Abstract
The overtaking maneuver can be often dangerous and risky, especially when driving in rural roads, with a significant probability of leading to fatalities and serious injuries. Vehicle communication emerges as one of the leading technologies to improve the driving experience and road safety. This article presents a system designed to augment road safety in an Overtaking Maneuver, providing the ahead vehicle's Line-of-Sight by real-time video-streaming. It uses the C-V2X Transmission Mode 4 that, building upon cellular technology, has evolved as one of the main standards for vehicle communication. Results show an effective application of the system in real overtaking scenarios, enhancing the safety and proving its usefulness. Whilst having some impact on the latency, the solution incurred in low packet losses and provided good video quality.

Abstract
Mobile Augmented Reality (MAR) systems have emerged and greatly evolved in the last two decades. They have application in many domains, most notably in the field of Cultural Heritage (CH) and tourism, where people tend to rely on smartphones when visiting a new city to obtain additional information on the city landmarks. Expectations are that they obtain precise and tailored information to the visitor’s needs. Therefore, researchers started to investigate innovative approaches for presenting and suggesting digital content related to cultural and historical places. This article presents a novel MAR application, NearHeritage, which uses emergent technologies to assist visitors in finding and exploring Cultural Heritage. The research focuses on combining the use of context-awareness with Augmented Reality (AR). By sensing the context surrounding the user, the NearHeritage app discloses not only the list of nearby points-of-interest (POI) but also detailed information about the POIs in the form of AR content adapted to the user context. The solution presented uses built-in sensors of Android devices and takes advantage of various APIs (Foursquare API, Google Maps API and IntelContextSensing SDK) to retrieve information about the landmarks and the visitor context. Results from initial experimentation indicate that the concept of a context-aware MAR application can improve the user experience in discovering and learning more about Cultural Heritage, creating an interactive, enjoyable and unforgettable adventure. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
Introduction: Previous studies have shown that adverse childhood experiences negatively impact child development, with consequences throughout the lifespan. Some of these consequences include the exacerbation or onset of several pathologies and risk behaviors. Materials and Methods: A convenience sample of 398 individuals aged 20 years or older from the Porto metropolitan area, with quotas, was collected. The evaluation was conducted using an anonymous questionnaire that included sociodemographic questions about exposure to adverse childhood experiences, a list of current health conditions, questions about risk behaviors, the AUDIT-C test, the Fagerstrom test and the Childhood Trauma Questionnaire-brief form. Variables were quantified to measure adverse childhood experiences, pathologies, and risk behaviors in adult individuals for comparison purposes. Results: Individuals with different forms of adverse childhood experiences present higher rates of smoking dependence, self-harm behaviors, victimization of/aggression toward intimate partners, early onset of sexual life, sexually transmitted infections, multiple sexual partners, abortions, anxiety, depression, diabetes, arthritis, high cholesterol, hypertension, and stroke. Different associations are analyzed and presented. Discussion and Conclusions: The results show that individuals with adverse childhood experiences have higher total scores for more risk behaviors and health conditions than individuals without traumatic backgrounds. These results are relevant for health purposes and indicate the need for further research to promote preventive and protective measures.

Abstract
The FotoInMotion (FiM) project is building a novel media creation platform, leveraging the use of semi-automated analysis and editing tools to empower creators to easily transform static visual acquisitions of real-world events into rich, animated and engaging objects, distributable through common channels. FiM transforms the content creative chain into an integrated pipeline across which media and metadata seamlessly flow and are exploited to produce more complex media objects. One of the addressed challenges consists the need for a seamless and efficient communication across such pipeline and on how to preserve, in a structured manner, all of the involved media and metadata. Existing standardized metadata tools and content wrappers are limited in expressivity and scope and incapable of fully supporting the needs of the content creative pipeline. This paper describes FiM's new structured data object, i.e. the Digital Event (DE), which acts as a universal vehicle for media and metadata. It builds on well-established and emergent MPEG standards (MPEG-21, MPEG-V, MPEG-7 and MPEG HEIF), to support data diversity, interoperability, packaging and sharing, within complex, Machine Learning enhanced, creative pipelines. Our solution has been validated by creative professionals (photojournalism, fashion marketing and festivals), who have conducted experiments within the context of different creative workflows in real world scenarios. DE's employment revealed to be advantageous, particularly in the homogenization of the media and metadata representation and packaging and in the normalization of the interaction between different pipeline components.

Abstract
Applying machine learning (ML), and especially deep learning, to understand visual content is becoming common practice in many application areas. However, little attention has been given to its use within the multimedia creative domain. It is true that ML is already popular for content creation, but the progress achieved so far addresses essentially textual content or the identification and selection of specific types of content. A wealth of possibilities are yet to be explored by bringing the use of ML into the multimedia creative process, allowing the knowledge inferred by the former to influence automatically how new multimedia content is created. The work presented in this article provides contributions in three distinct ways towards this goal: firstly, it proposes a methodology to re-train popular neural network models in identifying new thematic concepts in static visual content and attaching meaningful annotations to the detected regions of interest; secondly, it presents varied visual digital effects and corresponding tools that can be automatically called upon to apply such effects in a previously analyzed photo; thirdly, it defines a complete automated creative workflow, from the acquisition of a photograph and corresponding contextual data, through the ML region-based annotation, to the automatic application of digital effects and generation of a semantically aware multimedia story driven by the previously derived situational and visual contextual data. Additionally, it presents a variant of this automated workflow by offering to the user the possibility of manipulating the automatic annotations in an assisted manner. The final aim is to transform a static digital photo into a short video clip, taking into account the information acquired. The final result strongly contrasts with current standard approaches of creating random movements, by implementing an intelligent content- and context-aware video.

Abstract
The subjectiveness of multimedia content description has a strong negative impact on tag-based information retrieval. In our work, we propose enhancing available descriptions by adding semantically related tags. To cope with this objective, we use a word embedding technique based on the Word2Vec neural network parameterized and trained using a new dataset built from online newspapers. A large number of news stories was scraped and pre-processed to build a new dataset. Our target language is Portuguese, one of the most spoken languages worldwide. The results achieved significantly outperform similar existing solutions developed in the scope of different languages, including Portuguese. Contributions include also an online application and API available for external use. Although the presented work has been designed to enhance multimedia content annotation, it can be used in several other application areas.