LA MINERÍA DE DATOS APLICADA A LA GESTIÓN DOCENTE DE LA UNIVERSIDAD NACIONAL EXPERIMENTAL DE LAS FUERZAS ARMADAS (UNEFA)

La Universidad Nacional Experimental de las Fuerzas Armadas (UNEFA) cuenta con un sistema estandarizado de gestión para la docencia, que registra toda la información relacionada con los estudiantes, sin embargo, en el momento de tomar alguna decisión sobre el aprovechamiento docente de los estudiantes en los primeros años de la carrera, no tiene en cuenta el conocimiento oculto (información no evidente, desconocida a priori y potencialmente útil) de los datos que mantiene almacenados para sustentar determinadas líneas estratégicas en el proceso enseñanza-aprendizaje. El objetivo de la presente investigación es crear una herramienta de evaluación de los estudiantes de nuevo ingreso, a partir del empleo de técnicas de minería de datos que permita a los profesores mejorar los métodos pedagógicos y didácticos en la formación de profesional altamente calificados en la UNEFA. Se realizan cuatro tareas de minería, relacionadas con la asociación, el agrupamiento, la selección de atributos y la clasificación, desarrollándose un total de 48 experimentos, aplicando la metodología CRISP-MD. Como resultados de la la investigación se obtuvieron un conjunto de reglas que presentan parámetros aceptados para ser consideradas útiles durante la toma de decisiones por la Junta Directiva de la UNEFA. La investigación realizada aporta nuevos conocimientos, que permiten redefinir algunos objetivos del negocio, y replantearse un nuevo proceso de KDD

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse

The Multifaceted Nature of Food and Nutrition Insecurity around the World and Foodservice Business

The international concept of food security is a situation where all people have physical, social, and economic access at all times to sufficient, safe, and nutritious food that meets their dietary needs and food preferences for an active and healthy life. All four parameters (availability, access, utilization, and stability) should therefore be measured to determine food security status.Taking into account these premises, this book aims to present original research articles and reviews concerning the following: Agriculture and food security; Agri-tourism and its potential to assist with food security; Business–science cooperation to advance food security; Competing demands and tradeoffs for land and water resources; Consumer behavior, nutritional security and food assistance programs; Food and health; Global and local analyses of food security and its drivers; Global governance and food security; Infectious and non-infectious diseases and food security; Reducing food loss and waste; Reducing risks to food production and distribution from climate change; Supply chains and food security; Technological breakthroughs to help feed the globe; Tourism food security relationship; Urbanization, food value chains, and the sustainable, secure sourcing of food; Food and service quality at food catering establishments; Consumer behavior at foodservice operations (restaurants, cafés, hotels)

European Distance and E-Learning Network (EDEN). Conference Proceedings

Erasmus+ Programme of the European UnionThe powerful combination of the information age and the consequent disruption caused by these unstable environments provides the impetus to look afresh and identify new models and approaches for education (e.g. OERs, MOOCs, PLEs, Learning Analytics etc.). For learners this has taken a fantastic leap into aggregating, curating and co-curating and co-producing outside the boundaries of formal learning environments – the networked learner is sharing voluntarily and for free, spontaneously with billions of people.Supported by Erasmus+ Programme of the European Unioninfo:eu-repo/semantics/publishedVersio

Graph-based Approaches to Text Generation

Deep Learning advances have enabled more fluent and flexible text generation. However, while these neural generative approaches were initially successful in tasks such as machine translation, they face problems – such as unfaithfulness to the source, repetition and incoherence – when applied to generation tasks where the input is structured data, such as graphs. Generating text from graph-based data, including Abstract Meaning Representation (AMR) or Knowledge Graphs (KG), is a challenging task due to the inherent difficulty of properly encoding the input graph while maintaining its original semantic structure. Previous work requires linearizing the input graph, which makes it complicated to properly capture the graph structure since the linearized representation weakens structural information by diluting the explicit connectivity, particularly when the graph structure is complex. This thesis makes an attempt to tackle these issues focusing on two major challenges: first, the creation and improvement of neural text generation systems that can better operate when consuming graph-based input data. Second, we examine text-to-text pretrained language models for graph-to-text generation, including multilingual generation, and present possible methods to adapt these models pretrained on natural language to graph-structured data. In the first part of this thesis, we investigate how to directly exploit graph structures for text generation. We develop novel graph-to-text methods with the capability of incorporating the input graph structure into the learned representations, enhancing the quality of the generated text. For AMR-to-text generation, we present a dual encoder, which incorporates different graph neural network methods, to capture complementary perspectives of the AMR graph. Next, we propose a new KG-to-text framework that learns richer contextualized node embeddings, combining global and local node contexts. We thus introduce a parameter-efficient mechanism for inserting the node connections into the Transformer architecture operating with shortest path lengths between nodes, showing strong performance while using considerably fewer parameters. The second part of this thesis focuses on pretrained language models for text generation from graph-based input data. We first examine how encoder-decoder text-to-text pretrained language models perform in various graph-to-text tasks and propose different task-adaptive pretraining strategies for improving their downstream performance. We then propose a novel structure-aware adapter method that allows to directly inject the input graph structure into pretrained models, without updating their parameters and reducing their reliance on specific representations of the graph structure. Finally, we investigate multilingual text generation from AMR structures, developing approaches that can operate in languages beyond English

