So far, most research on recommender systems focused on maintaining long-term user engagement and satisfaction, by promoting relevant and personalized content. However, it is still very challenging to evaluate the quality and the reliability of this content. In this paper, we propose FEBR (Expert-Based Recommendation Framework), an apprenticeship learning framework to assess the quality of the recommended content on online platforms. The framework exploits the demonstrated trajectories of an expert (assumed to be reliable) in a recommendation evaluation environment, to recover an unknown utility function. This function is used to learn an optimal policy describing the expert's behavior, which is then used in the framework to provide high-quality and personalized recommendations. We evaluate the performance of our solution through a user interest simulation environment (using RecSim). We simulate interactions under the aforementioned expert policy for videos recommendation, and compare its efficiency with standard recommendation methods. The results show that our approach provides a significant gain in terms of content quality, evaluated by experts and watched by users, while maintaining almost the same watch time as the baseline approaches.

Federated learning (FL) is a distributed model for deep learning that integrates client-server architecture, edge computing, and real-time intelligence. FL has the capability of revolutionizing machine learning (ML) but lacks in the practicality of implementation due to technological limitations, communication overhead, non-IID (independent and identically distributed) data, and privacy concerns. Training a ML model over heterogeneous non-IID data highly degrades the convergence rate and performance. The existing traditional and clustered FL algorithms exhibit two main limitations, including inefficient client training and static hyper-parameter utilization. To overcome these limitations, we propose a novel hybrid algorithm, namely genetic clustered FL (Genetic CFL), that clusters edge devices based on the training hyper-parameters and genetically modifies the parameters cluster-wise. Then, we introduce an algorithm that drastically increases the individual cluster accuracy by integrating the density-based clustering and genetic hyper-parameter optimization. The results are bench-marked using MNIST handwritten digit dataset and the CIFAR-10 dataset. The proposed genetic CFL shows significant improvements and works well with realistic cases of non-IID and ambiguous data.

Wildfires are more and more present in modern society, mainly caused by heat waves, lightning, droughts, climate change, or even human actions like car fires and cigarette butts. We've seen it everywhere recently Brazil, Australia, United States, Canada, etc., destroying plant, human, and animal life, property damage, and contributing to global warming through the high amount of CO2 produced. These countries all have walls of videos like the one below in the county's fire emergency to see if something is going on. The most common problem is that they are spotted too late and already widely spread out. This is because you cannot have somebody staring at that wall all day, waiting to spot smoke or fire.

Brussels – The European Union is using its strength as a wealthy trade bloc of half a billion consumers to set the global pace of climate change action, challenging others to match the ambitions of its latest carbon cutting plans. In its most ambitious bid yet to hit a goal of cutting net greenhouse gas emissions by 55% from 1990 levels by 2030, the EU on Wednesday laid out proposals that would consign the internal combustion engine to history and raise the cost of emitting carbon for heating, transport and factories. The question now is whether the EU gambit becomes an established benchmark upon which investors and sectors like the auto industry set transition strategies, and how big emitters like the United States and China respond ahead of U.N. climate talks later this year. "Amongst G7 and G20 nations, the EU position is now the explicit global benchmark," said Julian Poulter, Head of Investor Relations at Inevitable Policy Response, a consultancy on environmental economics. "It will exert a new influence on that basis, in other industrialized nations and their financial sectors, and increase pressure on those nations that remain as climate outliers and spoilers," he added.

Labeled data is essential in modern systems that rely on Machine Learning (ML) for predictive modelling. Such systems may suffer from the cold-start problem: supervised models work well but, initially, there are no labels, which are costly or slow to obtain. This problem is even worse in imbalanced data scenarios. Online financial fraud detection is an example where labeling is: i) expensive, or ii) it suffers from long delays, if relying on victims filing complaints. The latter may not be viable if a model has to be in place immediately, so an option is to ask analysts to label events while minimizing the number of annotations to control costs. We propose an Active Learning (AL) annotation system for datasets with orders of magnitude of class imbalance, in a cold start streaming scenario. We present a computationally efficient Outlier-based Discriminative AL approach (ODAL) and design a novel 3-stage sequence of AL labeling policies where it is used as warm-up. Then, we perform empirical studies in four real world datasets, with various magnitudes of class imbalance. The results show that our method can more quickly reach a high performance model than standard AL policies. Its observed gains over random sampling can reach 80% and be competitive with policies with an unlimited annotation budget or additional historical data (with 1/10 to 1/50 of the labels).

