Modélisation des comportements de recherche basé sur les interactions des utilisateurs

Les utilisateurs de systèmes d'information divisent normalement les tâches en une séquence de plusieurs étapes pour les résoudre. En particulier, les utilisateurs divisent les tâches de recherche en séquences de requêtes, en interagissant avec les systèmes de recherche pour mener à bien le processus de recherche d'informations. Les interactions des utilisateurs sont enregistrées dans des journaux de requêtes, ce qui permet de développer des modèles pour apprendre automatiquement les comportements de recherche à partir des interactions des utilisateurs avec les systèmes de recherche. Ces modèles sont à la base de multiples applications d'assistance aux utilisateurs qui aident les systèmes de recherche à être plus interactifs, faciles à utiliser, et cohérents. Par conséquent, nous proposons les contributions suivantes : un modèle neuronale pour apprendre à détecter les limites des tâches de recherche dans les journaux de requête ; une architecture de regroupement profond récurrent qui apprend simultanément les représentations de requête et regroupe les requêtes en tâches de recherche ; un modèle non supervisé et indépendant d'utilisateur pour l'identification des tâches de recherche prenant en charge les requêtes dans seize langues ; et un modèle de tâche de recherche multilingue, une approche non supervisée qui modélise simultanément l'intention de recherche de l'utilisateur et les tâches de recherche. Les modèles proposés améliorent les méthodes existantes de modélisation, en tenant compte de la confidentialité des utilisateurs, des réponses en temps réel et de l'accessibilité linguistique. Le respect de la vie privée de l'utilisateur est une préoccupation majeure, tandis que des réponses rapides sont essentielles pour les systèmes de recherche qui interagissent avec les utilisateurs en temps réel, en particulier dans la recherche par conversation. Dans le même temps, l'accessibilité linguistique est essentielle pour aider les utilisateurs du monde entier, qui interagissent avec les systèmes de recherche dans de nombreuses langues. Les contributions proposées peuvent bénéficier à de nombreuses applications d'assistance aux utilisateurs, en aidant ces derniers à mieux résoudre leurs tâches de recherche lorsqu'ils accèdent aux systèmes de recherche pour répondre à leurs besoins d'information.Users of information systems normally divide tasks in a sequence of multiple steps to solve them. In particular, users divide search tasks into sequences of queries, interacting with search systems to carry out the information seeking process. User interactions are registered on search query logs, enabling the development of models to automatically learn search patterns from the users' interactions with search systems. These models underpin multiple user assisting applications that help search systems to be more interactive, user-friendly, and coherent. User assisting applications include query suggestion, the ranking of search results based on tasks, query reformulation analysis, e-commerce applications, retrieval of advertisement, query-term prediction, mapping of queries to search tasks, and so on. Consequently, we propose the following contributions: a neural model for learning to detect search task boundaries in query logs; a recurrent deep clustering architecture that simultaneously learns query representations through self-training, and cluster queries into groups of search tasks; Multilingual Graph-Based Clustering, an unsupervised, user-agnostic model for search task identification supporting queries in sixteen languages; and Language-agnostic Search Task Model, an unsupervised approach that simultaneously models user search intent and search tasks. Proposed models improve on existing methods for modeling user interactions, taking into account user privacy, realtime response times, and language accessibility. User privacy is a major concern in Ethics for intelligent systems, while fast responses are critical for search systems interacting with users in realtime, particularly in conversational search. At the same time, language accessibility is essential to assist users worldwide, who interact with search systems in many languages. The proposed contributions can benefit many user assisting applications, helping users to better solve their search tasks when accessing search systems to fulfill their information needs

Establishing Cueing Skills When Treating Bilingual Speech Sound Disorders

Purpose: This study sought to train cueing skills in first-year graduate students when working with bilingual children with speech sound disorders to ensure fidelity of intervention of a larger research investigation. Method: Before explicitly training cueing skills, three students were randomly assigned bilingual clients that had been previously diagnosed with a speech sound disorder and asked to administer trial therapy. During the instructional phase, we gave students a cueing protocol, a scoring template, and feedback. We assessed performance according to challenge-point criteria and adherence to our cueing protocol. Results: Performance varied per student, but overall scores were higher during the instructional phases than during the baseline phase for all students. Performance was also higher when the students participated in individual conferencing versus group conferencing. Conclusion: Although the data are limited, the results suggest that a cueing protocol is supportive in establishing cueing skills in first-year graduate students administering speech sound intervention

Classification in biological networks with hypergraphlet kernels

Abstract Motivation Biological and cellular systems are often modeled as graphs in which vertices represent objects of interest (genes, proteins and drugs) and edges represent relational ties between these objects (binds-to, interacts-with and regulates). This approach has been highly successful owing to the theory, methodology and software that support analysis and learning on graphs. Graphs, however, suffer from information loss when modeling physical systems due to their inability to accurately represent multiobject relationships. Hypergraphs, a generalization of graphs, provide a framework to mitigate information loss and unify disparate graph-based methodologies. Results We present a hypergraph-based approach for modeling biological systems and formulate vertex classification, edge classification and link prediction problems on (hyper)graphs as instances of vertex classification on (extended, dual) hypergraphs. We then introduce a novel kernel method on vertex- and edge-labeled (colored) hypergraphs for analysis and learning. The method is based on exact and inexact (via hypergraph edit distances) enumeration of hypergraphlets; i.e. small hypergraphs rooted at a vertex of interest. We empirically evaluate this method on fifteen biological networks and show its potential use in a positive-unlabeled setting to estimate the interactome sizes in various species.This work was partially supported by the National Science Foundation (NSF) [DBI-1458477], National Institutes of Health (NIH) [R01 MH105524], the Indiana University Precision Health Initiative, the European Research Council (ERC) [Consolidator Grant 770827], UCL Computer Science, the Slovenian Research Agency project [J1-8155], the Serbian Ministry of Education and Science Project [III44006] and the Prostate Project.Peer ReviewedPostprint (author's final draft

