The oceans are a source of an impressive mixture of complex data that could be used to uncover relationships yet to be discovered. Such data comes from the oceans and their surface, such as Automatic Identification System (AIS) messages used for tracking vessels' trajectories. AIS messages are transmitted over radio or satellite at ideally periodic time intervals but vary irregularly over time. As such, this paper aims to model the AIS message transmission behavior through neural networks for forecasting upcoming AIS messages' content from multiple vessels, particularly in a simultaneous approach despite messages' temporal irregularities as outliers. We present a set of experiments comprising multiple algorithms for forecasting tasks with horizon sizes of varying lengths. Deep learning models (e.g., neural networks) revealed themselves to adequately preserve vessels' spatial awareness regardless of temporal irregularity. We show how convolutional layers, feed-forward networks, and recurrent neural networks can improve such tasks by working together. Experimenting with short, medium, and large-sized sequences of messages, our model achieved 36/37/38% of the Relative Percentage Difference - the lower, the better, whereas we observed 92/45/96% on the Elman's RNN, 51/52/40% on the GRU, and 129/98/61% on the LSTM. These results support our model as a driver for improving the prediction of vessel routes when analyzing multiple vessels of diverging types simultaneously under temporally noise data.

There has been a surge in research in multimodal machine translation (MMT), where additional modalities such as images are used to improve translation quality of textual systems. A particular use for such multimodal systems is the task of simultaneous machine translation, where visual context has been shown to complement the partial information provided by the source sentence, especially in the early phases of translation. In this paper, we propose the first Transformer-based simultaneous MMT architecture, which has not been previously explored in simultaneous translation. Additionally, we extend this model with an auxiliary supervision signal that guides the visual attention mechanism using labelled phrase-region alignments. We perform comprehensive experiments on three language directions and conduct thorough quantitative and qualitative analyses using both automatic metrics and manual inspection. Our results show that (i) supervised visual attention consistently improves the translation quality of the simultaneous MMT models, and (ii) fine-tuning the MMT with supervision loss enabled leads to better performance than training the MMT from scratch. Compared to the state-of-the-art, our proposed model achieves improvements of up to 2.3 BLEU and 3.5 METEOR points.

A distinguishing characteristic of federated learning is that the (local) client data could have statistical heterogeneity. This heterogeneity has motivated the design of personalized learning, where individual (personalized) models are trained, through collaboration. There have been various personalization methods proposed in literature, with seemingly very different forms and methods ranging from use of a single global model for local regularization and model interpolation, to use of multiple global models for personalized clustering, etc. In this work, we begin with a generative framework that could potentially unify several different algorithms as well as suggest new algorithms. We apply our generative framework to personalized estimation, and connect it to the classical empirical Bayes' methodology. We develop private personalized estimation under this framework. We then use our generative framework for learning, which unifies several known personalized FL algorithms and also suggests new ones; we propose and study a new algorithm AdaPeD based on a Knowledge Distillation, which numerically outperforms several known algorithms. We also develop privacy for personalized learning methods with guarantees for user-level privacy and composition. We numerically evaluate the performance as well as the privacy for both the estimation and learning problems, demonstrating the advantages of our proposed methods.

Defining the metaverse is no easy task, with a mix of academics, journalists and tech experts weighing in differently on what it is or will become. The assortment of opinions may be due to the fact that the metaverse is still in its early stages of development – and there is already more than one in existence, not unlike the distributed ledger technologies popularly known as "blockchain." Most current definitions for the metaverse include a long list of technologies and principles. One definition tech experts seem to agree on is "an online 3D virtual world in which real people interact in real time to do an unlimited variety of virtual activities such as work, travel and play, all supported by its own digital economy." The metaverse is expected to become the next big breakthrough in the Internet's evolution, with seemingly endless potential to transform how we live, transact, learn, and even benefit from government services.