Causal discovery automates the learning of causal Bayesian networks from data and has been of active interest from their beginning. With the sourcing of large data sets off the internet, interest in scaling up to very large data sets has grown. One approach to this is to parallelize search using Markov Blanket (MB) discovery as a first step, followed by a process of combining MBs in a global causal model. We develop and explore three new methods of MB discovery using Minimum Message Length (MML) and compare them empirically to the best existing methods, whether developed specifically as MB discovery or as feature selection. Our best MML method is consistently competitive and has some advantageous features.

Modern data analytics underpinned by machine learning techniques has become a key enabler to the automation of data-led decision making. As an important branch of state-of-the-art data analytics, business process predictions are also faced with a challenge in regard to the lack of explanation to the reasoning and decision by the underlying `black-box' prediction models. With the development of interpretable machine learning techniques, explanations can be generated for a black-box model, making it possible for (human) users to access the reasoning behind machine learned predictions. In this paper, we aim to present an approach that allows us to use model explanations to investigate certain reasoning applied by machine learned predictions and detect potential issues with the underlying methods thus enhancing trust in business process prediction models. A novel contribution of our approach is the proposal of model inspection that leverages both the explanations generated by interpretable machine learning mechanisms and the contextual or domain knowledge extracted from event logs that record historical process execution. Findings drawn from this work are expected to serve as a key input to developing model reliability metrics and evaluation in the context of business process predictions.

The economic recession that follows as a consequence of the Covid-19 crisis and in particular the demise of certain sectors of the economy (physical retail, hospitality sector, etc) means that there will be greater pressure on politicians around the world to consider how to stimulate GPD growth in the post-pandemic world. However, there are also increasing pressures on politicians to combat the threat posed by climate change. Are the desired objectives of GDP and employment growth as well as reducing pollution at odds with each other? What if there is a pathway to GDP growth with the creation of new jobs and yet at the same time we are able to reduce emissions of Green House Gasses (GHGs)? A report entitled "How AI can enable a sustainable future" by PWC and commissioned by Microsoft (lead authors Celine Herweijer of PWC and Lucas Joppa of Microsoft) estimates that using AI for environmental applications across four sectors – agriculture, water, energy and transport. The report estimated that such applications could contribute up to $5.2 trillion USD to the global economy in 2030, a 4.4% increase relative to business as usual.

It has been over a year since international travel as we knew it ground to a halt. When the COVID-19 pandemic hit, air travel in the US dropped by 95% – from around two million travellers per day to fewer than 100,000. Until recently, flights in and out of Australia have been limited to those trying to get home, reuniting with their loved ones, or fleeing places that were no longer safe. Slowly, vaccination is making the possibility of taking to the skies again seem within reach. But what might have changed?

In the years after it was founded in 1999, the Swedish video game company Paradox Interactive quietly built a reputation for developing some of the best, and most hardcore, strategy games on the market. "Deep, endless, complex, unyielding games," is how Shams Jorjani, the company's chief business development officer, describes Paradox's offerings. Most of its biggest hits, such as the middle ages-themed Crusader Kings, or Sengoku, in which you play as a 16th-century Japanese noble, were loosely based on history. But in 2016, Paradox decided to try something a little different. Its new game, Stellaris, was a work of sprawling science fiction, set 200 years in the future. In this virtual universe, players could explore richly detailed galaxies, command their own fusion-powered starship fleets and fight with extraterrestrials to expand their space empires. Gamers could choose to play as the human race, or one of many alien species. Another type of alien is a sentient crystal that eats rocks.) The game was an instant hit, selling more than 200,000 copies in its first 24 hours. Later that year, Paradox decided to take Stellaris to China. This would mean navigating the country's notoriously tricky censorship rules, but given that China was, at the time, home to an estimated 560 million gamers, the commercial appeal was irresistible. Paradox had been burned in China before.