Mobile edge computing has become an effective and fundamental paradigm for futuristic autonomous vehicles to offload computing tasks. However, due to the high mobility of vehicles, the dynamics of the wireless conditions, and the uncertainty of the arrival computing tasks, it is difficult for a single vehicle to determine the optimal offloading strategy. In this paper, we propose a Digital Twin (DT) empowered task offloading framework for Internet of Vehicles. As a software agent residing in the cloud, a DT can obtain both global network information by using communications among DTs, and historical information of a vehicle by using the communications within the twin. The global network information and historical vehicular information can significantly facilitate the offloading. In specific, to preserve the precious computing resource at different levels for most appropriate computing tasks, we integrate a learning scheme based on the prediction of futuristic computing tasks in DT. Accordingly, we model the offloading scheduling process as a Markov Decision Process (MDP) to minimize the long-term cost in terms of a trade off between task latency, energy consumption, and renting cost of clouds. Simulation results demonstrate that our algorithm can effectively find the optimal offloading strategy, as well as achieve the fast convergence speed and high performance, compared with other existing approaches.

Online reviews have a significant influence on customers' purchasing decisions for any products or services. Several models have been developed to detect fake reviews using machine learning approaches. Many of these models have some limitations resulting in low accuracy in distinguishing between fake and genuine reviews. These models focused only on linguistic features to detect fake reviews and failed to capture the semantic meaning of the reviews. To deal with this, this paper proposes a new ensemble model that employs transformer architecture to discover the hidden patterns in a sequence of fake reviews and detect them precisely. The proposed approach combines three transformer models to improve the robustness of fake and genuine behaviour profiling and modelling to detect fake reviews. The experimental results using semi-real benchmark datasets showed the superiority of the proposed model over state-of-the-art models. NTRODUCTION The Internet's size and importance has exploded in recent years, and it exerts a significant and growing influence on people's daily lives. Customers usually spend a substantial amount of time online, searching for information on a variety of products, communicating with others, and reading reviews.

Facial feature tracking is a key component of imaging ballistocardiography (BCG) where accurate quantification of the displacement of facial keypoints is needed for good heart rate estimation. Skin feature tracking enables video-based quantification of motor degradation in Parkinson's disease. Traditional computer vision algorithms include Scale Invariant Feature Transform (SIFT), Speeded-Up Robust Features (SURF), and Lucas-Kanade method (LK). These have long represented the state-of-the-art in efficiency and accuracy but fail when common deformations, like affine local transformations or illumination changes, are present. Over the past five years, deep convolutional neural networks have outperformed traditional methods for most computer vision tasks. We propose a pipeline for feature tracking, that applies a convolutional stacked autoencoder to identify the most similar crop in an image to a reference crop containing the feature of interest. The autoencoder learns to represent image crops into deep feature encodings specific to the object category it is trained on. We train the autoencoder on facial images and validate its ability to track skin features in general using manually labeled face and hand videos. The tracking errors of distinctive skin features (moles) are so small that we cannot exclude that they stem from the manual labelling based on a $\chi^2$-test. With a mean error of 0.6-4.2 pixels, our method outperformed the other methods in all but one scenario. More importantly, our method was the only one to not diverge. We conclude that our method creates better feature descriptors for feature tracking, feature matching, and image registration than the traditional algorithms.

The prevalent paradigm of machine learning today is to use past observations to predict future ones. What if, however, we are interested in knowing the past given the present? This situation is indeed one that astronomers must contend with often. To understand the formation of our universe, we must derive the time evolution of the visible mass content of galaxies. However, to observe a complete star life, one would need to wait for one billion years! To overcome this difficulty, astrophysicists leverage supercomputers and evolve simulated models of galaxies till the current age of the universe, thus establishing a mapping between observed radiation and star formation histories (SFHs). Such ground-truth SFHs are lacking for actual galaxy observations, where they are usually inferred -- with often poor confidence -- from spectral energy distributions (SEDs) using Bayesian fitting methods. In this investigation, we discuss the ability of unsupervised domain adaptation to derive accurate SFHs for galaxies with simulated data as a necessary first step in developing a technique that can ultimately be applied to observational data.

Visual illusions remind us that we are not passive decoders of reality but active interpreters. Our eyes capture information from the environment, but our brain can play tricks on us. Perception doesn't always match reality. Scientists have used illusions for decades to explore the psychological and cognitive processes that underlie human visual perception. More recently, evidence is emerging that suggests many animals, like us, can perceive and create a range of visual illusions.

Australia has reported its first confirmed death from the new Omicron variant of COVID-19 amid another surge in daily infections. The authorities, however, refrained from imposing new restrictions, saying hospital admission rates remained low. The death on Monday of a man in his 80s with underlying health conditions marked a grim milestone for Australia that has had to reverse some parts of a staged reopening after nearly two years of stop-start lockdowns due to the fresh outbreak. Omicron, which health experts say appears more contagious but less virulent than previous strains, began to spread in the country just as it lifted restrictions on most domestic borders and allowed Australians to return from overseas without quarantine, driving case numbers to the highest levels since the start of the pandemic. The authorities gave no additional details about the Omicron death, except to say that the man caught the virus at an aged care facility and died in a Sydney hospital.

Deep reinforcement learning (RL) has recently shown great promise in robotic continuous control tasks. Nevertheless, prior research in this vein center around the centralized learning setting that largely relies on the communication availability among all the components of a robot. However, agents in the real world often operate in a decentralised fashion without communication due to latency requirements, limited power budgets and safety concerns. By formulating robotic components as a system of decentralised agents, this work presents a decentralised multiagent reinforcement learning framework for continuous control. To this end, we first develop a cooperative multiagent PPO framework that allows for centralized optimisation during training and decentralised operation during execution. However, the system only receives a global reward signal which is not attributed towards each agent. To address this challenge, we further propose a generic game-theoretic credit assignment framework which computes agent-specific reward signals. Last but not least, we also incorporate a model-based RL module into our credit assignment framework, which leads to significant improvement in sample efficiency. We demonstrate the effectiveness of our framework on experimental results on Mujoco locomotion control tasks. For a demo video please visit: https://youtu.be/gFyVPm4svEY.

Artificial Intelligence in Video Games Market research is an intelligence report with meticulous efforts undertaken to study the right and valuable information. The data which has been looked upon is done considering both, the existing top players and the upcoming competitors. Business strategies of the key players and the new entering market industries are studied in detail. Well explained SWOT analysis, revenue share and contact information are shared in this report analysis. It also provides market information in terms of development and its capacities.

The market of AI in Asia-Pacific is estimated to grow from US$18.7 billion in 2018 to US$43.7 billion in 2023 at a CAGR of 13.0% during the forecast period. The AI market is mainly driven by technological advancements in countries like China, India, Japan, Australia, South Korea, and the rest of Asia-Pacific. Artificial intelligence (AI) focuses on simulating human intelligence for building smart machines capable of performing tasks that require human intelligence. It has the potential of planning, learning, recognizing human-like speech, and solving problems based on past experiences. It includes hardware components such as chipsets with high computing capabilities.

Wind power is a powerful tool for reducing carbon emissions that cause climate change. The turbines, however, can be a threat to birds and bats, which is why experts are looking for--and finding--ways to eliminate the danger. The US government has allocated $13.5 million to look for solutions. But, already a Boulder, Colorado company has produced a camera- and AI-based technology that can recognize eagles, hawks and other raptors as they approach in enough time to pause turbines in their flight path. Their tool, called IdentiFlight, can detect 5.62 times more bird flights than human observers alone, and with an accuracy rate of 94 percent.