Twitch-palvelun katsomiseen tarkoitetun Windows Store -sovelluksen toteutus

Tämä työ kertoo Twitch.tv-palvelun videolähetyksien katsomiseen tarkoitetun sovelluksen kehittämisestä. Sovellus on tarkoitettu tablet-laitteille, jotka käyttävät Windows 8 -käyttöjärjestelmää. Tarkoituksena on mahdollistaa palvelun käyttäminen ilman selainta suoraan Windows App Store -sovelluksen kautta. Toteutuksessa keskitytään tutkimaan Microsoftin työkaluja ohjelmistonkehitykseen Windowsille, Twitch:n tarjoaman rajapinnan käyttöä ja käyttömahdollisuuksia. Työssä kerrotaan näiden työkalujen rajoittuneisuudesta ja tästä aiheutuvista ongelmista edellä kuvattua sovellusta kehittäessä. Ohjelmistossa panostetaan käytettävyyteen erityisesti tablet-laitteen näkökulmasta, käyttöliittymän suunnittelussa otetaan huomioon yhtenevä ulkonäkö ja Metro UI:n tyyli.

tecniche di machine learning per la predizione del rating delle applicazioni all’interno di un play store

Il mio progetto di tesi ha come obiettivo quello di creare un modello in grado di predire il rating delle applicazioni presenti all’interno del Play Store, uno dei più grandi servizi di distribuzione digitale Android. A tale scopo ho utilizzato il linguaggio Python, che grazie alle sue librerie, alla sua semplicità e alla sua versatilità è certamen- te uno dei linguaggi più usati nel campo dell’intelligenza artificiale. Il punto di partenza del mio studio è stato il Dataset (Insieme di dati strutturati in forma relazionale) “Google Play Store Apps” reperibile su Kaggle al seguente indirizzo: https://www.kaggle.com/datasets/lava18/google-play-store-apps, contenente 10841 osservazioni e 13 attributi. Dopo una prima parte relativa al caricamen- to, alla visualizzazione e alla preparazione dei dati su cui lavorare, ho applica- to quattro di↵erenti tecniche di Machine Learning per la stima del rating delle applicazioni. In particolare, sono state utilizzate:https://www.kaggle.com/datasets/lava18/google-play-store-apps, contenente 10841 osservazioni e 13 attributi. Dopo una prima parte relativa al caricamento, alla visualizzazione e alla preparazione dei dati su cui lavorare, ho applicato quattro differenti tecniche di Machine Learning per la stima del rating delle applicazioni: Ridje, Regressione Lineare, Random Forest e SVR. Tali algoritmi sono stati applicati attuando due tipi diversi di trasformazioni (Label Encoding e One Hot Encoding) sulla variabile ‘Category’, con lo scopo di analizzare come le suddette trasformazioni riescano a influire sulla bontà del modello. Ho confrontato poi l’errore quadratico medio (MSE), l’errore medio as- soluto (MAE) e l’errore mediano assoluto (MdAE) con il fine di capire quale sia l’algoritmo più efficiente.

Desenvolupament d'una app

El TFM ha consistit en participar i conèixer de prop tots el passos de desenvolupament d’una aplicació: elaboració d’un guió, realització de la part gràfica, programació i màrqueting. Per aquest motiu he realitzat unes hores de pràctiques a l'empresa Factoria Interactive SL. Durant aquest periòde vaig realitzar diversos guions per diferents apps relacionades amb continguts matemàtics i vaig participar en el programació de l'app "Dress up". I l'últim pas va consistir en penjar l'app a la botiga de Google Play. Per poder accedir a l'aplicació cal seguir el següent enllaç de Google Play: https://play.google.com/store/apps/details?id=eu.lafactoria.dressup&hl=es

