title: Neural multi-class classification approach to blood glucose level forecasting with prediction uncertainty visualisation

authors: Mayo, Michael

subjects: computer science

abstract: A machine learning-based method for blood glucose level prediction thirty and sixty minutes in advance based on highly multiclass classification (as opposed to the more traditional regression approach) is proposed. An advantage of this approach is the possibility of modelling and visualising the uncertainty of a prediction across the entire range of blood glucose levels without parametric assumptions such as normality. To demonstrate the approach, a long-short term memory-based neural network classifier is used in conjunction with a blood glucose-specific data preprocessing technique (risk domain transform) to train a set of models and generate predictions for the 2018 and 2020 Blood Glucose Level Prediction Competition datasets. Numeric accuracy results are reported along with examples of the uncertainty visualisation possible using this technique.

text: Neural multi-class classification approach to blood glucose level forecasting with prediction uncertainty visualisation A machine learning-based method for blood glucose level prediction thirty and sixty minutes in advance based on highly multiclass classification (as opposed to the more traditional regression approach) is proposed. An advantage of this approach is the possibility of modelling and visualising the uncertainty of a prediction across the entire range of blood glucose levels without parametric assumptions such as normality. To demonstrate the approach, a long-short term memory-based neural network classifier is used in conjunction with a blood glucose-specific data preprocessing technique (risk domain transform) to train a set of models and generate predictions for the 2018 and 2020 Blood Glucose Level Prediction Competition datasets. Numeric accuracy results are reported along with examples of the uncertainty visualisation possible using this technique.

year: 2020

title: Insider threat modeling: An adversarial risk analysis approach

authors: Joshi, Chaitanya

abstract: Insider threats entail major security issues in geopolitics, cyber security and business organizations. Most earlier work in the field has focused on standard game theoretic approaches. We provide here two alternative, more realistic models based on adversarial risk analysis (ARA). ARA does not assume common knowledge and solves the problem from the point of view of just one of the players, the defender (typically), taking into account their knowledge and uncertainties regarding the choices available to them, to their adversaries, the possible outcomes, their payoffs/utilities and their opponents payoffs/utilities. The first model depicts the problem as a standard Defend-AttackDefend model. The second model segments the set of involved agents in three classes of users and considers both sequential as well as simultaneous actions. A data security example illustrates the discussion.

text: Insider threat modeling: An adversarial risk analysis approach Insider threats entail major security issues in geopolitics, cyber security and business organizations. Most earlier work in the field has focused on standard game theoretic approaches. We provide here two alternative, more realistic models based on adversarial risk analysis (ARA). ARA does not assume common knowledge and solves the problem from the point of view of just one of the players, the defender (typically), taking into account their knowledge and uncertainties regarding the choices available to them, to their adversaries, the possible outcomes, their payoffs/utilities and their opponents payoffs/utilities. The first model depicts the problem as a standard Defend-AttackDefend model. The second model segments the set of involved agents in three classes of users and considers both sequential as well as simultaneous actions. A data security example illustrates the discussion.

year: 2019

title: The social meaning potential of loanwords: Empirical explorations of lexical borrowing as expression of (social) identity

authors: Zenner, Eline

abstract: The use of loanwords is not merely a lexical act (filling a lexical gap in a given language, or using a shorter word in place of a longer expression) but also a socially meaningful one – a contextual expression of self, social identity and language regard. Recent lexical borrowing research has drawn attention to this social meaning potential of loanwords. What motivates language users to select a borrowed form over a receptor language equivalent, what is the (perceived) social meaning of this choice and how can we empirically address these questions? This special issue seeks to bring into debate the interface between speakers (the social dimension) and language (the linguistic dimension) with regard to lexical borrowing, and to probe how language regard and speaker identity influence and explain the use of loanwords. In a bid to better understand this complex interface, the special issue includes papers that explore a range of empirical methodologies drawn from different subfields of (socio)linguistics and closely related scientific domains (linguistic anthropology, conversation analysis, corpus linguistics, social psychology and psycholinguistics) and documents a variety of contact situations: English loans into French and Finnish, Māori loanwords into New Zealand English, German loans into Dutch. Together, the different perspectives presented in this issue help advance our understanding of the relationship between lexical change on the one hand, and language regard and (social) identity on the other hand.

text: The social meaning potential of loanwords: Empirical explorations of lexical borrowing as expression of (social) identity The use of loanwords is not merely a lexical act (filling a lexical gap in a given language, or using a shorter word in place of a longer expression) but also a socially meaningful one – a contextual expression of self, social identity and language regard. Recent lexical borrowing research has drawn attention to this social meaning potential of loanwords. What motivates language users to select a borrowed form over a receptor language equivalent, what is the (perceived) social meaning of this choice and how can we empirically address these questions? This special issue seeks to bring into debate the interface between speakers (the social dimension) and language (the linguistic dimension) with regard to lexical borrowing, and to probe how language regard and speaker identity influence and explain the use of loanwords. In a bid to better understand this complex interface, the special issue includes papers that explore a range of empirical methodologies drawn from different subfields of (socio)linguistics and closely related scientific domains (linguistic anthropology, conversation analysis, corpus linguistics, social psychology and psycholinguistics) and documents a variety of contact situations: English loans into French and Finnish, Māori loanwords into New Zealand English, German loans into Dutch. Together, the different perspectives presented in this issue help advance our understanding of the relationship between lexical change on the one hand, and language regard and (social) identity on the other hand.

year: 2019