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Brisbane


A Little About Me -- Amena Khatun

#artificialintelligence

My name is Amena Khatun, and I currently live in Australia with my partner and our son. I am working as a'Postdoctoral Fellow' at Queensland University and Technology (QUT), Brisbane, Australia. My research interest is computer vision, deep learning, person re-identification, and security surveillance. In February 2017, my Ph.D. journey started in computer vision and deep learning at QUT. I am so thankful for the Australian Government RTP Scholarship, QUT HDR tuition Fees Sponsorship, and QUT Top-up Scholarship.


VeriDoc Global and VIEWTRACK Form Technology Partnership

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VeriDoc Global is pleased to announce a partnership with VIEWTRACK. VIEWTRACK is a provider of IoT solutions, 4G GPS Tracking, Fleet Management, and Artificial Intelligence Solutions for business and personal assets. Headquartered and based in Brisbane, Australia, the company supplies all forms of tracking technology worldwide. The partnership will enhance existing solutions by combining cutting-edge IoT devices with blockchain technology in areas such as transport, safety and manufacturing. To find out more information about VIEWTRACK please visit https://viewtrack.com.au and for more on VeriDoc Global https://veridocglobal.com


Brisbane AI User Group (Brisbane, Australia)

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This is a group for anyone interested in Artificial Intelligence (AI). All skill levels are welcome. The main focus of this group is to help people understand AI scenarios through real, hands-on technical solutions and explore how to develop AI solutions in today's world. We will look at a variety of technology from different providers. Guest speakers will include people working in the industry to demonstrate new technology.


#302: Robots That Can See, Do, and Win, with Juxi Leitner

Robohub

Juxi Leitner is co-founder of LYRO Robotics, a deep-tech startup based in Brisbane, Australia, creating robotic picking and packing solutions. LYRO is a spin-out of the Australian Centre of Excellence for Robotic Vision (ACRV), where Juxi is the research lead for the manipulation research stream (previously Vision and Action project). His research focus is on integrating Robotics, Computer Vision and Machine Learning/Artificial Intelligence (AI) for robust grasping and manipulation in real-world scenarios. In 2017 his team won the Amazon Robotics Challenge. Juxi is active in the local Brisbane deep-tech ecosystem and started Brisbane.AI and the Brisbane robotics interest group.


Deep learning method transforms shapes

#artificialintelligence

Called LOGAN, the deep neural network, i.e., a machine of sorts, can learn to transform the shapes of two different objects, for example, a chair and a table, in a natural way, without seeing any paired transforms between the shapes. All the machine had seen was a bunch of tables and a bunch of chairs, and it could automatically translate shapes between the two unpaired domains. LOGAN can also automatically perform both content and style transfers between two different types of shapes without any changes to its network architecture. The team of researchers behind LOGAN, from Simon Fraser University, Shenzhen University, and Tel Aviv University, are set to present their work at ACM SIGGRAPH Asia held Nov. 17 to 20 in Brisbane, Australia. SIGGRAPH Asia, now in its 12th year, attracts the most respected technical and creative people from around the world in computer graphics, animation, interactivity, gaming, and emerging technologies.


