Detecting real user tasks by training on laboratory contextual attention metadata

Detecting the current task of a user is essential for providing her with contextualized and personalized support, and using Contextual Attention Metadata (CAM) can help doing so. Some recent approaches propose to perform automatic user task detection by means of task classifiers using such metadata. In this paper, we show that good results can be achieved by training such classifiers offline on CAM gathered in laboratory settings. We also isolate a combination of metadata features that present a significantly better discriminative power than classical ones

Lesbian women choosing motherhood: the journey to conception

Increasingly, lesbian women are choosing to have children in the context of a same-sex relationship, and their journey to conception and on to motherhood involves a range of decisions that are unique to lesbian couples. While creating a de novo family is burdened with decisions, choosing to be parents was a deliberate and conscious decision made by lesbian women participating in our study. The findings presented in this article focus on choosing which partner would be pregnant, donor decisions, as well as methods of conception used by lesbian women participating in a qualitative study that examined the experiences of lesbian mothers in Australia. This article is not intended to be interpretive, but rather a description of the processes engaged by participants

Maximum-Likelihood Sequence Detection of Multiple Antenna Systems over Dispersive Channels via Sphere Decoding

Multiple antenna systems are capable of providing high data rate transmissions over wireless channels. When the channels are dispersive, the signal at each receive antenna is a combination of both the current and past symbols sent from all transmit antennas corrupted by noise. The optimal receiver is a maximum-likelihood sequence detector and is often considered to be practically infeasible due to high computational complexity (exponential in number of antennas and channel memory). Therefore, in practice, one often settles for a less complex suboptimal receiver structure, typically with an equalizer meant to suppress both the intersymbol and interuser interference, followed by the decoder. We propose a sphere decoding for the sequence detection in multiple antenna communication systems over dispersive channels. The sphere decoding provides the maximum-likelihood estimate with computational complexity comparable to the standard space-time decision-feedback equalizing (DFE) algorithms. The performance and complexity of the sphere decoding are compared with the DFE algorithm by means of simulations

The Programmer's Assistant: Conversational Interaction with a Large Language Model for Software Development

Large language models (LLMs) have recently been applied in software engineering to perform tasks such as translating code between programming languages, generating code from natural language, and autocompleting code as it is being written. When used within development tools, these systems typically treat each model invocation independently from all previous invocations, and only a specific limited functionality is exposed within the user interface. This approach to user interaction misses an opportunity for users to more deeply engage with the model by having the context of their previous interactions, as well as the context of their code, inform the model's responses. We developed a prototype system -- the Programmer's Assistant -- in order to explore the utility of conversational interactions grounded in code, as well as software engineers' receptiveness to the idea of conversing with, rather than invoking, a code-fluent LLM. Through an evaluation with 42 participants with varied levels of programming experience, we found that our system was capable of conducting extended, multi-turn discussions, and that it enabled additional knowledge and capabilities beyond code generation to emerge from the LLM. Despite skeptical initial expectations for conversational programming assistance, participants were impressed by the breadth of the assistant's capabilities, the quality of its responses, and its potential for improving their productivity. Our work demonstrates the unique potential of conversational interactions with LLMs for co-creative processes like software development.Comment: 43 pages, 3 figures. To be published in IUI 202

Appropriate Reliance on AI Advice: Conceptualization and the Effect of Explanations

AI advice is becoming increasingly popular, e.g., in investment and medical treatment decisions. As this advice is typically imperfect, decision-makers have to exert discretion as to whether actually follow that advice: they have to "appropriately" rely on correct and turn down incorrect advice. However, current research on appropriate reliance still lacks a common definition as well as an operational measurement concept. Additionally, no in-depth behavioral experiments have been conducted that help understand the factors influencing this behavior. In this paper, we propose Appropriateness of Reliance (AoR) as an underlying, quantifiable two-dimensional measurement concept. We develop a research model that analyzes the effect of providing explanations for AI advice. In an experiment with 200 participants, we demonstrate how these explanations influence the AoR, and, thus, the effectiveness of AI advice. Our work contributes fundamental concepts for the analysis of reliance behavior and the purposeful design of AI advisors

