Modern young people (“digital natives”) have grown in an era dominated by new technologies where communications are pushed to quite a real-time level, and pose no limits in establishing relationships with other people or communities. However, the speed of evolution does not allow young people to split consciously acceptable behaviors from potentially harmful ones and a new phenomenon known as cyber bullying is emerging with increasing evidence, attracting the attention of educators, and media. Cyber bullying is defined as “willful and repeated harm inflicted through the use of electronic devices” [1]. In this paper we propose a possible solution for automatic detection of bully traces over a social network, using techniques derived from NLP (Natural Language Processing) and machine learning. Specifically, we shall design a model inspired by Growing Hierarchical SOMs, able to cluster efficiently documents containing bully traces, built upon semantic and syntactic features of textual sentences. We fine-tuned our model to work with the social network Twitter, but we also tested the model against other social networks such as YouTube and Formspring. Finally, we report our results, showing that the proposed unsupervised approach could be effectively used with good performances in some scenarios.

Two methods were compared to predict a ship’s fuel consumption: the simplified naval architecture method (SNAM) and the deep neural network (DNN) method. The SNAM relied on limited operational data and employed a simplified technique to estimate a ship’s required power by determining its resistance in calm water. Here, the Holtrop–Mennen technique obtained hydrostatic information for each selected voyage, the added resistance in the encountered natural seaways, and the brake power required for each scenario. Additional characteristics, such as efficiency factors, were derived from literature surveys and from assumed working hypotheses. The DNN method comprised multiple fully connected layers with the nonlinear activation function rectified linear unit (ReLU). This machine-learning-based method was trained on more than 12,000 sample voyages, and the tested data were validated against realistic operational data. Our results demonstrated that, for some ship topologies (general cargo and containerships), the physical models predicted more accurately the realistic data than the machine learning approach despite the lack of relevant operational parameters. Nevertheless, the DNN method was generally capable of yielding reasonably accurate predictions of fuel consumption for oil tankers, bulk carriers, and RoRo ships.