Silas: High Performance, Explainable and Verifiable Machine Learning

arXiv.org Machine Learning

Silas: High Performance, Explainable and V erifiable Machine Learning Hadrien Bride, Zh e H ou Griffith University, Nathan, Brisbane, Australia Jie Dong Dependable Intelligence Pty Ltd, Brisbane, Australia Jin Song Dong National University of Singapore, Singapore Ali Mirjalili Griffith University, Nathan, Brisbane, AustraliaAbstract This paper introduces a new classification tool named Silas, which is built to provide a more transparent and dependable data analytics service. A focus of Silas is on providing a formal foundation of decision trees in order to support logical analysis and verification of learned prediction models. This paper describes the distinct features of Silas: The Model Audit module formally verifies the prediction model against user specifications, the Enforcement Learning module trains prediction models that are guaranteed correct, the Model Insight and Prediction Insight modules reason about the prediction model and explain the decision-making of predictions. We also discuss implementation details ranging from programming paradigm to memory management that help achieve high-performance computation.1. Introduction Machine learning has enjoyed great success in many research areas and industries, including entertainment [1], self-driving cars [2], banking [3], medical diagnosis [4], shopping [5], and among many others. However, the wide adoption of machine learn-Preprint submitted to Elsevier October 4, 2019 arXiv:1910.01382v1 The ramifications of the black-box approach are multifold. First, it may lead to unexpected results that are only observable after the deployment of the algorithm. For instance, Amazon's Alexa offered porn to a child [6], a self-driving car had a deadly accident [7], etc. Some of these accidents result in lawsuits or even lost lives, the cost of which is immeasurable. Second, it prevents the adoption in some applications and industries where an explanation is mandatory or certain specifications must be satisfied. For example, in some countries, it is required by law to give a reason why a loan application is rejected. In recent years, eXplainable AI (XAI) has been gaining attention, and there is a surge of interest in studying how prediction models work and how to provide formal guarantees for the models. A common theme in this space is to use statistical methods to analyse prediction models.


Queensland AI (Brisbane, Australia)

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From the curious beginner to the advanced practitioner, this is a group for anyone interested in learning and sharing ideas in AI. We aim to run events to get people together and to support all the great AI initiatives taking place in Queensland. Our Queensland.AI community will also serve as the foundation for the new Queensland AI centre, due to launch in 2019. Check out our website: https://brisbane.ai/


Using Machine Learning to Drive Retention

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Halfbrick Studios is a professional game development studio based in Brisbane, Australia. Founded in 2001, Halfbrick has developed many popular games, including Fruit Ninja, Jetpack Joyride, and Dan the Man. When Halfbrick first learned about Firebase Predictions, they were excited about targeting users based on predicted behavior, rather than historic. Re-engagement is tough, so intervening before a user churned - based on predictions instead of ad hoc heuristics - seemed like a strong strategy. They had been trying to create their own churn prediction models, but like many companies, didn't have the time or resources to properly devote to the problem.


Attention-based Recurrent Neural Network for Urban Vehicle Trajectory Prediction

arXiv.org Artificial Intelligence

As the number of various positioning sensors and location-based devices increase, a huge amount of spatial and temporal information data is collected and accumulated. These data are expressed as trajectory data by connecting the data points in chronological sequence, and thses data contain movement information of any moving object. Particularly, in this study, urban vehicle trajectory prediction is studied using trajectory data of vehicles in urban traffic network. In the previous work, Recurrent Neural Network model for urban vehicle trajectory prediction is proposed. For the further improvement of the model, in this study, we propose Attention-based Recurrent Neural Network model for urban vehicle trajectory prediction. In this proposed model, we use attention mechanism to incorporate network traffic state data into urban vehicle trajectory prediction. The model is evaluated by using the Bluetooth data collected in Brisbane, Australia, which contains the movement information of private vehicles. The performance of the model is evaluated with 5 metrics, which are BLEU-1, BLEU-2, BLEU-3, BLEU-4, and METEOR. The result shows that ARNN model have better performance compared to RNN model.


New creepy, crawly search and rescue robot: RSTAR can navigate large obstacles and carry payloads necessary for search and rescue operations

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The new Rising Sprawl-Tuned Autonomous Robot (RSTAR) utilizes adjustable sprawling wheel legs attached to a body that can move independently and reposition itself to run on flat surfaces, climb over large obstacles and up closely-spaced walls, and crawl through a tunnel, pipe or narrow gaps. The innovative BGU robot was introduced at the International Conference on Robotics and Automation (ICRA 2018) in Brisbane, Australia, May 21-25. "The RSTAR is ideal for search and rescue operations in unstructured environments, such as collapsed buildings or flooded areas, where it must adapt and overcome a variety of successive obstacles to reach its target," says Dr. David Zarrouk, a lecturer in BGU's Department of Mechanical Engineering, and head of the Bio-Inspired and Medical Robotics Lab. "It is the newest member of our family of STAR robots." Dr. Zarrouk and BGU student and robotics lab worker Liran Yehezkel designed RSTAR to function simply and reliably, change shape and overcome common obstacles without any external mechanical intervention.