Since Facebook was renamed Meta, a lot of attention, debate, and exploration have intensified about what the Metaverse is, how it works, and the possible ways to exploit it. It is anticipated that Metaverse will be a continuum of rapidly emerging technologies, usecases, capabilities, and experiences that will make it up for the next evolution of the Internet. Several researchers have already surveyed the literature on artificial intelligence (AI) and wireless communications in realizing the Metaverse. However, due to the rapid emergence and continuous evolution of technologies, there is a need for a comprehensive and in-depth survey of the role of AI, 6G, and the nexus of both in realizing the immersive experiences of Metaverse. Therefore, in this survey, we first introduce the background and ongoing progress in augmented reality (AR), virtual reality (VR), mixed reality (MR) and spatial computing, followed by the technical aspects of AI and 6G. Then, we survey the role of AI in the Metaverse by reviewing the state-of-the-art in deep learning, computer vision, and Edge AI to extract the requirements of 6G in Metaverse. Next, we investigate the promising services of B5G/6G towards Metaverse, followed by identifying the role of AI in 6G networks and 6G networks for AI in support of Metaverse applications, and the need for sustainability in Metaverse. Finally, we enlist the existing and potential applications, usecases, and projects to highlight the importance of progress in the Metaverse. Moreover, in order to provide potential research directions to researchers, we underline the challenges, research gaps, and lessons learned identified from the literature review of the aforementioned technologies.

Obstacle detection is a fundamental capability of an autonomous maritime surface vessel (AMSV). State-of-the-art obstacle detection algorithms are based on convolutional neural networks (CNNs). While CNNs provide higher detection accuracy and fast detection speed, they require enormous amounts of data for their training. In particular, the availability of domain-specific datasets is a challenge for obstacle detection. The difficulty in conducting onsite experiments limits the collection of maritime datasets. Owing to the logistic cost of conducting on-site operations, simulation tools provide a safe and cost-efficient alternative for data collection. In this work, we introduce SimuShips, a publicly available simulation-based dataset for maritime environments. Our dataset consists of 9471 high-resolution (1920x1080) images which include a wide range of obstacle types, atmospheric and illumination conditions along with occlusion, scale and visible proportion variations. We provide annotations in the form of bounding boxes. In addition, we conduct experiments with YOLOv5 to test the viability of simulation data. Our experiments indicate that the combination of real and simulated images improves the recall for all classes by 2.9%.

The lesser spotted eagle is endangered in Germany.Hinze, K/DPA via ZUMA Press This story was originally published by the Guardian and is reproduced here as part of the Climate Desk collaboration. Small in size, sensitive of constitution and with only 130 breeding pairs surviving locally in the wild, the lesser spotted eagle of the Oder delta lives up to its name. In Germany, key questions over the country's energy future hang on the question of whether artificial intelligence systems can do a better job of spotting the reclusive animal than birdwatchers do. Lesser spotted eagles (named after the drop-shaped spots on their feathers) are fond of riding thermals over many of the flatlands earmarked for a mass expansion of onshore windfarms by a German government under pressure to compensate for a pending loss of nuclear power, coal plants and Russian gas. Because lesser spotted eagles in mid-flight are unused to vertical obstacles, and keep their eyes focused on mice, lizard or frog-shaped prey below, conservationists say, they are known to occasionally collide with the rotor blades of wind turbines.

Small in size, sensitive of constitution and with only 130 breeding pairs surviving locally in the wild, the lesser spotted eagle of the Oder delta lives up to its name. In Germany, key questions over the country's energy future hang on the question of whether artificial intelligence systems can do a better job of spotting the reclusive animal than birdwatchers do. Lesser spotted eagles (named after the drop-shaped spots on their feathers) are fond of riding thermals over many of the flatlands earmarked for a mass expansion of onshore windfarms by a German government under pressure to compensate for a pending loss of nuclear power, coal plants and Russian gas. Because lesser spotted eagles in mid-flight are unused to vertical obstacles, and keep their eyes focused on mice, lizard or frog-shaped prey below, conservationists say, they are known to occasionally collide with the rotor blades of wind turbines. German researchers list eight dead specimens found in the vicinity of windfarms since 2002, a small but not insignificant number given the species' endangered status in the country.

Oysters are the living vacuum cleaners of the oceans. There is an exponential decline in the oyster population due to over-harvesting. With the current development of the automation and AI, robots are becoming an integral part of the environmental monitoring process that can be also utilized for oyster reef preservation. Nevertheless, the underwater environment poses many difficulties, both from the practical - dangerous and time consuming operations, and the technical perspectives - distorted perception and unreliable navigation. To this end, we present a simulated environment that can be used to improve oyster reef monitoring. The simulated environment can be used to create photo-realistic image datasets with multiple sensor data and ground truth location of a remotely operated vehicle(ROV). Currently, there are no photo-realistic image datasets for oyster reef monitoring. Thus, we want to provide a new benchmark suite to the underwater community.

