867 resultados para Ticket, Android, App


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The Web of Data currently comprises ? 62 billion triples from more than 2,000 different datasets covering many fields of knowledge3. This volume of structured Linked Data can be seen as a particular case of Big Data, referred to as Big Semantic Data [4]. Obviously, powerful computational configurations are tradi- tionally required to deal with the scalability problems arising to Big Semantic Data. It is not surprising that this ?data revolution? has competed in parallel with the growth of mobile computing. Smartphones and tablets are massively used at the expense of traditional computers but, to date, mobile devices have more limited computation resources. Therefore, one question that we may ask ourselves would be: can (potentially large) semantic datasets be consumed natively on mobile devices? Currently, only a few mobile apps (e.g., [1, 9, 2, 8]) make use of semantic data that they store in the mobile devices, while many others access existing SPARQL endpoints or Linked Data directly. Two main reasons can be considered for this fact. On the one hand, in spite of some initial approaches [6, 3], there are no well-established triplestores for mobile devices. This is an important limitation because any po- tential app must assume both RDF storage and SPARQL resolution. On the other hand, the particular features of these devices (little storage space, less computational power or more limited bandwidths) limit the adoption of seman- tic data for different uses and purposes. This paper introduces our HDTourist mobile application prototype. It con- sumes urban data from DBpedia4 to help tourists visiting a foreign city. Although it is a simple app, its functionality allows illustrating how semantic data can be stored and queried with limited resources. Our prototype is implemented for An- droid, but its foundations, explained in Section 2, can be deployed in any other platform. The app is described in Section 3, and Section 4 concludes about our current achievements and devises the future work.

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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.

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Questa tesi si occupa principalmente della revisione grafica in ottica Modern UI dell'app tuProlog Android, nella prospettiva di renderlo in futuro disponibile anche in modalità as-a-service. Dopo una attenta analisi preliminare dell'architettura di tuProlog in generale e in particolare della struttura dell'app tuProlog preesistente e del relativo progetto in ambiente Eclipse, ci si è focalizzati sulla riprogettazione dell'app, dall'analisi dei requisiti - ivi incluso il nuovo strumento di sviluppo da utilizzare, Android Studio - alla successiva analisi e progettazione della nuova soluzione, seguita da implementazione e collaudo.

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Questa tesi si occupa della realizzazione, in ottica Modern UI, di una nuova interfaccia per l'applicazione Android del sistema domotico Home Manager. Dopo una prima fase di analisi preliminare, si affronta la progettazione dell'app, dall'analisi dei requisiti - ivi incluso il nuovo strumento di sviluppo da utilizzare, Android Studio - alla successiva analisi e progettazione della nuova soluzione, seguita da implementazione e collaudo.

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Desde hace unos años, parece que la informática ha ido invadiendo numerosos campos de la ciencia, se ha ido consolidando como parte fundamental en el desarrollo y la tecnología y ya se puede ver como motor de la economía, las comunicaciones, el comercio e incluso, la medicina. Este trabajo se centra en la aplicación de la informática en el área de las enfermedades infecciosas, intentando contribuir con una nueva idea que revolucione el mundo de las mismas dentro de la medicina, en una situación en la que el término “Smart City” cada vez es más importante. Se trata de un sistema formado por una app para Android y una web controlada y gestionada por un médico, la cual permite identificar posibles usuarios contagiados, localizar focos de contagios y gestionar de una manera óptima, el estado y los avisos de cada usuario. En esta memoria se describe el trabajo realizado, la arquitectura del sistema, las tecnologías utilizadas, el diseño de la aplicación móvil y de la página web, su implementación, las pruebas realizadas, las conclusiones a las que se ha llegado y las posibles mejoras que se podrían incluir en el proyecto en un futuro.

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Malware detection is a growing problem particularly on the Android mobile platform due to its increasing popularity and accessibility to numerous third party app markets. This has also been made worse by the increasingly sophisticated detection avoidance techniques employed by emerging malware families. This calls for more effective techniques for detection and classification of Android malware. Hence, in this paper we present an n-opcode analysis based approach that utilizes machine learning to classify and categorize Android malware. This approach enables automated feature discovery that eliminates the need for applying expert or domain knowledge to define the needed features. Our experiments on 2520 samples that were performed using up to 10-gram opcode features showed that an f-measure of 98% is achievable using this approach.

