Slow construction and design of a family house turns into a spatial education experience: three challenges 6 facts.

Ponencia presentada a Session 1: Educación y arquitectura: fundamentos teóricos / Education and architecture: theoretical foundation

Why do architectural schools bother to teach theory?

Ponencia presentada a Session 1: Educación y arquitectura: fundamentos teóricos / Education and architecture: theoretical foundation

Urban spaces and the levels of the historic city

Ponencia presentada a Session 8: Dimensiones psicosociales de la arquitectura y el urbanismo / Psycological dimensions of architecture and plannin

The structure of ordinary: Hui vernacular settlements and architecture in China

Ponència presentada a: Session 8: Dimensiones psicosociales de la arquitectura y el urbanismo / Psycological dimensions of architecture and plannin

Tactical urbanism as a catalyst for democratic urban spaces

Ponència presentada a: Session 8: Dimensiones psicosociales de la arquitectura y el urbanismo / Psycological dimensions of architecture and plannin

News Session-Based Recommendations using Deep Neural Networks

News recommender systems are aimed to personalize users experiences and help them to discover relevant articles from a large and dynamic search space. Therefore, news domain is a challenging scenario for recommendations, due to its sparse user profiling, fast growing number of items, accelerated item's value decay, and users preferences dynamic shift. Some promising results have been recently achieved by the usage of Deep Learning techniques on Recommender Systems, specially for item's feature extraction and for session-based recommendations with Recurrent Neural Networks. In this paper, it is proposed an instantiation of the CHAMELEON -- a Deep Learning Meta-Architecture for News Recommender Systems. This architecture is composed of two modules, the first responsible to learn news articles representations, based on their text and metadata, and the second module aimed to provide session-based recommendations using Recurrent Neural Networks. The recommendation task addressed in this work is next-item prediction for users sessions: "what is the next most likely article a user might read in a session?" Users sessions context is leveraged by the architecture to provide additional information in such extreme cold-start scenario of news recommendation. Users' behavior and item features are both merged in an hybrid recommendation approach. A temporal offline evaluation method is also proposed as a complementary contribution, for a more realistic evaluation of such task, considering dynamic factors that affect global readership interests like popularity, recency, and seasonality. Experiments with an extensive number of session-based recommendation methods were performed and the proposed instantiation of CHAMELEON meta-architecture obtained a significant relative improvement in top-n accuracy and ranking metrics (10% on Hit Rate and 13% on MRR) over the best benchmark methods.Comment: Accepted for the Third Workshop on Deep Learning for Recommender Systems - DLRS 2018, October 02-07, 2018, Vancouver, Canada. https://recsys.acm.org/recsys18/dlrs

A Service Component-based Accounting and Charging Architecture to Support Interim Mechanisms across Multiple Domains

Today, telematics services are often compositions of different chargeable service components offered by different service providers. To enhance component-based accounting and charging, the service composition information is used to match with the corresponding charging structure of a service session. This enables the sharing of revenues among the service providers, and calculation of the total cost for the end-user. When multiple independent service providers are involved, it is a great challenge to apply interim accounting and charging during a service session in order to minimize financial risks between business partners. Another interesting development is the trend towards outsourcing accounting and charging processes to specialized business partners. This requires a decoupling between provisioning and accounting and charging processes. In this paper, we propose a comprehensive component-based accounting and charging architecture to support service session provisioning across multiple domains. The architecture, modeled in UML, incorporates an interim accounting and charging mechanism to enable the processing and exchange of accounting information needed to update intermediate charges for separate service components and the user's credit, even during the service provisioning phase

Service oriented interactive media (SOIM) engines enabled by optimized resource sharing

In the same way as cloud computing, Software as a Service (SaaS) and Content Centric Networking (CCN) triggered a new class of software architectures fundamentally different from traditional desktop software, service oriented networking (SON) suggests a new class of media engine technologies, which we call Service Oriented Interactive Media (SOIM) engines. This includes a new approach for game engines and more generally interactive media engines for entertainment, training, educational and dashboard applications. Porting traditional game engines and interactive media engines to the cloud without fundamentally changing the architecture, as done frequently, can enable already various advantages of cloud computing for such kinds of applications, for example simple and transparent upgrading of content and unified user experience on all end-user devices. This paper discusses a new architecture for game engines and interactive media engines fundamentally designed for cloud and SON. Main advantages of SOIM engines are significantly higher resource efficiency, leading to a fraction of cloud hosting costs. SOIM engines achieve these benefits by multilayered data sharing, efficiently handling many input and output channels for video, audio, and 3D world synchronization, and smart user session and session slot management. Architecture and results of a prototype implementation of a SOIM engine are discussed