term negative health outcomes, and healthcare system costs. Level of Evidence Level IV / Prognostic and Epidemiological

Cette thèse est réalisée dans le cadre du projet BPI DeepTech, en collaboration avec la société Fair&Smart, veillant principalement à la protection des données personnelles conformément au Règlement Général sur la Protection des Données (RGPD). Dans ce contexte, nous avons proposé un modèle neuronal profond pour l'extraction d'informations dans les documents administratifs semi-structurés (DSSs). En raison du manque de données d'entraînement publiques, nous avons proposé un générateur artificiel de DSSs qui peut générer plusieurs classes de documents avec une large variation de contenu et de mise en page. Les documents sont générés à l'aide de variables aléatoires permettant de gérer le contenu et la mise en page en respectant des contraintes visant à garantir leur proximité avec des documents réels. Des métriques ont été introduites pour évaluer la diversité des DSSs générés en termes de contenu et de mise en page. Les résultats de l'évaluation ont montré que les jeux de données générés pour trois types de DSSs (fiches de paie, tickets de caisse et factures) présentent un degré élevé de diversité, ce qui permet d'éviter le sur-apprentissage lors de l'entraînement des systèmes d'extraction d'informations. En s'appuyant sur le format spécifique des DSSs, constitué de paires de mots (mots-clés, informations) situés dans des voisinages proches spatialement, le document est modélisé sous forme de graphe où les nœuds représentent les mots et les arcs, les relations de voisinage. Le graphe est incorporé dans un réseau d'attention à graphe (GAT) multi-couches (Multi-GAT). Celui-ci applique le mécanisme d'attention multi-têtes permettant d'apprendre l'importance des voisins de chaque mot pour mieux le classer. Une première version de ce modèle a été utilisée en mode supervisé et a obtenu un score F1 de 96 % sur deux jeux de données de factures et de fiches de paie générées, et de 89 % sur un ensemble de tickets de caisse réels (SROIE). Nous avons ensuite enrichi le Multi-GAT avec un plongement multimodal de l'information au niveau des mots (avec des composantes textuelle, visuelle et positionnelle), et l'avons associé à un auto-encodeur variationnel à graphe (VGAE). Ce modèle fonctionne en mode semi-supervisé, capable d'apprendre à partir des données annotées et non annotées simultanément. Pour optimiser au mieux la classification des nœuds du graphe, nous avons proposé un semi-VGAE dont l'encodeur partage ses premières couches avec le classifieur Multi-GAT. Cette optimisation est encore renforcée par la proposition d'une fonction de perte VGAE gérée par la perte de classification. En utilisant une petite base de données non annotées, nous avons pu améliorer de plus de 3 % le score F1 obtenu sur un ensemble de factures générées. Destiné à fonctionner dans un environnement protégé, nous avons adapté l'architecture du modèle pour son chiffrement homomorphe. Nous avons étudié une méthode de réduction de la dimensionnalité du modèle Multi-GAT. Ensuite, nous avons proposé une approche d'approximation polynomiale des fonctions non-linéaires dans le modèle. Pour réduire la dimension du modèle, nous avons proposé une méthode de fusion de caractéristiques multimodales qui nécessite peu de paramètres supplémentaires et qui réduit les dimensions du modèle tout en améliorant ses performances. Pour l'adaptation au chiffrement, nous avons étudié des approximations polynomiales de degrés faibles aux fonctions non-linéaires avec une utilisation des techniques de distillation de connaissance et de fine tuning pour mieux adapter le modèle aux nouvelles approximations. Nous avons pu minimiser la perte lors de l'approximation d'environ 3 % pour deux jeux de données de factures ainsi qu'un jeu de données de fiches de paie et de 5 % pour SROIE
This 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

In the last decade, there has been a huge amount of documents with different types of rich metadata information, which belongs to the Semi-Structured Documents (SSDs), appearing in many real applications. It is an interesting research work to model this type of text data following the way how humans understand text with informative metadata. In the paper, we introduce a Self-paced Compensatory Deep Boltzmann Machine (SCDBM) architecture that learns a deep neural network by using metadata information to learn deep structure layer-wisely for Semi-Structured Documents (SSDs) embedding in a self-paced way. Inspired by the way how humans understand text, the model defines a deep process of document vector extraction beyond the space of words by jointing the metadata where each layer selects different types of metadata. We present efficient learning and inference algorithms for the SCDBM model and empirically demonstrate that using the representation discovered by this model has better performance on semi-structured document classification and retrieval, and tag prediction comparing with state-of-the-art baselines.