Miracle-dz/UniFAR-FTUs huggingface dataset:
An Intersectional Look at Use of and Satisfaction with Digital Mental Health Platforms: A Survey of Perinatal Black Women

In the United States (U.S.), Black women in the perinatal period disproportionately experience higher rates of mental health challenges like anxiety and postpartum depression. Digital platforms present promising opportunities for mental health support. However, the extent to which these tools are being adopted and satisfying the mental health needs amongst perinatal Black women is underexplored. To address this gap, we surveyed 101 pregnant and postpartum Black women in the U.S. Despite prior work showing low utilization of mental health services amongst Black women, our results show more than half of our participants using specific digital tools for mental health support (e.g., mobile apps and social media). Importantly, use and satisfaction with these tools varied by subgroups (e.g., income and education level). We use our findings to present recommendations for digital mental health intervention research that incorporates an understanding of intersectional identities during the perinatal period.
Disagreement, Agreement, and Elaboration in Crowdsourced Deliberation: Ideation Through Elaborated Perspectives

In this study, we examined disagreement, agreement, and elaboration (rationale sharing) and their association with idea generation in a crowdsourced deliberation that took place within a crowdsourced policymaking process led by a national government. We analyzed the comments posted to the crowdsourced deliberation process and found that the elaboration of perspectives was a key element in idea generation. Disagreement contributed to ideation most when it was elaborated—i.e., when the participants justified their stances—and when it was accompanied by elaborated agreement. The findings suggest that in the design of the technologies and processes facilitating crowdsourced policymaking and other applications for civic engagement, there should be a particular focus on encouraging elaboration because elaboration can contribute to productive ideation as well as constructive argumentation. Elaboration could be fostered by deploying features from deliberation and argumentation technologies, which are designed to encourage participants to elaborate on their stances.
RanSAM: Randomized Search for ABAC Policy Mining

This paper presents a novel approach for generating Attribute-based Access Control policies from a given Access Control Matrix (ACM). In contrast to the existing techniques for policy mining, which group the desired accesses in the ACM using certain heuristics, we pose it as a search problem in the policy space. A randomized algorithm is then used to identify the policy that best represents the given ACM. Our initial experiments show promising results.
“I See Me Here”: Mental Health Content, Community, and Algorithmic Curation on TikTok

Social media platforms are a place where people look for information and social support for mental health, resulting in both positive and negative effects on users. TikTok has gained notoriety for an abundance of mental health content and discourse. We present findings from a semi-structured interview study with 16 participants about mental health content and participants’ perceptions of community on TikTok. We find that TikTok’s community structure is permeable, allowing for self-discovery and understanding not found in traditional online communities. However, participants are wary of mental health information due to conflicts between a creator’s vulnerability and credibility. Our interviews suggest that the “For You Page" is a runaway train that encourages diverse community and content engagement but also displays harmful content that participants feel they cannot escape. We propose design implications to support better mental health, as well as implications for social computing research on community in algorithmic landscapes.
Research on Lightweight Intervertebral Disc Segmentation Method for Spine Magnetic Resonance Images: Lightweight Segmentation Method for Spine MRI

To address the problem of conflicting segmentation accuracy and model parametric numbers in many lumbar MR image disc segmentation methods, we propose a lightweight segmentation pipeline named Lite-snake for lumbar MR image disc instances. We use Nanodet as a lightweight target detection model, a parallel feature aggregation module to aggregate multi-scale features, and a depth-separable cyclic convolution to reconstruct deep snake. The method achieves an IoU of 79.49% and a DSC of 88.50% on the test set of lumbar MR images with only 5,670,000 parameters. The experimental results show that compared to many current deep-learning image segmentation methods, our method dramatically reduces the number of parameters of the model while ensuring segmentation accuracy. Therefore it is easy to deploy to lightweight mobile devices and has good application prospects.
Advancing HCI Research and Education within and across South Asia

With the advancements in technologies, the need for Human’s perspective and human-centered designs are in much demand and its essential to understand diverse cultural needs. The community in South Asia has been recognized to have a unique and diverse socio-cultural, political, infrastructural, and geographical background of the region. However, we continue to see that the studies presented to the CHI community about South Asia primarily focus on working with and unpacking the regional contextual constraints (of the users and the infrastructures), thus taking a developmental stance. We witness a lack of HCI work presented by South Asian research community promoting diverse methods, cultures and behaviors. We believe this is due to limited experience in the field and resources. In this online workshop, we take advanced steps to operationalize collaborations and resource sharing between HCI researchers by presenting their half-baked ideas, presenting published HCI work on other venues, or even sharing challenges faced in a Rejected HCI work in the context of South Asia. Our aim is to broaden the perspective of the CHI research and community towards the contributions from the region including and beyond development, by bringing together researchers, designers, and practitioners working or are interested in working within these regions on diverse topics.
HandAvatar: Embodying Non-Humanoid Virtual Avatars through Hands

