Sensors are the key to sensing the environment and imparting benefits to smart cities in many aspects, such as providing real-time air quality information throughout an urban area. However, a prerequisite is to obtain fine-grained knowledge of the environment. There is a limit to how many sensors can be installed in the physical world due to non-negligible expenses. In this paper, we propose to infer real-time information of any given location in a city based on historical and current observations from the available sensors (termed spatiotemporal inference). Our approach decouples the modeling of short-term and long-term patterns, relying on two major components. Firstly, unlike previous studies that separated the spatial and temporal relation learning, we introduce a joint spatiotemporal graph attention network that learns the short-term dependencies across both the spatial and temporal dimensions. Secondly, we propose an adaptive graph recurrent network with a time skip for capturing long-term patterns. The adaptive adjacency matrices are learned inductively first as the inputs of a recurrent network to learn dynamic dependencies. Experimental results on four public read-world datasets show that our method reduces state-of-the-art baseline mean absolute errors by 5%~12%.

Pandemics and natural disasters over the years have changed the behavior of people, which has had a tremendous impact on all life aspects. With the technologies available in each era, governments, organizations, and companies have used these technologies to track, control, and influence the behavior of individuals for a benefit. Nowadays, the use of the Internet of Things (IoT), cloud computing, and artificial intelligence (AI) have made it easier to track and change the behavior of users through changing IoT behavior. This article introduces and discusses the concept of the Internet of Behavior (IoB) and its integration with Explainable AI (XAI) techniques to provide trusted and evident experience in the process of changing IoT behavior to ultimately improving users' behavior. Therefore, a system based on IoB and XAI has been proposed in a use case scenario of electrical power consumption that aims to influence user consuming behavior to reduce power consumption and cost. The scenario results showed a decrease of 522.2 kW of active power when compared to original consumption over a 200-hours period. It also showed a total power cost saving of 95.04 Euro for the same period. Moreover, decreasing the global active power will reduce the power intensity through the positive correlation.

Building surrogate models is one common approach when we attempt to learn unknown black-box functions. Bayesian optimization provides a framework which allows us to build surrogate models based on sequential samples drawn from the function and find the optimum. Tuning algorithmic parameters to optimize the performance of large, complicated "black-box" application codes is a specific important application, which aims at finding the optima of black-box functions. Within the Bayesian optimization framework, the Gaussian process model produces smooth or continuous sample paths. However, the black-box function in the tuning problem is often non-smooth. This difficult tuning problem is worsened by the fact that we usually have limited sequential samples from the black-box function. Motivated by these issues encountered in tuning, we propose a novel additive Gaussian process model called clustered Gaussian process (cGP), where the additive components are induced by clustering. In the examples we studied, the performance can be improved by as much as 90% among repetitive experiments. By using this surrogate model, we want to capture the non-smoothness of the black-box function. In addition to an algorithm for constructing this model, we also apply the model to several artificial and real applications to evaluate it.

Machine learning techniques allow a direct mapping of atomic positions and nuclear charges to the potential energy surface with almost ab-initio accuracy and the computational efficiency of empirical potentials. In this work we propose a machine learning method for constructing high-dimensional potential energy surfaces based on feed-forward neural networks. As input to the neural network we propose an extendable invariant local molecular descriptor constructed from geometric moments. Their formulation via pairwise distance vectors and tensor contractions allows a very efficient implementation on graphical processing units (GPUs). The atomic species is encoded in the molecular descriptor, which allows the restriction to one neural network for the training of all atomic species in the data set. We demonstrate that the accuracy of the developed approach in representing both chemical and configurational spaces is comparable to the one of several established machine learning models. Due to its high accuracy and efficiency, the proposed machine-learned potentials can be used for any further tasks, for example the optimization of molecular geometries, the calculation of rate constants or molecular dynamics.

From James Bond to The Jetsons, jetpacks have been a staple feature in blockbuster movies for years. Now, the technology is slowly but surely becoming a reality, with one company unveiling what it claims is the world's first hands-free jetpack prototype. Maverick Aviation has developed a device called the Maverick Jetpack, which it claims will travel at speeds of up to 30mph and could be ready by 2022. Unlike most existing jetpacks, which require intense training to get the hang of, the Maverick Jetpack has an in-built autopilot system and is intuitive to control, according to the team. The developers believe the device could be used to enter structures that are difficult to access in the near future, including wind turbines and construction sites.

