Hate speech detection on Twitter is often treated in monolingual (in English generally) ignoring the fact that Twitter is a global platform where everyone expresses himself with his natal language. In this paper, we created a model which, taking benefits of the advantages of neural networks, classifies tweets written in seven different languages (and even those that contains more than one language at the same time) to hate speech or non hate speech. We used Convolutional Neural Networks (CNN) and character level representation. We carried out several experiments in order to adjust the parameters according to our case study. Our best results were (in terms of accuracy) 0.8893 for a dataset containing five languages and 0.8300 for a dataset of seven languages. Our model solves properly the problem of hate speech on Twitter and its results are, compared to the state of the art, more than satisfactory.
Computation offloading is the solution for IoT devices of limited resources and high-cost processing requirements. However, the network related issues such as latency and bandwidth consumption need to be considered. Data transmission reduction is one of the solutions aiming to solve network related problems by reducing the amount of data transmitted. In this paper, we propose a generalized formal data transmission reduction model independent of the system and the data type. This formalization is based on two main ideas: 1) Not sending data until a significant change occurs, 2) Sending a lighter size entity permitting the cloud to deduct the data captured by the IoT device without actually receiving it. This paper includes the mathematical representation of the model, general evaluation metrics formulas as well as detailed projections on real world use cases.
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