Open Finance Apps: Chancen und Voraussetzungen bei der Visualisierung von Finanzdaten

Die Open Government Bewegung soll der Verwaltungsführung mehr Transparenz und Verständnis entgegenbringen. Durch Open Finance Apps werden Finanzangaben und dazugehörige Informationen verständlich zugänglich gemacht und Grössenverhältnisse veranschaulicht.

Dangers of ‘Facebook Login’ for Mobile Apps: Is There a Price Tag for Social Information?

Social networks offer horizontal integration for any mobile platform providing app users with a convenient single sign-on point. Nonetheless, there are growing privacy concerns regarding its use. These vulnerabilities trigger alarm among app developers who fight for their user base: While they are happy to act on users’ information collected via social networks, they are not always willing to sacrifice their adoption rate for this goal. So far, understanding of this trade-off has remained ambiguous. To fill this gap, we employ a discrete choice experiment to explore the role of Facebook Login and investigate the impact of accompanying requests for different information items / actions in the mobile app adoption process. We quantify users’ concerns regarding these items in monetary terms. Beyond hands-on insights for providers, our study contributes to the theoretical discourse on the value of privacy in the growing world of Social Media and mobile web.

Mehr Transparenz und Mitwirkung bei oeffentlichen Finanzen: Open Finance Apps in der Schweiz und im Ausland

Die Finanzfluesse in oeffentlichen Haushalten sind heute hochkomplexe Gebilde. Mit interaktiven Visualisierungen können oeffentliche Finanzen transparenter und verstaendlicher werden. Diese sogenannten Open Finance Apps helfen mit, dass sich Bevoelkerung und Politik rasch ein objektives Bild der relevanten Finanzen verschaffen und vertiefte Informationen abrufen koennen. Immer haeufiger werden Open Finance Apps auch für partizipative und kollaborative Projekte eingesetzt.

Documenting sound change with smartphone apps

Crowdsourcing linguistic phenomena with smartphone applications is relatively new. Apps have been used to train acoustic models for automatic speech recognition (de Vries et al. 2014) and to archive endangered languages (Iwaidja Inyaman Team 2012). Leemann and Kolly (2013) developed a free app for iOS—Dialäkt Äpp (DÄ) (>78k downloads)—to document language change in Swiss German. Here, we present results of sound change based on DÄ data. DÄ predicts the users’ dialects: for 16 variables, users select their dialectal variant. DÄ then tells users which dialect they speak. Underlying this prediction are maps from the Linguistic Atlas of German-speaking Switzerland (SDS, 1962-2003), which documents the linguistic situation around 1950. If predicted wrongly, users indicate their actual dialect. With this information, the 16 variables can be assessed for language change. Results revealed robustness of phonetic variables; lexical and morphological variables were more prone to change. Phonetic variables like to lift (variants: /lupfə, lʏpfə, lipfə/) revealed SDS agreement scores of nearly 85%, i.e., little sound change. Not all phonetic variables are equally robust: ladle (variants: /xælə, xællə, xæuə, xæɫə, xæɫɫə/) exhibited significant sound change. We will illustrate the results using maps that show details of the sound changes at hand.

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.

La evaluación continua en la docencia del urbanismo: experiencia aplicada a través de Google Apps

El sistema tradicional de evaluación continua implica asumir, en muchas ocasiones, algunas dificultades en cuanto al seguimiento y valoración del trabajo del alumnado. La existencia de grupos numerosos y la necesidad de agilizar la comunicación profesor-alumno y entre alumnos, nos lleva a investigar las posibilidades que ofrecen las nuevas tecnologías con el fin de actualizar los métodos de aprendizaje y facilitar la valoración del trabajo de los alumnos dentro y fuera del aula. La presente experiencia docente se centra en estudiar las posibilidades que ofrece Google, a través de distintas aplicaciones web o Apps -Groups, Forms, Drive, Mail y Calendar- e incorporarlas como herramientas integradas en el trabajo del aula. Concretamente, se aplica simultáneamente esta metodología a dos asignaturas diferentes del área de Urbanística y Ordenación del Territorio -Urbanismo 2 y Urbanismo 4- en las que se integra al alumnado en un grupo virtual a través del cual, se realizan distintas actividades -resolución de dudas, intercambio de información, exámenes, etc.- que permiten al profesor gestionar y evaluar, de forma eficiente, cuestiones como la asistencia y la participación en la asignatura conociendo de forma inmediata la respuesta de los estudiantes ante los conceptos trabajados.

Participatory design with older adults for healthcare apps

The global population of people aged 60 years and older is growing rapidly [1]. Ongoing advances in mobile technologies have the potential to improve independence and quality of life of older adults by supporting the delivery of personalised and ubiquitous healthcare solutions. Suggested healthcare reforms reflect the need for a future model of healthcare delivery wherein older adults take more responsibility for their own healthcare in their own homes in an attempt to moderate healthcare costs without impairing healthcare quality. For such a paradigm shift to be realised, the supporting technology must address the needs of older patients efficiently and effectively to ensure technology acceptance and use. We argue this is not possible without employing participatory approaches for the informed and effective design and development of such technologies and outline recommendations for engaging in participatory design with older adults with impairments based on practical experience.

Towards a threat assessment framework for apps collusion

App collusion refers to two or more apps working together to achieve a malicious goal that they otherwise would not be able to achieve individually. The permissions based security model (PBSM) for Android does not address this threat, as it is rather limited to mitigating risks due to individual apps. This paper presents a technique for assessing the threat of collusion for apps, which is a first step towards quantifying collusion risk, and allows us to narrow down to candidate apps for collusion, which is critical given the high volume of Android apps available. We present our empirical analysis using a classified corpus of over 29000 Android apps provided by Intel Security.

Threats and thrills : pregnancy apps, risk and consumption