Implementation of a Topology Independent MAC (TiMAC) Policy on a Low-Cost IoT System

The emerging new paradigm under the fifth generation of wireless communications technologies (5G) and high expectations for massively expanding today’s Internet of Things (IoT) under 5G, are expected to support a large plurality of low-cost devices for an all-increasing number of new IoT applications. Many emerging IoT applications are going to take advantage of techniques and technologies that have high demands from low-cost devices in terms of processing large amounts of data and communication. For example, in systems based on fog computing technology, low-cost devices have to assign some of their limited resources for processing purposes. Considering the drawbacks emerging from using low-cost devices and the fact that many applications are in need for time-constrained approaches, TDMA-based Medium Access Control (MAC) policies need to be revisited and implemented in low-cost devices of today. In this sense, a policy independent of the underlying topology, TiMAC policy, is considered here and is implemented in low-cost devices using 433 MHz RF modules. Even though the implementation is limited by synchronization issues and a small number of nodes, the obtained experimental results demonstrate the potential for employing TDMA-based MAC policies on IoT systems consisting of low-cost devices

Language Processing for Predicting Suicidal Tendencies: A Case Study in Greek Poetry

Part 2: 8th Mining Humanistic Data WorkshopInternational audienceNatural language processing has previously been used with fairly high success to predict a writer’s likelihood of committing suicide, using a wide variety of text types, including suicide notes, micro-blog posts, lyrics and even poems. In this study, we extend work done in previous research to a language that has not been tackled before in this setting, namely Greek. A set of language-dependent (but easily portable across languages) and language-independent linguistic features is proposed to represent the poems of 13 Greek poets of the 20th century. Prediction experiments resulted in an overall classification rate of 84.5% with the C4.5 algorithm, after having tested multiple machine-learning algorithms. These results differ significantly from previous research, as some features investigated did not play as significant a role as was expected. This kind of task presents multiple difficulties, especially for a language where no previous research has been conducted. Therefore, a significant part of the annotation process was performed manually, which likely explains the somewhat higher classification rates compared to previous efforts