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Key feature of a context-aware application is the ability to adapt based on the change of context. Two approaches that are widely used in this regard are the context-action pair mapping where developers match an action to execute for a particular context change and the adaptive learning where a context-aware application refines its action over time based on the preceding action’s outcome. Both these approaches have limitation which makes them unsuitable in situations where a context-aware application has to deal with unknown context changes. In this paper we propose a framework where adaptation is carried out via concurrent multi-action evaluation of a dynamically created action space. This dynamic creation of the action space eliminates the need for relying on the developers to create context-action pairs and the concurrent multi-action evaluation reduces the adaptation time as opposed to the iterative approach used by adaptive learning techniques. Using our reference implementation of the framework we show how it could be used to dynamically determine the threshold price in an e-commerce system which uses the name-your-own-price (NYOP) strategy.

Drawing on critical realism, complexity theory, and emergence, this chapter supports the call to re-imagine doctoral writing by arguing that academic writing in general is a complex open and emergent social system that can change. Several reasons to re-imagine doctoral writing are discussed. The first reason is that academic writings already exhibit considerable diversity. This suggests that the conditions of possibility for re-imagining them are already in place and provide a conceptual space from which to further imagine. Second, there are\ud epistemic reasons for re-thinking how we write, as evidenced by research on socio-semiotics. Several examples of doctoral writers\ud who have re-imagined their writing for epistemic reasons are given. To explain how change in social phenomena is possible and how it can continue to be justified, I draw on the theory of complex permeable open systems. These systems are emergent and, as such, allow us to think of social phenomena, such as writing, as non-reductive organic unities whose characteristics emerge from but cannot be reduced to any single constituent feature (such as grammar or lexis). By re-thinking academic writings in this way, we can provide a rationale to explain how they can continue to change. The chapter concludes by sharing the work of scholars engaged in re-imagining doctoral writings. The significance for writing studies is that critical realism offers a systematic and critical space within which to explain change\ud in social phenomena and provides a theoretical foundation for continuing to re-imagine conditions of possibility.

Part 12: Workshops; International audience; The manipulation of information and the dissemination of “fake news” are practices that trace back to the early records of human history. Significant changes in the technological environment enabling ubiquity, immediacy and considerable anonymity, have facilitated the spreading of misinformation in unforeseen ways, raising concerns around people’s (mis)perception of social issues worldwide. As a wicked problem, limiting the harm caused by misinformation goes beyond technical solutions, requiring also regulatory and behavioural changes. This workshop proposes to unpack the challenge at hand by bringing together diverse perspectives to the problem. Based on participatory design principles, it will challenge participants to critically reflect the limits of existing socio-technical approaches and co-create scenarios in which digital platforms support misinformation resilience.

Classifying research papers according to their research topics is an important task to improve their retrievability, assist the creation of smart analytics, and support a variety of approaches for analysing and making sense of the research environment. In this paper, we present the CSO Classifier, a new unsupervised approach for automatically classifying research papers according to the Computer Science Ontology (CSO), a comprehensive ontology of re-search areas in the field of Computer Science. The CSO Classifier takes as input the metadata associated with a research paper (title, abstract, keywords) and returns a selection of research concepts drawn from the ontology. The approach was evaluated on a gold standard of manually annotated articles yielding a significant improvement over alternative methods. Comment: Conference paper at TPDL 2019

Abstract This chapter deals with the processes by which the European vernaculars gained prestige vis-à-vis Latin from the late Middle Ages onwards. It addresses the “idea” of a standard language and how this has been advanced by particular actors and institutions across Europe, focusing primarily on the period from the Renaissance to the eighteenth century. It also considers whether standard languages are now outmoded concepts and suggests that we are now in a period characterized by destandardization.

Introduces the notion of OER and situates it within a wider open education movement, which has more recently seen a turn to the consideration of 'open educational practices'.

Birds embody particular phonic and visual traits that distinguish them from 10,000 distinct bird species worldwide. Birds are also perceived to be indicators of biodiversity due to their propensity for responding to changes in their environment. An effective, automatic wildlife monitoring system based on bird bioacoustics, which can support manual classification, can be pivotal for the protection of the environment and endangered species. In modern machine learning, real-life bird audio classification is still considered as an esoteric challenge owing to the convoluted patterns present in bird song, and the complications that arise when numerous bird species are present in a common setting. Existing avian bioacoustic monitoring systems struggle when multiple bird species are present in an audio segment. To overcome these challenges, we propose a novel Faster Region-Based Convolutional Neural Network bird audio diarization system that incorporates object detection in the spectral domain and performs diarization of 50 bird species to effectively tackle the ‘which bird spoke when?’ problem. Benchmark results are presented using the Bird Songs from Europe dataset achieving a Diarization Error Rate of 21.81, Jaccard Error Rate of 20.94 and F1, precision and recall values of 0.85, 0.83 and 0.87 respectively.

Martian gullies were initially hypothesized to be carved by liquid water, due to their resemblance to gullies on Earth. Recent observations have highlighted significant sediment transport events occurring in Martian gullies at times and places where CO2 ice should be actively sublimating. Here we explore the role of CO2 sublimation in mobilizing sediment through laboratory simulation. In our previous experimental work, we reported the first observations of sediment slope movement triggered by the sublimation of CO2 frost. We used a Mars regolith simulant near the angle of repose. The current study extends our previous work by including two additional substrates, fine and coarse sand, and by testing slope angles down to 10°. We find that the Mars regolith simulant is active down to 17°, the fine sand is active only near the angle of repose and the coarse sand shows negligible movement. Using an analytical model, we show that under Martian gravity motion should be possible at even lower slope angles. We conclude that these mass-wasting processes could be involved in shaping Martian gullies at the present day and intriguingly the newly reported CO2-creep process could provide an alternative explanation for putative solifluction lobes on Mars.

The Multilayer Perceptron (MLP) is a neural network architecture that is widely used for regression, classification and time series forecasting. One often cited disadvantage of the MLP, however, is the difficulty associated with human understanding of a particular MLP’s function. This so called black box limitation is due to the fact that the weights of the network reveal little about structure of the function they implement. This paper proposes a method for understanding the structure of the function learned by MLPs that model functions of the class \(f:\{-1,1\}^n \rightarrow \mathbb {R}^m\). This includes regression and classification models. A Walsh decomposition of the function implemented by a trained MLP is performed and the coefficients analysed. The advantage of a Walsh decomposition is that it explicitly separates the contribution to the function made by each subset of input neurons. It also allows networks to be compared in terms of their structure and complexity. The method is demonstrated on some small toy functions and on the larger problem of the MNIST handwritten digit classification data set.

In this chapter we explore the future for innovation in two related, but distinct, sectors. We consider the linkages between medical technology(MedTech) and agricultural technology (Agri-Tech) innovation in the UK. We ask and discuss questions: Who are the key actors in the innovation systems of Medtech and Agri-Tech in the UK? What are the core technologies driving the current waves of innovation in these two sectors? Can one industry learn from the other? Where is the scope for cooperation and synergies? We notice that both sectors are technologically linked through foundational technologies underpinning the majority of the observed innovation e.g. big data, AI, IoT and robotics. The outputs of these technologies rely crucially on digital data for insight and decision support. However, Agri-Tech benefits from less complex stakeholder issues regarding data security and privacy. Both sectors are important to the UK going forwards, and both will be exposed to Brexit and the consequences of the COVID pandemic. Our discussion on the future of innovation should be of particular interest to start-up leaders, entrepreneurs, investors, managers and policy-makers in MedTech, Agri-Tech and cognate sectors.