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Exploring the Design of Vibrotactile Cues for Visio-Haptic Sensory Substitution

Description

This paper presents the design and evaluation of a haptic interface for augmenting human-human interpersonal interactions by delivering facial expressions of an interaction partner to an individual who is blind using a visual-to-tactile mapping of facial action units and emotions.

This paper presents the design and evaluation of a haptic interface for augmenting human-human interpersonal interactions by delivering facial expressions of an interaction partner to an individual who is blind using a visual-to-tactile mapping of facial action units and emotions. Pancake shaftless vibration motors are mounted on the back of a chair to provide vibrotactile stimulation in the context of a dyadic (one-on-one) interaction across a table. This work explores the design of spatiotemporal vibration patterns that can be used to convey the basic building blocks of facial movements according to the Facial Action Unit Coding System. A behavioral study was conducted to explore the factors that influence the naturalness of conveying affect using vibrotactile cues.

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2014-05

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Facial Expression Recognition Using Machine Learning

Description

In recent years, the development of new Machine Learning models has allowed for new technological advancements to be introduced for practical use across the world. Multiple studies and experiments have been conducted to create new variations of Machine Learning models

In recent years, the development of new Machine Learning models has allowed for new technological advancements to be introduced for practical use across the world. Multiple studies and experiments have been conducted to create new variations of Machine Learning models with different algorithms to determine if potential systems would prove to be successful. Even today, there are still many research initiatives that are continuing to develop new models in the hopes to discover potential solutions for problems such as autonomous driving or determining the emotional value from a single sentence. One of the current popular research topics for Machine Learning is the development of Facial Expression Recognition systems. These Machine Learning models focus on classifying images of human faces that are expressing different emotions through facial expressions. In order to develop effective models to perform Facial Expression Recognition, researchers have gone on to utilize Deep Learning models, which are a more advanced implementation of Machine Learning models, known as Neural Networks. More specifically, the use of Convolutional Neural Networks has proven to be the most effective models for achieving highly accurate results at classifying images of various facial expressions. Convolutional Neural Networks are Deep Learning models that are capable of processing visual data, such as images and videos, and can be used to identify various facial expressions. The purpose of this project, I focused on learning about the important concepts of Machine Learning, Deep Learning, and Convolutional Neural Networks to implement a Convolutional Neural Network that was previously developed by a recommended research paper.

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2020-05

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Multimodal Data Analysis of Dyadic Interactions for an Automated Feedback System Supporting Parent Implementation of Pivotal Response Treatment

Description

Parents fulfill a pivotal role in early childhood development of social and communication

skills. In children with autism, the development of these skills can be delayed. Applied

behavioral analysis (ABA) techniques have been created to aid in skill acquisition.

Among these, pivotal response

Parents fulfill a pivotal role in early childhood development of social and communication

skills. In children with autism, the development of these skills can be delayed. Applied

behavioral analysis (ABA) techniques have been created to aid in skill acquisition.

Among these, pivotal response treatment (PRT) has been empirically shown to foster

improvements. Research into PRT implementation has also shown that parents can be

trained to be effective interventionists for their children. The current difficulty in PRT

training is how to disseminate training to parents who need it, and how to support and

motivate practitioners after training.

Evaluation of the parents’ fidelity to implementation is often undertaken using video

probes that depict the dyadic interaction occurring between the parent and the child during

PRT sessions. These videos are time consuming for clinicians to process, and often result

in only minimal feedback for the parents. Current trends in technology could be utilized to

alleviate the manual cost of extracting data from the videos, affording greater

opportunities for providing clinician created feedback as well as automated assessments.

The naturalistic context of the video probes along with the dependence on ubiquitous

recording devices creates a difficult scenario for classification tasks. The domain of the

PRT video probes can be expected to have high levels of both aleatory and epistemic

uncertainty. Addressing these challenges requires examination of the multimodal data

along with implementation and evaluation of classification algorithms. This is explored

through the use of a new dataset of PRT videos.

The relationship between the parent and the clinician is important. The clinician can

provide support and help build self-efficacy in addition to providing knowledge and

modeling of treatment procedures. Facilitating this relationship along with automated

feedback not only provides the opportunity to present expert feedback to the parent, but

also allows the clinician to aid in personalizing the classification models. By utilizing a

human-in-the-loop framework, clinicians can aid in addressing the uncertainty in the

classification models by providing additional labeled samples. This will allow the system

to improve classification and provides a person-centered approach to extracting

multimodal data from PRT video probes.

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Created

Date Created
2019

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Language Image Transformer

Description

Humans perceive the environment using multiple modalities like vision, speech (language), touch, taste, and smell. The knowledge obtained from one modality usually complements the other. Learning through several modalities helps in constructing an accurate model of the environment. Most of

Humans perceive the environment using multiple modalities like vision, speech (language), touch, taste, and smell. The knowledge obtained from one modality usually complements the other. Learning through several modalities helps in constructing an accurate model of the environment. Most of the current vision and language models are modality-specific and, in many cases, extensively use deep-learning based attention mechanisms for learning powerful representations. This work discusses the role of attention in associating vision and language for generating shared representation. Language Image Transformer (LIT) is proposed for learning multi-modal representations of the environment. It uses a training objective based on Contrastive Predictive Coding (CPC) to maximize the Mutual Information (MI) between the visual and linguistic representations. It learns the relationship between the modalities using the proposed cross-modal attention layers. It is trained and evaluated using captioning datasets, MS COCO, and Conceptual Captions. The results and the analysis offers a perspective on the use of Mutual Information Maximisation (MIM) for generating generalizable representations across multiple modalities.