We propose HandAvatar to enable users to embody non-humanoid avatars using their hands. HandAvatar leverages the high dexterity and coordination of users’ hands to control virtual avatars, enabled through our novel approach for automatically-generated joint-to-joint mappings. We contribute an observation study to understand users’ preferences on hand-to-avatar mappings on eight avatars. Leveraging insights from the study, we present an automated approach that generates mappings between users’ hands and arbitrary virtual avatars by jointly optimizing control precision, structural similarity, and comfort. We evaluated HandAvatar on static posing, dynamic animation, and creative exploration tasks. Results indicate that HandAvatar enables more precise control, requires less physical effort, and brings comparable embodiment compared to a state-of-the-art body-to-avatar control method. We demonstrate HandAvatar’s potential with applications including non-humanoid avatar based social interaction in VR, 3D animation composition, and VR scene design with physical proxies. We believe that HandAvatar unlocks new interaction opportunities, especially for usage in Virtual Reality, by letting users become the avatar in applications including virtual social interaction, animation, gaming, or education.
Reddit Insights: Improving Online Discussion Culture by Contextualizing User Profiles

More than forty years after the creation of the first online messaging board, the quality of online discussion culture remains a topic of significant scientific and public debate. Social media platforms have struggled to maintain a free marketplace of ideas while addressing issues such as censorship, propaganda, and misinformation. Traditional methods of content moderation have been ineffective in addressing these issues, with incivility in online discussions remaining a major concern. In an effort to address the ongoing challenges facing online discussion culture, we have developed a browser extension called Reddit Insights, designed to improve discourse quality through design interventions. The extension provides context to user profiles by aggregating information from past commenting behavior. Results from a small-scale qualitative assessment with 9 users indicate that the extension helped users contextualize posts, identify implicit political tendencies and decide with whom to interact and choose in which discussions to engage more wisely.
“A Switch Went off in my Whole Body”: Lived Experiences of Fatigue and Post-Exertional Malaise in Long Covid

The growing HCI agenda on health has focused on different chronic conditions but less so on Long Covid, despite its severe impact on the quality of life. We report findings from 2 workshops with 13 people living with Long Covid, indicating the challenges of making sense of their physical, cognitive, and emotional symptoms, and of monitoring the triggers of post-exertional malaise. While most participants engage in pacing activities for the self-management of fatigue, only a few are aware of the importance of planning all their daily activities and routines in order to avoid post-exertional malaise. We conclude with design implications to support lightweight tracking and sensemaking of fatigue symptoms, novel data analytics for monitoring the triggers of post-exertional malaise and the worsening of symptoms, and support for self-management in order to prevent post-exertional malaise.
Surfacing Livingness in Microbial Displays: A Design Taxonomy for HCI

In recent years, there has been a notable proliferation and diversification of works in HCI, that integrate living microorganisms; an imperative lifeform dominating ecosystems of our planet. Yet despite the growing interest, there is a lack of structured lenses with which designers can strategize their processes of surfacing livingness; a material quality inherent in living artefacts with a potential to enrich user experiences and to initiate mutualistic care between humans and microorganisms. Through a systematic artefacts review and a case study on Flavobacteria, we have developed and instantiated a Taxonomy of Surfacing Livingness in Microbial Displays, consisting of six microbe-sensitive, tuneable mechanisms for human noticing of microorganisms: 1) Canvassing, 2) Marking, 3) Magnifying, 4) Translating, 5) Nudging, and 6) Molecular Programming. The taxonomy invites diverse and adaptable ways of generating and crafting microbial displays; towards overcoming microbe-specific surfacing constraints, integrating diverse stakeholders’ values, and enabling nuanced address of microbial welfare.
Amortized Inference with User Simulations

There have been significant advances in simulation models predicting human behavior across various interactive tasks. One issue remains, however: identifying the parameter values that best describe an individual user. These parameters often express personal cognitive and physiological characteristics, and inferring their exact values has significant effects on individual-level predictions. Still, the high complexity of simulation models usually causes parameter inference to consume prohibitively large amounts of time, as much as days per user. We investigated amortized inference for its potential to reduce inference time dramatically, to mere tens of milliseconds. Its principle is to pre-train a neural proxy model for probabilistic inference, using synthetic data simulated from a range of parameter combinations. From examining the efficiency and prediction performance of amortized inference in three challenging cases that involve real-world data (menu search, point-and-click, and touchscreen typing), the paper demonstrates that an amortized-inference approach permits analyzing large-scale datasets by means of simulation models. It also addresses emerging opportunities and challenges in applying amortized inference in HCI.
COVID-19 image classification based on Deep Learning

