Abstract
This documents introduces \Energyware" as a software engineering discipline aiming at defining, analyzing and optimizing the energy consumption by software systems. In this paper we present energyware analysis in the context of programming languages, software data structures and program's source code. For each of these areas we describe the research work done in the context of the Green Software Laboratory at Minho University: we describe energyaware techniques, tools, libraries, and repositories. © 2018 by the paper's authors.

Abstract
Software developers are more and more eager to understand their code's energy performance. However, even with such knowledge it is difficult to know how to improve the code. Indeed, little tool support exists to understand the energy consumption profile of a software system and to eventually (automatically) improve its code. In this paper we present a tool termed jStanley which automatically finds collections in Java programs that can be replaced by others with a positive impact on the energy consumption as well as on the execution time. In seconds, developers obtain information about energy-eager collection usage. jStanley will further suggest alternative collections to improve the code, making it use less time, energy, or a combination of both. The preliminary evaluation we ran using jStanley shows energy gains between 2% and 17%, and a reduction in execution time between 2% and 13%. A video can be seen at https://greensoftwarelab.github.io/jStanley. © 2018 Association for Computing Machinery.

Abstract
The current technologic proliferation has originated new paradigms concerning the production and consumption of multimedia content. This paper proposes a multisensory 360 video editor that allows producers to edit such contents with high levels of customization. This authoring tool allows the edition and visualization of 360 video with the novelty of allowing to complement the 360 video with multiple stimuli such as audio, haptics, and olfactory. In addition to this multisensory feature, the authoring tool allows customizing individually each of the stimuli to provide an optimal multisensory user experience. A usability evaluation has revealed the pertinence of the editor, where it was verified an effectiveness rate of 100%, only one help request out of 10 participants, and positive efficiency. Satisfaction-wise, results equally revealed high level of satisfaction as the average score was 8.3 out of 10. © Springer International Publishing AG, part of Springer Nature 2018.

Abstract
Head-mounted displays enable a user to view a complete environment as if he/she was there; providing an immersive experience. However, the lighting in a full environment can vary significantly. Panoramic images captured with conventional, Low Dynamic Range (LDR), imaging of scenes with a large range of lighting conditions, can include areas of under- or over-exposed pixels. High Dynamic Range (HDR) imaging, on the other hand, is able to capture the full range of detail in a scene. However, HMDs are not currently HDR and thus the HDR panorama needs to be tone mapped before it can be displayed on the LDR HMD. While a large number of tone mapping operators have been proposed in the last 25 years, these were not designed for panoramic images, or for use with HMDs. This paper undertakes a two part subjective study to investigate which of the current, state-of-the-art tone mappers is most suitable for use with HMDs.

Abstract
In agricultural applications hyperspectral imaging is used in cases where differences in spectral reflectance of the examined objects are small. However, the large amount of data generated by hyperspectral sensors requires advance processing methods. Machine learning approaches may play an important role in this task. They are known for decades, but they need high volume of data to compute accurate results. Until recently, the availability of hyperspectral data was a big drawback. It was first used in satellites, later in manned aircrafts and data availability from those platforms was limited because of logistics complexity and high price. Nowadays, hyperspectral sensors are available for unmanned aerial vehicles, which enabled to reach a high volume of data, thus overcoming these issues. This way, the aim of this paper is to present the status of the usage of machine learning approaches in the hyperspectral data processing, with a focus on agriculture applications. Nevertheless, there are not many studies available applying machine learning approach to hyperspectral data for agricultural applications. This apparent limitation was in fact the inspiration for making this survey. Preliminary results using UAV-based data are presented, showing the suitability of machine learning techniques in remote sensed data. © 2018 Association for Computing Machinery.

Abstract
Social media platforms are having a profound impact on the so-called information ecosystem, specifically on how information is produced, distributed and consumed. Social media in particular has contributed to the rise of user generated content and consequently to a greater diversity in online content. On the other hand, social media networks, such as Twitter or Facebook, have become information management tools that allow users to setup and configure information sources to their particular interests. A Twitter user can handpick the sources he wishes to follow, thus creating a custom information channel. However, this opportunity to create personalized information channels effectively results in different consumption profiles? Is the information consumed by users through social media networks distinct from the information consumed though traditional mainstream media? In this work, we set out to investigate this question using Twitter as a case study. We prepare two samples of users, one based on a uniform random selection of user IDs, and another one based on a selection of mainstream media followers. We analyze the home timelines of the users in each sample, focusing on characterizing information consumption habits. We find that information consumption volume is higher, while diversity is consistently lower, for mainstream media followers when compared to random users. When analyzing daily behavior, however, the samples slightly approximate, while clearly maintaining a lower diversity for mainstream media followers and a higher diversity for random users. Copyright © 2018 for the individual papers by the papers’ authors.