We propose a model architecture Although knowledge graphs (KGs) and ontologies have that combines the advantages of graph convolutional networks been exploited successfully for data integration [Trivedi (GCNs) [Kipf and Welling 2017] and siamese networks et al. 2018; Azmy et al. 2019], entity matching involving [Bromley et al. 1993] to address the entity-matching structured and unstructured sources has usually been task. GCNs are a type of graph neural network that shares performed by treating records without explicitly taking filter parameters among all the nodes, regardless of their location into account the natural graph representation of structured in the graph. Our Siamese Graph Convolutional Network sources and the potential graph representation of unstructured (S-GCN) incorporates two identical GCNs, as shown data [Mudgal et al. 2018; Gschwind et al. 2019].

Recent advances in Named Entity Recognition (NER) show that document-level contexts can significantly improve model performance. In many application scenarios, however, such contexts are not available. In this paper, we propose to find external contexts of a sentence by retrieving and selecting a set of semantically relevant texts through a search engine, with the original sentence as the query. We find empirically that the contextual representations computed on the retrieval-based input view, constructed through the concatenation of a sentence and its external contexts, can achieve significantly improved performance compared to the original input view based only on the sentence. Furthermore, we can improve the model performance of both input views by Cooperative Learning, a training method that encourages the two input views to produce similar contextual representations or output label distributions. Experiments show that our approach can achieve new state-of-the-art performance on 8 NER data sets across 5 domains.

Natural language processing (NLP) methods for analyzing legal text offer legal scholars and practitioners a range of tools allowing to empirically analyze law on a large scale. However, researchers seem to struggle when it comes to identifying ethical limits to using natural language processing (NLP) systems for acquiring genuine insights both about the law and the systems' predictive capacity. In this paper we set out a number of ways in which to think systematically about such issues. We place emphasis on three crucial normative parameters which have, to the best of our knowledge, been underestimated by current debates: (a) the importance of academic freedom, (b) the existence of a wide diversity of legal and ethical norms domestically but even more so internationally and (c) the threat of moralism in research related to computational law. For each of these three parameters we provide specific recommendations for the legal NLP community. Our discussion is structured around the study of a real-life scenario that has prompted recent debate in the legal NLP research community.

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An information cascade is a circumstance where agents make decisions in a sequential fashion by following other agents. Bikhchandani et al., predict that once a cascade starts it continues, even if it is wrong, until agents receive an external input such as public information. In an information cascade, even if an agent has its own personal choice, it is always overridden by observation of previous agents' actions. This could mean agents end up in a situation where they may act without valuing their own information. As information cascades can have serious social consequences, it is important to have a good understanding of what causes them. We present a detailed Bayesian model of the information gained by agents when observing the choices of other agents and their own private information. Compared to prior work, we remove the high impact of the first observed agent's action by incorporating a prior probability distribution over the information of unobserved agents and investigate an alternative model of choice to that considered in prior work: weighted random choice. Our results show that, in contrast to Bikhchandani's results, cascades will not necessarily occur and adding prior agents' information will delay the effects of cascades.

It has been argued that one role of social constructs, such as institutions, trust and norms, is to coordinate the expectations of autonomous entities in order to resolve collective action situations (such as collective risk dilemmas) through the coordination of behaviour. While much work has addressed the formal representation of these social constructs, in this paper we focus specifically on the formal representation of, and associated reasoning with, the expectations themselves. In particular, we investigate how explicit reasoning about expectations can be used to encode both traditional game theory solution concepts and social mechanisms for the social dilemma situation. We use the Collective Action Simulation Platform (CASP) to model a collective risk dilemma based on a flood plain scenario and show how using expectations in the reasoning mechanisms of the agents making decisions supports the choice of cooperative behaviour.

The trustworthiness of Robots and Autonomous Systems (RAS) has gained a prominent position on many research agendas towards fully autonomous systems. This research systematically explores, for the first time, the key facets of human-centered AI (HAI) for trustworthy RAS. In this article, five key properties of a trustworthy RAS initially have been identified. RAS must be (i) safe in any uncertain and dynamic surrounding environments; (ii) secure, thus protecting itself from any cyber-threats; (iii) healthy with fault tolerance; (iv) trusted and easy to use to allow effective human-machine interaction (HMI), and (v) compliant with the law and ethical expectations. Then, the challenges in implementing trustworthy autonomous system are analytically reviewed, in respects of the five key properties, and the roles of AI technologies have been explored to ensure the trustiness of RAS with respects to safety, security, health and HMI, while reflecting the requirements of ethics in the design of RAS. While applications of RAS have mainly focused on performance and productivity, the risks posed by advanced AI in RAS have not received sufficient scientific attention. Hence, a new acceptance model of RAS is provided, as a framework for requirements to human-centered AI and for implementing trustworthy RAS by design. This approach promotes human-level intelligence to augment human's capacity. while focusing on contributions to humanity.

Data augmentation has recently seen increased interest in NLP due to more work in low-resource domains, new tasks, and the popularity of large-scale neural networks that require large amounts of training data. Despite this recent upsurge, this area is still relatively underexplored, perhaps due to the challenges posed by the discrete nature of language data. In this paper, we present a comprehensive and unifying survey of data augmentation for NLP by summarizing the literature in a structured manner. We first introduce and motivate data augmentation for NLP, and then discuss major methodologically representative approaches. Next, we highlight techniques that are used for popular NLP applications and tasks. We conclude by outlining current challenges and directions for future research. Overall, our paper aims to clarify the landscape of existing literature in data augmentation for NLP and motivate additional work in this area.

The first image of China's rouge Long March 5B rocket in orbit has been released by astronomers. The Italy-based Virtual Telescope Project captured the craft, which appears like a glowing light, as it passed above the group's'Elena' robotic telescope. The Chinese rocket made headlines this week when new surfaced the massive 21-ton vehicle would make an uncontrolled reentry weekend, with the possibility of landing in inhabited areas. The rocket was moving'extremely fast' when it soared 435 miles above the Virtual Telescopes Project's telescope Wednesday evening, researchers said. Gianluca Masi, an astronomer with the Virtual Telescope Project who snapped the image, stated that'while the Sun was just a few degrees below the horizon, so the sky was incredibly bright: these conditions made the imaging quite extreme, but our robotic telescope succeeded in capturing this huge debris.' 'This is another bright success, showing the amazing capabilities of our robotic facility in tracking these objects.'

While healthcare has lagged behind other industries in the deployment of artificial intelligence (AI) and many other advanced technologies, the COVID-19 pandemic is proving to be the mother of invention when it comes to technological innovation. Machine learning -- a key part of AI where computer algorithms automatically improve through experience -- has been called upon to leverage healthcare data to help deal with many of the challenges COVID-19 has presented. Public health systems have turned to machine learning to complement their contact tracing and other efforts to control the disease and track outbreaks. Private healthcare operators have embraced machine learning to remain competitive when faced with a drop in demand for elective surgery or, in many countries, a reluctance or inability to visit hospitals or clinics. The pace of AI and machine learning adoption is also accelerating in hospitals.