Data augmentation and semi-supervised learning for deep neural networks-based text classifier

User feedback is essential for understanding user needs. In this paper, we use free-text obtained from a survey on sleep-related issues to build a deep neural networks-based text classifier. However, to train the deep neural networks model, a lot of labelled data is needed. To reduce manual data labelling, we propose a method which is a combination of data augmentation and pseudo-labelling: data augmentation is applied to labelled data to increase the size of the initial train set and then the trained model is used to annotate unlabelled data with pseudo-labels. The result shows that the model with the data augmentation achieves macro-averaged f1 score of 65.2% while using 4,300 training data, whereas the model without data augmentation achieves macro-averaged f1 score of 68.2% with around 14,000 training data. Furthermore, with the combination of pseudo-labelling, the model achieves macro-averaged f1 score of 62.7% with only using 1,400 training data with labels. In other words, with the proposed method we can reduce the amount of labelled data for training while achieving relatively good performance

Using Augmented Reality as a Medium to Assist Teaching in Higher Education

In this paper we describe the use of a high-level augmented reality (AR) interface for the construction of collaborative educational applications that can be used in practice to enhance current teaching methods. A combination of multimedia information including spatial three-dimensional models, images, textual information, video, animations and sound, can be superimposed in a student-friendly manner into the learning environment. In several case studies different learning scenarios have been carefully designed based on human-computer interaction principles so that meaningful virtual information is presented in an interactive and compelling way. Collaboration between the participants is achieved through use of a tangible AR interface that uses marker cards as well as an immersive AR environment which is based on software user interfaces (UIs) and hardware devices. The interactive AR interface has been piloted in the classroom at two UK universities in departments of Informatics and Information Science

Influence of augmented humans in online interactions during voting events

The advent of the digital era provided a fertile ground for the development of virtual societies, complex systems influencing real-world dynamics. Understanding online human behavior and its relevance beyond the digital boundaries is still an open challenge. Here we show that online social interactions during a massive voting event can be used to build an accurate map of real-world political parties and electoral ranks. We provide evidence that information flow and collective attention are often driven by a special class of highly influential users, that we name "augmented humans", who exploit thousands of automated agents, also known as bots, for enhancing their online influence. We show that augmented humans generate deep information cascades, to the same extent of news media and other broadcasters, while they uniformly infiltrate across the full range of identified groups. Digital augmentation represents the cyber-physical counterpart of the human desire to acquire power within social systems.Comment: 11 page

Knowledge Graph semantic enhancement of input data for improving AI

Intelligent systems designed using machine learning algorithms require a large number of labeled data. Background knowledge provides complementary, real world factual information that can augment the limited labeled data to train a machine learning algorithm. The term Knowledge Graph (KG) is in vogue as for many practical applications, it is convenient and useful to organize this background knowledge in the form of a graph. Recent academic research and implemented industrial intelligent systems have shown promising performance for machine learning algorithms that combine training data with a knowledge graph. In this article, we discuss the use of relevant KGs to enhance input data for two applications that use machine learning -- recommendation and community detection. The KG improves both accuracy and explainability

Sensory augmentation and the tactile sublime

This paper responds to recent developments in the field of sensory augmentation by analysing several technological devices that augment the sensory apparatus using the tactile sense. First, I will define the term sensory augmentation, as the use of technological modification to enhance the sensory apparatus, and elaborate on the preconditions for successful tactile sensory augmentation. These are the adaptability of the brain to unfamiliar sensory input and the specific qualities of the skin lending themselves to be used for the perception of additional sensory information. Two devices, Moon Ribas’ Seismic Sense and David Eagleman’s vest, will then be discussed as potential facilitators of aesthetic experiences in virtue of the tactile sensory augmentation that these devices allow. I will connect the experiences afforded by these devices to the Kantian categories of the mathematical and the dynamical sublime, and to existing accounts of tactile sublimity. Essentially, the objects these devices make sensible, earthquakes for the Seismic Sense and digital information for the vest, produce pleasurable feelings of potential danger, awe, and respect. The subsequent acclimation to this new way of sensing and the aim to comprehend its sensed object are then discussed as possible objections to the interpretation of these experiences as sublime, and as aesthetic in general. To exemplify these issues and concretise my thesis of tactile sensory augmentation as a trigger of the sublime, I will outline an experiment to use the vest as an aid for faster decision making on the stock market