CAMBRIA E.

Learning paradigms that use random basis functions provide effective tools to deal with large datasets, as they combine efficient training algorithms with remarkable generalization performances. The paper first considers the affinity between the paradigm of learning with similarity functions and the Extreme Learning Machine (ELM) model, and...

Thanks to recent advances in machine learning, some say AI is the new engine and data is the new coal. Mining this 'coal' from the ever-growing Social Web, however, can be a formidable task. In this work, we address this problem in the context of sentiment analysis using convolutional online adaptation learning (COAL). In particular, we...

The availability of new datasets and deep learning techniques have led to a surge of effort directed towards the creation of new models that can exploit the large amount of data. However, little attention has been given to the development of models that are not only accurate, but also suitable for user-specific use or geared towards...

Deep convolutional neural networks (CNNs) provide an effective tool to extract complex information from images. In the area of image polarity detection, CNNs are customarily utilized in combination with transfer learning techniques to tackle a major problem: the unavailability of large sets of labeled data. Thus, polarity predictors in general...

Emotion recognition, among other natural language processing tasks, has greatly benefited from the use of large transformer models. Deploying these models on resource-constrained devices, however, is a major challenge due to their computational cost. In this paper, we show that the combination of large transformers, as high-quality feature...

Expressiveness varies from one person to another. Most images posted on Twitter lack good labels and the accompanying tweets have a lot of noise. Hence, in this paper we identify the contents and sentiments in images through the fusion of both image and text features. We leverage on the fact that AlexNet is a pre-trained model with great...

In the past few years, the use of transformer-based models has experienced increasing popularity as new state-of-the-art performance was achieved in several natural language processing tasks. As these models are often extremely large, however, their use for applications within embedded devices may not be feasible. In this work, we look at one...

Forecasting stock market behavior is an interesting and challenging problem. Regression of prices and classification of daily returns have been widely studied with the main goal of supplying forecasts useful in real trading scenarios. Unfortunately, the outcomes are not directly related with the maximization of the financial gain. Firstly, the...

Stock market prediction is one of the most challenging problems which has been distressing both researchers and financial analysts for more than half a century. To tackle this problem, two completely opposite approaches, namely technical and fundamental analysis, emerged. Technical analysis bases its predictions on mathematical indicators...

Predicting stock market movements is an interesting and challenging problem: Researchers and traders have approached this task with different techniques, from time series prediction to technical and fundamental analysis. Nowadays, a huge amount of textual data can be used to lead a new source of information on this task, well known to be highly...

Over the last twenty years, researchers and practitioners have attempted in many ways to effectively predict market trends. Till date, however, no satisfactory solution has been found. Many approaches have been applied to predict market trends, from technical analysis to fundamental analysis passing through sentiment analysis. A promising...