249 research outputs found
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Stakeholders Classification System Based on Clustering Techniques
No full textStakeholder classification is carried out by project managers using methods such as interviews with experts, brainstorming and checklists. These methods are carried out manually and present a subjective character as they depend on the appreciation of the interviewees. It affects the accuracy of the classification and the making-decisions. The objective of this research is to propose a fuzzy inference system for the classification of stakeholders, which will improve the quality of such classification in the projects. The proposal performs the automatic learning and the adjustment of the fuzzy inference system to classify the stakeholders executing two clustering algorithms: SBC and DENFIS. It examines the results of applying them in 10 iterations by calculating the measures: accuracy, false positive cases, false negative cases, mean square error and symmetric mean absolute percentage error. The best results are shown by the SBC algorithm. The fuzzy inference system for stakeholder’s classification generated improves the quality of this classification as well as the tools to support decision-making in organizations oriented to projects.Trabajo de investigaciónDoble cieg
Estudio del clima organizacional en Greenland Perú SAC.
Get PDFLa presente investigación se realizó en Greenland Perú, una agroexportadora de productos
perecibles. Es de tipo mixta bajo el enfoque de complementariedad que tuvo como
principal objetivo la medición del Clima Organizacional. Para la medición se aplicó dos
instrumentos; el primero fue la adaptación de metodología para la Evaluación del Clima
Organizacional (EDCO) formulada por el Ministerio de Salud, consta de una encuesta
validad por expertos y aplicada a todos los colaboradores de la organización; el segundo
fue una entrevista semiestructurada a los tres jefes de áreas, con el objetivo de contrastar
la información obtenida de la EDCO con la realidad. Los resultados reflejan que el Clima
Organizacional en Greenland es “Saludable”; a pesar de ello, hay variables “Por Mejorar”
que se reflejan de forma positiva en los resultados de la EDCO, al igual que variables
“Saludables” que no se reflejan en la realidad.ÍNDICE
CAPÍTULO I. PLANTEAMIENTO TEORICO 5
Descripción de la Problemática 5
Pregunta de Investigación 5
Objetivos 5
Objetivo General. 5
Objetivos Específicos. 6
CAPITULO II 7
Fundamentación Teórica 7
Relación del Clima Organizacional y Cultura Organizacional. 18
Metodologías para la medición del Clima Organizacional. 19
Variables y Dimensiones 23
Cultura de la organización. 23
Diseño organizacional (estructura). 24
Potencial humano. 25
Literatura referida a la metodología. 26
CAPÍTULO III 31
PLANTEAMIENTO METODOLÓGICO Y OPERATIVO 31
Tipo de Investigación 31
Diseño de la Investigación 31
Unidad de Estudio 32
Población 34
Muestra 34
Métodos e Instrumento 34
Datos Cuantitativos 34
Datos Cualitativos 36
Validación del Instrumento 36
Aplicación del Instrumento 37
Instrumento Cuantitativo 37
Instrumento Cualitativo 38
CAPITULO IV 40
RESULTADOS DE LA INVESTIGACIÓN 40
Población Encuestada 40
Resultados del Instrumento Cuantitativo (EDCO) 40
Descripción de la Variable Cultura de la Organización 43
Descripción de la Variable Diseño Organizacional 47
Descripción de la Variable Potencial Humano. 50
Escala de Sinceridad. 54
Promedio de Priorización de las Dimensiones 55
Resultados del Instrumento Cualitativo (Entrevista) 56
CAPÍTULO V 60
CONCLUSIONES Y RECOMENDACIONES 60
Conclusiones 60
Limitaciones del Estudio 61
Recomendaciones para futuras investigaciones 62
REFERENCIAS 63Tesi
On Semantic Solutions for Efficient Approximate Similarity Search on Large-Scale Datasets
No full textApproximate similarity search algorithms based on hashing were proposed to query high-dimensional datasets due to its fast retrieval speed and low storage cost. Recent studies, promote the use of Convolutional Neural Network (CNN) with hashing techniques to improve the search accuracy. However, there are challenges to solve in order to find a practical and efficient solution to index CNN features, such as the need for heavy training process to achieve accurate query results and the critical dependency on data-parameters. Aiming to overcome these issues, we propose a new method for scalable similarity search, i.e., Deep frActal based Hashing (DAsH), by computing the best data-parameters values for optimal sub-space projection exploring the correlations among CNN features attributes using fractal theory. Moreover, inspired by recent advances in CNNs, we use not only activations of lower layers which are more general-purpose but also previous knowledge of the semantic data on the latest CNN layer to improve the search accuracy. Thus, our method produces a better representation of the data space with a less computational cost for a better accuracy. This significant gain in speed and accuracy allows us to evaluate the framework on a large, realistic, and challenging set of datasets.Trabajo de investigaciónDoble cieg
A Multi-layer Representation Model for the ISO/IEC 33000 Assessment Framework: Analysing Composition and Behaviour
No full textSoftware Process Improvement (SPI) models are a very important topic for SPI workers as software engineering students that require a good comprehension of the process assessment models. This paper proposes a visual multi-layered representational model describing in a highly practical way the ISO/IEC 33000 Assessment Framework through of structural and behavioural views. This kind of representation comprises several semantic layers based on the dimensions of the key elements of the model with an additional dimension for specifying measurements. The structural elements of the model are assigned on distinct layers and connected through dependence and co-occurrence connector in applying the Assessment Model on software processes. The representation model was tested with software developers, academic experts and students.Doble- Cieg
