RMIT Vietnam, an entity of RMIT University, has campuses in Ho Chi Minh City and Hanoi since 2001. We provide internationally recognised, high-quality, education and professional training for students, clients and the community, and assist in the development of human resources capability in Vietnam and the region by hosting students from Australia and many other countries. The School of Science, Engineering & Technology at RMIT Vietnam is associated with the STEM College of RMIT University Melbourne, delivering the College's award programs and developing research in the field(s) of science, engineering and health. Programs currently taught on the Vietnam campus include the Bachelor of Information Technology, Bachelor of Engineering (Electrical and Electronics), Bachelor of Engineering (Software Engineering), Bachelor of Engineering (Robotics & Mechatronics), Bachelor of Applied Science (Aviation), Bachelor of Applied Science (Psychology), Bachelor of Science (Food Technology and Nutrition, Master of Artificial Intelligence and the Doctor of Philosophy (Engineering) (Electrical and Electronic Engineering). As a senior RMIT Vietnam staff member in Master of AI, the Program Manager (PM) will provide leadership of the Artificial Intelligence discipline across education, research, engagement and administration.

Role requiring'No experience data provided' months of experience in None We're looking for a Build and Release Engineer to join our team as a master of packages and containers who always delivers the goods. A key member of the CI/CD pipeline from planning all the way through deployment, you'll collaborate with other skilled engineers to identify technical needs, develop solutions, and deploy them using the latest tools available. Sure, you'll use your head to build and maintain the tools, infrastructure, and processes that directly impact our development teams and customers. But you're a developer at heart, and this is a role in which you'll craft code aplenty and keep your finger on the pulse of modern software build engineering practices. Inductive Automation is an innovation company. We are champions for industrial automation software, and we believe in building sensible solutions that provide value for our customers. Our workforce shares a passion for technology.

In a recent filing, Microsoft told New Zealand's Commerce Commission that Blizzard Activision produces no "must-have" games. Weird thing to say when the company plans to spend $68.7 billion to buy the gaming giant behind Call of Duty, Overwatch, Diablo, World of Warcraft and plenty more. In the document, Microsoft said: "There is nothing unique about the video games developed and published by Activision Blizzard that is a'must have' for rival PC and console video game distributors that give rise to a foreclosure concern." Attempting to downplay the importance of Call of Duty is just one of the ways Microsoft has tried to placate regulators. In February, the company pledged it would continue to make the franchise available on PlayStation consoles beyond any existing agreements between Sony and Activision. Apple's 10.2-inch iPad is back on sale for $300 at Amazon Sony is retiring the PlayStation 5's Accolades feature because people aren't nice An e-bike- and scooter-sharing startup co-founded by Olympian Usain Bolt appears to have stopped operations.

Proxymity is bringing technological innovation to the market infrastructure of a long -established sector--Proxy Voting, Shareholder Disclosure, and Identification. Founded in London and spun out of Citi, from very beginning our mission focused on benefitting the whole ecosystem, rather than just one part or one player within it. This ethos is endorsed by investment from a unique and global consortium of the industry's most influential financial institutions. We currently serve 29 markets and are growing our global footprint fast. With offices in UK, Israel and Australia, our Proxymity Shareholder ID and Vote Connect products can serve all markets globally, with our flagship Vote Connect Total product providing complete end-to-end digital connectivity in 12 key markets around the world.

The manufacturing industry includes a broad range of businesses varying in size, from small manufacturers producing limited quantities of items to multi-national organisations involved in the large-scale production of manufactured goods. This industry encompasses manufacturers of food and beverages to textiles, chemical manufacturing and even heavy machinery. As an industry, manufacturing contributes around $100 billion to the Australian GDP annually (ABS, 2020). Thus, an industry with such varying degrees of complexity, breadth and scale is one that seeks to maximise the broad benefits of cloud technologies. Regardless of the end product, commonly faced challenges experienced within the manufacturing industry include innovation, risk management, operational efficiency and cost optimisation, remaining sustainable and time to market.

