Abstract
Smartphones are increasingly present in human’s life. For example, for entertainment many people use their smartphones to watch videos or listen to music. Many users, however, stream or play videos with the intention to only listen to the audio track. This way, some battery energy, which is critical to most users, is unnecessarily consumed thus and switching between video and audio can increase the time of use of the smartphone between battery recharges. In this paper, we present a first approach that, based on the user context, can automatically switch between video and audio. A supervised learning approach is used along with the classifiers K-Nearest Neighbors, Hoeffding Trees and Naive Bayes, individually and combined to create an ensemble classifier. We investigate the accuracy for recognizing the context of the user and the overhead that this system can have on the smartphone energy consumption. We evaluate our approach with several usage scenarios and an average accuracy of 88.40% was obtained for the ensemble classifier. However, the actual overhead of the system on the smartphone energy consumption highlights the need for researching further optimizations and techniques. © 2019, Springer Nature Switzerland AG.

Abstract
The Classifier kNN is largely used in Human Activity Recognition systems. Research efforts have proposed methods to decrease the high computational costs of the original kNN by focusing, e.g., on approximate kNN solutions such as the ones relying on Locality-sensitive Hashing (LSH). However, embedded kNN implementations need to address the target device memory constraints and power/energy consumption savings. One of the important aspects is the constraint regarding the maximum number of instances stored in the kNN learning process (being it offline or online and incremental). This paper presents simple, energy/computationally efficient and real-time feasible schemes to maintain a maximum number of learning instances stored by kNN. Experiments in the context of HAR show the efficiency of our best approaches, and their capability to avoid the kNN storage runs out of training instances for a given activity, a situation not prevented by typical default schemes.

Abstract
Nowadays most people carry a smartphone with built-in sensors (e.g., accelerometers, gyroscopes) capable of providing useful data for Human Activity Recognition (HAR). Machine learning classification methods have been intensively researched and developed for HAR systems, each with different accuracy and performance levels. However, acquiring sensor data and executing machine learning classifiers require computational power and consume energy. As such, a number of factors, such as inadequate preprocessing, can have a negative impact on the overall HAR performance, even on high-end handheld devices. While high accuracy can be extremely important in some applications, the device’s battery life can be highly critical to the end-user. This paper is focused on the k-nearest neighbors’ algorithm (kNN), one of the most used algorithms in HAR systems, and research and develop energy-efficient implementations for mobile devices. We focus on a kNN implementation based on Locality-Sensitive Hashing (LSH) with a significant positive impact on the device’s battery life, fully integrated into a mobile HAR Android application able to classify human activities in real-time. The proposed kNN implementation was able to achieve execution time reductions of 50% over other versions of kNN with average accuracy of 96.55% when considering 8 human activities. © 2019, Springer Nature Switzerland AG.

Abstract
FPGAs are becoming a popular solution for accelerating the execution of software applications. The use of high level synthesis (HLS) tools intends to provide levels of abstraction comfortable to software developers when targeting FPGA-based hardware accelerators. However, the need to restructure the software code and to use adequate directives require both mastering the HLS tool used and FPGA hardware. This paper presents our efforts to provide a new approach for code restructuring, intended to help software developers in achieving efficient hardware implementations. Our approach uses an unfolded graph representation, which is generated from program execution traces, together with graph-based optimizations such as folding to generate suitable C code to input to HLS tools, such as Vivado HLS. The experiments show that our approach is capable of generating C code that results in efficient hardware implementations only otherwise achievable using manual restructuring of the input software code and manual insertion of adequate directives. © VDE VERLAG GMBH · Berlin · Offenbach

Abstract
The ANTAREX project relies on a Domain Specific Language (DSL) based on Aspect Oriented Programming (AOP) concepts to allow applications to enforce extra functional properties such as energy-efficiency and performance and to optimize Quality of Service (QoS) in an adaptive way. The DSL approach allows the definition of energy-efficiency, performance, and adaptivity strategies as well as their enforcement at runtime through application autotuning and resource and power management. In this paper, we present an overview of the key outcome of the project, the ANTAREX DSL, and some of its capabilities through a number of examples, including how the DSL is applied in the context of the project use cases.

Abstract
Pedestrian behaviour modelling and simulation play a fundamental role in reducing traffic risks and new policies implementation costs. However, representing human behaviour in this dynamic environment is not a trivial task and such models require an accurate representation of pedestrian behaviour. Virtual environments have been gaining notoriety as a behaviour elicitation tool, but it is still necessary to understand the validity of this technique in the context of pedestrian studies, as well as to create guidelines for its use. This work proposes a proper methodology for pedestrian behaviour elicitation using virtual reality environments in conjunction with surveys or questionnaires. The methodology focuses on gathering data about the subject, the context, and the action taken, as well as on analyzing the collected data to finally output a behavioural model. The resulting model can be used as a feedback signal to improve environment conditions for experiment iterations. A concrete implementation was built based on this methodology, serving as an example for future studies. A virtual reality traffic environment and two surveys were used as data sources for pedestrian crossing experiments. The subjects controlled a virtual avatar using an HTC Vive and were asked to traverse the distance between two points in a city. The data collected during the experiment was analyzed and used as input to a machine learning model capable of predicting pedestrian speed, taking into account their actions and perceptions. The proposed methodology allowed for the successful data gathering and its use to predict pedestrian behaviour with fairly acceptable accuracy. © 2019 IEEE.