Usability analysis of contending electronic health record systems

In this paper, we report measured usability of two leading EHR systems during procurement. A total of 18 users participated in paired-usability testing of three scenarios: ordering and managing medications by an outpatient physician, medicine administration by an inpatient nurse and scheduling of appointments by nursing staff. Data for audio, screen capture, satisfaction rating, task success and errors made was collected during testing. We found a clear difference between the systems for percentage of successfully completed tasks, two different satisfaction measures and perceived learnability when looking at the results over all scenarios. We conclude that usability should be evaluated during procurement and the difference in usability between systems could be revealed even with fewer measures than were used in our study. © 2019 American Psychological Association Inc. All rights reserved.Peer reviewe

Integrating Data Mining and Social Network Techniques into the Development of a Web-based Adaptive Play-based Assessment tool for School Readiness.

A major challenge that faces most families is effectively anticipating how ready to start school a given child is. Traditional tests are not very effective as they depend on the skills of the expert conducting the test. It is argued that automated tools are more attractive especially when they are extended with games capabilities that would be the most attractive for the children to be seriously involved in the test. The first part of this thesis reviews the school readiness approaches applied in various countries. This motivated the development of the sophisticated system described in the thesis. Extensive research was conducted to enrich the system with features that consider machine learning and social network aspects. A modified genetic algorithm was integrated into a web-based stealth assessment tool for school readiness. The research goal is to create a web-based stealth assessment tool that can learn the user's skills and adjust the assessment tests accordingly. The user plays various sessions from various games, while the Genetic Algorithm (GA) selects the upcoming session or group of sessions to be presented to the user according to his/her skills and status. The modified GA and the learning procedure were described. A penalizing system and a fitness heuristic for best choice selection were integrated into the GA. Two methods for learning were presented, namely a memory system and a no-memory system. Several methods were presented for the improvement of the speed of learning. In addition, learning mechanisms were introduced in the social network aspect to address further usage of stealth assessment automation. The effect of the relatives and friends on the readiness of the child was studied by investigating the social communities to which the child belongs and how the trend in these communities will reflect on to the child under investigation. The plan is to develop this framework further by incorporating more information related to social network construction and analysis. Also, it is planned to turn the framework into a self adaptive one by utilizing the feedback from the usage patterns to learn and adjust the evaluation process accordingly

Decoding Legalese Without Borders: Multilingual Evaluation of Language Models on Long Legal Texts

Pretrained transformers have sparked an explosion of research in the field of Natural Language Processing (NLP). Scaling up language models based on the transformer architecture in terms of size, compute, and data led to impressive emergent capabilities that were considered unattainable in such a brief span, a mere three years ago, prior to the launch of GPT-3. These advances catapulted the previously niche field of legal NLP into the mainstream, at the latest, with GPT-4 passing the bar. Many products based on GPT-4 and other large language models are entering the market at an increasing pace, many of those targeting the legal field. This dissertation makes contributions in two key areas within Natural Language Processing (NLP) focused on legal text: resource curation and detailed model analysis. First, we curate an extensive set of multilingual legal datasets, train a variety of language models on these, and establish comprehensive benchmarks for evaluating Large Language Models (LLMs) in the legal domain. Second, we conduct a multidimensional analysis of model performance, focusing on metrics like explainability and calibration in the context of Legal Judgment Prediction. We introduce novel evaluation frameworks and find that while our trained models exhibit high performance and better calibration than human experts, they do not necessarily offer improved explainability. Furthermore, we investigate the feasibility of re-identification in anonymized legal texts, concluding that large-scale re-identification using LLMs is currently unfeasible. For future work, we propose exploring domain adaptation and instruction tuning to enhance language model performance on legal benchmarks, while also advocating for a detailed examination of dataset overlaps and model interpretability. Additionally, we emphasize the need for dataset extension to unexplored legal tasks and underrepresented jurisdictions, aiming for a more comprehensive coverage of the global legal landscape in NLP resources