Abstract

Abstract
Abstract Recently, research concerning the navigation of autonomous surface vehicles (ASVs) has been increasing. However, a large-scale implementation of these vessels is still held back by several challenges such as multi-object tracking. Attaining accurate object detection plays a big role in achieving successful tracking. This article presents the development of a detection model with an image-based Convolutional Neural Network trained through transfer learning, a deep learning technique. To train, test, and validate the detector module, data were collected with the SENSE ASV by sailing through two nearby ports, Leixões and Viana do Castelo, and recording video frames through its on-board cameras, along with a Light Detection And Ranging, GPS, and Inertial Measurement Unit data. Images were extracted from the collected data, composing a manually annotated dataset with nine classes of different vessels, along with data from other open-source maritime datasets. The developed model achieved a class mAP@[.5 .95] (mean average precision) of 89.5% and a clear improvement in boat detection compared to a multi-purposed state-of-the-art detector, YOLO-v4, with a 22.9% and 44.3% increase in the mAP with an Intersection over Union threshold of 50% and the mAP@[.5 .95], respectively. It was integrated in a detection and tracking system, being able to continuously detect nearby vessels and provide sufficient information for simple navigation tasks.

Abstract
Language is closely related to how we perceive ourselves and signify our reality. In this scope, we created Desiring Machines, an interactive media art project that allows the experience of affective virtual environments adopting speech emotion recognition as the leading input source. Participants can share their emotions by speaking, singing, reciting poetry, or making any vocal sounds to generate virtual environments on the run. Our contribution combines two machine learning models. We propose a long-short term memory and a convolutional neural network to predict four main emotional categories from high-level semantic and low-level paralinguistic acoustic features. Predicted emotions are mapped to audiovisual representations by an end-to-end process encoding emotion in virtual environments. We use a generative model of chord progressions to transfer speech emotion into music based on the tonal interval space. Also, we implement a generative adversarial network to synthesize an image from the transcribed speech-to-text. The generated visuals are used as the style image in the style-transfer process onto an equirectangular projection of a spherical panorama selected for each emotional category. The result is an immersive virtual space encapsulating emotions in spheres disposed into a 3D environment. Users can create new affective representations or interact with other previously encoded instances (This ArtsIT publication is an extended version of the earlier abstract presented at the ACM MM22 [1]). © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

Abstract
The papers in this special section focus on advances in renewable energy forecasting, predictability, business models, and applications in the power industry. During the last 25 years, research has been conducted for developing renewable energy source (RES) forecasting algorithms, especially for wind and solar energy, seeking an improvement of predictability and uncertainty forecasting products. Research on wave energy forecasting is also being conducted, although this technology is not at the same maturity levels of wind and solar energy technologies. Furthermore, the number of companies selling forecasting services has proliferated and the reliability and availability of the services have improved. Currently, power system operators and electrical energy traders use weather and power forecasts embedded in their decision-making processes. Despite all this research and adoption by the energy industry, deterministic forecasts are still predominant in utility practice mainly due to: i) lack of understanding and standardization of uncertainty forecast products; and ii) high computational time associated with stochastic and robust optimization approaches. Moreover, proven business cases are also needed to demonstrate the benefits of uncertainty forecasts to end-users.

Abstract
Typical VR interactions can be tiring, including standing up, walking, and mid-air gestures. Such interactions result in decreased comfort and session duration compared with traditional non-VR interfaces, which may, in turn, reduce productivity. Nevertheless, current approaches often neglect this aspect, making the VR experience not as promising as it can be. As we see it, desk VR experiences provide the convenience and comfort of a desktop experience and the benefits of VR immersion, being a good compromise between the overall experience and ergonomics. In this work, we explore navigation techniques targeted at desk VR users, using both controllers and a large multi-touch surface. We address travel and orientation techniques independently, considering only continuous approaches for travel as these are better suited for exploration and both continuous and discrete approaches for orientation. Results revealed advantages for a continuous controller-based travel method and a trend for a dragging-based orientation technique. Also, we identified possible trends towards task focus affecting overall cybersickness symptomatology.

Abstract
This paper investigates prosumers' flexibility provision for the optimal operation of active distribution networks in a transactive energy (TE) market. From a prosumer point of view, flexibility can be provided to operators using renewable energy resources (RES) and demand response (DR) through home appliances with the ability to modify their consumption profiles. In the TE market model, the distribution system operator (DSO) is responsible for market-clearing mechanisms and controlling the net power exchange between the distribution network and the upstream grid. The contribution of this work is the enhancement of a strategy to reduce operational costs of an active distribution network by using prosumers' flexibility provision through an aggregator or a smart building coordinator. To this end, a TE market for both energy and flexibility trading at distribution networks is presented, demonstrating the possibility to fulfill DSO requirements through the flexibility contributions in the day-ahead (DA) and real-time (RT) markets.