Affective Conversational Agents: Understanding Expectations and Personal Influences

The rise of AI conversational agents has broadened opportunities to enhance human capabilities across various domains. As these agents become more prevalent, it is crucial to investigate the impact of different affective abilities on their performance and user experience. In this study, we surveyed 745 respondents to understand the expectations and preferences regarding affective skills in various applications. Specifically, we assessed preferences concerning AI agents that can perceive, respond to, and simulate emotions across 32 distinct scenarios. Our results indicate a preference for scenarios that involve human interaction, emotional support, and creative tasks, with influences from factors such as emotional reappraisal and personality traits. Overall, the desired affective skills in AI agents depend largely on the application's context and nature, emphasizing the need for adaptability and context-awareness in the design of affective AI conversational agents

ICE: Enabling Non-Experts to Build Models Interactively for Large-Scale Lopsided Problems

Quick interaction between a human teacher and a learning machine presents numerous benefits and challenges when working with web-scale data. The human teacher guides the machine towards accomplishing the task of interest. The learning machine leverages big data to find examples that maximize the training value of its interaction with the teacher. When the teacher is restricted to labeling examples selected by the machine, this problem is an instance of active learning. When the teacher can provide additional information to the machine (e.g., suggestions on what examples or predictive features should be used) as the learning task progresses, then the problem becomes one of interactive learning. To accommodate the two-way communication channel needed for efficient interactive learning, the teacher and the machine need an environment that supports an interaction language. The machine can access, process, and summarize more examples than the teacher can see in a lifetime. Based on the machine's output, the teacher can revise the definition of the task or make it more precise. Both the teacher and the machine continuously learn and benefit from the interaction. We have built a platform to (1) produce valuable and deployable models and (2) support research on both the machine learning and user interface challenges of the interactive learning problem. The platform relies on a dedicated, low-latency, distributed, in-memory architecture that allows us to construct web-scale learning machines with quick interaction speed. The purpose of this paper is to describe this architecture and demonstrate how it supports our research efforts. Preliminary results are presented as illustrations of the architecture but are not the primary focus of the paper

Emerging Perspectives in Human-Centered Machine Learning

Current Machine Learning (ML) models can make predictions that are as good as or better than those made by people. The rapid adoption of this technology puts it at the forefront of systems that impact the lives of many, yet the consequences of this adoption are not fully understood. Therefore, work at the intersection of people's needs and ML systems is more relevant than ever. This area of work, dubbed Human-Centered Machine Learning (HCML), re-thinks ML research and systems in terms of human goals. HCML gathers an interdisciplinary group of HCI and ML practitioners, each bringing their unique, yet related perspectives. This one-day workshop is a successor of Gillies et al. (2016) and focuses on recent advancements and emerging areas in HCML. We aim to discuss different perspectives on these areas and articulate a coordinated research agenda for the XXI century

Identifying a low-risk group for parametrial involvement in microscopic Stage IB1 cervical cancer using criteria from ongoing studies and a new MRI criterion

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.Abstract Background There are currently three ongoing studies on less radical surgery in cervical cancer: ConCerv, GOG-278, and SHAPE. The aim of this study was to evaluate the performance of the criteria used in ongoing studies retrospectively and suggest a new, simplified criterion in microscopic Stage IB1 cervical cancer. Methods A retrospective analysis was performed in 125 Stage IB1 cervical cancer patients who had no clinically visible lesions and were allotted based on microscopic findings after conization. All patients had magnetic resonance imaging (MRI) after conization and underwent type C2 radical hysterectomy. We suggested an MRI criterion for less radical surgery candidates as patients who had no demonstrable lesions on MRI. The rates of parametrial involvement (PMI) were estimated for patients that satisfied the inclusion criteria for ongoing studies and the MRI criterion. Results The rate of pathologic PMI was 5.6% (7/125) in the study population. ConCerv and GOG-278 identified 11 (8.8%) and 14 (11.2%) patients, respectively, as less radical surgery candidates, and there were no false negative cases. SHAPE and MRI criteria identified 78 (62.4%) and 74 (59.2%) patients, respectively, as less radical surgery candidates; 67 patients were identified as less radical surgery candidates by both sets of criteria. Of these 67 patients, only one had pathologic PMI with tumor emboli. Conclusions This study suggests that the criteria used in three ongoing studies and a new, simplified criterion using MRI can identify candidates for less radical surgery with acceptable false negativity in microscopic Stage IB1 disease