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Android is becoming ubiquitous and currently has the largest share of the mobile OS market with billions of application downloads from the official app market. It has also become the platform most targeted by mobile malware that are becoming more sophisticated to evade state-of-the-art detection approaches. Many Android malware families employ obfuscation techniques in order to avoid detection and this may defeat static analysis based approaches. Dynamic analysis on the other hand may be used to overcome this limitation. Hence in this paper we propose DynaLog, a dynamic analysis based framework for characterizing Android applications. The framework provides the capability to analyse the behaviour of applications based on an extensive number of dynamic features. It provides an automated platform for mass analysis and characterization of apps that is useful for quickly identifying and isolating malicious applications. The DynaLog framework leverages existing open source tools to extract and log high level behaviours, API calls, and critical events that can be used to explore the characteristics of an application, thus providing an extensible dynamic analysis platform for detecting Android malware. DynaLog is evaluated using real malware samples and clean applications demonstrating its capabilities for effective analysis and detection of malicious applications.

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Nowadays, a lot of interesting and useful and imaginative applications are springing to Android software market. And for guitar fans, some related apps bring great connivence to them, like a guitar tuner can save people from carrying a entity tuner all the time, some apps can simulate a real guitar, and some apps provide some simple lessons allowing people to learn some basic things. But these apps which can teach people, they can't really “monitor ” people, that is, they just give some instructions and hope people would follow them. So my project is to design an app which can detect if users are playing wrong and right real-timely. Guitar chords are always the first for new guitar beginners to learn, and a chord is a set of notes combined together in a regulated way ( get from the music theory having millions of developing ), and 'pitch' is the term for determining if the note different from other notes or noise, so the problem here is to manage the multi-pitch analysis in real time. And it's necessary to know some basics of digital signal processing ( DSP ) because digital signals are always more convenient for computers to analyze compared to analog signals. Then I found an audio processing Java library – TarsosDSP, and try to apply it to my Android project.

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Smartphones are increasingly playing a role in healthcare and previous studies assessing medical applications (apps) have raised concerns about lack of expert involvement and low content accuracy. However, there are no such studies in Urology. We reviewed Urology apps with the aim of assessing the level of participation of healthcare professionals (HCP) and scientific Urology associations in their development.

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Attualmente, la maggior parte dei dati che transitano sulla rete appartiene a contenuti multimediali. Più nello specifico, è lo Streaming Video ad avere la predominanza nella condivisione di Internet; vista la crescita che tale servizio ha subìto negli ultimi anni, si sono susseguiti diversi studi volti allo sviluppo di tecniche e metodologie che potessero migliorarlo. Una di queste è sicuramente l'Adaptive Video Streaming, tecnica utilizzata per garantire all'utente una buona Quality of Experience (QoE) mediante l'utilizzo dei cosiddetti "algoritmi di rate adaptation". Il lavoro svolto in questi studi si è voluto concentrare su due filoni distinti, ma allo stesso tempo confrontabili: la prima parte della tesi riguarda lo sviluppo e l'analisi di alcuni algoritmi di rate adaptation per DASH, mentre la seconda è relativa all'implementazione di un nuovo algoritmo che li possa affiancare, migliorando la QoE nel monitorare lo stato della connessione. Si è quindi dovuta implementare un'applicazione Android per lo streaming video, che fosse conforme allo standard MPEG-DASH e potesse fornire le informazioni di testing da utilizzare per le analisi. La tesi è suddivisa in quattro capitoli: il primo introduce l'argomento e definisce la terminologia necessaria alla comprensione degli studi; il secondo descrive alcuni dei lavori correlati allo streaming adattivo e introduce i due filoni principali della tesi, ovvero gli algoritmi di rate adaptation e la proposta di algoritmo per la selezione dinamica del segmento; il terzo presenta l'app SSDash, utilizzata come mezzo per le analisi sperimentali; infine, il quarto ed ultimo capitolo mostra i risultati delle analisi e le corrispondenti valutazioni.

