El impacto de la asignación universal por hijo en la provincia de Mendoza

La universalización de las asignaciones familiares a través de la implementación de la Asignación Universal por Hijo (en adelante AUH), teniendo en cuenta el número de menores que se ven beneficiados por esta política (más de 3,5 millones), generó un amplio debate por diferentes investigadores, periodistas y referentes políticos, en relación a los efectos que se producirán en el corto y largo plazo en nuestra sociedad. Los investigadores del CIFRA (Centro de Investigación y Formación de la República Argentina), indican que “frente a la falta de una política clara tendiente a garantizar la demanda agregada interna, la implementación de la AUH implica un cambio importante". De este análisis se desprende la posibilidad y necesidad de conjugar la equidad social con el crecimiento económico. En el mismo sentido, resaltan “más allá de combatir la pobreza y la indigencia de forma directa, la transferencia de ingresos hacia los sectores más desprotegidos implica un fuerte impulso al consumo, generando un efecto multiplicador positivo". Existen diferentes investigaciones, a nivel nacional, que analizan el impacto de la AUH sobre los niveles de pobreza, indigencia, consumo, entre otros. El objetivo de este documento es presentar un análisis de la asignación universal por hijo y su potencial impacto sobre la Provincia de Mendoza. Para ello, en los dos primeros capítulos se describe 5 el sistema contributivo de asignaciones familiares por hijo y la nueva asignación universal no contributiva. En el capítulo tres se describen los programas asistenciales similares a la AUH, referentes a siete países de América Latina. En el capítulo cuatro se presenta la estimación del impacto potencial de la AUH en la pobreza e indigencia de nuestra provincia, como así también el impacto sobre la demanda agregada. Para finalizar se abordarán las conclusiones y recomendaciones.

Amēnun taretsʻuytsʻě : zbōsali u pitani.

Published: Paris : H.B. Tiwrapean, 1924; Vienna : Mkhitʻarean Tparan, 1925; Venice : Tp. Mkhitʻarean, 1926; Pʻariz : Tparan "Masis", 1927-1929.

The Illinois Gap Analysis final report, 2006.

U.S. Geological Survey, Department of Interior Cooperative Agreement No. 14-48-0003-95-1090

Kochʻnak.

Published: New York

Middleware for distributed context-aware systems

Context-aware systems represent extremely complex and heterogeneous distributed systems, composed of sensors, actuators, application components, and a variety of context processing components that manage the flow of context information between the sensors/actuators and applications. The need for middleware to seamlessly bind these components together is well recognised. Numerous attempts to build middleware or infrastructure for context-aware systems have been made, but these have provided only partial solutions; for instance, most have not adequately addressed issues such as mobility, fault tolerance or privacy. One of the goals of this paper is to provide an analysis of the requirements of a middleware for context-aware systems, drawing from both traditional distributed system goals and our experiences with developing context-aware applications. The paper also provides a critical review of several middleware solutions, followed by a comprehensive discussion of our own PACE middleware. Finally, it provides a comparison of our solution with the previous work, highlighting both the advantages of our middleware and important topics for future research.

Modelling context information with ORM

Context-aware applications rely on implicit forms of input, such as sensor-derived data, in order to reduce the need for explicit input from users. They are especially relevant for mobile and pervasive computing environments, in which user attention is at a premium. To support the development of context-aware applications, techniques for modelling context information are required. These must address a unique combination of requirements, including the ability to model information supplied by both sensors and people, to represent imperfect information, and to capture context histories. As the field of context-aware computing is relatively new, mature solutions for context modelling do not exist, and researchers rely on information modelling solutions developed for other purposes. In our research, we have been using a variant of Object-Role Modeling (ORM) to model context. In this paper, we reflect on our experiences and outline some research challenges in this area.

Personalising context-aware applications

The immaturity of the field of context-aware computing means that little is known about how to incorporate appropriate personalisation mechanisms into context-aware applications. One of the main challenges is how to elicit and represent complex, context-dependent requirements, and then use the resulting representations within context-aware applications to support decision-making processes. In this paper, we characterise several approaches to personalisation of context-aware applications and introduce our research on personalisation using a novel preference model.

A design pattern for efficient retrieval of large data sets from remote data sources

Retrieving large amounts of information over wide area networks, including the Internet, is problematic due to issues arising from latency of response, lack of direct memory access to data serving resources, and fault tolerance. This paper describes a design pattern for solving the issues of handling results from queries that return large amounts of data. Typically these queries would be made by a client process across a wide area network (or Internet), with one or more middle-tiers, to a relational database residing on a remote server. The solution involves implementing a combination of data retrieval strategies, including the use of iterators for traversing data sets and providing an appropriate level of abstraction to the client, double-buffering of data subsets, multi-threaded data retrieval, and query slicing. This design has recently been implemented and incorporated into the framework of a commercial software product developed at Oracle Corporation.

A comparison of microphone and speech recognition engine efficacy for mobile data entry

The research presented in this paper is part of an ongoing investigation into how best to incorporate speech-based input within mobile data collection applications. In our previous work [1], we evaluated the ability of a single speech recognition engine to support accurate, mobile, speech-based data input. Here, we build on our previous research to compare the achievable speaker-independent accuracy rates of a variety of speech recognition engines; we also consider the relative effectiveness of different speech recognition engine and microphone pairings in terms of their ability to support accurate text entry under realistic mobile conditions of use. Our intent is to provide some initial empirical data derived from mobile, user-based evaluations to support technological decisions faced by developers of mobile applications that would benefit from, or require, speech-based data entry facilities.

A comparison of pseudo-paper and paper prototyping methods for mobile evaluations

The research presented in this paper is part of an ongoing investigation into how best to support meaningful lab-based usability evaluations of mobile technologies. In particular, we report on a comparative study of (a) a standard paper prototype of a mobile application used to perform an early-phase seated (static) usability evaluation, and (b) a pseudo-paper prototype created from the paper prototype used to perform an early-phase,contextually-relevant, mobile usability evaluation. We draw some initial conclusions regarding whether it is worth the added effort of conducting a usability evaluation of a pseudo-paper prototype in a contextually-relevant setting during early-phase user interface development.

A comparison of microphone and speech recognition engine efficacy for mobile data entry

The research presented in this paper is part of an ongoing investigation into how best to incorporate speech-based input within mobile data collection applications. In our previous work [1], we evaluated the ability of a single speech recognition engine to support accurate, mobile, speech-based data input. Here, we build on our previous research to compare the achievable speaker-independent accuracy rates of a variety of speech recognition engines; we also consider the relative effectiveness of different speech recognition engine and microphone pairings in terms of their ability to support accurate text entry under realistic mobile conditions of use. Our intent is to provide some initial empirical data derived from mobile, user-based evaluations to support technological decisions faced by developers of mobile applications that would benefit from, or require, speech-based data entry facilities.