Identifying which words present in a text may be difficult to understand by common readers is well-known subtask complexity analysis. The advent of deep language models has also established the new state-of-the-art this task means end-to-end semi-supervised (pre-trained) and downstream training of, mainly, transformer-based neural networks. Nevertheless, usefulness traditional linguistic featur...

In this paper we present a Convolutional Neural Network for multilingual emotion recognition from spoken sentences. The purpose of work was to build model capable recognising emotions combining textual and acoustic information compatible with multiple languages. derive has an end-to-end deep architecture, hence it takes raw text audio data uses convolutional layers extract hierarchy classificat...

Probabilistic logic programs without negation can have cycles (with a preference for false), but cannot represent all conditional distributions. Probabilistic logic programs with negation can represent arbitrary conditional probabilities, but with cycles they create logical inconsistencies. We show how allowing negative noise probabilities allows us to represent arbitrary conditional probabilit...

In this study, we introduce a new approach to combine multi-classifiers in an ensemble system. Instead of using numeric membership values encountered in fixed combining rules, we construct interval membership values associated with each class prediction at the level of meta-data of observation by using concepts of information granules. In the proposed method, uncertainty (diversity) of findings...

in the last two decade the use of aerial laser scanner (als) or lidar (light detection and ranging) sensor in geomatics engineering and surveying application has augmented significantly . the main reason of the mentioned phenomenon is the reliability and accuracy of the data obtained by lidar sensors. the output of lidar is unclassified 3d point cloud. classification of the lidar point clouds i...