Human-Centered and Computational Understanding for the Design and Adaptation of Mental Health and Well-being Interventions

Thesis (Ph.D.)--University of Washington, 2022As many as 20% of Americans suffer from diagnosable mental health disorders, but those overwhelmed with physiological and economic burdens cannot prioritize seeking support for their mental health and well-being. There are many evidence-based psychosocial interventions (EBPIs) that have been proven to be effective in treating mental health conditions. Recent initiatives to improve engagement in mental health care through technology have generated an abundance of promising digital mental health solutions. However, symptoms of stress, anxiety, and depression remain overlooked and in constant tension with life demands and disruptions, making it challenging to integrate such solutions into everyday life. My dissertation research examines the tensions between everyday life demands and mental health and well-being, where I design systems that integrate adaptations of EBPIs into everyday contexts to promote engagement. My work intersects three well-being contexts: (1) the COVID-19 pandemic, (2) co-morbid cancer and depression, and (3) workplace stress. First, I examine the situated contexts using human-centered and computational methods grounded on holistic frameworks to reveal challenges rooted in tensions among multiple needs that get in the way of engaging in mental health and well-being activities. I conduct this research in the COVID-19 pandemic and co-morbid cancer and depression contexts to demonstrate that these challenges are present at the individual, organizational, and population scales. Second, I identify modification targets to existing evidence-based psychosocial interventions that can be enhanced through the use of technology to ease the tensions among needs and to directly integrate adapted interventions into the relevant contexts. I describe the development of the collaborative behavioral activation system aimed at improving the collaboration and engagement of patients and providers in depression care. I also describe the development of a just-in-time micro-intervention system aimed at reducing stress in the workplace. Lastly, I deploy these technology-enhanced mental health and well-being systems in real-world contexts to evaluate their effectiveness in improving engagement. Through such deployment, I highlight implementation challenges to integrating patient-provider collaborative technology into a clinical care practice as well as individual, contextual, and intervention-related factors that may influence real-time engagement in digitized interventions. Across three well-being contexts, my dissertation demonstrates that contextual and continuous adaptations of EBPIs can improve engagement in mental health and well-being care. My dissertation makes theoretical contributions through the development of holistic frameworks, methodological contributions through the development of computational frameworks, and artifact contributions through the development of technology-enhanced mental health and well-being intervention systems and through the design recommendations that arise from real-world deployments

Efficacy of the iDBT-Pain skills training intervention to reduce emotional dysregulation and pain intensity in people with chronic pain: protocol for a single-case experimental design with multiple baselines

Introduction Difficulties in emotional regulation are key to the development and maintenance of chronic pain. Recent evidence shows internet-delivered dialectic behaviour therapy (iDBT) skills training can reduce emotional dysregulation and pain intensity. However, further studies are needed to provide more definitive evidence regarding the efficacy of iDBT skills training in the chronic pain population.Methods and analysis A single-case experimental design (SCED) with multiple baselines will be used to examine the efficacy of a 4-week iDBT-Pain skills training intervention (iDBT-Pain intervention) to reduce emotional dysregulation and pain intensity in individuals with chronic pain. The iDBT-Pain intervention encompasses two components: (1) iDBT-Pain skills training sessions (iDBT-Pain sessions) and (2) the iDBT-Pain skills training web application (iDBT-Pain app). Three individuals with chronic pain will be recruited and randomly allocated to different baseline phases (5, 9 or 12 days). Following the baseline phase, participants will receive six 60–90 min iDBT-Pain sessions approximately 4 or 5 days apart, delivered by a psychologist via Zoom. To reinforce learnings from the iDBT-Pain sessions, participants will have unlimited use of the iDBT-Pain app. A 7-day follow-up phase (maintenance) will follow the intervention, whereby the iDBT-Pain sessions cease but the iDBT-Pain app is accessible. Emotional regulation, as the primary outcome measure, will be assessed using the Difficulties in Emotion Regulation Scale. Pain intensity, as the secondary outcome measure, will be assessed using a visual analogue scale. Generalisation measures will assess psychological state factors (depression, anxiety and coping behaviour), alongside sleep quality, well-being and harm avoidance. SCEDs are increasingly considered effective designs for internet-delivered psychological interventions because SCED enables the investigation of interindividual variability in a heterogeneous population such as chronic pain.Ethics and dissemination This trial was approved by the University of New South Wales (HC200199). Results will be published in peer-reviewed journals.Trial registration number ACTRN12620000604909

Efficacy of the iDBT-Pain skills training intervention to reduce emotional dysregulation and pain intensity in people with chronic pain: protocol for a single-case experimental design with multiple baselines.

