Graph-Based Mutations for Music Generation

Our study aims to compare the effects of direct mutation and graphbased mutation on representations of music domain. We focus on short tunes from the Irish folk tradition, represented as integer sequences, and use a graph-based representation based on Pathway Assembly (a directed acyclic graph) and the Sequitur algorithm. We define multiple mutation operators to work directly on the sequences or on the graphs, hypothesizing that graph-based mutations will tend to preserve the pattern used per tune, while direct mutation of sequences will tend to destroy patterns, resulting in new generated tunes that are more complex. We perform experiments on a corpus of tunes and apply the mutation operators many times consecutively to analyze their effects

Exploring the Personality of Virtual Tutors in Conversational Foreign Language Practice

Fluid interaction between virtual agents and humans requires the understanding of many issues of conversational pragmatics. One such issue is the interaction between communication strategy and personality. As a step towards developing models of personality driven pragmatics policies, in this paper, we present our initial experiment to explore differences in user interaction with two contrasting avatar personalities. Each user saw a single personality in a video-call setting and gave feedback on the interaction. Our expectations, that a more extroverted outgoing positive personality would be a more successful tutor, were only partially confirmed. While this personality did induce longer conversations in the participants, we found that interactions with both were enjoyed and that user perception of them differed less than intended

Finally a Case for Collaborative VR?: The Need to Design for Remote Multi-Party Conversations

Amid current social distancing measures requiring people to work from home, there has been renewed interest on how to effectively converse and collaborate remotely utilizing currently available technologies. On the surface, VR provides a perfect platform for effective remote communication. It can transfer contextual and environmental cues and facilitate a shared perspective while also allowing people to be virtually co-located. Yet we argue that currently VR is not adequately designed for such a communicative purpose. In this paper, we outline three key barriers to using VR for conversational activity : (1) variability of social immersion, (2) unclear user roles, and (3) the need for effective shared visual reference. Based on this outline, key design topics are discussed through a user experience design perspective for considerations in a future collaborative design framework

FAIR4PGHD: A framework for FAIR implementation over PGHD

Patient Generated Health Data (PGHD) are being considered for integration with health facilities, however little is known about how such data can be made machine-actionable in a way that meets FAIR guidelines. This article proposes a 5-stage framework that can be used to achieve this

Comparing Abstractive Summaries Generated by ChatGPT to Real Summaries Through Blinded Reviewers and Text Classification Algorithms

Large Language Models (LLMs) have gathered significant attention due to their impressive performance on a variety of tasks. ChatGPT, developed by OpenAI, is a recent addition to the family of language models and is being called a disruptive technology by a few, owing to its human-like text-generation capabilities. Although, many anecdotal examples across the internet have evaluated ChatGPT's strength and weakness, only a few systematic research studies exist. To contribute to the body of literature of systematic research on ChatGPT, we evaluate the performance of ChatGPT on Abstractive Summarization by the means of automated metrics and blinded human reviewers. We also build automatic text classifiers to detect ChatGPT generated summaries. We found that while text classification algorithms can distinguish between real and generated summaries, humans are unable to distinguish between real summaries and those produced by ChatGPT

Cough Monitoring Through Audio Analysis

The detection of cough events in audio recordings requires the analysis of a significant amount of data as cough is typically monitored continuously over several hours to capture naturally occurring cough events. The recorded data is mostly composed of undesired sound events such as silence, background noise, and speech. To reduce computational costs and to address the ethical concerns raised from the collection of audio data in public environments, the data requires pre-processing prior to any further analysis. Current cough detection algorithms typically use pre-processing methods to remove undesired audio segments from the collected data but do not preserve the privacy of individuals being recorded while monitoring respiratory events. This study reveals the need for an automatic pre-processing method that removes sensitive data from the recording prior to any further analysis to ensure privacy preservation of individuals. Specific characteristics of cough sounds can be used to discard sensitive data from audio recordings at a pre-processing stage, improving privacy preservation, and decreasing ethical concerns when dealing with cough monitoring through audio analysis. We propose a pre-processing algorithm that increases privacy preservation and significantly decreases the amount of data to be analysed, by separating cough segments from other non-cough segments, including speech, in audio recordings. Our method verifies the presence of signal energy in both lower and higher frequency regions and discards segments whose energy concentrates only on one of them. The method is iteratively applied on the same data to increase the percentage of data reduction and privacy preservation. We evaluated the performance of our algorithm using several hours of audio recordings with manually pre-annotated cough and speech events. Our results showed that 5 iterations of the proposed method can discard up to 88.94% of the speech content present in the recordings, allowing for a strong privacy preservation while considerably reducing the amount of data to be further analysed by 91.79%. The data reduction and privacy preservation achievements of the proposed pre-processing algorithm offers the possibility to use larger datasets captured in public environments and would beneficiate all cough detection algorithms by preserving the privacy of subjects and by-stander conversations recorded during cough monitoring

Towards Exchanging Wearable-PGHD with EHRs: Developing a Standardized Information Model for Wearable-Based Patient Generated Health Data

Wearables have become commonplace for tracking and making sense of patient lifestyle, wellbeing and health data. Most of this tracking is done by individuals outside of clinical settings, however some data from wearables may be useful in a clinical context. As such, wearables may be considered a prominent source of Patient Generated Health Data (PGHD). Studies have attempted to maximize the use of the data from wearables including integrating with Electronic Health Records (EHRs). However, usually a limited number of wearables are considered for integration and, in many cases, only one brand is investigated. In addition, we find limited studies on integration of metadata including data quality and provenance, despite such data being very relevant for clinical decision making. This paper describes a proposed design and development of a generic information model for wearable based PGHD integration with EHRs. We propose a vendor-neutral model that can work with a wider range of wearables and discuss our proposed method to employ an ontology-based approach and provide insights to future work

An Empirical Study of Topic Transition in Dialogue

Transitioning between topics is a natural component of human-human dialog. Although topic transition has been studied in dialogue for decades, only a handful of corpora based studies have been performed to investigate the subtleties of topic transitions. Thus, this study annotates 215 conversations from the switchboard corpus and investigates how variables such as length, number of topic transitions, topic transitions share by participants and turns/topic are related. This work presents an empirical study on topic transition in switchboard corpus followed by modelling topic transition with a precision of 83% for in-domain(id) test set and 82% on 10 out-of-domain}(ood) test set. It is envisioned that this work will help in emulating human-human like topic transition in open-domain dialog systems.Comment: 5 pages, 4 figures, 3 table