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8 Historic Elections in Science
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Generos periodísticos
gui_es_06_strumenti_per_il_web_sketch
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Segunda tarea obligatoria
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Developing a gesture-based game platform with collision detection in promoting environmental awareness
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Abstract
The Protector of the Sea is a gesture-based game with a concept of environmental awareness that examines each user’s gestures to qualify for collision detection. The system focuses on assessing the accuracy of the gesture recognition through testing methods, providing questionnaire for an overall feedback with a rating of 4 being Strongly Agree and 1 being Strong Disagree, and calculating paired T-distribution statistics for the learning of the system through a questionnaire given to the same set of users before and after the game. Although there are existing approaches for gesture recognition that provides a relatively accurate feedback for the gestures, none had utilized collision detection for recognition. Usage of the system shows that it was an interesting and knowledgeable for the users. Alongside, it also provides entertainment and uniqueness to the users. The researchers see this study as significant to those who would like to explore gesture recognition using motion capture devices using a similar approach in the future.
Forty-five (45) users from grade levels 7, 9, 10 and Alternative Learning Students (ALS) participated in the study, each of them played the game. These users’ gestures were assessed and evaluated all throughout the game. Seven different gestures were characterized from the system, namely, Catch Right, Catch Middle, Catch Left, Catch Right Forward, Catch Middle Forward, Catch Left Forward and Unload. Based on the results of the testing, the system performed 85.73% accuracy. The overall rating of the system produced was 84%. The calculated result t of -5.6552, and the critical value, 2.015 of the statistical test showed that the means of the before and after data has a significant difference in the existing knowledge of the users’ before and after the game was presented.
Keywords: collision detection, environmental awareness, gesture, recognition, 3d motion capture, technology, Kinect
Elementos de conteo Martin Seveso 180043 Fernando Rodriguez 198238
Conteo y Grafos - Obligatorio 2
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Este principio también llamado ”principio de Dirichlet ”, en honor a un matemático alemán de principios del siglo XIX; establece que : "Si tenemos p palomas, las que se disponen en n nidos , donde p \(\)> n , entonces por lo menos dos palomas se deben ubicar en el mismo nido."
Entrega 2 Conteo y Grafos Sebastián Machiñena (165010) Gonzalo Lamela (152427)
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Relaciones de orden, equivalencia y Hasse
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A continuacion se presenta un resumen breve sobre relaciones de equivalencia y relaciones de orden parcial, esto implica definir una relación y exponer sus propiedades. El material elaborado por la Cátedra de Matemática de Universidad ORT, se utiliza como referencia para extraer conceptos e imágenes.
“El concepto de relacion implica la idea de correspondencia entre los elementos de dos conjuntos que forman parejas ordenadas” (Enciclopedia, 2011). Teniendo en cuenta el concepto anteriormente expresado, veremos las relaciones binarias de un conjunto en si mismo. nos centraremos en explicar sus propiedades y cómo reconocerlas a través de las distintas formas de representación de una relación.
Tomemos un conjunto X no vacío, de allí surge que una relación es un subconjunto del mismo. Estas serán relaciones binarias en el conjunto X. Existen cuatro formas de representar una relación binaria: Extensión, Comprensión, Diagrama y Matricial. En el primer caso, se muestra cada pareja de la relación, a través de pares ordenados. Si lo expresamos por comprensión, se muestran todos los elementos del conjunto y además, se determinan las condiciones que tendrán las parejas para pertenecer a la relación. En el diagrama, se muestra la relación a través de vértices y aristas. Por último, se representa a través de una matriz cuadrada dónde solo se ingresan unos (cuando hay relación) y ceros (cuando no hay relación).
A modo de ejemplo:
Sea C = {1,2,3}, tal que: (x, y)∈R ↔ x ≤ y
R = {(1,1), (1,2), (1,3) (2,2), (2,3), (3,3)}
\[ M_r =
\left( \begin{array}{ccc}
1 & 1 & 1 \\
0 & 1 & 1 \\
0 & 0 & 1
\end{array} \right) \]
Welcome to Authorea!
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Elementos de conteo Martin Sevese Fernando Rodriguez 198238\(s;:wer\)
Welcome to Authorea!
5. Theory of three-electrone bond in the four works with brief comments
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Multi-class Adaboost
The name of the analysed article is “Multi-class AdaBoost” witch is a special issue on data mining and machine learning. It was written by Ji Zhu who is a professor of statistics and EECS in department of statistics at Michigan University, Hui Zou who is a professor of statistics at Minnesota University, Saharon Rosset who is associate professor in the department of statistics and Operations research at Tel Aviv University and TREVOR HASTIE who is a professor of statistics and biomedical data science at Stanford University.
The article was published in the “Statistics and Its Interface (2009)”, Volume 2 p(349–360). This journal as defined in [5]: "is a quarterly peer-reviewed open access scientific journal covering the interface between the field of statistics and other disciplines. The journal was established in 2008 and is published by International Press. The editor-in-chief is Heping Zhang (Yale University)".
In this paper, the authors develops a new algorithm that directly extends the AdaBoost algorithm from solving the two-class classification problems to solving the multi-class classification problems. Before the authors spoke about their new algorithm, they started by giving us some information about other similar existent algorithms and their problems, then some information about the AdaBoost algorithm .
The paper is organized as follows: In section 2 , we will see the scientific context of the article. Some of the existing works on the subject are listed in section 3 . Section 4 will present the contribution of the article. In section 5, we will see the experiments and the validations that are presented in the article. The last Section (section 6) will summarize all the work.
Implementing Socially Aware LSTMs for effective Crowd Navigation
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