The number of rooftop photovoltaic (PV) systems has significantly increased in recent years around the globe, including in Australia. This trend is anticipated to continue in the next few years. Given their high share of generation in power systems, detecting malfunctions and abnormalities in rooftop PV systems is essential for ensuring their high efficiency and safety. In this paper, we present a novel anomaly detection method for a large number of rooftop PV systems installed in a region using big data and a time series complexity measure called weighted permutation entropy (WPE). This efficient method only uses the historical PV generation data in a given region to identify anomalous PV systems and requires no new sensor or smart device. Using a real-world PV generation dataset, we discuss how the hyperparameters of WPE should be tuned for the purpose. The proposed PV anomaly detection method is then tested on rooftop PV generation data from over 100 South Australian households. The results demonstrate that anomalous systems detected by our method have indeed encountered problems and require a close inspection. The detection and resolution of potential faults would result in better rooftop PV systems, longer lifetimes, and higher returns on investment.

Machine learning models work better when curated features are provided to them. Feature engineering methods have been usually used as a preprocessing step to obtain or build a proper feature set. In late years, autoencoders (a specific type of symmetrical neural network) have been widely used to perform representation learning, proving their competitiveness against classical feature engineering algorithms. The main obstacle in the use of autoencoders is finding a good architecture, a process that most experts confront manually. An automated autoencoder architecture search procedure, based on evolutionary methods, is proposed in this paper. The methodology is tested against nine heterogeneous data sets. The obtained results show the ability of this approach to find better architectures, able to concentrate most of the useful information in a minimized coding, in a reduced time.

Random walk kernels have been introduced in seminal work on graph learning and were later largely superseded by kernels based on the Weisfeiler-Leman test for graph isomorphism. We give a unified view on both classes of graph kernels. We study walk-based node refinement methods and formally relate them to several widely-used techniques, including Morgan's algorithm for molecule canonization and the Weisfeiler-Leman test. We define corresponding walk-based kernels on nodes that allow fine-grained parameterized neighborhood comparison, reach Weisfeiler-Leman expressiveness, and are computed using the kernel trick. From this we show that classical random walk kernels with only minor modifications regarding definition and computation are as expressive as the widely-used Weisfeiler-Leman subtree kernel but support non-strict neighborhood comparison. We verify experimentally that walk-based kernels reach or even surpass the accuracy of Weisfeiler-Leman kernels in real-world classification tasks.

Distribution shift occurs when the test distribution differs from the training distribution, and it can considerably degrade performance of machine learning models deployed in the real world. Temporal shifts -- distribution shifts arising from the passage of time -- often occur gradually and have the additional structure of timestamp metadata. By leveraging timestamp metadata, models can potentially learn from trends in past distribution shifts and extrapolate into the future. While recent works have studied distribution shifts, temporal shifts remain underexplored. To address this gap, we curate Wild-Time, a benchmark of 5 datasets that reflect temporal distribution shifts arising in a variety of real-world applications, including patient prognosis and news classification. On these datasets, we systematically benchmark 13 prior approaches, including methods in domain generalization, continual learning, self-supervised learning, and ensemble learning. We use two evaluation strategies: evaluation with a fixed time split (Eval-Fix) and evaluation with a data stream (Eval-Stream). Eval-Fix, our primary evaluation strategy, aims to provide a simple evaluation protocol, while Eval-Stream is more realistic for certain real-world applications. Under both evaluation strategies, we observe an average performance drop of 20% from in-distribution to out-of-distribution data. Existing methods are unable to close this gap. Code is available at https://wild-time.github.io/.

Chapter 3: Displaying the Market Dynamics- Drivers, Trends and Challenges & Opportunities of the Artificial Intelligence in Aviation Chapter 4: Presenting the Artificial Intelligence in Aviation Market Factor Analysis, Porters Five Forces, Supply/Value Chain, PESTEL analysis, Market Entropy, Patent/Trademark Analysis.

"We live in an age of Apple and Tesla," says Marcus Byrne of creative agency Thinkerbell in Australia. "Minimal design has taken charge and logos are stripped back to live on mobile devices." He's challenged that ideal by asking: "What would Gaudí do?" – using the Midjourney AI software and Photoshop to create a set of modern gadgets in the Spanish architect's colourful, curvilinear style. "It turns minimal appliances into art-nouveau styled sculptures," he says. Byrne is uncomfortable calling the Gaudí gadgets his work, though.

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There are also unresolved questions about how spectator bans can be enforced at lower-tier facilities for women's, youth, state league or other grassroots matches, where the security presence is greatly reduced in comparison to the stadiums where the full-time professionals play. FA chief executive James Johnson admitted on Tuesday that stadium bans were "complex" but not impossible to implement, and required close collaboration and information sharing between sporting bodies, security and police to allow those manning stadium entry points to identify banned supporters. Victoria Police say the enforcement of stadium bans is a matter for host clubs and venues.Credit:Getty Images According to industry sources, however, the system is imperfect and, in many ways, archaic, heavily reliant on the memories of security staff and their ability to quickly match patrons with photos, names or other data related to people on banned lists. Inevitably, some slip through the net, and often they are the ones who can cause trouble at a sporting event. One obvious but highly controversial and almost dystopian solution appears to be gaining traction in Australia and throughout the world: facial recognition technology, which was rolled out in cameras throughout the SCG precinct in 2018 and has been in place at Sydney's major stadia ever since.

Abstract--Federated learning (FL) enables the training of models among distributed clients without compromising the privacy of training datasets, while the invisibility of clients' datasets and the training process poses a variety of security threats. This survey provides the taxonomy of poisoning attacks and experimental evaluation to discuss the need for robust FL. The FL has emerged as a promising solution to a authors also conducted an experimental evaluation in order number of applications to solve data silos while protecting to draw a conclusion on how to select the suitable method the privacy of data. Since its emergence, FL has been in each category of adversarial attacks. Furthermore, a brief employed in a variety of applications including but not limited overview of threats to FL is discussed in [2] and focuses on to healthcare, crowdsourcing systems, natural language poisoning and inference attacks in order to comprehend the processing (NLP), and the Internet of Things (IoT).

Standard fine-tuning of language models typically performs well on in-distribution data, but suffers with generalization to distribution shifts. In this work, we aim to improve generalization of adapter-based cross-lingual task transfer where such cross-language distribution shifts are imminent. We investigate scheduled unfreezing algorithms -- originally proposed to mitigate catastrophic forgetting in transfer learning -- for fine-tuning task adapters in cross-lingual transfer. Our experiments show that scheduled unfreezing methods close the gap to full fine-tuning and achieve state-of-the-art transfer performance, suggesting that these methods can go beyond just mitigating catastrophic forgetting. Next, aiming to delve deeper into those empirical findings, we investigate the learning dynamics of scheduled unfreezing using Fisher Information. Our in-depth experiments reveal that scheduled unfreezing induces different learning dynamics compared to standard fine-tuning, and provide evidence that the dynamics of Fisher Information during training correlate with cross-lingual generalization performance. We additionally propose a general scheduled unfreezing algorithm that achieves an average of 2 points improvement over four datasets compared to standard fine-tuning and provides strong empirical evidence for a theory-based justification of the heuristic unfreezing schedule (i.e., the heuristic schedule is implicitly maximizing Fisher Information). Our code will be publicly available.