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La gran cantidad de personas interesadas actualmente en cuidar de su salud por medio de la Bicicleta, es una tendencia que cada vez toma más fuerza y por esto, se tomara como ventaja que la ciudad de Bogotá con sus últimos alcaldes han decidió apoyar el uso de esta. Aplicación para realizar grupos de ciclistas y poder salir cualquier día de la semana acompañado de más personas por un tema de transporte pero también de ocio. El objetivo es que las personas que no utilizan la bicicleta por miedo a salir solos, puedan unirse a diferentes grupos y hacer bici paseos por la ciudad, también contemplamos el hecho de que existen personas que quieren montar bicicleta como aficionados, es decir que salen por la carreteras aledañas a la capital pero muchas veces no tienen grupo con quien rodar. Queremos para el año 2020, lograr ser una de las aplicaciones de Bicicletas más exitosa de la ciudadanía de Bogotá, siendo una de las aplicaciones que mas apoya el uso diario y deportivo de la bicicleta. Nuestra aplicación ofrece el servicio principalmente de reunir gente y hacer paseos con diferentes personas haciendo uso de las bicicletas, dentro de la ciudad como ocio y transporte se realizarían en la mañana y noches, pero así mismo realizar grupos para hacer uso de la bicicleta por carreteras, es decir más como modo profesional o aficionado. Nuestra aplicación cuenta con una interface para seleccionar el tipo de bici usuario y segundo por donde o a donde quiere dirigirse para así mismo mostrarle las rutas cercanas a él. Nuestra característica principal es la unión de diferentes grupos, personas y entidades para hacer uso de la Bicicleta, tenemos una plataforma interactiva y fácil de usar, tan fácil que cualquier persona que no esté inmersa en el mundo de los Smartphone o aplicaciones pueda aprender a usarla. Unas de las ventajas con las que cuento es que desde muy joven me ha gustado montar en bicicletas y es así como decido crear una aplicación ya que compañeros, familiares y conocidos no salían a montar bicicleta solo conmigo, preferían que fuese un grupo más grande. Así mismo cuento con 3 compañeros de la universidad Javeriana que son programadores y ellos me van a brindar apoyo con la programación de la aplicación, y una compañera cercana a mí que estudio diseño industrial y me brindara apoyo con el diseño e imagen corporativa de la aplicación. Cuento con planta física para ubicar la oficina de nuestra empresa. Esta aplicación va dirigida principalmente a los habitantes de la ciudad de Bogotá, interesados en el cuidado de su salud combinado con medio de transporte, y personas correspondientes al estrato 2 en adelante, ya que son las personas que normalmente hacen uso de la bicicleta o que son personas potenciales para empezar hacer uso de la bicicleta. . La ciudad de Bogotá cuenta con 8’037.732 habitantes y este proyecto va ser desarrollado en toda la ciudad, toca tener en cuenta que la ciudad esta mesclada entre la diferente estratificación, no dirigimos a los estratos 2 y 3 que tenga la posibilidad de tener su Bicicleta y un celular tipo Smartphone, para los estratos 4, 5 y 6 sabemos que tiene la facilidad de obtener una bicicleta y ellos son nuestro usuario potencial el cual generaríamos un cambio y tomarían su bicicleta para hacer ejercicio como ocio y como medio de transporte. En cuanto a la proyección financiera para la aplicación, como se mencionó anteriormente, contamos con una gran ventaja, ya que la inversión requerida será menor debido a la propiedad con la que cuento para llevar a cabo el proyecto. Una propiedad de 60 metros cuadrados para empezar, la cual cuenta con salas de reunión y auditorio, un parqueaderos exteriores. Al hacer la calculación se va tener en cuenta el pago de un arriendo sin importan que sea de nuestra propiedad y así poder evidenciar realmente como es el estado financiero y no subsidiarla. Por otro lado, la inversión en efectivo que se necesitara será aproximadamente de $100.000.000 que serán $50.000.000 de mis padres y el restante saldrán de mis ahorros, Javier Amortegui Babativa, los cuales serán distribuidos para adecuación de planta, equipos y sistemas $17.616.880, para publicidad y mercadeo: $30.000.000, creación de la aplicación $34.000.000, sistemas IOS + Android $421.600, Gastos de composición empresarial y bancarios $11.000.000 y por último se va tener un provisión para imprevistos por el restante $6.961.520. Nuestras proyecciones de ventas han sido basadas en aplicaciones similares con un mismo formato de lucro con el tema de bicicletas pero no con la misma idea de negocio, nuestras proyecciones de ventas estimadas serán de $20.000.000 a $25.000.000 los primeros 3 meses, mientras tomamos fuerza en el mercado.

