No abstract

Abstract. We prove that the expected number of braid moves in the commutation class of the reduced word (s 1 s 2 · · · s n−1 )(s 1 s 2 · · · s n−2 ) · · · (s 1 s 2 )(s 1 ) for the long element in the symmetric group Sn is one. This is a variant of a similar result by V. Reiner, who proved that the expected number of braid moves in a random reduced word for the long element is one. The proof is bijective and uses X. Viennot's theory of heaps and variants of the promotion operator. In addition, we provide a refinement of this result on orbits under the action of even and odd promotion operators. This gives an example of a homomesy for a nonabelian (dihedral) group that is not induced by an abelian subgroup. Our techniques extend to more general posets and to other statistics.

Concepts of space are fundamental to our understanding of human action and interaction. The common sense concept of uniform, metric, physical space is inadequate for design. It fails to capture features of social norms and practices that can be critical to the success of a technology. The concept of 'place' addresses these limitations by taking account of the different ways a space may be understood and used. This paper argues for the importance of a third concept: communication space. Motivated by Heidegger's discussion of 'being-with' this concept addresses differences in interpersonal 'closeness' or mutual-involvement that are a constitutive feature of human interaction. We apply the concepts of space, place and communication space to the analysis of a corpus of interactions from an online community, 'Walford', which has a rich communicative ecology. A novel measure of sequential integration of conversational turns is proposed as an index of mutal-involvement. We demonstrate systematic differences in mutual-involvement that cannot be accounted for in terms of space or place and conclude that a concept of communication space is needed to address the organisation of human encounters in this community.

This chapter discusses the emergence of the Internet of Things, using a case study of a citizen science initiative, focusing in particular on issues involved in measuring air quality. The core of the citizen science initiative was formed by a world-wide network of early adaptors of the Internet of Things who, motivated by public health issues, set out to create widely available tools for air quality measuring. With these tools, they established a global, citizen-led, air quality measurement network. Besides highlighting a number of social and technological issues which are involve any such enterprise, this chapter engages with the discourse surrounding the use of IoT in collective sensing projects. Two questions are salient here. Firstly, can IoT technology be used in a citizen science context to monitor air quality? And secondly, does the construction of these devices lead to a successful mobilisation around issues of air quality?

A Federated Learning approach consists of creating an AI model from multiple data sources, without moving large amounts of data across to a central environment. Federated learning can be very useful in a tactical coalition environment, where data can be collected individually by each of the coalition partners, but network connectivity is inadequate to move the data to a central environment. However, such data collected is often dirty and imperfect. The data can be imbalanced, and in some cases, some classes can be completely missing from some coalition partners. Under these conditions, traditional approaches for federated learning can result in models that are highly inaccurate. In this paper, we propose approaches that can result in good machine learning models even in the environments where the data may be highly skewed, and study their performance under different environments.