94 research outputs found

    Graph Deep Learning: State of the Art and Challenges

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    The last half-decade has seen a surge in deep learning research on irregular domains and efforts to extend convolutional neural networks (CNNs) to work on irregularly structured data. The graph has emerged as a particularly useful geometrical object in deep learning, able to represent a variety of irregular domains well. Graphs can represent various complex systems, from molecular structure, to computer and social and traffic networks. Consequent on the extension of CNNs to graphs, a great amount of research has been published that improves the inferential power and computational efficiency of graph- based convolutional neural networks (GCNNs).The research is incipient, however, and our understanding is relatively rudimentary. The majority of GCNNs are designed to operate with certain properties. In this survey we review of the state of graph representation learning from the perspective of deep learning. We consider challenges in graph deep learning that have been neglected in the majority of work, largely because of the numerous theoretical difficulties they present. We identify four major challenges in graph deep learning: dynamic and evolving graphs, learning with edge signals and information, graph estimation, and the generalization of graph models. For each problem we discuss the theoretical and practical issues, survey the relevant research, while highlighting the limitations of the state of the art. Advances on these challenges would permit GCNNs to be applied to wider range of domains, in situations where graph models have previously been limited owing to the obstructions to applying a model owing to the domains’ natures

    Present and Future of Formal Argumentation

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    This report documents the program and the outcomes of Dagstuhl Perspectives Workshop 15362 “Present and Future of Formal Argumentation”. The goal of this Dagstuhl Perspectives Workshop was to gather the world leading experts in formal argumentation in order to develop a SWOT (Strength, Weaknesses, Opportunities, Threats) analysis of the current state of the research in this field and to draw accordingly some strategic lines to ensure its successful development in the future. A critical survey of the field has been carried out through individual presentations and collective discussions. Moreover, working group activity lead to identify several open problems in argumentation

    A proof system for the language POOL

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    Ologs: a categorical framework for knowledge representation

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    In this paper we introduce the olog, or ontology log, a category-theoretic model for knowledge representation (KR). Grounded in formal mathematics, ologs can be rigorously formulated and cross-compared in ways that other KR models (such as semantic networks) cannot. An olog is similar to a relational database schema; in fact an olog can serve as a data repository if desired. Unlike database schemas, which are generally difficult to create or modify, ologs are designed to be user-friendly enough that authoring or reconfiguring an olog is a matter of course rather than a difficult chore. It is hoped that learning to author ologs is much simpler than learning a database definition language, despite their similarity. We describe ologs carefully and illustrate with many examples. As an application we show that any primitive recursive function can be described by an olog. We also show that ologs can be aligned or connected together into a larger network using functors. The various methods of information flow and institutions can then be used to integrate local and global world-views. We finish by providing several different avenues for future research.Comment: 38 page

    Compositionality in the temporal logic of concurrent systems

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    A proof theory for a sequential version of POOL

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    Context-Aware and Secure Workflow Systems

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    Businesses do evolve. Their evolution necessitates the re-engineering of their existing "business processes”, with the objectives of reducing costs, delivering services on time, and enhancing their profitability in a competitive market. This is generally true and particularly in domains such as manufacturing, pharmaceuticals and education). The central objective of workflow technologies is to separate business policies (which normally are encoded in business logics) from the underlying business applications. Such a separation is desirable as it improves the evolution of business processes and, more often than not, facilitates the re-engineering at the organisation level without the need to detail knowledge or analyses of the application themselves. Workflow systems are currently used by many organisations with a wide range of interests and specialisations in many domains. These include, but not limited to, office automation, finance and banking sector, health-care, art, telecommunications, manufacturing and education. We take the view that a workflow is a set of "activities”, each performs a piece of functionality within a given "context” and may be constrained by some security requirements. These activities are coordinated to collectively achieve a required business objective. The specification of such coordination is presented as a set of "execution constraints” which include parallelisation (concurrency/distribution), serialisation, restriction, alternation, compensation and so on. Activities within workflows could be carried out by humans, various software based application programs, or processing entities according to the organisational rules, such as meeting deadlines or performance improvement. Workflow execution can involve a large number of different participants, services and devices which may cross the boundaries of various organisations and accessing variety of data. This raises the importance of _ context variations and context-awareness and _ security (e.g. access control and privacy). The specification of precise rules, which prevent unauthorised participants from executing sensitive tasks and also to prevent tasks from accessing unauthorised services or (commercially) sensitive information, are crucially important. For example, medical scenarios will require that: _ only authorised doctors are permitted to perform certain tasks, _ a patient medical records are not allowed to be accessed by anyone without the patient consent and _ that only specific machines are used to perform given tasks at a given time. If a workflow execution cannot guarantee these requirements, then the flow will be rejected. Furthermore, features/characteristics of security requirement are both temporal- and/or event-related. However, most of the existing models are of a static nature – for example, it is hard, if not impossible, to express security requirements which are: _ time-dependent (e.g. A customer is allowed to be overdrawn by 100 pounds only up-to the first week of every month. _ event-dependent (e.g. A bank account can only be manipulated by its owner unless there is a change in the law or after six months of his/her death). Currently, there is no commonly accepted model for secure and context-aware workflows or even a common agreement on which features a workflow security model should support. We have developed a novel approach to design, analyse and validate workflows. The approach has the following components: = A modelling/design language (known as CS-Flow). The language has the following features: – support concurrency; – context and context awareness are first-class citizens; – supports mobility as activities can move from one context to another; – has the ability to express timing constrains: delay, deadlines, priority and schedulability; – allows the expressibility of security policies (e.g. access control and privacy) without the need for extra linguistic complexities; and – enjoy sound formal semantics that allows us to animate designs and compare various designs. = An approach known as communication-closed layer is developed, that allows us to serialise a highly distributed workflow to produce a semantically equivalent quasi-sequential flow which is easier to understand and analyse. Such re-structuring, gives us a mechanism to design fault-tolerant workflows as layers are atomic activities and various existing forward and backward error recovery techniques can be deployed. = Provide a reduction semantics to CS-Flow that allows us to build a tool support to animate a specifications and designs. This has been evaluated on a Health care scenario, namely the Context Aware Ward (CAW) system. Health care provides huge amounts of business workflows, which will benefit from workflow adaptation and support through pervasive computing systems. The evaluation takes two complementary strands: – provide CS-Flow’s models and specifications and – formal verification of time-critical component of a workflow
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