Using Naming Patterns for Identifying Architectural Technical Debt
No full textHasty software development can produce immediate implementations with source code unnecessarily complex and hardly readable. These small kinds of software decay generate a technical debt that could be big enough to seriously affect future maintenance activities. This work presents an analysis technique for identifying architectural technical debt related to non-uniformity of naming patterns; the technique is based on term frequency over package hierarchies. The proposal has been evaluated on projects of two popular organizations, Apache and Eclipse. The results have shown that most of the projects have frequent occurrences of the proposed naming patterns, and using a graph model and aggregated data could enable the elaboration of simple queries for debt identification. The technique has features that favor its applicability on emergent architectures and agile software development.Doble- Cieg
Extracting word clouds in Git repositories
No full textGit based social coding platforms (e.g.; GitHub, BitBucket) have been broadly adopted by many open source projects, and have became an important source of technical and social information about software development. That information could be employed for identifying programming conventions used by software development teams. This work intends leverage the naming patterns immersed in a Git repository of source code, for getting word clouds as a summary of it. Git repositories of several open source projects have been employed to evaluate the proposal. The results show significant words mined from the repositories, and frequent implementation features for code artifacts which use these words.Doble- Cieg
Characterization of climatological time series using autoencoders
No full textCommon problems in climatological time series data are high dimensionality, correlation between the sequential values and noise due to calibration of meteorological stations influencing dramatically in the quality of clustering, classification, climate pattern finding and data processing. One way to deal with this problem is through feature extraction technique. In order to extract features from large climatological time series data, we propose a feature extraction method based on autoencoder neural network (AUTOE). As a first step, time series is standardized. Then, different architectures of autoencoder is applied on it to reduce dimensionality. Finally, k-means clustering algorithm are used to evaluate them through quality measures. As a result, autoencoder performs well and is competitive with other feature extraction techniques over Synthetic Control Chart Time Series.Trabajo de investigaciónDoble cieg
A Technique Based on Naming Patterns for Finding Candidates to Components from Source Code
No full textComponent identification is relevant in maintenance activities but can be hard in scenarios where the volume of source code is high and there is no architecture documentation. This work presents a technique for finding concepts as candidates of architectural components. The technique is based in the frequency of terms that follows naming patterns that are proposed in this work. Projects from Eclipse and Apache, were used for evaluating the technique. The results show terms which match with some popular component types and domain concepts of the software in treatment. The technique has used source code without any architectural documentation showing that could be applied in emergent architectures.Doble- Cieg
A cross-cultural review of lightbot for introducing functions and code reuse
No full textFunctions and code reuse are often among the first topics that students find challenging in introductory programming courses. This paper describes the use of Lightbot to introduce students to these concepts in two post-secondary classrooms in different countries, cultural contexts, and curricula. This work suggests that Lightbot might offer an effective and engaging introduction to these concepts regardless of cultural setting. In this paper we discuss and analyze student responses.Doble- Cieg
Valoración de los Ciudadanos: Crédito Social o Estado Policial de este siglo
No full textParece ser que nos estamos acercando peligrosamente a la realidad de Black Mirror, una sociedad en la que lo único importante es lograr una puntuación de cinco estrellas en base a la valoración colectiva que es determinante para lograr el nivel de vida deseado.
Si bien el crédito social aparejaría un sistema de alcances orwelianos, debe tenerse presente que en una variante de naturaleza económica-comercial se ha venido consolidando como parte del crecimiento del comercio electrónico, por lo que en cierta forma ya se ha implantado dentro de nuestra sociedad y sus alcances están pasando inadvertidos.
Este sistema sobrepasa el enfoque Big Data para convertirse probablemente en manos de cualquier gobierno en la estigmatización del ciudadano con cuestionables repercusiones en torno a la confianza social y la integridad de los individuos. Tal vez este contexto demande la necesidad de centrar nuestra atención en las implicancias de la economía de la reputación a efecto de determinar sus verdaderos alcances en la actualidad.
Nos cuestionamos en torno a las consecuencias jurídicas del posible establecimiento de este sistema al interior de los estados producto de una migración del sector comercial -en el cual los resultados parecen haber sido ya validados- hacia la esfera política-estatal así como la consecuente desnaturalización del rol del estado como hasta ahora ha sido concebido.Doble- Cieg