SeeChart: Enabling Accessible Visualizations Through Interactive Natural Language Interface For People with Visual Impairments

Web-based data visualizations have become very popular for exploring data and communicating insights. Newspapers, journals, and reports regularly publish visualizations to tell compelling stories with data. Unfortunately, most visualizations are inaccessible to readers with visual impairments. For many charts on the web, there are no accompanying alternative (alt) texts, and even if such texts exist they do not adequately describe important insights from charts. To address the problem, we first interviewed 15 blind users to understand their challenges and requirements for reading data visualizations. Based on the insights from these interviews, we developed SeeChart, an interactive tool that automatically deconstructs charts from web pages and then converts them to accessible visualizations for blind people by enabling them to hear the chart summary as well as to interact through data points using the keyboard. Our evaluation with 14 blind participants suggests the efficacy of SeeChart in understanding key insights from charts and fulfilling their information needs while reducing their required time and cognitive burden.Comment: 28 pages, 13 figure

Regional variation in rates of IVF treatment across Australia: a population-based study

Background: There is variation in uptake of in vitro fertilisation (IVF) between countries, and Australia has high incidence rates of IVF due to universal public funding. However, it remains unclear whether there is regional variation and, if present, what might cause this. Objectives: We sought to determine whether regional variations in treatment rates existed and what might influence these. Methods: The number of cycles of fresh IVF and intrauterine insemination (IUI) for women were obtained for the period 2011 until 2014 in two age groups (25 to 34 years and 35 to 44 years) to calculate incidence rates. Proxy indicators that might influence treatment affordability were: unemployment rates; average weekly total earnings; coverage of private health insurance; and, percentage of women in the highest socioeconomic quintile. Measures of accessibility considered were percentage of the population remote from urban areas and average state population density. Linear regressions were performed using log-transformed ratio of IVF and IUI incidence rates. Results: Variations were found in IVF uptake between states with greater differences in older women. There was no significant association between IVF procedures and population density or geographic isolation. Economic factors were not associated with IVF uptake. Conclusion: These findings suggest that factors such as physician preference, clinical practice guidelines, and cryopreservation protocols of ART units might explain the national variation in uptake of IV

Dental bacterial DNA are present in the amniotic cavity of healthy pregnant women at term

Aims: To determine if dental bacterial DNA are present in the amniotic cavity of healthy pregnant women undergoing an elective caesarean section at term utilising culture independent techniques. Methods: Pregnant Australian women undergoing an elective caesarean section were recruited. Women completed questionnaires addressing demographics, past and current pregnancies and medical history. One high vaginal swab and three amniotic cavity swabs (amniotic fluid, newborn axilla and placental) were collected under sterile conditions. Samples were analysed using culture-independent techniques to detect the presence of predefined pathogenic bacterial taxa of the oral microbiome. Taxa isolated from the amniotic cavity swabs were compared to those isolated from the vaginal swab. Results: DNA from taxa isolated from the amniotic cavity but not vagina included A. xylosoxidans, A. tumefaciens, B. subtilis, Bartonella sp, Bergeyella sp, C. concisus, C. curvus, C. durum, D. microaerophilus, G. haemolysans, G. morbillorum, G. adiacens, G. elegans, K. pneumoniae, L. casei, L. paracasei, L. fermentum, P. aeruginosa, P. fluorescens, P. pseudoalcaligenes, P. stutzeri, R. microluginosa, S. maltophilia, S. pneumoniae, S. salivarius, S. sanguinis, V. dispar, V. parvula and Xanthomonas sp. Conclusion: The DNA of many pathogenic oral bacteria can be identified in the amniotic cavity of healthy pregnant women at term when utilising culture-independent techniques. Given DNA is not always present in the vagina, the study findings fulfill one criterion necessary for oral haematogenous spread to the amniotic cavity