It is an online romance that has overcome a 3,500-mile distance, and also the Covid pandemic - which meant they had to get married virtually. Welsh cheesemaker Lewis Relfe struck up a relationship with Ameila Henderson, from Virginia, USA, while playing the Friday the 13th horror video game in 2017. She made a number of visits across the Atlantic, including one for six months, and he proposed on Aberystwyth Pier, dressed as the game's main character, Jason Voorhees. While they admit to seeing the humour in being the couple that met and married virtually, they now live together in Ceredigion, with daughter Evelyn. But because of parental responsibilities, they no longer get to enjoy the thing that brought them together.

Context. As software systems become more integrated into society's infrastructure, the responsibility of software professionals to ensure compliance with various non-functional requirements increases. These requirements include security, safety, privacy, and, increasingly, non-discrimination. Motivation. Fairness in pricing algorithms grants equitable access to basic services without discriminating on the basis of protected attributes. Method. We replicate a previous empirical study that used black box testing to audit pricing algorithms used by Italian car insurance companies, accessible through a popular online system. With respect to the previous study, we enlarged the number of tests and the number of demographic variables under analysis. Results. Our work confirms and extends previous findings, highlighting the problematic permanence of discrimination across time: demographic variables significantly impact pricing to this day, with birthplace remaining the main discriminatory factor against individuals not born in Italian cities. We also found that driver profiles can determine the number of quotes available to the user, denying equal opportunities to all. Conclusion. The study underscores the importance of testing for non-discrimination in software systems that affect people's everyday lives. Performing algorithmic audits over time makes it possible to evaluate the evolution of such algorithms. It also demonstrates the role that empirical software engineering can play in making software systems more accountable.

Relation extraction is essential for extracting and understanding biographical information in the context of digital humanities and related subjects. There is a growing interest in the community to build datasets capable of training machine learning models to extract relationships. However, annotating such datasets can be expensive and time-consuming, in addition to being limited to English. This paper applies guided distant supervision to create a large biographical relationship extraction dataset for German. Our dataset, composed of more than 80,000 instances for nine relationship types, is the largest biographical German relationship extraction dataset. We also create a manually annotated dataset with 2000 instances to evaluate the models and release it together with the dataset compiled using guided distant supervision. We train several state-of-the-art machine learning models on the automatically created dataset and release them as well. Furthermore, we experiment with multilingual and cross-lingual experiments that could benefit many low-resource languages.

Technology has the power to improve lives and tackle the great challenges of our time. At the University of Exeter, for example, Tim Dodwell is about to start a five-year project looking into how technology can make the aviation industry more sustainable and safer. He is one of the first five recipients of a new government fund for research fellowships into artificial intelligence (AI). His work will focus on how we can use machine learning and data processing to understand complex mathematical problems and build lighter and faster aircraft. The UK is the birthplace of AI, and it is fitting that these new fellowships are named after the man credited with inventing machine learning: Alan Turing.

Prior to the conference, Assistant Professor of Mathematics at Dartmouth John McCarthy and Claude Shannon from MIT had been co-editing the then forthcoming Volume 34 of the Annals of Mathematics Studies journal, on Automata Studies (Shannon & McCarthy, 1956). Automata are self-operating machines designed to automatically follow predetermined sequences of operations or respond to predetermined instructions. As engineering mechanisms they appear in a wide variety of everyday applications such as mechanical clocks where a hammer strikes a bell or a cuckoo appears to sing. "At the time I believed if only we could get everyone who was interested in the subject together to devote time to it and avoid distractions, we could make real progress" -- John McCarthy The initial group McCarthy had in mind included Marvin Minsky whom he had known since they were graduate students together at Fine Hall in the early 1950s. The two had talked about artificial intelligence then, and Minsky's PhD dissertation in mathematics had been on neural nets (Moor, 2006) and the structure of the human brain (Nasar, 1998).

Knowledge graphs encode uniquely identifiable entities to other entities or literal values by means of relationships, thus enabling semantically rich querying over the stored data. Typically, the semantics of such queries are often crisp thereby resulting in crisp answers. Query log statistics show that a majority of the queries issued to knowledge graphs are often entity centric queries. When a user needs additional answers the state-of-the-art in assisting users is to rewrite the original query resulting in a set of approximations. Several strategies have been proposed in past to address this. They typically move up the taxonomy to relax a specific element to a more generic element. Entities don't have a taxonomy and they end up being generalized. To address this issue, in this paper, we propose an entity centric reformulation strategy that utilizes schema information and entity features present in the graph to suggest rewrites. Once the features are identified, the entity in concern is reformulated as a set of features. Since entities can have a large number of features, we introduce strategies that select the top-k most relevant and {informative ranked features and augment them to the original query to create a valid reformulation. We then evaluate our approach by showing that our reformulation strategy produces results that are more informative when compared with state-of-the-art

Manchester was the world's first industrial city. It is the birthplace of the modern computer, and has the birthright for Graphene. Manchester is a product of the first industrial revolution, home of the third industrial revolution and now marks the time for it to be the focus of the fourth industrial revolution – 4IR. The 4IR will be the transformational changes caused by Artificial Intelligence. We are already experiencing major changes in our daily lives as AI is increasingly incorporated into new products.

Instead, they're put on show as part of an installation that takes the lifework of original AI scientists Allen Newell and Herbert Simon and brings it to life within the very technology they dedicated their lives to creating. "This is kind of like a history room," says artist Daniel Pillis, who spent three years researching and creating the installation. Pillis's interest in the history of robotics and artificial intelligence began to really take off when he started graduate school at Carnegie Mellon University in 2012. The installation is based of Newell and Simon's original office space, which Pillis replicated through physical archives and oral accounts of the room from former Carnegie Mellon employees. The recreation features minute details about the way the scientists worked from the 1950s through the 1980s as they created the building blocks for artificial intelligence technology, featuring things such as Simon's standing desk workspace with actual chairs he used and messy piles of papers, which Newell was known to keep around his work station.

As the amount of knowledge bases (KBs) grows rapidly, the problem of question answering (QA) over multiple KBs has drawn more attention. The most significant distinction between multiple KB-QA and single KB-QA is that the former must consider the alignments between KBs. The pipeline strategy first constructs the alignments independently, and then uses the obtained alignments to construct queries. However, alignment construction is not a trivial task, and the introduced noises would be passed on to query construction. By contrast, we notice that alignment construction and query construction are interactive steps, and jointly considering them would be beneficial. To this end, we present a novel joint model based on integer linear programming (ILP), uniting these two procedures into a uniform framework. The experimental results demonstrate that the proposed approach outperforms state-of-the-art systems, and is able to improve the performance of both alignment construction and query construction.

Finding interesting rule in the sixth strategy step about threshold control on generalized relations in attribute oriented induction, there is possibility to select candidate attribute for further generalization and merging of identical tuples until the number of tuples is no greater than the threshold value, as implemented in basic attribute oriented induction algorithm. At this strategy step there is possibility the number of tuples in final generalization result still greater than threshold value. In order to get the final generalization result which only small number of tuples and can be easy to transfer into simple logical formula, the seventh strategy step about rule transformation is evolved where there will be simplification by unioning or grouping the identical attribute. Our approach to measure interesting rule is opposite with heuristic measurement approach by Fudger and Hamilton where the more complex concept hierarchies, more interesting results are likely to be found, but our approach the simpler concept hierarchies, more interesting results are likely to be found and the more complex concept hierarchies, more complex process generalization in concept tree. The decision to find interesting rule is influenced with wide or length and depth or level of concept tree.