Marta Campos Ferreira

Abstract
In recent years, with the growth of digital media and modern imaging equipment, the use of video processing algorithms and semantic film and image management has expanded. The usage of different video datasets in training artificial intelligence algorithms is also rapidly expanding in various fields. Due to the high volume of information in a video, its processing is still expensive for most hardware systems, mainly in terms of its required runtime and memory. Hence, the optimal selection of keyframes to minimize redundant information in video processing systems has become noteworthy in facilitating this problem. Eliminating some frames can simultaneously reduce the required computational load, hardware cost, memory and processing time of intelligent video-based systems. Based on the aforementioned reasons, this research proposes a method for selecting keyframes and adaptive cropping input video for human action recognition (HAR) systems. The proposed method combines edge detection, simple difference, adaptive thresholding and 1D and 2D average filter algorithms in a hierarchical method. Some HAR methods are trained with videos processed by the proposed method to assess its efficiency. The results demonstrate that the application of the proposed method increases the accuracy of the HAR system by up to 3% compared to random image selection and cropping methods. Additionally, for most cases, the proposed method reduces the training time of the used machine learning algorithm. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

Abstract
The analysis of ambient sounds can be very useful when developing sound base intelligent systems. Acoustic scene classification (ASC) is defined as identifying the area of a recorded sound or clip among some predefined scenes. ASC has huge potential to be used in urban sound event classification systems. This research presents a hybrid method that includes a novel mathematical fusion step which aims to tackle the challenges of ASC accuracy and adaptability of current state-of-the-art models. The proposed method uses a stereo signal, two ensemble classifiers (random subspace), and a novel mathematical fusion step. In the proposed method, a stable, invariant signal representation of the stereo signal is built using Wavelet Scattering Transform (WST). For each mono, i.e., left and right, channel, a different random subspace classifier is trained using WST. A novel mathematical formula for fusion step was developed, its parameters being found using a Genetic algorithm. The results on the DCASE 2017 dataset showed that the proposed method has higher classification accuracy (about 95%), pushing the boundaries of existing methods.

Abstract

Abstract
Technological advances and the use of mobile solutions to make smartphone users’ daily life easier is a mindset that has revolutionized the society lifestyle in the past years. In the public transport sector, mobile ticketing is an example of the applicability of mobile solutions in a real context. Using one smartphone to purchase and validate tickets is a revolutionary idea that has acquired fans around the world. The convenience of use and time savings throughout the process are positive aspects, however, the success of the adoption of such services is limited. Based on the case of Porto, Portugal and particularly of the mobile app And, this study intends to understand customer churn factors of mobile ticketing services by analysing data from customer complaints and from usage history. Thus, an analysis of the complaints, the complainers and the effects of complaints is presented. A strategy for capturing and retaining users is also proposed considering four stages of mobile ticketing apps lifecycle: user onboarding, user engagement, user retention and user reinstall. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

Abstract
Mobile ticketing services allow urban transport passengers to travel in a convenient and easy way, enhancing their travelling experience. In recent years several mobile ticketing services have started to be developed and launched, but there is still a lot to be done in terms of its effectiveness, efficiency and innovation. This paper presents a micro-location mobile ticketing solution based on Near Field Communication (NFC) and Bluetooth Low Energy (BLE) technologies, called Anda. This solution is based on a check-in/be-out scheme and requires the minimum intervention from the passenger. It is really innovative in the urban transport field, as it takes advantage of BLE technology not usually used for this purpose, it is based on a concept of post-billing with a fare optimization algorithm associated and it allows the micro-location of passengers throughout their journeys. This paper details the architecture of the solution and its mode of operation. It also presents the evaluation methodology that was followed during the pilot trial that took place in the Metropolitan Area of Porto (AMP), Portugal, during one year with 140 real passengers. A set of design lessons were identified as a result of the field tests and materialized in five mobile ticketing design dimensions, constituting important contributions to the design of future mobile ticketing services. Anda was commercially deployed in the AMP in 2018 and is used by thousands of passengers every day. IEEE