Abstract
During the last decades, local, regional, and national governments promoted the development of smart cities, aiming the integration of traditional urban infrastructures and information technologies to provide high quality and sustainable urban services. Smart cities' implementations may change the way the individuals experience the urban spaces. Looking specifically to older adults, smart cities' applications have the potential of promoting their autonomy, independence, safety, well-being, social participation, and inclusion. This paper presents a survey of the scientific literature aiming to analyse current evidence related to smart cities' applications to support older adults and to identify issues for future research.

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Embodied Communication Agents are being used to support older adults but there is still a lack of knowledge about their impact. Therefore, this paper presents a scoping review of the literature on user centred evaluation of the impact of Embodied Communication Agents when embedded in digital solutions to support older adults. The ten studies that were included in this scoping review reported on the implementation of digital solutions to promote physical activity, minimize loneliness, provide spiritual comfort, facilitate problem solving and knowledge acquisition, and support the diagnosis of neurodegenerative diseases. In terms of impact assessment, different outcomes were considered, and the included studies reported significant impact in terms of steps walked and acknowledge acquisition, a trend in the reduction of loneliness, and good sensitivity and specificity to diagnose neurodegenerative diseases.

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The aim of this study is to analyze the typical process of the practical part of software development courses at universities and to evaluate whether the current process meets the expectations of students or whether the needs of students would be better met by the benefits of automated code feedback. A semiautomated survey was conducted in German, involving bachelor students from different universities who had attended an introductory programming class within the last 6 semesters. The results clearly show that the students would like to have individual assignments instead of the due to time constraints, usual group exercises as they see more advantages for their learning progress. The use of automated code feedback could not only solve this time problem but would also bring other benefits.

Abstract
Decision trees are often preferred when implementing Machine Learning in embedded systems for their simplicity and scalability. Hoeffding Trees are a type of Decision Trees that take advantage of the Hoeffding Bound to allow them to learn patterns in data without having to continuously store the data samples for future reprocessing. This makes them especially suitable for deployment on embedded devices. In this work we highlight the features of a HLS implementation of the Hoeffding Tree. The implementation parameters include the feature size of the samples (D), the number of output classes (K), and the maximum number of nodes to which the tree is allowed to grow (Nd). We target a Xilinx MPSoC ZCU102, and evaluate: the design's resource requirements and clock frequency for different numbers of classes and feature size, the execution time on several synthetic datasets of varying sizes (N) and the execution time and accuracy for two datasets from UCI. For a problem size of D=3, K=5, and N=40000, a single decision tree operating at 103MHz is capable of 8.3x faster inference than the 1.2 GHz ARM Cortex-A53 core. Compared to a reference implementation of the Hoeffding tree, we achieve comparable classification accuracy for the UCI datasets.

Abstract
Energy efficiency is a non-functional requirement that developers must consider. This requirement is particularly relevant when building software for battery-operated devices like mobile ones: a long-lasting battery is an essential requirement for an enjoyable user experience. It has been shown that many mobile applications include inefficiencies that cause battery to be drained faster than necessary. Some of these inefficiencies result from software patterns that have been catalogued in the literature. The catalogues often provide more energy-efficient alternatives. While the related literature is vast, most approaches so far assume as a fundamental requirement that one has access to the source code of an application in order to be able to analyse it. This requirement makes independent energy analysis challenging, or even impossible, e.g. for a mobile user or, most significantly, an App Store trying to provide information on how efficient an application being submitted for publication is. Our work studies the viability of looking for known energy patterns in applications by decompiling them and analysing the resulting code. For this, we decompiled and analysed 236 open-source applications. We extended an existing tool to aid in this process, making it capable of seamlessly decompiling and analysing android applications. With the collected data, we performed a comparative analysis of the presence of energy patterns between the source code and the decompiled code. While further research is required to more assertively say if this type of static analysis is viable, our results point in a promising direction with 163 applications, approximately 69%, containing the same number of detected patterns in both source code and the release APK. © 2022 ACM.