Identification of previously unseen Asian elephants using visual data and semi-supervised learning

This paper presents a novel method to identify unseen Asian elephants that are not previously captured or identified in available data sets and re-identify previously seen Asian elephants using images of elephant ears, leveraging a semi-supervised learning approach. Ear patterns of unseen elephants are learnt for future re-identification. To aid our process, elephant ear patterns are used as a biomarker to uniquely identify individual Asian elephant, each of which is attached a descriptor. The main challenge is to learn and use a clustering technique to identify new classes (i.e., elephants) in unlabelled elephant ear image sets and leveraging this data in verifying the labelled images. This study proposes a systematic approach to address the problem to uniquely identify elephants, where we developed: (a) a self-supervised learning approach for training the representation of labelled and unlabelled image data to avoid unWanted, bias labelled data, (b) rank statistics for transferring the models’ knowledge of the labelled classes when clustering the unlabelled images, and, (c) improving the identification accuracy of both the classification and clustering algorithms by introducing a optimization problem when training with the data representation on the labelled and unlabelled image data sets. This approach was evaluated on seen (labelled) and unseen (unlabelled) elephants, where we achieved a significant accuracy of 86.89% with an NMI (Normalized Mutual Information) score of 0.9132 on identifying seen elephants. Similarly, an accuracy of 54.29% with an NMI score of 0.6250 was achieved on identifying unseen elephants from the unlabelled Asian elephant ear image data set. Findings of this research provides the ability to accurately identify elephants without having expert knowledge on the field. Our method can be used to uniquely identify elephants from their herds and then use it to track their travel patterns Which is greatly applicable in understanding the social organization of elephant herds, individual behavioural patterns, and estimating demographic parameters as a measure to reducing the human-elephant conflict in Sri Lanka

Modelling and prediction of pain related neural firings using deep learning

We propose a deep learning approach to model and predict pain related neural firings from EEG data. In particular, we target for the first time differentiation between acute and chronic pain. Our modelling strategy followed three steps: 1) Feature extraction of EEG data using Petrosian Fractal Dimension (PFD) and Hjorth activity functions. 2) Source localization of neural firings to differentiate between acute and chronic pain. 3) Modelling and training of a deep learning model for the prediction of the related pain according to the feature extracted neural firings. Based on our results, an occipital brain activation for chronic pain and a temporal activation in the case of acute pain were recognized. Moreover, our long short-term memory (LSTM) based prediction model achieved an accuracy of 91.29% for identification of related pain. The performance of the model was evaluated using precision, recall and F1 scores. For acute pain it achieved scores of 0.90, 0.82, 0.86 and for chronic pain scores of 0.86, 0.93, 0.89 respectively. It is concluded that our approach not only shows better predictive accuracy than the results reported by previous studies, but also represents an important step towards identifying and evaluating pain when patients are incapable of self-reporting it or when the clinical observations are unobtainable or unreliable

A critical analysis of music recommendation systems and new perspectives

Many businesses enhance on-line user experience using various recommender systems which have a growing innovation and research interest. Recommender systems in music streaming applications proactively suggest new selections to users by attempting to predict user preferences. While current music recommendation systems help users to efficiently discover fascinating music, challenges remain in this research area. This paper presents a critical analysis of current music recommender systems and proposes a new hybrid recommender system with efficient and enhanced prediction capabilities

An augmented reality-based fashion design interface with artistic contents generated using deep generative models

Fashion design is an art that reshapes the designers’ imagination into visible content which requires a significant amount of time and effort. The assistance provided by the available design tools are limited in the sense of visualizing and fitting of the generated cloth on the human body. We present, ARGAN-an Augmented Reality (AR) based Fashion Design system which is able to generate a new dress when a sketch and a theme image are provided as the input into a Controllable Generative Adversarial Network. Further, this system can visualize the generated virtual 2D apparel in realtime on a real human body using Augmented Reality. To the best of our knowledge, this work is the first attempt at utilizing Deep Generative Models (e.g. GANs) in an Augmented Reality prototype in fashion designing for generate creative fashion content in 2D and exploiting the possibility of Deep Generative Models to generate fashion designs align to a theme. Our findings show that the use of the ARGAN can support fashion designers’ during their designing process

The use of augmented reality to deliver enhanced user experiences in fashion industry

With the advancement of new technologies, industries are developing rapidly. Among them, the fashion industry is a vast area that involves the production of raw materials, the production of fashion goods by designers, and retail sales. One of the most contributed technologies used in industry is Augmented reality. The use of new technologies can address the limitations of traditional fashion experience and enhance user experience. Undertaking a comparative thematic analysis of AR research in the fashion industry, this paper considers how AR applications evolve to enhance designer skills and knowledge throughout the past decades and the customization of clothes by users themselves to make a satisfactory and comfortable product. Secondly, the paper considers the enhancement of customer experience by analyzing how clothing retail has progressed with the adoption of AR applications. Finally, we have concluded the review paper by addressing the future research ideas. The results of the review show that possible improvements can be done for fashion designing and enhancing customer experience using AR and hybrid technologies

New interaction tools for preserving an old language

The Penan people of Malaysian Borneo were traditionally nomads of the rainforest. They would leave messages in the jungle for each other by shaping natural objects into language tokens and arranging these symbols in specific ways – much like words in a sentence. With settlement, the language is being lost as it is not being used by the younger generation. We report here, a tangible system designed to help the Penans preserve their unique object writing language. The key features of the system are that: the tangibles are made of real objects; it works in the wild; and new tangibles can be fabricated and added to the system by the users. Our evaluations show that the system is engaging and encourages intergenerational knowledge transfer, thus has the potential to help preserve this language

Confucius computer: a philosophical digital agent for intergenerational philosophical play

Confucianism is commonly defined as “... a system of philosophical, ethical and political thought based on the teachings of Confucius,” which originated through the teachings of Confucius during the sixth-century BCE. It is a way of life or a philosophy of human nature that considers human relationships as the foundation of the society. Confucius teachings had highly influenced the development of several cultures in Asia, making Confucianism an intangible cultural heritage. In this paper, we are re-acquainting users with an intangible heritage that is part of their everyday, by developing a system that permits experiencing Confucius teachings virtually and interactively. The system can measure philosophical intent of the human and generate meaningful philosophical answers. It is also aimed for intergenerational sharing of Confucius heritage through a simple interactive process with the virtual sage making the experience enjoyable and entertaining. Previous research in natural language processing (NLP) mainly focused on the understanding and delivering of human natural language accurately. In this research, we explored how to apply NLP to model the knowledge and teachings of Confucius, through the natural conversation between human and computer. This virtual Confucius, a chat agent that generates outputs based on Confucius teachings, using a series of algorithms and techniques to improve the matching accuracy between user input and computer output, introduces a novel way of interacting with intangible cultures. Our user evaluation results revealed that there is a positive correlation between relevance and enjoyment, finding their experiences interacting with virtual Confucius very encouraging. Adults who participated in experiencing the virtual Confucius together with their children believed that this system has the potential to improve intergenerational interactions through shared play