This scholarship has been established to provide financial assistance to a PhD student to undertake research funded by an ARC Grant headed by Buhui Qiu within the University of Sydney Business School. The project will develop an innovative machine-learning-based approach for measuring, monitoring and evaluating bank lending activities and risk disclosures to take advantage of the big data available. It will use multidimensional data to produce more relevant metrics for assessing bank risks and risk disclosure quality and apply them in regulatory policy evaluation. The project findings will significantly advance the knowledge on mitigating banking misconduct. They will also equip regulatory authorities with an efficient monitoring tool and an early-warning device to promote better lending and risk disclosure practices, and foster a more transparent and stable financial system to support financial intermediation in Australia and worldwide.

Yoshua Bengio is one of the world's leading experts in artificial intelligence and deep learning. Also known as the father of deep learning, he says that for the world to change for the better with AI, a global shift in how organizations and governments share their research needs to come. In many countries, private companies, government entities, and academic institutions conduct AI research. These places must foster a global culture of open science. These research places the need to rethink how to encourage the development of impactful artificial intelligence.

Purpose: The development of metaheuristic algorithms has increased by researchers to use them extensively in the field of business, science, and engineering. One of the common metaheuristic optimization algorithms is called Grey Wolf Optimization (GWO). The algorithm works based on imitation of the wolves' searching and the process of attacking grey wolves. The main purpose of this paper to overcome the GWO problem which is trapping into local optima. Design or Methodology or Approach: In this paper, the K-means clustering algorithm is used to enhance the performance of the original Grey Wolf Optimization by dividing the population into different parts. The proposed algorithm is called K-means clustering Grey Wolf Optimization (KMGWO). Findings: Results illustrate the efficiency of KMGWO is superior to GWO. To evaluate the performance of the KMGWO, KMGWO applied to solve 10 CEC2019 benchmark test functions. Results prove that KMGWO is better compared to GWO. KMGWO is also compared to Cat Swarm Optimization (CSO), Whale Optimization Algorithm-Bat Algorithm (WOA-BAT), and WOA, so, KMGWO achieves the first rank in terms of performance. Statistical results proved that KMGWO achieved a higher significant value compared to the compared algorithms. Also, the KMGWO is used to solve a pressure vessel design problem and it has outperformed results. Originality/value: Results prove that KMGWO is superior to GWO. KMGWO is also compared to cat swarm optimization (CSO), whale optimization algorithm-bat algorithm (WOA-BAT), WOA, and GWO so KMGWO achieved the first rank in terms of performance. Also, the KMGWO is used to solve a classical engineering problem and it is superior

The Varroa destructor mite is one of the most dangerous Honey Bee (Apis mellifera) parasites worldwide and the bee colonies have to be regularly monitored in order to control its spread. Here we present an object detector based method for health state monitoring of bee colonies. This method has the potential for online measurement and processing. In our experiment, we compare the YOLO and SSD object detectors along with the Deep SVDD anomaly detector. Based on the custom dataset with 600 ground-truth images of healthy and infected bees in various scenes, the detectors reached a high F1 score up to 0.874 in the infected bee detection and up to 0.727 in the detection of the Varroa Destructor mite itself. The results demonstrate the potential of this approach, which will be later used in the real-time computer vision based honey bee inspection system. To the best of our knowledge, this study is the first one using object detectors for this purpose. We expect that performance of those object detectors will enable us to inspect the health status of the honey bee colonies.

Another is the family of optimal transport central to many machine learning algorithms, with distances (Villani, 2008), including the Wasserstein-1 metric. Lipschitz constrained variants recently gaining In general, variational representations are supremums attention. Inspired by this, we generalize the of integral formulas taken over sets of functions, such as the so-called tight variational representation of f-Donsker-Varadhan formula (Donsker & Varadhan, 1976) divergences in the case of probability measures for the Kullback-Leibler divergence or the Kantorovich-on compact metric spaces to be taken over the Rubinstein formula (Villani, 2008) for the Wasserstein-1 space of Lipschitz functions vanishing at an arbitrary metric. Informally speaking, one can implement (Nowozin base point, characterize functions achieving et al., 2016; Arjovsky et al., 2017) such a formula by constructing the supremum in the variational representation, a real-valued neural network taking samples from propose a practical algorithm to calculate the the two probability measures as inputs, which is then trained tight convex conjugate of f-divergences compatible to maximize the integral formula in order to approximate with automatic differentiation frameworks, the supremum, resulting in a learned proxy to the actual define the Moreau-Yosida approximation of f-divergence of said probability measures. Implementing the divergences with respect to the Wasserstein-1 metric, Kantorovich-Rubinstein formula in such a way involves and derive the corresponding variational formulas, restricting the Lipschitz constant of the neural network (Gulrajani providing a generalization of a number et al., 2017; Petzka et al., 2018; Miyato et al., 2018), of recent results, novel special cases of interest which effectively stabilizes the approximation procedure.

