Existential rules are a positive fragment of first-order logic that generalizes function-free Horn rules by allowing existentially quantified variables in rule heads. This family of languages has recently attracted significant interest in the context of ontology-mediated query answering. Forward chaining, also known as the chase, is a fundamental tool for computing universal models of knowledge bases, which consist of existential rules and facts. Several chase variants have been defined, which differ on the way they handle redundancies. A set of existential rules is bounded if it ensures the existence of a bound on the depth of the chase, independently from any set of facts. Deciding if a set of rules is bounded is an undecidable problem for all chase variants. Nevertheless, when computing universal models, knowing that a set of rules is bounded for some chase variant does not help much in practice if the bound remains unknown or even very large. Hence, we investigate the decidability of the k-boundedness problem, which asks whether the depth of the chase for a given set of rules is bounded by an integer k. We identify a general property which, when satisfied by a chase variant, leads to the decidability of k-boundedness. We then show that the main chase variants satisfy this property, namely the oblivious, semi-oblivious (aka Skolem), and restricted chase, as well as their breadth-first versions. This paper is under consideration for publication in Theory and Practice of Logic Programming.

The spread of the novel coronavirus that causes COVID-19 has prompted state and local governments around the U.S. to institute shelter-in-place orders and business closures. As millions suddenly find themselves confined to their homes, the shift has strained not only internet service providers, streaming platforms, and online retailers, but the utilities supplying power to the nation's electrical grid, as well. U.S. electricity use on March 27, 2020 was 3% lower than it was on March 27, 2019, a loss of about three years of sales growth. Peter Fox-Penner, director of the Boston University Institute for Sustainable Energy, asserted in a recent op-ed that utility revenues will suffer because providers are halting shutoffs and deferring rate increases. Moreover, according to research firm Wood Mackenzie, the rise in household electricity demand won't offset reduced business electricity demand, mainly because residential demand makes up just 40% of the total demand across North America.

On 9th April 2020, Queensland AI hosted a special online panel, exploring a very topical question, "Can AI help in the fight against COVID-19?" During the hour-long webinar discussion, Nicholas Therkelsen-Terry, CEO of Max Kelsen and Head of Queensland AI, hosted a multi-disciplinary panel of medical and AI experts directly involved in the COVID-19 response. Over the hour, the panellists discussed the role of artificial intelligence and data science in the fight against COVID-19. The panellists brought a range of perspectives and a wealth of experience to the discussion, creating a balanced conversation that considered both the clinical and technical sides of the equation. A shift away from classic statistical analysis using P-value confidence indicators and a movement towards a more precautionary, cost-benefit analysis approach for guiding health policy is key to gaining control of the pandemic.

Data storage, processing and management are critical now as most activities worldwide move online during coronavirus lockdowns. Migration to the cloud was happening even before this crisis but now, organizations are depending on cloud services more than ever. Whether it is working from home or streaming videos from Netflix, these services are underpinned by software and hardware on the cloud. Although there may be a reduction of cloud usage as airlines and others see business dwindle, the dominant theme in these times is digitalization. The best-known cloud infrastructure providers are Amazon, Google and Microsoft.

Velodyne Lidar, Inc. today announced a multi-year sales agreement with NAVYA, a leading company in autonomous driving systems. Since 2015, NAVYA has been using Velodyne lidar sensors in production for its autonomous shuttle fleet that provides mobility services to cities and private sites. This press release features multimedia. Since 2015, NAVYA has been using Velodyne lidar sensors in production for its autonomous shuttle fleet that provides mobility services to cities and private sites. NAVYA plans to pursue the worldwide expansion of its shuttle with Velodyne's state-of-the-art sensors for precise real-time localization and object detection.

Every time you open Instagram, there are some new ads for you. All these ads relate to what you search for. You move to the explore section and can find thousands of related posts that interest you. But, how is that possible? This is not just the story of Instagram but pretty-much every social media platform today.

Recently, there have been some breakthroughs in graph analysis by applying the graph neural networks (GNNs) following a neighborhood aggregation scheme, which demonstrate outstanding performance in many tasks. However, we observe that the parameters of the network and the embedding of nodes are represented in real-valued matrices in existing GNN-based graph embedding approaches which may limit the efficiency and scalability of these models. It is well-known that binary vector is usually much more space and time efficient than the real-valued vector. This motivates us to develop a binarized graph neural network to learn the binary representations of the nodes with binary network parameters following the GNN-based paradigm. Our proposed method can be seamlessly integrated into the existing GNN-based embedding approaches to binarize the model parameters and learn the compact embedding. Extensive experiments indicate that the proposed binarized graph neural network, namely BGN, is orders of magnitude more efficient in terms of both time and space while matching the state-of-the-art performance.

Customers of any type of business expect help instantly and access to their services in a growing number of ways. Banks are turning to chatbots to help deal with massive volumes of customer interactions. Conversational banking frees up agents for more complex issues, while the move to app-based and web banking sees customers more used to dealing with digital interfaces, of which chatbots and AI virtual assistants are just the latest step. Established banks and their challenger rivals are all keen to develop a conversational banking strategy. Those that have been experimenting for some years find themselves with key advantages over banks stepping fresh into the conversational customer service arena.

Creativity isn't the only route to discovery – automated analysis of huge amounts of data works, too. "Can machines think?", asked the famous mathematician, code breaker and computer scientist Alan Turing almost 70 years ago. Today, some experts have no doubt that artificial intelligence (AI) will soon be able to develop the kind of general intelligence that humans have. But others argue that machines will never measure up. Although AI can already outperform humans on certain tasks – just like calculators – they can't be taught human creativity.

Identifying semantically identical questions on, Question and Answering social media platforms like Quora is exceptionally significant to ensure that the quality and the quantity of content are presented to users, based on the intent of the question and thus enriching overall user experience. Detecting duplicate questions is a challenging problem because natural language is very expressive, and a unique intent can be conveyed using different words, phrases, and sentence structuring. Machine learning and deep learning methods are known to have accomplished superior results over traditional natural language processing techniques in identifying similar texts. In this paper, taking Quora for our case study, we explored and applied different machine learning and deep learning techniques on the task of identifying duplicate questions on Quora's dataset. By using feature engineering, feature importance techniques, and experimenting with seven selected machine learning classifiers, we demonstrated that our models outperformed previous studies on this task. Xgboost model with character level term frequency and inverse term frequency is our best machine learning model that has also outperformed a few of the Deep learning baseline models. We applied deep learning techniques to model four different deep neural networks of multiple layers consisting of Glove embeddings, Long Short Term Memory, Convolution, Max pooling, Dense, Batch Normalization, Activation functions, and model merge. Our deep learning models achieved better accuracy than machine learning models. Three out of four proposed architectures outperformed the accuracy from previous machine learning and deep learning research work, two out of four models outperformed accuracy from previous deep learning study on Quora's question pair dataset, and our best model achieved accuracy of 85.82% which is close to Quora state of the art accuracy.