Geoscape Australia, a government-owned geospatial data company, has announced it has partnered with an Israeli artificial intelligence start-up to use machine vision and deep learning technology to enhance its 3D digital maps of Australia. The CEO of Geoscape Australia said that the partnership will advance what is known about every address across the country. Applying the Israeli AI start-up's patented AI technology to the highest quality aerial imagery will significantly evolve the current digital model of Australia. The company says more accurate digital models of Australia's urban environment will enable the data-driven foundation of Digital Twin applications that better reflect the real world. The up-to-date data will also improve the assessment of risk for insurers, allow architects to visualise new developments in the context of their surroundings, help noise modellers better understand what will be impacted by noise, and power modelling of energy use patterns in commercial and residential buildings.

Abstract--There are numerous methods for detecting anomalies in time series, but that is only the first step to understanding them. We strive to exceed this by explaining those anomalies. Thus we develop a novel attribution scheme for multivariate time series relying on counterfactual reasoning. We aim to answer the counterfactual question of would the anomalous event have occurred if the subset of the involved variables had been more similarly distributed to the data outside of the anomalous interval. By determining which variables yield the lowest anomaly score Finding causes of extreme weather events, power outages after the replacement, we can conclude that the subset of and abnormal fluctuations in financial data can be of crucial variables in question was the reason why the anomaly had importance for their understanding and taking precautionary occurred. We propose a novel anomaly attribution scheme Our attribution method can be applied to any multivariate to analyze anomalous intervals of multivariate temporal and time series data regardless of potential outliers and missing spatio-temporal data and attribute those anomalies to a set of values.

While Deep Reinforcement Learning (DRL) provides transformational capabilities to the control of Robotics and Autonomous Systems (RAS), the black-box nature of DRL and uncertain deployment-environments of RAS pose new challenges on its dependability. Although there are many existing works imposing constraints on the DRL policy to ensure a successful completion of the mission, it is far from adequate in terms of assessing the DRL-driven RAS in a holistic way considering all dependability properties. In this paper, we formally define a set of dependability properties in temporal logic and construct a Discrete-Time Markov Chain (DTMC) to model the dynamics of risk/failures of a DRL-driven RAS interacting with the stochastic environment. We then do Probabilistic Model Checking based on the designed DTMC to verify those properties. Our experimental results show that the proposed method is effective as a holistic assessment framework, while uncovers conflicts between the properties that may need trade-offs in the training. Moreover, we find the standard DRL training cannot improve dependability properties, thus requiring bespoke optimisation objectives concerning them. Finally, our method offers a novel dependability analysis to the Sim-to-Real challenge of DRL.

Young cows have learned to urinate in a dedicated "latrine" that whisks the waste away before it can pollute waterways or trigger the release of harmful gases. What's more, nitrous oxide that arises when livestock urine and faeces mix can cause respiratory problems and contribute to global warming. By training cattle to void directly into a sort of "cow toilet", however, Lindsay Matthews at the University of Auckland in New Zealand and his colleagues have potentially found a way to keep water and air cleaner, improving health and welfare for both humans and animals. Matthews's team taught 16 5-month-old Holstein heifers to use a custom-built, plastic-grass-floored latrine when they felt the need to urinate, using a three-step training process. First, the team placed pairs of calves in the latrine until they urinated; then gave them a treat – either diluted molasses or barley – through an automatic dispenser and opened the exit door.

They may seem like something out of The Avengers film franchise, but these ambitious concepts of revolutionary warships are actually part of the Royal Navy's vision of what the British fleet could look like in the future. Detailed proposals for four potential vehicles, created by young engineers, have been released, including a stealth submarine carrier and a huge flying drone station which would be attached to a helium balloon and based in the stratosphere. The idea is that attack drones shaped like conventional airplanes could then be launched from the station'at a moment's notice' before shooting down towards Earth and potentially gliding just beneath the water in a stealth mode and smashing into an enemy ship. The Royal Navy hasn't disclosed anticipated costs of bringing to life the newly-revealed concepts, which have been described as one expert involved in British defence and security operations as very much'in the realm of speculative thinking'. They have been put forward by young engineers from industry and academia as part of a challenge posed by the UK Naval Engineering Science and Technology (UKNEST), aimed at helping the Royal Navy to develop ideas for an autonomous fleet that could shape how it operates over the next 50 years.