Mobile Glaucoma Detection Application

Glaucoma is a debilitating optical degeneration disease that can lead to vision loss and eventually blindness. Given its asymptomatic nature, most people with Glaucoma aren’t even aware that they have the disease. As a result, the disease is often left untreated until it is too late. Detecting the presence of Glaucoma is one of the most important steps in treating Glaucoma, but is unfortunately also the most difficult to enforce. The Mobile Glaucoma Detection application aims to reduce the growing number of individuals who are unaware that they have Glaucoma by providing a simple detection mechanism to notify users if they are at risk. The system does this by enabling its users to independently conduct Tonometry exams through the application. Tonometry examinations allow doctors to determine if the intra-ocular pressure levels in a person’s eyes put them at risk for Glaucoma. The M.G.D.A(Mobile Glaucoma Detection Application) allows users to determine their intra-ocular pressure levels from the comfort of their own home via a special contact lens paired with a smartphone application. The system also offers users the opportunity to monitor, regulate, and track their use and progress through the system

The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems

This paper introduces the Ubuntu Dialogue Corpus, a dataset containing almost 1 million multi-turn dialogues, with a total of over 7 million utterances and 100 million words. This provides a unique resource for research into building dialogue managers based on neural language models that can make use of large amounts of unlabeled data. The dataset has both the multi-turn property of conversations in the Dialog State Tracking Challenge datasets, and the unstructured nature of interactions from microblog services such as Twitter. We also describe two neural learning architectures suitable for analyzing this dataset, and provide benchmark performance on the task of selecting the best next response.Comment: SIGDIAL 2015. 10 pages, 5 figures. Update includes link to new version of the dataset, with some added features and bug fixes. See: https://github.com/rkadlec/ubuntu-ranking-dataset-creato

Outcasts of the Universe: Shyness in Hawthorne and James

In Outcasts of the Universe, I argue that shyness is a modern dilemma, problematized by the broader shifts in American society: the expanding marketplace, the idealization of the self-made man, the rise of feminism and ever-changing gender roles, and a slow consolidation of the bachelor, the artist, and the aesthete into the stigmatized figure of the homosexual. By drawing on both the lives and works of Hawthorne and James, I theorize shyness as an alternative model of social and sexual engagement in the nineteenth century. In particular, I adapt the queer theory concept of closetedness, a concept that has no equivalent in heterosexual terms--unless it is shyness itself. In doing so, I contribute new insights to the fields of gender studies and to queer theory, both by expanding theories of the closet to heterosexual narratives and by exploring how closetedness might be psychologically overdetermined by shyness, melancholy, and introversion

Trajectories of Psychological Distress among Low-Income, Female Survivors of Hurricane Katrina

The purpose of this study was to investigate trajectories of psychological distress among low-income women, primarily unmarried and African American, who survived Hurricane Katrina (N = 386). Data were collected in the year prior to the hurricane, as well as approximately one and three years thereafter. Using Latent Class Growth Analysis (LCGA), we detected six distinct trajectory groups. Over half of participants fit into a trajectory consistent with resilience; that is, they maintained low levels of psychological distress over the course of the study, but experienced an elevation in symptoms at the first pre-disaster time point, followed by a return to pre-disaster levels. The other trajectories reflected the range in psychological responses to disasters, and suggested pre-disaster functioning as having a major influence on post-disaster psychological outcomes. Exposure to hurricane-related stressors, experiences of human and pet bereavement, perceived social support, and socioeconomic status were significant predictors of trajectory group membership. Based on these findings, we recommend policies that protect against hurricane exposure, promote the rebuilding of social support networks, and assist survivors in identifying employment and educational opportunities, as well as well as empirically supported clinical interventions that help survivors cope with longstanding or emergent symptoms. Further longitudinal quantitative studies, as well as qualitative analysis of survivors\u27 accounts of post-disaster psychological experiences, would advance our understanding of resilience and other trajectories of functioning in the aftermath of traumatic events

Towards an Automatic Turing Test: Learning to Evaluate Dialogue Responses

Automatically evaluating the quality of dialogue responses for unstructured domains is a challenging problem. Unfortunately, existing automatic evaluation metrics are biased and correlate very poorly with human judgements of response quality. Yet having an accurate automatic evaluation procedure is crucial for dialogue research, as it allows rapid prototyping and testing of new models with fewer expensive human evaluations. In response to this challenge, we formulate automatic dialogue evaluation as a learning problem. We present an evaluation model (ADEM) that learns to predict human-like scores to input responses, using a new dataset of human response scores. We show that the ADEM model's predictions correlate significantly, and at a level much higher than word-overlap metrics such as BLEU, with human judgements at both the utterance and system-level. We also show that ADEM can generalize to evaluating dialogue models unseen during training, an important step for automatic dialogue evaluation.Comment: ACL 201

A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues

Sequential data often possesses a hierarchical structure with complex dependencies between subsequences, such as found between the utterances in a dialogue. In an effort to model this kind of generative process, we propose a neural network-based generative architecture, with latent stochastic variables that span a variable number of time steps. We apply the proposed model to the task of dialogue response generation and compare it with recent neural network architectures. We evaluate the model performance through automatic evaluation metrics and by carrying out a human evaluation. The experiments demonstrate that our model improves upon recently proposed models and that the latent variables facilitate the generation of long outputs and maintain the context.Comment: 15 pages, 5 tables, 4 figure