COVID-19 is diagnosed by nucleic acid testing, aided by Computed Tomography. In order to rapidly screen CT images of COVID-19, Squeeze-and-Excitation Network based network model combined with Deep Learning is proposed, which can adapt to learn important parts of the feature channel. Firstly, the feature Squeeze is carried out along the space dimension, and the output dimension matches the number of input feature channels. Secondly, the feature channel learns the feature channel characteristics by capturing the channel dependencies in the previous step. Finally, the weight is updated to model the correlation of feature channels. The Precision, Recall and Specificity were selected to be 92.8%, 92.8% and 93.7%, the Accuracy of the model was 93.24% for the whole sample specificity. Compared with the mainstream model, the experimental results of this model are improved greatly.
Learning Circular Tabular Embeddings for Heterogeneous Large-scale Structured Datasets

Deep-learning models, most recently with embeddings have been used and yield promising results for many data fusion problems. Some methods directly use the embeddings, pre-trained on large corpora, such as Wikipedia and naively treat table tuples as sentences, when it comes to structured data 1 . This leads to loss of valuable 2D contextual information, inherent to structured data. Other methods partially take into account 2D context, but insufficiently to attain high accuracy on downstream tasks, such as classification on Web-scale datasets, known to exhibit extreme heterogeneity . In this paper, we propose scalable algorithms to construct and train embeddings, fully embodying complex 2D tabular context, unique to structured data, which turns out to be especially important to attain high accuracy at scale. Compared to the baselines, we report a substantial gain in F-Measure of up to 34.4%. We also observed that not only the average downstream classification task accuracy is much higher with our embeddings, but also classification of some categories completely fail with state of the art embeddings.
DIY Ecological Momentary Assessment

Ecological Momentary Assessment (EMA) is an increasingly used methodology in HCI which captures an individual’s experiences, typically digitally. The benefits of EMA include: reducing recall bias with ‘in-the-moment’ measurement; improving ecological validity with ‘in-the-context’ measurement and enabling exploration of the dynamic interplay of variables from repeated measurement. This course will teach participants about EMA by providing hands-on practical skill development in how to use off-the-shelf software to develop their own EMA system. At the end of the course, participants will be able to rapidly prototype and run custom EMA studies securely and at scale without the usual associated costs.
The Impact of Navigation Aids on Search Performance and Object Recall in Wide-Area Augmented Reality

Head-worn augmented reality (AR) is a hotly pursued and increasingly feasible contender paradigm for replacing or complementing smartphones and watches for continual information consumption. Here, we compare three different AR navigation aids (on-screen compass, on-screen radar and in-world vertical arrows) in a wide-area outdoor user study (n=24) where participants search for hidden virtual target items amongst physical and virtual objects. We analyzed participants’ search task performance, movements, eye-gaze, survey responses and object recall. There were two key findings. First, all navigational aids enhanced search performance relative to a control condition, with some benefit and strongest user preference for in-world arrows. Second, users recalled fewer physical objects than virtual objects in the environment, suggesting reduced awareness of the physical environment. Together, these findings suggest that while navigational aids presented in AR can enhance search task performance, users may pay less attention to the physical environment, which could have undesirable side-effects.
Remote Breathing Rate Tracking in Stationary Position Using the Motion and Acoustic Sensors of Earables

Breathing rate is critical for the user’s respiratory health and is hard to track outside the clinical context, requiring specialized devices. Earables could provide a convenient solution to track the breathing rate anywhere by leveraging the user’s breathing-related motion and sound captured through the earables’ motion sensors and microphones. However, small non-breathing head movements or background noises during the assessment affect the estimation accuracy. While noise filtering improves accuracy, it can discard valid measurements. This paper presents a multimodal approach to tracking the user’s breathing rate using a signal-processing-based algorithm on motion sensors and a lightweight machine-learning algorithm on acoustic sensors from the earables that balances the accuracy and data retention. A user study with 30 participants shows that the system can accurately calculate breathing rate (Mean Absolute Error < 2 breaths per minute) while retaining most breathing sessions (75%) performed in real-world settings. This work provides an essential direction for remote breathing rate monitoring.
Rethinking Creative Labor: A Sociotechnical Examination of Creativity & Creative Work on TikTok

