Temporal Aspects of Smart Contracts for Financial Derivatives

Implementing smart contracts to automate the performance of high-value over-the-counter (OTC) financial derivatives is a formidable challenge. Due to the regulatory framework and the scale of financial risk if a contract were to go wrong, the performance of these contracts must be enforceable in law and there is an absolute requirement that the smart contract will be faithful to the intentions of the parties as expressed in the original legal documentation. Formal methods provide an attractive route for validation and assurance, and here we present early results from an investigation of the semantics of industry-standard legal documentation for OTC derivatives. We explain the need for a formal representation that combines temporal, deontic and operational aspects, and focus on the requirements for the temporal aspects as derived from the legal text. The relevance of this work extends beyond OTC derivatives and is applicable to understanding the temporal semantics of a wide range of legal documentation

Graph reasoning with context-aware linearization for interpretable fact extraction and verification

This paper presents an end-to-end system for fact extraction and verification using textual and tabular evidence, the performance of which we demonstrate on the FEVEROUS dataset. We experiment with both a multi-task learning paradigm to jointly train a graph attention network for both the task of evidence extraction and veracity prediction, as well as a single objective graph model for solely learning veracity prediction and separate evidence extraction. In both instances, we employ a framework for per-cell linearization of tabular evidence, thus allowing us to treat evidence from tables as sequences. The templates we employ for linearizing tables capture the context as well as the content of table data. We furthermore provide a case study to show the interpretability our approach. Our best performing system achieves a FEVEROUS score of 0.23 and 53% label accuracy on the blind test data

XAI.it 2020 - Preface to the first italian workshop on explainable artificial intelligence

Artificial Intelligence systems are increasingly playing an increasingly important role in our daily lives. As their importance in our everyday lives grows, it is fundamental that the internal mechanisms that guide these algorithms are as clear as possible. It is not by chance that the recent General Data Protection Regulation (GDPR) emphasized the users’ right to explanation when people face artificial intelligence-based technologies. Unfortunately, the current research tends to go in the opposite direction, since most of the approaches try to maximize the effectiveness of the models (e.g., recommendation accuracy) at the expense of the explainability and the transparency. The main research questions which arise from this scenario is straightforward: how can we deal with such a dichotomy between the need for effective adaptive systems and the right to transparency and interpretability? Several research lines are triggered by this question: building transparent intelligent systems, analyzing the impact of opaque algorithms on final users, studying the role of explanation strategies, investigating how to provide users with more control in the behavior of intelligent systems. XAI.it, the first Italian workshop on Explainable AI, tries to address these research lines and aims to provide a forum for the Italian community to discuss problems, challenges and innovative approaches in the various sub-fields of XAI

Identifying and mapping chemical bonding within phenolic resin using Secondary Electron Hyperspectral Imaging

The distributions of methylene and ether bridges have been shown to impact the mechanical properties of phenolic resin. This work demonstrates the ability of the novel SEM based technique, Secondary Electron Hyperspectral Imaging (SEHI), to characterise and map methylene and ether bridges within phenolic resin at the nanoscale

EPIdemiology of Surgery-Associated Acute Kidney Injury (EPIS-AKI) : Study protocol for a multicentre, observational trial

More than 300 million surgical procedures are performed each year. Acute kidney injury (AKI) is a common complication after major surgery and is associated with adverse short-term and long-term outcomes. However, there is a large variation in the incidence of reported AKI rates. The establishment of an accurate epidemiology of surgery-associated AKI is important for healthcare policy, quality initiatives, clinical trials, as well as for improving guidelines. The objective of the Epidemiology of Surgery-associated Acute Kidney Injury (EPIS-AKI) trial is to prospectively evaluate the epidemiology of AKI after major surgery using the latest Kidney Disease: Improving Global Outcomes (KDIGO) consensus definition of AKI. EPIS-AKI is an international prospective, observational, multicentre cohort study including 10 000 patients undergoing major surgery who are subsequently admitted to the ICU or a similar high dependency unit. The primary endpoint is the incidence of AKI within 72 hours after surgery according to the KDIGO criteria. Secondary endpoints include use of renal replacement therapy (RRT), mortality during ICU and hospital stay, length of ICU and hospital stay and major adverse kidney events (combined endpoint consisting of persistent renal dysfunction, RRT and mortality) at day 90. Further, we will evaluate preoperative and intraoperative risk factors affecting the incidence of postoperative AKI. In an add-on analysis, we will assess urinary biomarkers for early detection of AKI. EPIS-AKI has been approved by the leading Ethics Committee of the Medical Council North Rhine-Westphalia, of the Westphalian Wilhelms-University Münster and the corresponding Ethics Committee at each participating site. Results will be disseminated widely and published in peer-reviewed journals, presented at conferences and used to design further AKI-related trials. Trial registration number NCT04165369

Supporting Visual Information Extraction from Geospatial Data

"The Spatial Relation Query (SRQ) tool is a graphical. software system, supported by a SQL-like query language,. that enables users to perform information extraction driven. by the visual appearance and the spatial arrangement of the. information. The tool has been initially designed to support. visual information extraction from web pages. Indeed, its former. underlying spatial relation formalism relied on the bounding. boxes of the graphical objects, which is a suitable choice for the. web domain. In this paper we present a theoretical extension of. the SRQ spatial composition framework that has been enhanced. to work directly on the contours of the graphical objects. This. allows us to apply the tool to more general contexts, such as GIS. applications.

A Graphical Tool to Support Visual Information Extraction

In this paper we present the Spatial Relation Query tool, a graphical software system for Information Extraction based on the visual appearance of the information. The SRQ tool is provided with a query language similar to the wellknown SQL, which allows users to write queries based on the visual arrangement of the information in an intuitive way. Two applications showing the use of the tool on web pages and geospatial data are presented

A spatial relation-based framework to perform visual information extraction

"The Spatial Relation Query (SRQ) tool is a graphical software environment, supported by a SQL-like language, which enables users to perform information extraction driven by the visual appearance and the spatial arrangement of the information. The tool has been initially customised to work on specific application domains, like web pages and geospatial data. In this paper, we present the theoretical formalisation of the visual information extraction (VIE) task and accordingly the redesign of the SRQ tool, which is now a full-featured, general-purpose information extraction system. Moreover, we show a new application of the VIE framework to the analysis and visual information extraction from PDF files.