Abstract
Algorithmic systems based on artificial intelligence (AI) increasingly play a role in decision-making processes, both in government and industry. These systems are used in areas such as retail, finances, and manufacturing. In the latter domain, the main priority is that the solutions are interpretable, as this characteristic correlates to the adoption rate of users (e.g., schedulers). However, more recently, these systems have been applied in areas of public interest, such as education, health, public administration, and criminal justice. The adoption of these systems in this domain, in particular the data-driven decision models, has raised questions about the risks associated with this technology, from which ethical problems may emerge. We analyse two important characteristics, interpretability and trustability, of AI-based systems in the industrial and public domains, respectively.

Abstract

Abstract
Virtual Reality (VR) through head-mounted displays (HMDs) can be delivered via multiple setups such as smartphones, standalone VR or VR Workstations. The VR Workstation setup delivers the best performance of them all; however, as a drawback up until recently, it required cables to power up the VR equipment. The introduction of wireless solutions for VR Workstations came to solve one of the disadvantages of this setup. However, the impact of the wireless solution versus the HMD cables was not yet properly investigated. In this paper, we study the impact of using a wired vs wireless HMD on Presence, Cybersickness, and Game Experience. We conducted a quasi-experimental between-subjects study with 68 participants assigned to the following three groups that were balanced regarding gender and sample size: Cable (participants used a wired HMD), Cable & x002B; Help (a researcher assisted the participants with the HMD cable during the experience to reduce the participants; awareness of it), and Wireless (untethered HMD). Results showed no significant differences in presence and cybersickness. The game experience was significantly different, with Cable & x002B; Help performing better than Cable and Wireless. We conclude that sense of presence using wireless solutions is equated to wired HMD solutions.

Abstract
Accurate minutely solar irradiance forecasting is the basis of minute-level photovoltaic (PV) power forecasting. In this paper, a minutely solar irradiance forecasting method based on real-time surface irradiance mapping model is proposed, which is beneficial to achieve higher accuracy in solar power forecasting. First, we extract the red-green-blue (RGB) values and position information of pixels in sky images after background elimination and distortion rectification, to explore the mapping relationship between sky image and solar irradiance. Then a real-time sky image-irradiance mapping model is built, trained, and updated according to real-time sky images and solar irradiance. Finally, the future solar irradiance within the time horizons varying from 1 min to 10 min ahead are capable to be forecasted by using the latest updated surface irradiance mapping model with extracted input from the current sky image. The average measures of proposed method by using MAPE, RMSE, MBE are 22.66%, 92.72, -1.26% for blocky clouds; 20.44%, 132.15, -1.06% for thin clouds and 18.82%, 120.78, -0.98% for thick clouds, thus deliver much higher forecasting accuracy than other benchmarks.

Abstract
Video summarization is an important tool considering the amount of data to analyze. Techniques in this area aim to yield synthetic and useful visual abstraction of the videos contents. Hence, in this paper we present a new summarization algorithm, based on image features, which is composed by the following steps: (i) Query video processing using cosine similarity metric and total variation smoothing to identify classes in the query sequence; (ii) With this result, build a labeled training set of frames; (iii) Generate the unlabeled training set composed by samples of the video database; (iv) Training a deep semi-supervised autoencoder; (v) Compute the K-means for each video separately, in the encoder space, with the number of clusters set as a percentage of the video size; (vi) Select key-frames in the K-means clusters to define the summaries. In this methodology, the query video is used to incorporate prior knowledge in the whole process through the obtained labeled data. The step (iii) aims to include unknown patterns useful for the summarization process. We evaluate the methodology using some videos from OPV video database. We compare the performance of our algorithm with the VSum. The results indicate that the pipeline was well succeed in the summarization presenting a F-score value superior to VSum.

Abstract
The constant increase in the world population has progressively demanded that humanity develop new technologies to face challenges such as providing high-quality food to the consumer market. In this sense, the concept of precision agriculture arises, proposing the development of agricultural activities such as preparing the land, sowing, planting, treating plants and harvesting automatically through robotic systems. This study focuses on performing a systematic review of the state of the art of robotics applications to execute agricultural activities. Through a comparative analysis of the existing solutions it was possible to highlight the similarities, differences and limitations of several agricultural robots. After looking at the needs of agricultural tasks and the limitations of robots, the challenges that are still unresolved and their possible solutions are indicated. © CLAWAR Association Ltd.