Aplicación móvil para el acceso a juego de naipes sobre el desarrollo humano

Este Proyecto Fin de Carrera (PFC) tiene como objetivo el análisis, diseño e implementación de un videojuego móvil multijugador, con un enfoque educativo, para la sensibilización sobre el Índice de Desarrollo Humano (IDH). El sistema resultante se ha desarrollado para la Plataforma Android, utilizando el Framework AndEngine, que utiliza aceleración hardware de la GPU para garantizar un buen rendimiento en terminales de gama baja, de modo que pueda utilizarse en un amplio número de terminales móviles disponibles en el mercado. La aplicación se presenta como un juego de cartas con los diferentes países y sus datos humanitarios, los jugadores deben conocer el peso de los índices de desarrollo (esperanza de vida, renta, educación) de los países en comparación con los países de los otros jugadores. El sistema de juego premia a los jugadores con mayores conocimientos sobre los datos humanos de los diferentes países del mundo, de ese modo los mejores jugadores serán los que tengan más conocimientos de estos datos. El juego permite jugar partidas en solitario utilizando jugadores manejados por la CPU, o multijugador mediante WIFI o 3G. La actualización de la información y de los datos de las partidas se realiza a través de la comunicación con un servidor web ya implementado de forma complementaria a la realización de este proyecto. El sistema ha sido integrado y validado satisfactoriamente con diferentes terminales móviles y usuarios de diferente perfil de edad y uso. El videojuego se puede descargar de la página web creada en un proyecto complementario a éste (pendiente de publicación web), y ya se encuentra también disponible en Google Play. https://play.google.com/store/apps/details?id=xnetcom.pro.cartas&hl=es_419 ABSTRACT. This Project End of Career (PFC) takes as an aim the analysis, design and implementation of a multiplayer mobile videogame, with an educational approach, for the awareness on the Human Development Index (HDI). The resultant system has been developed for the Platform Android, using the AndEngine Framework, which uses hardware acceleration of the GPU to ensure a good performance on low-end terminals, so that it can be used in a wide range of mobile handsets available in the market. The application is presented as a card game with the different countries and his humanitarian information, the players must know the weight of the indexes of development (life expectancy, revenue, education) of the countries in comparison with the countries of other players. The game system rewards players with more knowledge on human information of different countries, thus the best players will be those with more knowledge of these information. The game allows to play items in solitarily using players handled by the CPU, or multiplayer by means of WIFI or 3G. The update of the information and data of the online games is done through communication with a web server implemented as a complement to the realization of this project. The system has been built and successfully validated with different mobile terminals and users of different age and usage profile. The game can be downloaded from the website created in a complementary project to this (web publication pending), and is now also available on Google Play https://play.google.com/store/apps/details?id=xnetcom.pro.cartas&hl=es_419

GPLSI Compendium App

GPLSI Compendium App se trata de una aplicación móvil que hará las veces de un sistema de gestión y difusión de contenidos digitales, en la que el usuario podrá realizar resúmenes de textos de diferentes webs, tales como artículos o noticias, y posteriormente compartir esos resúmenes en sus redes sociales o a través del correo. Se le proporcionarán varios métodos de resumen, así como las descripciones y un pequeño tutorial de cómo utilizar la aplicación.

Happy App

Aplicação de promoção de estilos de vida saudáveis e prevenção do cancro https://play.google.com/store/apps/details?id=com.ipatimup.happy

eMed.pt - Poupe na Receita

A consulta dos preços dos medicamentos e a comparação com outros fármacos disponíveis, com preços mais baixos, é a principal funcionalidade da aplicação eMed.pt - Poupe na Receita.

Sviluppo di funzionalità di Image Targeting con apps basate su realtà aumentata

Dopo una breve introduzione sulla realtà aumentata (definizione, storia e stato dell’arte) viene effettuata un'analisi delle librerie esistenti per l’implementazione su dispositivi mobile. Considerando compatibilità con i recenti SO, frequenza degli aggiornamenti, costi per le licenze e funzionalità offerte viene scelta la libreria Vuforia, originariamente sviluppata da Qualcomm e poi acquistata da PTC inc. Si conviene poi che le apps basate su realtà aumentata creano il contenuto “aumentato” in due maniere: o tramite riconoscimento di una specifica immagine oppure tramite localizzazione GPS. Di questi due metodi descritti, il primo risulta molto più affidabile e per questo viene sviluppata una app che crea un contenuto in 3D (aumentato) riconoscendo una immagine: funzionalità di Image Targeting. Il progetto considera le seguenti varianti: l’immagine da riconoscere, chiamata “target”, può essere in un database locale oppure cloud mentre il contenuto in 3D aumentato può essere sia statico che animato. Durante la fase di implementazione vengono fornite anche alcuni nozioni di base di Computer Graphic per il rendering del modello 3D. La tesi si conclude con una panoramica di apps presenti sullo store che funzionano secondo questo principio di Image Targeting, i possibili utilizzi in ambito educativo/ludico ed i costi di realizzazione.

A Cooperative and Adaptive Variable Neighborhood Search for the Multi Depot Vehicle Routing Problem with Time Windows

In this paper we propose two cooperation schemes to compose new parallel variants of the Variable Neighborhood Search (VNS). On the one hand, a coarse-grained cooperation scheme is introduced which is well suited for being enhanced with a solution warehouse to store and manage the so far best found solutions and a self-adapting mechanism for the most important search parameters. This makes an a priori parameter tuning obsolete. On the other hand, a fine-grained scheme was designed to reproduce the successful properties of the sequential VNS. In combination with the use of parallel exploration threads all of the best solutions and 11 out of 20 new best solutions for the Multi Depot Vehicle Routing Problem with Time Windows were found.