Social media platform success relies on users to consume, create, and share creative content. While some creatives aspire to become influencers, this is not the goal of all creatives, particularly those with smaller audiences. Through an interview study of 15 creatives on TikTok, we explore the often overlapping intentions for creating and sharing videos, as well as the challenges to maintaining these creative intentions and routines as they are shaped by platform logic. We find platforms introduce impediments which disrupt people’s creative routines and alienate people from their overlapping creative intentions; introducing challenges which alienate people from their sense of self, and their audiences. We construct a broader definition of creative labor - the work of professionalizing and monetizing a creative product shared on social media - reflecting on how the routine enactment of creative labor is impacted by infrastructural elements of technology.
Positional Control in Node-Based Programming

Visual programming languages enable novices to code with a lowered barrier. These languages typically employ one of two popular design approaches — block-based editing (e.g. Scratch), which allows users to control the execution order of code blocks, and node-based editing (e.g. Grasshopper), which enables users to control the data flow through nodes and wires. We propose integrating these two approaches by utilizing positional control in node-based programming to visualize and allow manipulation of both the execution order and data flow. A grid system organizes blocks and determines their sequence. Effect block is introduced, which controls other blocks within its effective range through positional constraints. As relocating blocks is easier than wiring that targets tiny inlets and outlets, we aim to shorten the feedback loop time and encourage exploration. We present b5, a web-based novel visual interface for creative coding, to demonstrate and evaluate this design.
Accessibility Evaluation of IoT Android Mobile Companion Apps

Internet of Things (IoT) devices use mobile companion apps to configure, update, and proxy communications between devices, cloud endpoints, and users. However, to the best of our knowledge, their accessibility characteristics have received little study. Thus, we report the analysis results of 248 IoT companion apps. Our approach involves manual analysis based on the Accessibility Insights tool and reports on: the presence of contextual information (descriptions of controls, images, and text input), size of touch elements, and color contrast. Our primary findings are: (i) most apps have reasonable accessibility posture, but there exists a long tail of apps with significant problems, (ii) only two apps do not present any accessibility errors, and (iii) nearly 87% of apps in the corpus exhibit errors involving a lack of names and descriptions of elements and/or images. We further provide actionable recommendations to enhance the accessibility posture of the IoT android apps.
Prediction of Ship Motion Attitude Based on Combined Model

Due to the influence of sea conditions, six dimensional movements, including heave, roll, pitch, sway, surge and yaw, are easy to be produced while ships sailing. These motions seriously affect the safety of its sailing, so the prediction of ship motion attitude is particularly important. In this case, a new combined model called CWGRU is proposed for predicting ship motion attitude with high accuracy. The CWGRU is based on complete ensemble empirical mode decomposition algorithm (CEEMD), whale optimization algorithm (WOA) and gated recurrent unit (GRU). Firstly, the CEEMD algorithm is used to decompose the ship’s sailing attitude data into a number of intrinsic mode functions (IMF) with different characteristics, so that the non-stationary time sequences have stability and periodicity. Then, the GRU based on WOA (WGRU) model is used to learn the short-term characteristics of each IMF component and predict it. Finally, the predicted values of each IMF component are added to obtain the prediction results. In order to verify the effectiveness of the CWGRU model proposed in this paper, the experiment based on real motion data collected in a ship are carried out. The first 80 of the data is used as the training set, and the last 20 is used for the test. Experimental results show that the performance of CWGRU is much better than that of GRU and WGRU.
Faster than the brighter-light beacon

We analyse the motion of a spot of light projected onto a flat screen by a rotating source. We find that the motion of the spot has many interesting features such as spot splitting and superluminal effects. Our discussion is well suited for undergraduates and can be an interesting add-on in their curriculum, giving them new insights into the effects of kinematics.
Assistive-Technology Aided Manual Accessibility Testing in Mobile Apps, Powered by Record-and-Replay

Billions of people use smartphones on a daily basis, including 15% of the world’s population with disabilities. Mobile platforms encourage developers to manually assess their apps’ accessibility in the way disabled users interact with phones, i.e., through Assistive Technologies (AT) like screen readers. However, most developers only test their apps with touch gestures and do not have enough knowledge to use AT properly. Moreover, automated accessibility testing tools typically do not consider AT. This paper introduces a record-and-replay technique that records the developers’ touch interactions, replays the same actions with an AT, and generates a visualized report of various ways of interacting with the app using ATs. Empirical evaluation of this technique on real-world apps revealed that while user study is the most reliable way of assessing accessibility, our technique can aid developers in detecting complex accessibility issues at different stages of development.