With the emergence of IoT, there is a rising interest in applying Internet of Things (IoT) technology in the smart homes for making occupants' life more convenient. The convenience is underpinned by the principle of the least effort, i.e. the premise that humans would usually want to achieve goals with the least cognitive and physical efforts [2]. IoT refers to the networked interconnection of everyday things, which are augmented with capabilities such as sensing, actuating, and communication [21]. The availability of IoT devices including switch sensors, infrared motion sensors, pressure sensor, wearable sensors, accelerators, temperature, humidity, and light sensors have the potential to realize the convenience. It is a challenge that IoT devices are highly diverse in supporting infrastructure such as different programming language and communication protocols [5].

We propose a novel activity learning framework based on Edge Cloud architecture for the purpose of recognizing and predicting human activities. Although activity recognition has been vastly studied by many researchers, the temporal features that constitute an activity, which can provide useful insights for activity models, have not been exploited to their full potentials by mining algorithms. In this paper, we utilize temporal features for activity recognition and prediction in a single smart home setting. We discover activity patterns and temporal relations such as the order of activities from real data to develop a prompting system. Analysis of real data collected from smart homes was used to validate the proposed method.

Adversarial attacks against conventional Deep Learning (DL) systems and algorithms have been widely studied, and various defenses were proposed. However, the possibility and feasibility of such attacks against Deep Reinforcement Learning (DRL) are less explored. As DRL has achieved great success in various complex tasks, designing effective adversarial attacks is an indispensable prerequisite towards building robust DRL algorithms. In this paper, we introduce two novel adversarial attack techniques to \emph{stealthily} and \emph{efficiently} attack the DRL agents. These two techniques enable an adversary to inject adversarial samples in a minimal set of critical moments while causing the most severe damage to the agent. The first technique is the \emph{critical point attack}: the adversary builds a model to predict the future environmental states and agent's actions, assesses the damage of each possible attack strategy, and selects the optimal one. The second technique is the \emph{antagonist attack}: the adversary automatically learns a domain-agnostic model to discover the critical moments of attacking the agent in an episode. Experimental results demonstrate the effectiveness of our techniques. Specifically, to successfully attack the DRL agent, our critical point technique only requires 1 (TORCS) or 2 (Atari Pong and Breakout) steps, and the antagonist technique needs fewer than 5 steps (4 Mujoco tasks), which are significant improvements over state-of-the-art methods.

With the increasing popularity of electric vehicles, distributed energy generation and storage facilities in smart grid systems, an efficient Demand-Side Management (DSM) is urgent for energy savings and peak loads reduction. Traditional DSM works focusing on optimizing the energy activities for a single household can not scale up to large-scale home energy management problems. Multi-agent Deep Reinforcement Learning (MA-DRL) shows a potential way to solve the problem of scalability, where modern homes interact together to reduce energy consumers consumption while striking a balance between energy cost and peak loads reduction. However, it is difficult to solve such an environment with the non-stationarity, and existing MA-DRL approaches cannot effectively give incentives for expected group behavior. In this paper, we propose a collective MA-DRL algorithm with continuous action space to provide fine-grained control on a large scale microgrid. To mitigate the non-stationarity of the microgrid environment, a novel predictive model is proposed to measure the collective market behavior. Besides, a collective behavior entropy is introduced to reduce the high peak loads incurred by the collective behaviors of all householders in the smart grid. Empirical results show that our approach significantly outperforms the state-of-the-art methods regarding power cost reduction and daily peak loads optimization.

Eurovision 2020 was unsurprisingly cancelled due to the pandemic, but AI has stepped in to fill its glittery shoes. Dutch broadcaster VPRO has just wrapped up a Eurovision-inspired AI Song Contest, with 13 teams from Europe and Australia training algorithms to become budding pop stars while experts judge their output. As BBC and Bloomberg point out, the results are a mix of surprisingly well-done and frighteningly dystopic tunes... a bit like the real thing, really. The winning entry came from Australian team Uncanny Valley, whose song "Beautiful the World" was built by an AI trained on a mix of Eurovision hits and local animals affected by wildfires, including koalas, kookaburras and Tasmanian devils. It has the same catchy dance pop riffs you'd expect to get the full douze points from a Eurovision vote, just with nonsensical lyrics.

Computer Vision also referred as Vision is the recent cutting edge field within computer science that deals with enabling computers, devices or machines, in general, to see, understand, interpret or manipulate what is being seen. Computer Vision technology implements deep learning techniques and in few cases also employs Natural Language Processing techniques as a natural progression of steps to analyze extracted text from images. With all the advancements of deep learning, building functions like image classification, object detection, tracking, and image manipulation has become more simpler and accurate thus leading way to exploring more complex autonomous applications like self-driving cars, humanoids or drones. With deep learning, we can now manipulate images, for example superimpose Tom Cruise's features onto another face. Or convert a picture into a sketch mode or water color painting mode.

An Australian team has won a competition to write a hit Eurovision song using artificial intelligence. An editor for Dutch broadcaster VPRO had the idea, after the Netherlands won last year's Eurovision Song Contest. And it grew into an international effort after this year's contest was cancelled because of the coronavirus pandemic. The winning song, Beautiful the World, was inspired by nature's recovery from the bushfires earlier this year. A total of 13 teams took part, from the Netherlands, Australia, Sweden, Belgium, the UK, France, Germany and Switzerland.

Artificial intelligence in the retail sector is being applied in new ways, from the whole product and service cycle to assembly-to-post customer service interactions, but the key questions for retail players. What AI applications play a role in the automation or growth of the retail process? How retail are companies today using this technology to stay ahead of their competitors, and what innovations are posed as potential retail game-changers over the next decade? Innovation is a double-edged sword, and like any innovation, the results are a mixed bag. Many AI applications have yielded increased ROI -- this case study of AI in the retail marketing department is an example -- while others have failed and failed to meet expectations, such innovations shed light on the obstacles that must be overcome before becoming industry drivers.

Facebook unveiled an initiative Tuesday to take on "hateful memes" by using artificial intelligence, backed by crowd sourcing, to identify maliciously motivated posts. The leading social network said it had already created a database of 10,000 memes -- images often blended with text to deliver a specific message -- as part of a ramped-up effort against hate speech. Facebook said it was releasing the database to researchers as part of a "hateful memes challenge" to develop improved algorithms to detect hate-driven visual messages, with a prize pool of $100,000. "These efforts will spur the broader AI research community to test new methods, compare their work, and benchmark their results in order to accelerate work on detecting multimodal hate speech," Facebook said in a blog post. Facebook's effort comes as it leans more heavily on AI to filter out objectionable content during the coronavirus pandemic that has sidelined most of its human moderators.

Behavior Trees (BTs) were invented as a tool to enable modular AI in computer games, but have received an increasing amount of attention in the robotics community in the last decade. With rising demands on agent AI complexity, game programmers found that the Finite State Machines (FSM) that they used scaled poorly and were difficult to extend, adapt and reuse. In BTs, the state transition logic is not dispersed across the individual states, but organized in a hierarchical tree structure, with the states as leaves. This has a significant effect on modularity, which in turn simplifies both synthesis and analysis by humans and algorithms alike. These advantages are needed not only in game AI design, but also in robotics, as is evident from the research being done. In this paper we present a comprehensive survey of the topic of BTs in Artificial Intelligence and Robotic applications. The existing literature is described and categorized based on methods, application areas and contributions, and the paper is concluded with a list of open research challenges.