In this week's geeked-out edition of the Good News Roundup, Ukraine's jaw-dropping battlefield victories with HIMARS are documented using OSINT, South Africa implements AI technology to track dangerous locust swarms, biologists and naturalists overwhelmingly agree that gay sex is normal throughout the animal kingdom, and BirdNet proves reliable at crowdsourcing the task of identifying wild birds by their songs. In wholesome news for sci fi/space fantasy fans everywhere, Ukraine's president Zelensky continues attending technology trade shows through holograms in which he promises that Ukraine will defeat the Empire. Ukrainians are also using 3d imaging technology to preserve the cultural heritage of their country from looters and bombs, storing their data in a digital archive that will support restoration work when the invaders have been defeated. And in good news for new Ukrainian parents, the non-profit Embrace Global is making headlines for using innovative technology to provide incubators for babies in Ukraine at a tiny fraction of their usual cost. You can see their TED talk by entrepreneur Jane Chen here.

Numerous examples of machine learning show that machine learning (ML) can be extremely useful in a variety of crucial applications, including data mining, natural language processing, picture recognition, and expert systems. In all of these areas and more, ML offers viable solutions, and it is destined to be a cornerstone of our post-apocalyptic civilization. The history of machine learning shows that a good grasp of the machine learning lifecycle increase machine learning benefits for businesses significantly. There are many uncommon machine learning examples that prove this, and you will find the best ones in this article. Machine learning uses statistical methods to increase a computer's intelligence, assisting in the automatic utilization of all business data. Due to growing reliance on machine learning technologies, humans' lifestyles have undergone a significant transformation. We use Google Assistant, which uses ML principles, as an example.

Research into drugs to treat mosquito-borne flaviviruses such as Zika and dengue as well as COVID-19will benefit from a major funding boost, says a group of international scientists using artificial intelligence to discover new oral antivirals. A research consortium led by the non-profit COVID Moonshot has been awarded more than US$68 million from the US National Institutes of Health (NIH) to discover and develop globally accessible and affordable novel oral antivirals to combat COVID-19 and future pandemics. The development comes as monkeypox outbreaks have been declared around the world, raising concerns about the rapid spread of such viruses. Monkeypox is a viral disease that the World Health Organization says has emerged in at least 23 countries where the disease is not regularly found since 13 May. The open-science COVID Moonshot was established in 2020 with the objective of developing a safe, globally accessible and affordable antiviral pill for COVID-19.

The explanation to an AI model's prediction used to support decision making in cyber security, is of critical importance. It is especially so when the model's incorrect prediction can lead to severe damages or even losses to lives and critical assets. However, most existing AI models lack the ability to provide explanations on their prediction results, despite their strong performance in most scenarios. In this work, we propose a novel explainable AI method, called PhilaeX, that provides the heuristic means to identify the optimized subset of features to form the complete explanations of AI models' predictions. It identifies the features that lead to the model's borderline prediction, and those with positive individual contributions are extracted. The feature attributions are then quantified through the optimization of a Ridge regression model. We verify the explanation fidelity through two experiments. First, we assess our method's capability in correctly identifying the activated features in the adversarial samples of Android malwares, through the features attribution values from PhilaeX. Second, the deduction and augmentation tests, are used to assess the fidelity of the explanations. The results show that PhilaeX is able to explain different types of classifiers correctly, with higher fidelity explanations, compared to the state-of-the-arts methods such as LIME and SHAP.

Oyinmiebi Elena Ebikefe is a robotics and artificial intelligence (AI) engineer from Nigeria. She holds a first-class honours degree in electrical and electronics engineering, which led her to explore industrial automation, where she subsequently became hooked on robotics and AI. She went on to secure an MSc in Control, Automation and Artificial Intelligence at Coventry University and is open to robotics opportunities whilst she continues to develop her career through self-directed study. "Robotics uncovered a whole new side of me -- I keep surprising myself with the level of discipline and focus to study, dedication, investment and results I have gained with each robotics project." My name is Oyinmiebi Elena Ebikefe, and I'm a robotics and artificial intelligence engineer from Nigeria.

FIFA has confirmed that a semi-automated offside system will be used at this year's football World Cup in Qatar. The new technology utilises a limb-tracking camera system to track player movements and a sensor in the ball. It then quickly shows 3D images on stadium screens at the tournament to help fans understand the referee's call. It is the third World Cup in a dispute that will see FIFA introduce new technology to help referees. The optical tracking system was trialled at the FIFA Club World Cup in Abu Dhabi earlier this year and had also been tested at the Arab Cup in Qatar last December.