CentralNet: a Multilayer Approach for Multimodal Fusion

This paper proposes a novel multimodal fusion approach, aiming to produce best possible decisions by integrating information coming from multiple media. While most of the past multimodal approaches either work by projecting the features of different modalities into the same space, or by coordinating the representations of each modality through the use of constraints, our approach borrows from both visions. More specifically, assuming each modality can be processed by a separated deep convolutional network, allowing to take decisions independently from each modality, we introduce a central network linking the modality specific networks. This central network not only provides a common feature embedding but also regularizes the modality specific networks through the use of multi-task learning. The proposed approach is validated on 4 different computer vision tasks on which it consistently improves the accuracy of existing multimodal fusion approaches

Opinion-Mining on Marglish and Devanagari Comments of YouTube Cookery Channels Using Parametric and Non-Parametric Learning Models

YouTube is a boon, and through it people can educate, entertain, and express themselves about various topics. YouTube India currently has millions of active users. As there are millions of active users it can be understood that the data present on the YouTube will be large. With India being a very diverse country, many people are multilingual. People express their opinions in a code-mix form. Code-mix form is the mixing of two or more languages. It has become a necessity to perform Sentiment Analysis on the code-mix languages as there is not much research on Indian code-mix language data. In this paper, Sentiment Analysis (SA) is carried out on the Marglish (Marathi + English) as well as Devanagari Marathi comments which are extracted from the YouTube API from top Marathi channels. Several machine-learning models are applied on the dataset along with 3 different vectorizing techniques. Multilayer Perceptron (MLP) with Count vectorizer provides the best accuracy of 62.68% on the Marglish dataset and Bernoulli Naïve Bayes along with the Count vectorizer, which gives accuracy of 60.60% on the Devanagari dataset. Multilayer Perceptron and Bernoulli Naïve Bayes are considered to be the best performing algorithms. 10-fold cross-validation and statistical testing was also carried out on the dataset to confirm the results

Insurance Meets Sentiment: An Empirical Study of Attitudes Toward Life, Health, and P&C Insurances

Sentiment Analysis, an up-and-coming subfield of Natural Language Processing (NLP), contains previously untapped potential that can be utilized to drive better business decision making. In this paper, we employ state-of-the-art sentiment analysis tools to compare the performances of traditional classification algorithms – namely Support Vector Machines (SVMs), bagging, boosting, random forest, and decision tree classifiers – on insurance-related textual data. We successfully demonstrate that algorithms such as bagging and boosting, which were constructed to enhance the performance of simpler algorithms such as decision tree classifiers, offer only marginal improvements in terms of classification accuracy and certain performance metrics for our data. However, the improved accuracy comes as the cost of slightly higher runtimes. Insurance companies could apply these findings to choose suitable algorithms and gain a more nuanced understanding of the needs of their insureds. Index Terms— Sentiment Analysis, Textual Analysis, Machine Learning, Natural Language Processing (NLP), Opinion Mining (OM

Leveraging Sociological Models for Predictive Analytics

Abstract—There is considerable interest in developing techniques for predicting human behavior, for instance to enable emerging contentious situations to be forecast or the nature of ongoing but “hidden ” activities to be inferred. A promising approach to this problem is to identify and collect appropriate empirical data and then apply machine learning methods to these data to generate the predictions. This paper shows the performance of such learning algorithms often can be improved substantially by leveraging sociological models in their development and implementation. In particular, we demonstrate that sociologically-grounded learning algorithms outperform gold-standard methods in three important and challenging tasks: 1.) inferring the (unobserved) nature of relationships in adversarial social networks, 2.) predicting whether nascent social diffusion events will “go viral”, and 3.) anticipating and defending future actions of opponents in adversarial settings. Significantly, the new algorithms perform well even when there is limited data available for their training and execution. Keywords—predictive analysis, sociological models, social networks, empirical analysis, machine learning. I

Multimodal human machine interactions in industrial environments

This chapter will present a review of Human Machine Interaction techniques for industrial applications. A set of recent HMI techniques will be provided with emphasis on multimodal interaction with industrial machines and robots. This list will include Natural Language Processing techniques and others that make use of various complementary interfaces: audio, visual, haptic or gestural, to achieve a more natural human-machine interaction. This chapter will also focus on providing examples and use cases in fields related to multimodal interaction in manufacturing, such as augmented reality. Accordingly, the chapter will present the use of Artificial Intelligence and Multimodal Human Machine Interaction in the context of STAR applications