Abstract
Although the growth of video content in online platforms has been happening for some time, searching and browsing these assets is still very inefficient as rich contextual data that describes the content is still not available. Furthermore, any available descriptions are, usually, not linked to timed moments of content. In this paper, we present an approach for making social web videos available on YouTube more accessible, searchable and navigable. By using the concept of crowdsourcing to collect the metadata, our proposal can contribute to easily enhance content uploaded in the YouTube platform. Metadata, collected as a collaborative annotation game, is added to the content as time-based information in the form of descriptions and captions using the YouTube API. This contributes for enriching video content and enabling navigation through temporal links.

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
The growing importance of Radar solutions in automotive applications results in new standards to be met and more demanding performances to be assured. As such, Radar developers require testers capable of guaranteeing compliance with the specified criteria. This paper presents a preliminary approach to allow over-the-air (OTA) testing and validation of 76-77 GHz and 77-81 GHz Radar units in production lines, covering not only pass/fail conditions, but also determination of the Radar antenna array radiation diagrams (in azimuth and elevation) through a calibrated external power sensor. Preliminary measurements on an open environment test bench are shown for different Radar bandwidths (1 GHz and 4 GHz), illustrating the importance of a clean and shielded anechoic chamber environment as a baseline approach.

Abstract
The development of efficient methods for searching and browsing large assets of video content has been considered by the academia and content owners for long. Different approaches that range from manual structured annotations, to unstructured metadata collected from several sources, as well as multimedia processing for automatic description of the content, can be identified. The growth on the number of hours of video content put online in video sharing platforms has however shown that video retrieval is still quite inefficient as rich contextual data that describes the content is most of the times still not available. Additionally, metadata is usually not linked to timed moments of content, making direct access to the most relevant moments not possible. In this paper, an approach for making web videos available in the YouTube platform more accessible is presented. The solution is based on a collaborative process presented as a game that enables collecting metadata from the crowd while implementing mechanisms that remove erroneous information usually encountered in this type of information. Metadata, exported to YouTube in the form of captions and descriptions, contributes to enhance video retrieval, guaranteeing a better user experience and exposure of the content.

Abstract
Over the last years, Deep Learning has become one of the most popular research fields of Artificial Intelligence. Several approaches have been developed to address conventional challenges of AI. In computer vision, these methods provide the means to solve tasks like image classification, object identification and extraction of features. In this paper, some approaches to face detection and recognition are presented and analyzed, in order to identify the one with the best performance. The main objective is to automate the annotation of a large dataset and to avoid the costy and time-consuming process of content annotation. The approach follows the concept of incremental learning and a R-CNN model was implemented. Tests were conducted with the objective of detecting and recognizing one personality within image and video content. Results coming from this initial automatic process are then made available to an auxiliary tool that enables further validation of the annotations prior to uploading them to the archive. Tests show that, even with a small size dataset, the results obtained are satisfactory. © 2020, Springer Nature Switzerland AG.

Abstract
This paper presents an experiment on newsreaders' behavior and preferences on the interaction with online personalized news. Different recommendation approaches, based on consumption profiles and user location, and the impact of personalized news on several aspects of consumer decision-making are examined on a group of volunteers. Results show a significant preference for reading recommended news over other news presented on the screen, regardless of the chosen editorial layout. In addition, the study also provides support for the creation of profiles taking into consideration the evolution of user's interests. The proposed solution is valid for users with different reading habits and can be successfully applied even to users with small consumption history. Our findings can be used by news providers to improve online services, thus increasing readers' perceived satisfaction.