There are many contexts in which dyadic data are present. Social networks are a well-known example. In these contexts, pairs of elements are linked building a network that reflects interactions. Explaining why these relationships are established is essential to obtain transparency, an increasingly important notion. These explanations are often presented using text, thanks to the spread of the natural language understanding tasks. Our aim is to represent and explain pairs established by any agent (e.g., a recommender system or a paid promotion mechanism), so that text-based personalisation is taken into account. We have focused on the TripAdvisor platform, considering the applicability to other dyadic data contexts. The items are a subset of users and restaurants and the interactions the reviews posted by these users. We propose the PTER (Personalised TExt-based Reviews) model. We predict, from the available reviews for a given restaurant, those that fit to the specific user interactions. PTER leverages the BERT (Bidirectional Encoders Representations from Transformers) transformer-encoder model. We customised a deep neural network following the feature-based approach, presenting a LTR (Learning To Rank) downstream task. We carried out several comparisons of our proposal with a random baseline and other models of the state of the art, following the EXTRA (EXplanaTion RAnking) benchmark. Our method outperforms other collaborative filtering proposals.

With world-class research institutes in artificial intelligence (AI), Sweden keeps up with all the latest ideas – and sometimes even steps out ahead. But blue-sky research doesn't always lead to practical solutions that can be used by industry. That's where AI Sweden plays a key role. An important first step in the development of AI Sweden came when Mikael Ljungblom was working as a political advisor to the Swedish minister for digital development. While travelling to see what other countries were doing, Ljungblom and his colleagues saw that countries such as Japan and China were investing in AI. Given the importance of the technology for competitiveness and societal development, they sensed a need to develop an AI centre in Sweden.

Over the past century, technological advancement has accelerated significantly. Additionally, over the past ten years, the world of information technology has experienced exponential growth, particularly in the fields of artificial intelligence (AI) and machine learning (ML). Our lives are being affected by these changes more and more; they have an effect on everything from eLearning to personal economics to leisure. Before discussing how to handle the expected and quick changes in the future, let's take a look back at the AI and machine learning milestones during the last 10 years. Are you passionate about artificial intelligence?

We introduce a method for long-distance maritime route planning in polar regions, taking into account complex changing environmental conditions. The method allows the construction of optimised routes, describing the three main stages of the process: discrete modelling of the environmental conditions using a non-uniform mesh, the construction of mesh-optimal paths, and path smoothing. In order to account for different vehicle properties we construct a series of data driven functions that can be applied to the environmental mesh to determine the speed limitations and fuel requirements for a given vessel and mesh cell, representing these quantities graphically and geospatially. In describing our results, we demonstrate an example use case for route planning for the polar research ship the RRS Sir David Attenborough (SDA), accounting for ice-performance characteristics and validating the spatial-temporal route construction in the region of the Weddell Sea, Antarctica. We demonstrate the versatility of this route construction method by demonstrating that routes change depending on the seasonal sea ice variability, differences in the route-planning objective functions used, and the presence of other environmental conditions such as currents. To demonstrate the generality of our approach, we present examples in the Arctic Ocean and the Baltic Sea. The techniques outlined in this manuscript are generic and can therefore be applied to vessels with different characteristics. Our approach can have considerable utility beyond just a single vessel planning procedure, and we outline how this workflow is applicable to a wider community, e.g. commercial and passenger shipping.

Graph embedding methods aim at finding useful graph representations by mapping nodes to a low-dimensional vector space. It is a task with important downstream applications, such as link prediction, graph reconstruction, data visualization, node classification, and language modeling. In recent years, the field of graph embedding has witnessed a shift from linear algebraic approaches towards local, gradient-based optimization methods combined with random walks and deep neural networks to tackle the problem of embedding large graphs. However, despite this improvement in the optimization tools, graph embedding methods are still generically designed in a way that is oblivious to the particularities of real-life networks. Indeed, there has been significant progress in understanding and modeling complex real-life networks in recent years. However, the obtained results have had a minor influence on the development of graph embedding algorithms. This paper aims to remedy this by designing a graph embedding method that takes advantage of recent valuable insights from the field of network science. More precisely, we present a novel graph embedding approach based on the popularity-similarity and local attraction paradigms. We evaluate the performance of the proposed approach on the link prediction task on a large number of real-life networks. We show, using extensive experimental analysis, that the proposed method outperforms state-of-the-art graph embedding algorithms. We also demonstrate its robustness to data scarcity and the choice of embedding dimensionality.