This paper provides the system description of "Silo NLP's" submission to the Workshop on Asian Translation (WAT2022). We have participated in the Indic Multimodal tasks (English->Hindi, English->Malayalam, and English->Bengali Multimodal Translation). For text-only translation, we trained Transformers from scratch and fine-tuned mBART-50 models. For multimodal translation, we used the same mBART architecture and extracted object tags from the images to use as visual features concatenated with the text sequence. Our submission tops many tasks including English->Hindi multimodal translation (evaluation test), English->Malayalam text-only and multimodal translation (evaluation test), English->Bengali multimodal translation (challenge test), and English->Bengali text-only translation (evaluation test).

Our goal is to enable robots to perform functional tasks in emotive ways, be it in response to their users' emotional states, or expressive of their confidence levels. Prior work has proposed learning independent cost functions from user feedback for each target emotion, so that the robot may optimize it alongside task and environment specific objectives for any situation it encounters. However, this approach is inefficient when modeling multiple emotions and unable to generalize to new ones. In this work, we leverage the fact that emotions are not independent of each other: they are related through a latent space of Valence-Arousal-Dominance (VAD). Our key idea is to learn a model for how trajectories map onto VAD with user labels. Considering the distance between a trajectory's mapping and a target VAD allows this single model to represent cost functions for all emotions. As a result 1) all user feedback can contribute to learning about every emotion; 2) the robot can generate trajectories for any emotion in the space instead of only a few predefined ones; and 3) the robot can respond emotively to user-generated natural language by mapping it to a target VAD. We introduce a method that interactively learns to map trajectories to this latent space and test it in simulation and in a user study. In experiments, we use a simple vacuum robot as well as the Cassie biped.

Internal cluster validity measures (such as the Calinski-Harabasz, Dunn, or Davies-Bouldin indices) are frequently used for selecting the appropriate number of partitions a dataset should be split into. In this paper we consider what happens if we treat such indices as objective functions in unsupervised learning activities. Is the optimal grouping with regards to, say, the Silhouette index really meaningful? It turns out that many cluster (in)validity indices promote clusterings that match expert knowledge quite poorly. We also introduce a new, well-performing variant of the Dunn index that is built upon OWA operators and the near-neighbour graph so that subspaces of higher density, regardless of their shapes, can be separated from each other better.

Machine Reading Comprehension with Unanswerable Questions is a difficult NLP task, challenged by the questions which can not be answered from passages. It is observed that subtle literal changes often make an answerable question unanswerable, however, most MRC models fail to recognize such changes. To address this problem, in this paper, we propose a span-based method of Contrastive Learning (spanCL) which explicitly contrast answerable questions with their answerable and unanswerable counterparts at the answer span level. With spanCL, MRC models are forced to perceive crucial semantic changes from slight literal differences. Experiments on SQuAD 2.0 dataset show that spanCL can improve baselines significantly, yielding 0.86-2.14 absolute EM improvements. Additional experiments also show that spanCL is an effective way to utilize generated questions.

Wide Area Networks (WAN) are a key infrastructure in today's society. During the last years, WANs have seen a considerable increase in network's traffic and network applications, imposing new requirements on existing network technologies (e.g., low latency and high throughput). Consequently, Internet Service Providers (ISP) are under pressure to ensure the customer's Quality of Service and fulfill Service Level Agreements. Network operators leverage Traffic Engineering (TE) techniques to efficiently manage network's resources. However, WAN's traffic can drastically change during time and the connectivity can be affected due to external factors (e.g., link failures). Therefore, TE solutions must be able to adapt to dynamic scenarios in real-time. In this paper we propose Enero, an efficient real-time TE solution based on a two-stage optimization process. In the first one, Enero leverages Deep Reinforcement Learning (DRL) to optimize the routing configuration by generating a long-term TE strategy. To enable efficient operation over dynamic network scenarios (e.g., when link failures occur), we integrated a Graph Neural Network into the DRL agent. In the second stage, Enero uses a Local Search algorithm to improve DRL's solution without adding computational overhead to the optimization process. The experimental results indicate that Enero is able to operate in real-world dynamic network topologies in 4.5 seconds on average for topologies up to 100 edges.