Writing online:a guide to effective digital communication at work

Online writing plays a complex and increasingly prominent role in the life of organizations. From newsletters to press releases, social media marketing and advertising, to virtual presentations and interactions via e-mail and instant messaging, digital writing intertwines and affects the day-to-day running of the company - yet we rarely pay enough attention to it. Typing on the screen can become particularly problematic because digital text-based communication increases the opportunities for misunderstanding: it lacks the direct audio-visual contact and the norms and conventions that would normally help people to understand each other. Providing a clear, convincing and approachable discussion, this book addresses arenas of online writing: virtual teamwork, instant messaging, emails, corporate communication channels, and social media. Instead of offering do and don’t lists, however, it teaches the reader to develop a practice that is observant, reflective, and grounded in the understanding of the basic principles of language and communication. Through real-life examples and case studies, it helps the reader to notice previously unnoticed small details, question previously unchallenged assumptions and practices, and become a competent digital communicator in a wide range of professional contexts.

Dynamic Bayesian network for semantic place classification in mobile robotics

In this paper, the problem of semantic place categorization in mobile robotics is addressed by considering a time-based probabilistic approach called dynamic Bayesian mixture model (DBMM), which is an improved variation of the dynamic Bayesian network. More specifically, multi-class semantic classification is performed by a DBMM composed of a mixture of heterogeneous base classifiers, using geometrical features computed from 2D laserscanner data, where the sensor is mounted on-board a moving robot operating indoors. Besides its capability to combine different probabilistic classifiers, the DBMM approach also incorporates time-based (dynamic) inferences in the form of previous class-conditional probabilities and priors. Extensive experiments were carried out on publicly available benchmark datasets, highlighting the influence of the number of time-slices and the effect of additive smoothing on the classification performance of the proposed approach. Reported results, under different scenarios and conditions, show the effectiveness and competitive performance of the DBMM.