Exploring new approaches to improve the capabilities and accuracy of robots, a team of researchers in Singapore has turned to an unexpected source: plants. Robots have been dispatched to move cars, lift weighty inventory in warehouses and assist in construction projects. But what if you need to delicately lift a tiny object 1/50th of an inch? To accomplish that task, the Singapore team turned to a Venus flytrap, one of nature's more fascinating plants. The flytrap, a native of North Carolina, contains tiny hairs on two leaf lobes that, when stimulated by an insect, shut tight and slowly devour the prey.

The idea that gamers are antisocial grumps who stay up all night eating junk food while playing Call of Duty in their mother's basement is woefully outdated. According to a new survey, about half of all gamers admit they've been playing more since the pandemic started, but nearly three-quarters use it to socialize. Only ten percent of respondents said they munched on junk while gaming, compared to the 37 percent who don't eat at all while playing. Nearly half of respondents kept their gaming to between 8pm and midnight, while just seven percent burned the midnight oil. UK game developer Jagex first came on the scene in 2001 with RuneScape, a popular fantasy massively multiplayer online roleplaying game (MMORPG).

Combining the closed-world reasoning of answer set programming (ASP) with the open-world reasoning of ontologies broadens the space of applications of reasoners. Disjunctive hybrid MKNF knowledge bases succinctly extend ASP and in some cases without increasing the complexity of reasoning tasks. However, in many cases, solver development is lagging behind. As the result, the only known method of solving disjunctive hybrid MKNF knowledge bases is based on guess-and-verify, as formulated by Motik and Rosati in their original work. A main obstacle is understanding how constraint propagation may be performed by a solver, which, in the context of ASP, centers around the computation of \textit{unfounded atoms}, the atoms that are false given a partial interpretation. In this work, we build towards improving solvers for hybrid MKNF knowledge bases with disjunctive rules: We formalize a notion of unfounded sets for these knowledge bases, identify lower complexity bounds, and demonstrate how we might integrate these developments into a solver. We discuss challenges introduced by ontologies that are not present in the development of solvers for disjunctive logic programs, which warrant some deviations from traditional definitions of unfounded sets. We compare our work with prior definitions of unfounded sets.

The method of random projection (RP) is the standard technique in machine learning and many other areas, for dimensionality reduction, approximate near neighbor search, compressed sensing, etc. Basically, RP provides a simple and effective scheme for approximating pairwise inner products and Euclidean distances in massive data. Closely related to RP, the method of random Fourier features (RFF) has also become popular, for approximating the Gaussian kernel. RFF applies a specific nonlinear transformation on the projected data from random projections. In practice, using the (nonlinear) Gaussian kernel often leads to better performance than the linear kernel (inner product), partly due to the tuning parameter $(\gamma)$ introduced in the Gaussian kernel. Recently, there has been a surge of interest in studying properties of RFF. After random projections, quantization is an important step for efficient data storage, computation, and transmission. Quantization for RP has also been extensive studied in the literature. In this paper, we focus on developing quantization algorithms for RFF. The task is in a sense challenging due to the tuning parameter $\gamma$ in the Gaussian kernel. For example, the quantizer and the quantized data might be tied to each specific tuning parameter $\gamma$. Our contribution begins with an interesting discovery, that the marginal distribution of RFF is actually free of the Gaussian kernel parameter $\gamma$. This small finding significantly simplifies the design of the Lloyd-Max (LM) quantization scheme for RFF in that there would be only one LM quantizer for RFF (regardless of $\gamma$). We also develop a variant named LM$^2$-RFF quantizer, which in certain cases is more accurate. Experiments confirm that the proposed quantization schemes perform well.

Triplet loss is an extremely common approach to distance metric learning. Representations of images from the same class are optimized to be mapped closer together in an embedding space than representations of images from different classes. Much work on triplet losses focuses on selecting the most useful triplets of images to consider, with strategies that select dissimilar examples from the same class or similar examples from different classes. The consensus of previous research is that optimizing with the \textit{hardest} negative examples leads to bad training behavior. That's a problem -- these hardest negatives are literally the cases where the distance metric fails to capture semantic similarity. In this paper, we characterize the space of triplets and derive why hard negatives make triplet loss training fail. We offer a simple fix to the loss function and show that, with this fix, optimizing with hard negative examples becomes feasible. This leads to more generalizable features, and image retrieval results that outperform state of the art for datasets with high intra-class variance.