Abstract
This paper describes an energy management system that is being developed in the AnyPLACE project to support new energy services, like demand response, in residential buildings. In the project end-user interfaces are designed and implemented to allow the input of preferences regarding the flexible use of shiftable and thermal appliances. Monitoring and self-learning algorithm are used to allow additional information to be collected and an automation platform is available for the management and control of appliances. An energy management algorithm is presented that processes end-user preferences and devices characteristics to produce an optimal dispatch considering demand response incentives. Results show the successful implementation of an optimized energy scheduling.

Abstract
This paper presents the development framework for an energy management platform that is being developed within the AnyPLACE project. In order to ensure that end-users become active participants in services like demand response, a combined approach is necessary in terms of monitoring, automation, and user interfacing. The success in engaging the end-user, as the centerpiece of the energy management challenge, is vital in taking advantage of a more efficient use of energy, as it is shown in this paper. The proposed framework can be run in a single board computer.

Abstract

Abstract
This Letter discusses the benefits of introducing Machine Learning techniques in multi-view streaming applications. Widespread use of machine learning techniques has contributed to significant gains in numerous scientific and industry fields. Nonetheless, these have not yet been specifically applied to adaptive interactive multimedia streaming systems where, typically, the encoding bit rate is adapted based on resources availability, targeting the efficient use of network resources whilst offering the best possible user quality of experience (QoE). Intrinsic user data could be coupled with such existing quality adaptation mechanisms to derive better results, driven also by the preferences of the user. Head-tracking data, captured from camera feeds available at the user side, is an example of such data to which Recurrent Attention Models could be applied to accurately predict the focus of attention of users within videos frames. Information obtained from such models could be used to assist a preemptive buffering approach of specific viewing angles, contributing to the joint goal of maximising QoE. Based on these assumptions, a research line is presented, focusing on obtaining better QoE in an already existing multi-view streaming system

Abstract
Multimedia content production is nowadays widespread due to technological advances, namely supported by smartphones and social media. Although the massive amount of media content brings new opportunities to the industry, it also obfuscates the relevance of marketing content, meant to maintain and lure new audiences. This leads to an emergent necessity of producing these kinds of contents as quickly and engagingly as possible. Creating these automatically would decrease both the production costs and time, particularly by using static media for the creation of short storytelling animated clips. We propose an innovative approach that uses context and content information to transform a still photo into an appealing context-aware video clip. Thus, our solution presents a contribution to the state-of-the-art in computer vision and multimedia technologies and assists content creators with a value-added service to automatically build rich contextualized multimedia stories from single photographs. © 2020 Owner/Author.

Abstract
The media sector is constantly evolving and, in the last few years, such evolution has been driven by a number of convergence paradigms, notably, that between broadband and broadcast technologies with the introduction of IT and IP technology. The present trend is to switch totally from a closed niche that uses highly specialized equipment to off-the-shelf IT-centric solutions, offering easy configuration and remote operation. The aim is to enable common computers to be turned into highly capable media devices and act as connected objects adopting an IoT-like paradigm. This vision, though, is not implemented easily, given that most media industry professionals do not yet feel comfortable operating in the IT technology space and also due to the stringent requirements that exist in this industry. The Joint Task Force on Networked Media is defining specifications that aim at overcoming such existing barriers. In this article we present a novel solution that follows the guidelines delivered by this group to set up a remotely operated media production facility, totally based on IP and IT technology, constituting a step forward the realization of the IoT concept in professional media environments. The focus is on two complementary components, namely, the GUI Agent and the MW Agent, which are not covered by the defined specifications but that are crucial to speed up the deployment of concrete solutions that can be easily operated by non-IT and non-IP experts in a transparent and ubiquitous way. © 2020 TRON Forum.