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The use of social media has increased dramatically in recent years in the areas of public health and injury prevention, with many organisations creating online and social media content. Despite the adoption of such media by modern society, research relating to the design and evaluation of social media for the promotion of health issues is very much in its infancy. A timely review of the available evidence in relation to the use of social media campaigns from both the road safety and broader public health context will be undertaken. In particular, this paper will address the questions of what social media should be developed, how it should be evaluated, as well as what should be the key measures of success and to what extent do these measures relate to practically significant outcomes, such as behaviour change. Much like more traditional media campaigns, social media may be best regarded as another approach within the array of potential approaches that a health advertising researcher or practitioner may utilise. With younger demographics becoming less likely to engage with more traditional advertising mediums relative to their preference for social media, social media‟s most important role in the road safety advertising context may be to offer the means of delivering road safety messages to high risk, younger road users.

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Our daily lives become more and more dependent upon smartphones due to their increased capabilities. Smartphones are used in various ways from payment systems to assisting the lives of elderly or disabled people. Security threats for these devices become increasingly dangerous since there is still a lack of proper security tools for protection. Android emerges as an open smartphone platform which allows modification even on operating system level. Therefore, third-party developers have the opportunity to develop kernel-based low-level security tools which is not normal for smartphone platforms. Android quickly gained its popularity among smartphone developers and even beyond since it bases on Java on top of "open" Linux in comparison to former proprietary platforms which have very restrictive SDKs and corresponding APIs. Symbian OS for example, holding the greatest market share among all smartphone OSs, was closing critical APIs to common developers and introduced application certification. This was done since this OS was the main target for smartphone malwares in the past. In fact, more than 290 malwares designed for Symbian OS appeared from July 2004 to July 2008. Android, in turn, promises to be completely open source. Together with the Linux-based smartphone OS OpenMoko, open smartphone platforms may attract malware writers for creating malicious applications endangering the critical smartphone applications and owners� privacy. In this work, we present our current results in analyzing the security of Android smartphones with a focus on its Linux side. Our results are not limited to Android, they are also applicable to Linux-based smartphones such as OpenMoko Neo FreeRunner. Our contribution in this work is three-fold. First, we analyze android framework and the Linux-kernel to check security functionalities. We survey wellaccepted security mechanisms and tools which can increase device security. We provide descriptions on how to adopt these security tools on Android kernel, and provide their overhead analysis in terms of resource usage. As open smartphones are released and may increase their market share similar to Symbian, they may attract attention of malware writers. Therefore, our second contribution focuses on malware detection techniques at the kernel level. We test applicability of existing signature and intrusion detection methods in Android environment. We focus on monitoring events on the kernel; that is, identifying critical kernel, log file, file system and network activity events, and devising efficient mechanisms to monitor them in a resource limited environment. Our third contribution involves initial results of our malware detection mechanism basing on static function call analysis. We identified approximately 105 Executable and Linking Format (ELF) executables installed to the Linux side of Android. We perform a statistical analysis on the function calls used by these applications. The results of the analysis can be compared to newly installed applications for detecting significant differences. Additionally, certain function calls indicate malicious activity. Therefore, we present a simple decision tree for deciding the suspiciousness of the corresponding application. Our results present a first step towards detecting malicious applications on Android-based devices.

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Smartphones are getting increasingly popular and several malwares appeared targeting these devices. General countermeasures to smartphone malwares are currently limited to signature-based antivirus scanners which efficiently detect known malwares, but they have serious shortcomings with new and unknown malwares creating a window of opportunity for attackers. As smartphones become host for sensitive data and applications, extended malware detection mechanisms are necessary complying with the corresponding resource constraints. The contribution of this paper is twofold. First, we perform static analysis on the executables to extract their function calls in Android environment using the command readelf. Function call lists are compared with malware executables for classifying them with PART, Prism and Nearest Neighbor Algorithms. Second, we present a collaborative malware detection approach to extend these results. Corresponding simulation results are presented.

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Smartphones started being targets for malware in June 2004 while malware count increased steadily until the introduction of a mandatory application signing mechanism for Symbian OS in 2006. From this point on, only few news could be read on this topic. Even despite of new emerging smartphone platforms, e.g. android and iPhone, malware writers seemed to lose interest in writing malware for smartphones giving users an unappropriate feeling of safety. In this paper, we revisit smartphone malware evolution for completing the appearance list until end of 2008. For contributing to smartphone malware research, we continue this list by adding descriptions on possible techniques for creating the first malware(s) for Android platform. Our approach involves usage of undocumented Android functions enabling us to execute native Linux application even on retail Android devices. This can be exploited to create malicious Linux applications and daemons using various methods to attack a device. In this manner, we also show that it is possible to bypass the Android permission system by using native Linux applications.