Introduction Difficulties in emotional regulation are key to the development and maintenance of chronic pain. Recent evidence shows internet-delivered dialectic behaviour therapy (iDBT) skills training can reduce emotional dysregulation and pain intensity. However, further studies are needed to provide more definitive evidence regarding the efficacy of iDBT skills training in the chronic pain population. Methods and analysis A single-case experimental design (SCED) with multiple baselines will be used to examine the efficacy of a 4-week iDBT-Pain skills training intervention (iDBT-Pain intervention) to reduce emotional dysregulation and pain intensity in individuals with chronic pain. The iDBT-Pain intervention encompasses two components: (1) iDBT-Pain skills training sessions (iDBT-Pain sessions) and (2) the iDBT-Pain skills training web application (iDBT-Pain app). Three individuals with chronic pain will be recruited and randomly allocated to different baseline phases (5, 9 or 12 days). Following the baseline phase, participants will receive six 60–90min iDBT-Pain sessions approximately 4 or 5 days apart, delivered by a psychologist via Zoom. To reinforce learnings from the iDBT-Pain sessions, participants will have unlimited use of the iDBT-Pain app. A 7-day follow-up phase (maintenance) will follow the intervention, whereby the iDBT-Pain sessions cease but the iDBT-Pain app is accessible. Emotional regulation, as the primary outcome measure, will be assessed using the Difficulties in Emotion Regulation Scale. Pain intensity, as the secondary outcome measure, will be assessed using a visual analogue scale. Generalisation measures will assess psychological state factors (depression, anxiety and coping behaviour), alongside sleep quality, well-being and harm avoidance. SCEDs are increasingly considered effective designs for internet-delivered psychological interventions because SCED enables the investigation of interindividual variability in a heterogeneous population such as chronic pain. Ethics and dissemination This trial was approved by the University of New South Wales (HC200199). Results will be published in peer-reviewed journals

FoundWright: A System to Help People Re-find Pages from Their Web-history

Re-finding information is an essential activity, however, it can be difficult when people struggle to express what they are looking for. Through a need-finding survey, we first seek opportunities for improving re-finding experiences, and explore one of these opportunities by implementing the FoundWright system. The system leverages recent advances in language transformer models to expand people's ability to express what they are looking for, through the interactive creation and manipulation of concepts contained within documents. We use FoundWright as a design probe to understand (1) how people create and use concepts, (2) how this expanded ability helps re-finding, and (3) how people engage and collaborate with FoundWright's machine learning support. Our probe reveals that this expanded way of expressing re-finding goals helps people with the task, by complementing traditional searching and browsing. Finally, we present insights and recommendations for future work aiming at developing systems to support re-finding.Comment: 26 page

Projections of suitable cultivation area for major fruit trees and climate-type in South Korea under representative concentration pathway scenarios using the ensemble of high-resolution regional climate models

This study projected the future changes in the climate-type distribution in South Korea according to the Koppen-Trewartha climate classification (KTCC) under the representative concentration pathway (RCP) 4.5/8.5 scenarios and the future change of cultivation area of apple (Malus domestica Borkh.) and mandarin (Citrus unshiu Marc.), which are major fruit crops in South Korea, using five regional climate models with a 12.5 km horizontal resolution. According to KTCC, type temperate (D)s is dominant in most of South Korea during the reference period (1981-2005). On the other hand, it is projected that the area of Type D and Type subtropical (C) will decrease and increase, respectively, towards higher latitudes and elevations in the future under RCP4.5/8.5 scenarios. Accordingly, the cultivation areas of major fruit crops in South Korea are projected to change significantly. The cultivation area of apple (mandarin), which is a major current fruit crop in Type D (C), is projected to be reduced (expanded) as it moves towards higher latitudes and elevations in the future. Apples grown throughout South Korea in the present climate (reference period) are not expected to be cultivated in the late-21C due to climate change. On the other hand, the cultivation area of mandarins is projected to increase steadily in the future. At present, mandarins are cultivated only in Jeju Island, which is located in the south of the South Korea. However, the cultivation area is expected to increase by 1323% in late-21C under the RCP8.5 scenario compared to the reference period. Moreover, mandarin cultivation is projected to be possible anywhere in South Korea. Nevertheless, in late-21C, excessive increases in temperature that exceeds the appropriate temperature for mandarin in Jeju Island and the southern part of South Korea will eventually decrease the cultivation area of mandarins