Mining mobile apps reviews to support release planning

The mobile apps market is a tremendous success, with millions of apps downloaded and used every day by users spread all around the world. For apps’ developers, having their apps published on one of the major app stores (e.g. Google Play market) is just the beginning of the apps lifecycle. Indeed, in order to successfully compete with the other apps in the market, an app has to be updated frequently by adding new attractive features and by fixing existing bugs. Clearly, any developer interested in increasing the success of her app should try to implement features desired by the app’s users and to fix bugs affecting the user experience of many of them. A precious source of information to decide how to collect users’ opinions and wishes is represented by the reviews left by users on the store from which they downloaded the app. However, to exploit such information the app’s developer should manually read each user review and verify if it contains useful information (e.g. suggestions for new features). This is something not doable if the app receives hundreds of reviews per day, as happens for the very popular apps on the market. In this work, our aim is to provide support to mobile apps developers by proposing a novel approach exploiting data mining, natural language processing, machine learning, and clustering techniques in order to classify the user reviews on the basis of the information they contain (e.g. useless, suggestion for new features, bugs reporting). Such an approach has been empirically evaluated and made available in a web-­‐based tool publicly available to all apps’ developers. The achieved results showed that the developed tool: (i) is able to correctly categorise user reviews on the basis of their content (e.g. isolating those reporting bugs) with 78% of accuracy, (ii) produces clusters of reviews (e.g. groups together reviews indicating exactly the same bug to be fixed) that are meaningful from a developer’s point-­‐of-­‐view, and (iii) is considered useful by a software company working in the mobile apps’ development market.

Tangible mobile app store design for China's new generation migrant worker

China has the largest numbers of Internet users and mobile phone subscribers in the world, as well as the most extensive peacetime internal migration on the planet. Mobile phone uses play a very important role in migrant responses to alienation and discrimination. There are 150 million to 200 million migrant workers in China, 60% of them are the second-generation migrant workers. They support the nation’s manufacturing and industry. The majority of these individuals is poor and is from rural areas of the country. These young people are currently ignored by mainstream mobile device manufacturers, even though this constituency will be a growing consumptive segment in the future. Thus, my research will target the new-generation migrant workers in China, and concentrates on their mobile lives to best develop a tangible mobile devices application store that will improve their mobile experiences.

Scatter search for a real-life heterogeneous fleet vehicle routing problem with time windows and split deliveries in Brazil

In this paper, we consider a real-life heterogeneous fleet vehicle routing problem with time windows and split deliveries that occurs in a major Brazilian retail group. A single depot attends 519 stores of the group distributed in 11 Brazilian states. To find good solutions to this problem, we propose heuristics as initial solutions and a scatter search (SS) approach. Next, the produced solutions are compared with the routes actually covered by the company. Our results show that the total distribution cost can be reduced significantly when such methods are used. Experimental testing with benchmark instances is used to assess the merit of our proposed procedure. (C) 2008 Published by Elsevier B.V.

Enterobacter sakazakii in Dried Infant Formulas and Milk Kitchens of Maternity Wards in Sao Paulo, Brazil

This study was the first conducted in Brazil to evaluate the presence of Enterobacter sakazakii in milk-based powdered infant formula manufactured for infants 0 to 6 months of age and to examine the conditions of formula preparation and service in three hospitals in Sao Paulo State, Brazil. Samples of dried and rehydrated infant formula, environments of milk kitchens, water, bottles and nipples, utensils, and hands of personnel were analyzed, and E. sakazakii and Enterobacteriaceae populations were determined. All samples of powdered infant formula purchased at retail contained E. sakazakii at <0.03 most probable number (MPN)/100 g. In hospital samples, E. sakazakii was found in one unopened formula can (0.3 MPN/100 g) and in the residue from one nursing bottle from hospital A. All other cans of formula from the same lot bought at a retail store contained E. sakazakii at <0.03 MPN/100 g. The pathogen also was found in one cleaning sponge from hospital B. Enterobacteriaceae populations ranged from 10(1) to 10(5) CFU/g in cleaning aids and <5 CFU/g in all formula types (dry or rehydrated), except for the sample that contained E. sakazakii, which also was contaminated with Enterobacteriaceae at 5 CFU/g. E. sakazakii isolates were not genetically related. In an experiment in which rehydrated formula was used as the growth medium, the temperature was that of the neonatal intensive care unit (25 C), and the incubation time was the average time that formula is left at room temperature while feeding the babies (up to 4 h), a 2-log increase in levels of E. sakazakii was found in the formula. Visual inspection of the facilities revealed that the hygienic conditions in the milk kitchens needed improvement. The length of time that formula is left at room temperature in the different hospitals while the babies in the neonatal intensive care unit are being fed (up to 4 h) may allow for the multiplication of E. sakazakii and thus may lead to an increased health risk for infants.

Timber-framed windows, C House, Coorparoo, Brisbane

Windows to North-East elevation, looking onto pool deck.

Windows, Neville Bonner Building, Brisbane

North elevation, Riverside Expressway building.