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Date Created
2020

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Generalized Domain Adaptation for Visual Domains

Description

Humans have a great ability to recognize objects in different environments irrespective of their variations. However, the same does not apply to machine learning models which are unable to generalize to images of objects from different domains. The generalization of

Humans have a great ability to recognize objects in different environments irrespective of their variations. However, the same does not apply to machine learning models which are unable to generalize to images of objects from different domains. The generalization of these models to new data is constrained by the domain gap. Many factors such as image background, image resolution, color, camera perspective and variations in the objects are responsible for the domain gap between the training data (source domain) and testing data (target domain). Domain adaptation algorithms aim to overcome the domain gap between the source and target domains and learn robust models that can perform well across both the domains.

This thesis provides solutions for the standard problem of unsupervised domain adaptation (UDA) and the more generic problem of generalized domain adaptation (GDA). The contributions of this thesis are as follows. (1) Certain and Consistent Domain Adaptation model for closed-set unsupervised domain adaptation by aligning the features of the source and target domain using deep neural networks. (2) A multi-adversarial deep learning model for generalized domain adaptation. (3) A gating model that detects out-of-distribution samples for generalized domain adaptation.

The models were tested across multiple computer vision datasets for domain adaptation.

The dissertation concludes with a discussion on the proposed approaches and future directions for research in closed set and generalized domain adaptation.

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Date Created
2020

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Neural Network Architecture with External Memory and Domain-aware Weight Switching Mechanism

Description

Humans have an excellent ability to analyze and process information from multiple domains. They also possess the ability to apply the same decision-making process when the situation is familiar with their previous experience.

Inspired by human's ability to remember past

Humans have an excellent ability to analyze and process information from multiple domains. They also possess the ability to apply the same decision-making process when the situation is familiar with their previous experience.

Inspired by human's ability to remember past experiences and apply the same when a similar situation occurs, the research community has attempted to augment memory with Neural Network to store the previously learned information. Together with this, the community has also developed mechanisms to perform domain-specific weight switching to handle multiple domains using a single model. Notably, the two research fields work independently, and the goal of this dissertation is to combine their capabilities.

This dissertation introduces a Neural Network module augmented with two external memories, one allowing the network to read and write the information and another to perform domain-specific weight switching. Two learning tasks are proposed in this work to investigate the model performance - solving mathematics operations sequence and action based on color sequence identification. A wide range of experiments with these two tasks verify the model's learning capabilities.

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Date Created
2020

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Incremental Learning With Sample Generation From Pretrained Networks

Description

In the last decade deep learning based models have revolutionized machine learning and computer vision applications. However, these models are data-hungry and training them is a time-consuming process. In addition, when deep neural networks are updated to augment their prediction

In the last decade deep learning based models have revolutionized machine learning and computer vision applications. However, these models are data-hungry and training them is a time-consuming process. In addition, when deep neural networks are updated to augment their prediction space with new data, they run into the problem of catastrophic forgetting, where the model forgets previously learned knowledge as it overfits to the newly available data. Incremental learning algorithms enable deep neural networks to prevent catastrophic forgetting by retaining knowledge of previously observed data while also learning from newly available data.

This thesis presents three models for incremental learning; (i) Design of an algorithm for generative incremental learning using a pre-trained deep neural network classifier; (ii) Development of a hashing based clustering algorithm for efficient incremental learning; (iii) Design of a student-teacher coupled neural network to distill knowledge for incremental learning. The proposed algorithms were evaluated using popular vision datasets for classification tasks. The thesis concludes with a discussion about the feasibility of using these techniques to transfer information between networks and also for incremental learning applications.

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Date Created
2020

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Deep domain fusion for adaptive image classification

Description

Endowing machines with the ability to understand digital images is a critical task for a host of high-impact applications, including pathology detection in radiographic imaging, autonomous vehicles, and assistive technology for the visually impaired. Computer vision systems rely on large

Endowing machines with the ability to understand digital images is a critical task for a host of high-impact applications, including pathology detection in radiographic imaging, autonomous vehicles, and assistive technology for the visually impaired. Computer vision systems rely on large corpora of annotated data in order to train task-specific visual recognition models. Despite significant advances made over the past decade, the fact remains collecting and annotating the data needed to successfully train a model is a prohibitively expensive endeavor. Moreover, these models are prone to rapid performance degradation when applied to data sampled from a different domain. Recent works in the development of deep adaptation networks seek to overcome these challenges by facilitating transfer learning between source and target domains. In parallel, the unification of dominant semi-supervised learning techniques has illustrated unprecedented potential for utilizing unlabeled data to train classification models in defiance of discouragingly meager sets of annotated data.

In this thesis, a novel domain adaptation algorithm -- Domain Adaptive Fusion (DAF) -- is proposed, which encourages a domain-invariant linear relationship between the pixel-space of different domains and the prediction-space while being trained under a domain adversarial signal. The thoughtful combination of key components in unsupervised domain adaptation and semi-supervised learning enable DAF to effectively bridge the gap between source and target domains. Experiments performed on computer vision benchmark datasets for domain adaptation endorse the efficacy of this hybrid approach, outperforming all of the baseline architectures on most of the transfer tasks.

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Date Created
2019