Augmented reality (AR) has been widely used in education, particularly for child education. This paper presents the design and implementation of a novel mobile app, Learn2Write, using machine learning techniques and augmented reality to teach alphabet writing. The app has two main features: (i) guided learning to teach users how to write the alphabet and (ii) on-screen and AR-based handwriting testing using machine learning. A learner needs to write on the mobile screen in on-screen testing, whereas AR-based testing allows one to evaluate writing on paper or a board in a real world environment. We implement a novel approach to use machine learning for AR-based testing to detect an alphabet written on a board or paper. It detects the handwritten alphabet using our developed machine learning model. After that, a 3D model of that alphabet appears on the screen with its pronunciation/sound. The key benefit of our approach is that it allows the learner to use a handwritten alphabet. As we have used marker-less augmented reality, it does not require a static image as a marker. The app was built with ARCore SDK for Unity. We further evaluated and quantified the performance of our app on multiple devices.

Pfizer Inc. is an American multinational pharmaceutical and biotechnology corporation headquartered on 42nd Street in Manhattan, New York City. Pfizer develops and produces medicines and vaccines for immunology, oncology, cardiology, endocrinology, and neurology. The company has several blockbuster drugs or products that each generate more than US$1 billion in annual revenues.

The enormous underwater volcano off Tonga last month not only caused record plumes of ash into the air, but also led to one of the largest volcanic lightning events ever seen. According to GLD360, the ground-based global lightning detection network owned and operated by Vaisala, the eruption triggered nearly 590,000 lighting strikes that were'unlike anything on record.' The lightning almost engulfed the surrounding islands in the Tonga archipelago, according to Chis Vagasky, a meteorologist at Vaisala. 'I can't imagine what the people on the islands would have been going through, with a huge ash cloud overhead, a tsunami flooding everything they own, and cloud-to-ground lightning coming down around them,' he said. 'It must have felt apocalyptic.' Ash sent spewing into the air from the massive underwater volcanic eruption in Tonga was photographed by International Space Station astronauts.

Retrieval-based place recognition is an efficient and effective solution for re-localization within a pre-built map, or global data association for Simultaneous Localization and Mapping (SLAM). The accuracy of such an approach is heavily dependent on the quality of the extracted scene-level representation. While end-to-end solutions - which learn a global descriptor from input point clouds - have demonstrated promising results, such approaches are limited in their ability to enforce desirable properties at the local feature level. In this paper, we introduce a local consistency loss to guide the network towards learning local features which are consistent across revisits, hence leading to more repeatable global descriptors resulting in an overall improvement in 3D place recognition performance. We formulate our approach in an end-to-end trainable architecture called LoGG3D-Net. Experiments on two large-scale public benchmarks (KITTI and MulRan) show that our method achieves mean $F1_{max}$ scores of $0.939$ and $0.968$ on KITTI and MulRan respectively, achieving state-of-the-art performance while operating in near real-time. The open-source implementation is available at: https://github.com/csiro-robotics/LoGG3D-Net.

Melody choralization, i.e. generating a four-part chorale based on a user-given melody, has long been closely associated with J.S. Bach chorales. Previous neural network-based systems rarely focus on chorale generation conditioned on a chord progression, and none of them realised controllable melody choralization. To enable neural networks to learn the general principles of counterpoint from Bach's chorales, we first design a music representation that encoded chord symbols for chord conditioning. We then propose DeepChoir, a melody choralization system, which can generate a four-part chorale for a given melody conditioned on a chord progression. Furthermore, with the improved density sampling, a user can control the extent of harmonicity and polyphonicity for the chorale generated by DeepChoir. Experimental results reveal the effectiveness of our data representation and the controllability of DeepChoir over harmonicity and polyphonicity. The code and generated samples (chorales, folk songs and a symphony) of DeepChoir, and the dataset we use now are available at https://github.com/sander-wood/deepchoir.

There is a growing interest in dataset generation recently due to the superior generative capacity of large pre-trained language models (PLMs). In this paper, we study a flexible and efficient zero-short learning method, ZeroGen. Given a zero-shot task, we first generate a dataset from scratch using PLMs in an unsupervised manner. Then, we train a tiny task model (e.g., LSTM) under the supervision of the synthesized dataset. This approach allows highly efficient inference as the final task model only has orders of magnitude fewer parameters comparing to PLMs (e.g., GPT2-XL). Apart from being annotation-free and efficient, we argue that ZeroGen can also provide useful insights from the perspective of data-free model-agnostic knowledge distillation, and unreferenced text generation evaluation. Experiments and analysis on different NLP tasks, namely, text classification, question answering, and natural language inference), show the effectiveness of ZeroGen.

AI technologies are being used in different areas like healthcare, pharmaceuticals, manufacturing, teaching, security, etc. However, the use of artificial intelligence is not just confined to some businesses and industries. Artificial Intelligence is also being used for saving the environment, and saving sea turtles as well. There are different ways to help the environment, and the use of AI software for saving sea turtles is one such way to use AI for conservation. The use of AI technologies has helped in protecting and saving sea turtles eggs from the feral pigs in North Queensland in Australia.

Across the world, companies are in a fierce battle to assemble the right mix of hardware and software technology to deliver autonomous vehicles. With dozens of companies working on one of the hardest problems, driverless vehicles will be here in the not too distant future. So how is Australia getting ready to facilitate their introduction and allow businesses and citizens to take advantage of the technology? Last Friday, the 16th meeting of Infrastructure and Transport Ministers was held and they have made a really important determination, available in the now-public documents at infrastruture.gov.au Automated vehicles Ministers agreed that the future Automated Vehicle Safety Law will be implemented through Commonwealth law.

At present, quantum computer systems make too many errors to ever be really helpful, however, a synthetic intelligence that may appropriate quantum errors might supply an answer. The duty is extra complicated in quantum computing as a result every qubit, or quantum bit, exists in a blended state of zero and 1, and any try to establish errors by instantly measuring qubits destroys the information. Researchers have developed a way to identify sources of error in quantum computers through Artificial intelligence and machine learning, providing hardware developers the ability to pinpoint performance degradation with unprecedented accuracy. A technique to detect the tiniest deviations from the precise conditions needed to execute quantum algorithms using trapped ion and superconducting quantum computing hardware. These are the core technologies used by industrial quantum computing efforts at IBM, Google, Honeywell, and others.

Digital health is defined as the space where digital technologies, daily life, and health care intersect. The objective of digital health is to enhance the efficiency of healthcare delivery and make medicine more individualised and precise. Information and communication technologies are used to help address the health problems faced by people undergoing treatment. The interconnectedness of health systems development, improved use of computational analysis, smart devices, and communication media are the techniques and tools used in digital health to aid clinicians and their clients with managing illnesses and health conditions while simultaneously promoting good health and well being. Generally, digital health is concerned about the development of interconnected health systems to improve the use of computational technologies, smart devices, computational analysis techniques, and communication media to aid healthcare professionals and their clients manage illnesses and health risks, as well as promote health and wellbeing.