Classification in biological networks with hypergraphlet kernels

MOTIVATION: Biological and cellular systems are often modeled as graphs in which vertices represent objects of interest (genes, proteins and drugs) and edges represent relational ties between these objects (binds-to, interacts-with and regulates). This approach has been highly successful owing to the theory, methodology and software that support analysis and learning on graphs. Graphs, however, suffer from information loss when modeling physical systems due to their inability to accurately represent multiobject relationships. Hypergraphs, a generalization of graphs, provide a framework to mitigate information loss and unify disparate graph-based methodologies. RESULTS: We present a hypergraph-based approach for modeling biological systems and formulate vertex classification, edge classification and link prediction problems on (hyper)graphs as instances of vertex classification on (extended, dual) hypergraphs. We then introduce a novel kernel method on vertex- and edge-labeled (colored) hypergraphs for analysis and learning. The method is based on exact and inexact (via hypergraph edit distances) enumeration of hypergraphlets; i.e. small hypergraphs rooted at a vertex of interest. We empirically evaluate this method on fifteen biological networks and show its potential use in a positive-unlabeled setting to estimate the interactome sizes in various species. AVAILABILITY AND IMPLEMENTATION: https://github.com/jlugomar/hypergraphlet-kernels. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online

Finanzas a Largo Plazo Análisis del proyecto de inversión Ice Martinez y Cia Ltda. para 2018-2022

El presente trabajo de seminario de graduación consiste en la valoración económica, financiera para un proyecto de inversión. Con el objetivo general de Evaluar la viabilidad financiera en el proyecto de inversión ICE MARTINEZ & CIA para el periodo comprendido del 2018 al 2022. El método investigativo utilizado es cualitativo con lineamientos cuantitativos, para abarcar la estructura del proyecto y una mayor comprensión en la interpretación de los resultados. Ice Martínez & Cía. Ltda. Es una compañía del sector industrial, que se dedicara a la producción y comercialización de hielo en cubo en presentaciones de 25 lb. Estamos orientados para atender el segmento hotelero, supermercado, restaurantes, gasolineras, bares, así también de venta directa. Con el objetivo que la empresa tenga una base a estudios futuros, se realiza una valoración financiera que le permite a la empresa conocer su situación actual al utilizar los resultados de la investigación, además, la empresa desea conocer la rentabilidad actual de su negocio y las recomendaciones resultantes. Como resultado tenemos que en Nicaragua el comercio se ha diversificado principalmente en pequeñas y medianas empresas que han aportado a sus dueños grandes beneficios, por ello las empresas quieren invertir en proyectos a largo plazo y disminuir su riesgo en la inversión, es necesario que realicen evaluaciones de la situación presente y cómo afectará en un futuro la toma de decisiones a los inversionistas y la empresa. En conclusión se determinó a través de las distintas herramientas financieras y de mercado que este proyecto de inversión es rentable

Effects of Exercise on Oxidative Stress in Rats Induced by Ozone

Oxidative stress (OS) induced by acute exercise is reduced by chronic exercise. Ozone (O3) exposure produces OS. The aim of this study was to determine if aerobic exercise (AE) reduced OS produced by O3. A pilot experiment was performed with male Wistar rats submitted to AE (trained to swim 90 min/day). Adaptation to exercise was demonstrated three weeks after training by means of changes in reduced nitrates (NOx) in plasma. Therefore, two-week training was chosen for the following experiments. Six of twelve trained rats were exposed to O3 (0.5 ppm, 4 h/day, one hour before exercise). Two groups of sedentary animals (n = 6 each) were used as controls, one of which was exposed to O3. At the end of the experiments NOx, 8-isoprostane (8-IP), malondialdehyde (MDA), superoxide dismutase (SOD) activity, and carbonyls (CBs) were measured in plasma. CBs did not change in any group. O3-induced OS was manifested by reduced NOx and SOD activity, as well as increased 8-IP and MDA. Exercise significantly blocked O3 effects although SOD was also decreased by exercise (a greater drop occurring in the O3 group). It is concluded that AE protects against OS produced by O3 and the effect is independent of SOD

Dynamic Bayesian networks for integrating multi-omics time-series microbiome data

. A key challenge in the analysis of longitudinal microbiomes data is to go beyond computing their compositional profiles and infer the complex web of interactions between the various microbial taxa, their genes, and the metabolites they consume and produce. To address this challenge, we developed a computational pipeline that first aligns multi-omics data and then uses dynamic Bayesian networks (DBNs) to integrate them into a unified model. We discuss how our approach handles the different sampling and progression rates between individuals, how we reduce the large number of different entities and parameters in the DBNs, and the construction and use of a validation set to model edges. Applying our method to data collected from Inflammatory Bowel Disease (IBD) patients, we show that it can accurately identify known and novel interactions between various entities and can improve on current methods for learning such interactions. Experimental validations support several predictions about novel metabolite-taxa interactions. The source code is freely available under the MIT Open Source license agreement and can be downloaded from https://github.com/DaniRuizPerez/longitudinal_multiomic_analysis_public