Abstract
Network slicing emerged in 5G networks as a key component to enable the use of multiple services with different performance requirements on top of a shared physical network infrastructure. A major challenge lies on ensuring wireless coverage and enough communications resources to meet the target Quality of Service (QoS) levels demanded by these services, including throughput and delay guarantees. The challenge is exacerbated in temporary events, such as disaster management scenarios and outdoor festivities, where the existing wireless infrastructures may collapse, fail to provide sufficient wireless coverage, or lack the required communications resources. Flying networks, composed of Unmanned Aerial Vehicles (UAVs), emerged as a solution to provide on-demand wireless coverage and communications resources anywhere, anytime. However, existing solutions mostly rely on best-effort networks. The main contribution of this paper is SLICER, an algorithm enabling the placement and allocation of communications resources in slicing-aware flying networks. The evaluation carried out by means of ns-3 simulations shows SLICER can meet the targeted QoS levels, while using the minimum amount of communications resources.

Abstract
Assessment is an important part of the educational process, playing a crucial role in student learning. The increase in the number of students in higher education has placed extreme pressure on assessment practices, often leading to a teacher having hundreds of assignments to correct, not only giving feedback too late, but also low quality feedback, as it is humanly impossible to correct all these assessments by giving quality feedback in such a short time. Due to the social confinement caused by the pandemic of COVID-19, there was the need to change the evaluation method initially associated with a thin exam, to a continuous evaluation method based on multiple weekly assignments. In order to deal with this situation, we developed AOCO, the first automatic correction tool for the ARMv8 AArch64 assembly language. This work presents the AOCO tool, as well as the results of the evaluation of a first use with students.

Abstract
The 2020/2021 academic year started full of uncertainties for new students of higher education in Portugal. The restrictions imposed by the COVID-19 pandemic, the fears of a new lockdown, all coupled with the well-known challenges that a university student faces in his first year, made this year a particularly challenging year in terms of the students' integration. In this paper, we present how the mentoring programme of the Integrated Master in Informatics and Computing Engineering at the Faculty of Engineering of the University of Porto was adapted to help the integration of first-year students in the university environment under the pandemic.

Abstract
Understanding how acting bridges the emotional bond between spectators and films is essential to depict how humans interact with this rapidly growing digital medium. In recent decades, the research community made promising progress in developing facial expression recognition (FER) methods. However, no emphasis has been put in cinematographic content, which is complex by nature due to the visual techniques used to convey the desired emotions. Our work represents a step towards emotion identification in cinema through facial expressions' analysis. We presented a comprehensive overview of the most relevant datasets used for FER, highlighting problems caused by their heterogeneity and to the inexistence of a universal model of emotions. Built upon this understanding, we evaluated these datasets with a standard image classification models to analyze the feasibility of using facial expressions to determine the emotional charge of a film. To cope with the problem of lack of datasets for the scope under analysis, we demonstrated the feasibility of using a generic dataset for the training process and propose a new way to look at emotions by creating clusters of emotions based on the evidence obtained in the experiments.

Abstract
Advertisements are often inserted in multimedia content, and this is particularly relevant in TV broadcasting as they have a key financial role. In this context, the flexible and efficient processing of TV content to identify advertisement segments is highly desirable as it can benefit different actors, including the broadcaster, the contracting company, and the end user. In this context, detecting the presence of the channel logo has been seen in the state-of-the-art as a good indicator. However, the difficulty of this challenging process increases as less prior data is available to help reduce uncertainty. As a result, the literature proposals that achieve the best results typically rely on prior knowledge or pre-existent databases. This paper proposes a flexible method for processing TV broadcasting content aiming at detecting channel logos, and consequently advertising segments, without using prior data about the channel or content. The final goal is to enable stream segmentation identifying advertisement slices. The proposed method was assessed over available state-of-the-art datasets as well as additional and more challenging stream captures. Results show that the proposed method surpasses the state-of-the-art.

Abstract