Aviation Maintenance Administrationman 3rd Class Shea Wright, assigned to the Skinny Dragons of Patrol Squadron (VP) 4, recovers a squadron P-8A Poseidon maritime patrol and reconnaissance aircraft following an anti-submarine warfare mission over the Atlantic Ocean, Nov. 30, 2019. The increasingly global reach of Chinese nuclear-armed ballistic missile submarines, armed with JL-2 weapons reportedly able to hit parts of the U.S., continues to inspire an ongoing Navy effort to accelerate production of attack submarines, prepare long-dwell drones for deployment to the Pacific and continue acquisition of torpedo-armed sub-hunting planes such as the P-8/A Poseidon. The Navy has been moving quickly to increase its fleet of Poseidon's on an accelerated timetable; in the Navy's 2020 budget, the service was authorized for a near term increase in Poseidon production by three, moving funding for the year up for nine Poseidons, as cited in a report from USNI news. Last year, the Navy awarded Boeing a $2.4 billion deal to produce 19 more P-8A Poseidon surveillance and attack planes. The Poseidon increase appears to align with the service's overall Pacific theater strategy, which makes a point to sustain peaceful, yet vital surveillance and Freedom of Navigation missions in the region.

Artificial intelligence and machine learning (AI/ML) systems are growing exponentially around the world and is estimated to generate AU$22.17 trillion to the global economy by 2030. The Australian Government's Artificial Intelligence Technology Roadmap, developed by Data61, identified Australia's need for up to 161,000 new specialist AI workers by 2030. Stuart Ayres, NSW Minister for Jobs, Investment, Tourism and Western Sydney, said: "NeurIPS 2021 will propel Australia's research and innovative discoveries to the forefront – bringing with it opportunities for trade and investment and talent attraction as well as helping to build Sydney's brand as an intellectual capital." Dr. Terrence Sejnowski, President of the NeurIPS Foundation, agreed that it was a "significant step" bringing the conference to Australia. It will be the first time NeurIPS is held in the Asia-Pacific region, and only the third time it has been held outside North America.

Innovation ecosystems can be naturally described as a collection of networked entities, such as experts, institutions, projects, technologies and products. Representing in a machine-readable form these entities and their relations is not entirely attainable, due to the existence of abstract concepts such as knowledge and due to the confidential, non-public nature of this information, but even its partial depiction is of strong interest. The representation of innovation ecosystems incarnated as knowledge graphs would enable the generation of reports with new insights, the execution of advanced data analysis tasks. An ontology to capture the essential entities and relations is presented, as well as the description of data sources, which can be used to populate innovation knowledge graphs. Finally, the application case of the Universidad Politecnica de Madrid is presented, as well as an insight of future applications.

Arguably, the most important scenario for search technology is lifelong learning and education, both for students and all citizens. Human learning is a complex multidimensional activity, which includes procedural learning (e.g., activity patterns associated with cooking, sports) and knowledge-based learning (e.g., mathematics, genetics). It also includes different levels of learning, such as the ability to solve an individual math problem correctly. It also includes the development of meta-cognitive self-regulatory abilities, such as recognizing the type of problem being solved and whether one is in an error state. These latter types of awareness enable correctly regulating ones approach to solving a problem, and recognizing when one is off track by repairing momentary errors as needed. Later stages of learning enable the generalization of learned skills or information from one context or domain to others such as applying math problem solving to calculations in the wild (e.g., calculation of garden space, engineering calculations required for a structurally sound building).

Mark Wallace 1 and Aldeida Aleti 1 1 Monash University, Wellington Road, Clayton, Vic and 3800, Australia Abstract For most practical optimisation problems local search outperforms random sampling - despite the "No Free Lunch Theorem". This paper introduces a property of search landscapes termed Neighbours' Similar Fitness (NSF) that underlies the good performance of neighbourhood search in terms of local improvement . Though necessary, NSF is not sufficient to ensure that searching for improvement among the neighbours of a good solution is better than random search. The paper introduces an additional (natural) property which supports a general proof that, for NSF landscapes, neighbourhood search beats random search. 1 Introduction Local Search is a successful class of methods used to solve many large complex optimisation problems. A problem (S,f) is defined as a set S of candidate solutions, termed its search space, and a fitness function f that maps candidate solutions to a fitness measure. Many researchers have explored why different forms of local search [ Burke and Kendal, 2014 ] are so effective, and deep theoretical studies have been published on the performance of algorithms on specific classes of problems [ Michiels et al., 2007 ] . Our focus is on challenging problems for which it is hard to find optimal (or just "good") solutions. In section 5 it will also be shown that all the example hard problems (classed as PLS-Complete) in [ Michiels et al., 2007 ] have this same property that solutions thin out towards the optimum.

Twenty IT leaders look into their crystal balls to predict the technologies and trends that will drive the sector in 2020. CIO Australia asked Australian technology bosses about their top line predictions for 2020, the technologies that will have the greatest impact next year, and what top trends will impact the IT and business landscape. Here are the predictions from IT leaders across vendor land to CIOs and CTOs across a host of industries. Intelligent systems (machine learning, artificial intelligence and automation) are the top trends in 2020. Intelligent systems will have a significant impact on increasing situational awareness (insights) and using these insights to enhance decision making – to deliver optimal outcomes for customers.

Road accidents constitute a major problem in our societies around the world. The World Health Organization(WHO) estimated that 1.25 million deaths were related to road traffic injuries in the year 2010. For the year 2016, the USA alone had recorded 37, 461 motor vehicle crash-related deaths, averaging around 102 people per day. In Europe, the statistics also indicate that each minute, there are 50 road deaths recorded in the year 2017. Can machine learning help us understand the causes and the factors that affect car crash severity?

An improved online learning algorithm for general fuzzy min-max neural network Thanh Tung Khuat Advanced Analytics Institute University of T echnology Sydney Sydney, Australia thanhtung.khuat@student.uts.edu.au Abstract --This paper proposes an improved version of the current online learning algorithm for a general fuzzy min-max neural network (GFMM) to tackle existing issues concerning expansion and contraction steps as well as the way of dealing with unseen data located on decision boundaries. These drawbacks lower its classification performance, so an improved algorithm is proposed in this study to address the above limitations. The proposed approach does not use the contraction process for overlapping hyperboxes, which is more likely to increase the error rate as shown in the literature. The empirical results indicated the improvement in the classification accuracy and stability of the proposed method compared to the original version and other fuzzy min-max classifiers. In order to reduce the sensitivity to the training samples presentation order of this new online learning algorithm, a simple ensemble method is also proposed. I NTRODUCTION Artificial neural networks (ANNs) are one of the most widely used methods for dealing with classification problems as well as real-world applications [1]. However, the main disadvantage of the original ANNs is that they do not have the capability of giving explanations of their predictive results to humans explicitly. This drawback restricts the widespread use of the ANNs for critical domains such as healthcare and criminal justice [2]. In a recent study, Rudin [2] has highlighted that there is a high demand for interpretable models to substitute black-box models in assisting decision-makers in areas with the requirement of high safety and trust.

The use of autonomous bots for automatic support in software development tasks is increasing. In the past, however, they were not always perceived positively and sometimes experienced a negative bias compared to their human counterparts. We conducted a qualitative study in which we deployed an autonomous refactoring bot for 41 days in a student software development project. In between and at the end, we conducted semi-structured interviews to find out how developers perceive the bot and whether they are more or less critical when reviewing the contributions of a bot compared to human contributions. Our findings show that the bot was perceived as a useful and unobtrusive contributor, and developers were no more critical of it than they were about their human colleagues, but only a few team members felt responsible for the bot.