Software Vulnerabilities (SVs) are increasing in complexity and scale, posing great security risks to many software systems. Given the limited resources in practice, SV assessment and prioritization help practitioners devise optimal SV mitigation plans based on various SV characteristics. The surge in SV data sources and data-driven techniques such as Machine Learning and Deep Learning have taken SV assessment and prioritization to the next level. Our survey provides a taxonomy of the past research efforts and highlights the best practices for data-driven SV assessment and prioritization. We also discuss the current limitations and propose potential solutions to address such issues.

Using satellites, drones and artificial intelligence, emerging technology is changing the way firefighting agencies and governments battle the ever-increasing threat of wildfires as hundreds of thousands of acres burn across the western United States. New programs are being developed by startups and research institutions to predict fire behavior, monitor drought and even detect fires when they first start. As climate change continues to increase the intensity and frequency of wildfires, these breakthroughs offer at least one tool in the growing arsenal of prevention and suppression strategies. "This is not to replace firefighting on the ground," said Ilkay Altintas, a computer scientist with the University of California, San Diego, who developed a fire map for the region. "The more science and data we can give firefighters and the public, the quicker we'll have solutions to combat and mitigate wildfires."

The one glaring gap in the Commonwealth government's AI strategy and action plan is a process to develop a coordinated governance framework around the development, use and procurement of AI services within commonwealth government agencies. This is where the NSW Government has taken a clear lead, setting out a mandatory customer service circular which all NSW Government agencies need to adhere to. There is practical guidance on adhering to principles, assessing risk, managing data, sourcing AI solutions, meeting legal obligations and more.

We address policy learning with logged data in contextual bandits. Current offline-policy learning algorithms are mostly based on inverse propensity score (IPS) weighting requiring the logging policy to have \emph{full support} i.e. a non-zero probability for any context/action of the evaluation policy. However, many real-world systems do not guarantee such logging policies, especially when the action space is large and many actions have poor or missing rewards. With such \emph{support deficiency}, the offline learning fails to find optimal policies. We propose a novel approach that uses a hybrid of offline learning with online exploration. The online exploration is used to explore unsupported actions in the logged data whilst offline learning is used to exploit supported actions from the logged data avoiding unnecessary explorations. Our approach determines an optimal policy with theoretical guarantees using the minimal number of online explorations. We demonstrate our algorithms' effectiveness empirically on a diverse collection of datasets.

Quadratic Unconstrained Binary Optimization (QUBO) is recognized as a unifying framework for modeling a wide range of problems. Problems can be solved with commercial solvers customized for solving QUBO and since QUBO have degree two, it is useful to have a method for transforming higher degree pseudo-Boolean problems to QUBO format. The standard transformation approach requires additional auxiliary variables supported by penalty terms for each higher degree term. This paper improves on the existing cubic-to-quadratic transformation approach by minimizing the number of additional variables as well as penalty coefficient. Extensive experimental testing on Max 3-SAT modeled as QUBO shows a near 100% reduction in the subproblem size used for minimization of the number of auxiliary variables.

Although Graph Convolutional Networks (GCNs) have demonstrated their power in various applications, the graph convolutional layers, as the most important component of GCN, are still using linear transformations and a simple pooling step. In this paper, we propose a novel generalization of Factorized Bilinear (FB) layer to model the feature interactions in GCNs. FB performs two matrix-vector multiplications, that is, the weight matrix is multiplied with the outer product of the vector of hidden features from both sides. However, the FB layer suffers from the quadratic number of coefficients, overfitting and the spurious correlations due to correlations between channels of hidden representations that violate the i.i.d. assumption. Thus, we propose a compact FB layer by defining a family of summarizing operators applied over the quadratic term. We analyze proposed pooling operators and motivate their use. Our experimental results on multiple datasets demonstrate that the GFB-GCN is competitive with other methods for text classification.

We propose a novel framework for image clustering that incorporates joint representation learning and clustering. Our method consists of two heads that share the same backbone network - a "representation learning" head and a "clustering" head. The "representation learning" head captures fine-grained patterns of objects at the instance level which serve as clues for the "clustering" head to extract coarse-grain information that separates objects into clusters. The whole model is trained in an end-to-end manner by minimizing the weighted sum of two sample-oriented contrastive losses applied to the outputs of the two heads. To ensure that the contrastive loss corresponding to the "clustering" head is optimal, we introduce a novel critic function called "log-of-dot-product". Extensive experimental results demonstrate that our method significantly outperforms state-of-the-art single-stage clustering methods across a variety of image datasets, improving over the best baseline by about 5-7% in accuracy on CIFAR10/20, STL10, and ImageNet-Dogs. Further, the "two-stage" variant of our method also achieves better results than baselines on three challenging ImageNet subsets.

In a highly competitive market, all energy generators rely on highly accurate predictions of how much electricity they'll be able to make. Australian researchers have figured out a way to improve these predictions for wind and solar farms, using artificial intelligence. The National Energy Market – "the grid" – requires automatic forecasts every five minutes from electricity generators. This ensures that electricity generation meets demand. It can be very costly if those five-minute forecasts prove to be incorrect.

Accurate image segmentation plays a crucial role in medical image analysis, yet it faces great challenges of various shapes, diverse sizes, and blurry boundaries. To address these difficulties, square kernel-based encoder-decoder architecture has been proposed and widely used, but its performance remains still unsatisfactory. To further cope with these challenges, we present a novel double-branch encoder architecture. Our architecture is inspired by two observations: 1) Since the discrimination of features learned via square convolutional kernels needs to be further improved, we propose to utilize non-square vertical and horizontal convolutional kernels in the double-branch encoder, so features learned by the two branches can be expected to complement each other. 2) Considering that spatial attention can help models to better focus on the target region in a large-sized image, we develop an attention loss to further emphasize the segmentation on small-sized targets. Together, the above two schemes give rise to a novel double-branch encoder segmentation framework for medical image segmentation, namely Crosslink-Net. The experiments validate the effectiveness of our model on four datasets. The code is released at https://github.com/Qianyu1226/Crosslink-Net.

The policy gradient (PG) is one of the most popular methods for solving reinforcement learning (RL) problems. However, a solid theoretical understanding of even the "vanilla" PG has remained elusive for long time. In this paper, we apply recent tools developed for the analysis of SGD in non-convex optimization to obtain convergence guarantees for both REINFORCE and GPOMDP under smoothness assumption on the objective function and weak conditions on the second moment of the norm of the estimated gradient. When instantiated under common assumptions on the policy space, our general result immediately recovers existing $\widetilde{\mathcal{O}}(\epsilon^{-4})$ sample complexity guarantees, but for wider ranges of parameters (e.g., step size and batch size $m$) with respect to previous literature. Notably, our result includes the single trajectory case (i.e., $m=1$) and it provides a more accurate analysis of the dependency on problem-specific parameters by fixing previous results available in the literature. We believe that the integration of state-of-the-art tools from non-convex optimization may lead to identify a much broader range of problems where PG methods enjoy strong theoretical guarantees.