Abstract
Advancements in semiconductor technology no longer occur at the pace the industry had been accustomed to. We have entered what is considered by many to be the post-Moore era. In order to continue scaling performance, increasingly heterogeneous architectures are being developed and the use of special purpose accelerators is on the rise. One notable example are Field-Programmable-Gate-Arrays (FPGAs), both in the data-center and embedded spaces. Advances in FPGA features and tools is allowing for critical kernels to be accelerated on specialized hardware without fabrication costs. However, re-targeting code to such heterogeneous platforms still requires significant refactoring of the compute intensive kernels, as well as knowledge of parallel compute and hardware design concepts for maximization of performance. We present Tribble, a source-to-source framework under active development, capable of transforming regular C/C++ programs for execution on heterogeneous architectures. This includes transforming the target kernel source code so that it is amenable for circuit generation while keeping the original version for software execution, inserting code for task and memory management and injecting a scheduler algorithm.

Abstract
In the last years, the number of machine learning algorithms and their parameters has increased significantly. On the one hand, this increases the chances of finding better models. On the other hand, it increases the complexity of the task of training a model, as the search space expands significantly. As the size of datasets also grows, traditional approaches based on extensive search start to become prohibitively expensive in terms of computational resources and time, especially in data streaming scenarios. This paper describes an approach based on meta-learning that tackles two main challenges. The first is to predict key performance indicators of machine learning models. The second is to recommend the best algorithm/configuration for training a model for a given machine learning problem. When compared to a state-of-the-art method (AutoML), the proposed approach is up to 130x faster and only 4% worse in terms of average model quality. Hence, it is especially suited for scenarios in which models need to be updated regularly, such as in streaming scenarios with big data, in which some accuracy can be traded for a much shorter model training time.

Abstract
Automatic energy management systems allow users' active participation in flexibility management while assuring their energy demands. We propose a transparent framework for automated energy management to increase trust and improve the learning process, combining machine learning, experts' knowledge, and semantic reasoning. A practical example of thermal comfort shows the advantages of the framework.

Abstract
In recent decades, many attempts have been made to automate the entire welding process, how-ever, there remain many non-automated welding operations that present a constant hazard to workers. This article presents an automated welding solution with collaborative robots, with this contribution, we intend to help companies in this sector increase productivity, improve quality, effectively reduce costs, and improve working conditions.

Abstract
Human Activity Recognition (HAR) has been a popular research field due to the widespread of devices with sensors and computational power (e.g., smartphones and smartwatches). Applications for HAR systems have been extensively researched in recent literature, mainly due to the benefits of improving quality of life in areas like health and fitness monitoring. However, since persons have different motion patterns when performing physical activities, a HAR system would need to adapt to the characteristics of the user in order to maintain or improve accuracy. Mobile devices, such as smartphones, used to implement HAR systems, have limited resources (e.g., battery life). They also have difficulty adapting to the device's constraints to work efficiently for long periods. In this work, we present a kNN-based HAR system and an extensive study of the influence of hyperparameters (window size, overlap, distance function, and the value of k) and parameters (sampling frequency) on the system accuracy, energy consumption, and response time. We also study how hyperparameter configurations affect the model's performance for the users and the activities. Experimental results show that adapting the hyperparameters makes it possible to adjust the system's behavior to the user, the device, and the target service. These results motivate the development of a HAR system capable of automatically adapting the hyperparameters for the user, the device, and the service.

Abstract
Currently, copper is approved as an active substance among plant protection products and is considered effective against more than 50 different diseases in different crops, conventional and organic. Tomato has been cultivated for centuries, but many fungal diseases still affect it, making it necessary to control them through antifungal agents, such as copper, making it the primary form of fungal control in organic farming systems (OFS). The objective of this work was to determine whether exogenous copper applications can affect AOX mechanisms and nitrogen use efficiency in tomato plant grown in OFS. For this purpose, plants were sprayed with 'Bordeaux' mixture (SP). In addition, two sets of plants were each treated with 8 mg/L copper in the root substrate (S). Subsequently, one of these groups was also sprayed with a solution of 'Bordeaux' mixture (SSP). Leaves and roots were used to determine NR, GS and GDH activities, as well as proline, H2O2 and AsA levels. The data gathered show that even small amounts of copper in the rhizosphere and copper spraying can lead to stress responses in tomato, with increases in total ascorbate of up to 70% and a decrease in GS activity down to 49%, suggesting that excess copper application could be potentially harmful in horticultural production by OFS.