Novel thin film polymer foaming technique for low and ultra low-k dielectrics

The results presented show a novel route for the preparation of thin ultra-low-k polymer films based on commercial and "non-exotic" (non-expensive) polyimide by a foaming technique. Dependent on the glass transition temperature of the polyimide mechanically and thermally stable (> 300 °C) films having porosities of ca. 40 % and k-values below 2.0 are formed. A further reduction into the ultra low k region may be accomplished by tailoring the shape of the pores from spherical into disc-like void

Proceed with caution:On the use of computational linguistics in threat assessment

Large-scale linguistic analyses are increasingly applied to the study of extremism, terrorism, and other threats of violence. At the same time, practitioners working in the field of counterterrorism and security are confronted with large-scale linguistic data, and may benefit from computational methods. This article highlights the challenges and opportunities associated with applying computational linguistics in the domain of threat assessment. Four current issues are identified, namely (1) the data problem, (2) the utopia of predicting violence, (3) the base rate fallacy, and (4) the danger of closed-sourced tools. These challenges are translated into a checklist of questions that should be asked by policymakers and practitioners who (intend to) make use of tools that leverage computational linguistics for threat assessment. The ‘VISOR-P’ checklist can be used to evaluate such tools through their Validity, Indicators, Scientific Quality, Openness, Relevance and Performance. Finally, some suggestions are outlined for the furtherance of the computational linguistic threat assessment field.</p

Proceed with caution: on the use of computational linguistics in threat assessment

Large-scale linguistic analyses are increasingly applied to the study of extremism, terrorism, and other threats of violence. At the same time, practitioners working in the field of counterterrorism and security are confronted with large-scale linguistic data, and may benefit from computational methods. This article highlights the challenges and opportunities associated with applying computational linguistics in the domain of threat assessment. Four current issues are identified, namely (1) the data problem, (2) the utopia of predicting violence, (3) the base rate fallacy, and (4) the danger of closed-sourced tools. These challenges are translated into a checklist of questions that should be asked by policymakers and practitioners who (intend to) make use of tools that leverage computational linguistics for threat assessment. The ‘VISOR-P’ checklist can be used to evaluate such tools through their Validity, Indicators, Scientific Quality, Openness, Relevance and Performance. Finally, some suggestions are outlined for the furtherance of the computational linguistic threat assessment field

Online influence, offline violence: Language Use on YouTube surrounding the 'Unite the Right' rally

The media frequently describes the 2017 Charlottesville ‘Unite the Right’ rally as a turning point for the alt-right and white supremacist movements. Social movement theory suggests that the media attention and public discourse concerning the rally may have engendered changes in social identity performance and visibility of the alt-right, but this has yet to be empirically tested. The presence of the movement on YouTube is of particular interest, as this platform has been referred to as a breeding ground for the alt-right. The current study investigates whether there are differences in language use between 7142 alt-right and progressive YouTube channels, in addition to measuring possible changes as a result of the rally. To do so, we create structural topic models and measure bigram proportions in video transcripts, spanning approximately 2 months before and after the rally. We observe differences in topics between the two groups, with the ‘alternative influencers’, for example, discussing topics related to race and free speech to a larger extent than progressive channels. We also observe structural breakpoints in the use of bigrams at the time of the rally, suggesting there are changes in language use within the two groups as a result of the rally. While most changes relate to mentions of the rally itself, the alternative group also shows an increase in promotion of their YouTube channels. In light of social movement theory, we argue that language use on YouTube shows that the Charlottesville rally indeed triggered changes in social identity performance and visibility of the alt-right

Sarcoidosis of the parotid glands in Heerfordt’s syndrome

Case Repor

Shedding Light on Terrorist and Extremist Content Removal

Social media and tech companies face the challenge of identifying and removing terrorist and extremist content from their platforms. This paper presents the findings of a series of interviews with Global Internet Forum to Counter Terrorism (GIFCT) partner companies and law enforcement Internet Referral Units (IRUs). It offers a unique view on current practices and challenges regarding content removal, focusing particularly on human-based and automated approaches and the integration of the two

An investigation of data-driven player positional roles within the Australian Football League Women's competition using technical skill match-play data

Understanding player positional roles are important for match-play tactics, player recruitment, talent identification, and development by providing a greater understanding of what each positional role constitutes. Currently, no analysis of competition technical skill data exists by player position in the Australian Football League Women's (AFLW) competition. The primary aim of the research was to use data-driven techniques to observe what positions and roles characterise AFLW match-play using detailed technical skill action data of players. A secondary aim was to comment on the application of clustering methods to achieve more interpretable, reflective positional clustering. A two-stage, unsupervised clustering approach was applied to meet these aims. Data cleaning resulted in 165 variables across 1296 player seasons in the 2019–2022 AFLW seasons which was used for clustering. First-stage clustering found four positions following a common convention (forwards, midfielders, defenders, and rucks). Second-stage clustering found roles within positions, resulting in a further 13 clusters with three forwards, three midfielders, four defenders, and three ruck positional roles. Key variables across all positions and roles included the field location of actions, number of contested possessions, clearances, interceptions, hitouts, inside 50s, and rebound 50s. Unsupervised clustering allowed the discovery of new roles rather than being constrained to pre-defined existing classifications of previous literature. This research assists coaches and practitioners by identifying key game actions players need to perform in match-play by position, which can assist in player recruitment, player development, and identifying appropriate match-play styles and tactics, while also defining new roles and suggestions of how to best use available data