Miracle-dz/UniFAR-FTUs huggingface dataset:
Demonstrating HydroMod : Constructive Modules for Prototyping Hydraulic Physical Interfaces

In recent years, actuators that handle fluids such as gases and liquids have been attracting attention for their applications in soft robots and shape-changing interfaces. In the field of HCI, there have been various inflatable prototyping tools that utilize air control, however, very few tools for liquid control have been developed. In this study, we propose HydroMod, novel constructive modules that can easily generate liquid flow and programmatically control liquid flow, with the aim of lowering the barrier to entry for prototyping with liquids. HydroMod consists of palm-sized small modules, which can generate liquid flow with the electrohydrodynamics (EHD) phenomenon by simply connecting the modules. Moreover, users can control the flow path by simply recombining the modules. In this paper, we demonstrate three applications that can be easily prototyped with liquids by recombining HydroMod. These applications showcase the characteristics of using liquids: weight manipulation, color expression, and heating/cooling control.
FlexHaptics: A Design Method for Passive Haptic Inputs Using Planar Compliant Structures

This paper presents FlexHaptics, a design method for creating custom haptic input interfaces. Our approach leverages planar compliant structures whose force-deformation relationship can be altered by adjusting the geometries. Embedded with such structures, a FlexHaptics module exerts a fine-tunable haptic effect (i.e., resistance, detent, or bounce) along a movement path (i.e., linear, rotary, or ortho-planar). These modules can work separately or combine into an interface with complex movement paths and haptic effects. To enable the parametric design of FlexHaptic modules, we provide a design editor that converts user-specified haptic properties into underlying mechanical structures of haptic modules. We validate our approach and demonstrate the potential of FlexHaptic modules through six application examples, including a slider control for a painting application and a piano keyboard interface on touchscreens, a tactile low vision timer, VR game controllers, and a compound input device of a joystick and a two-step button.
Responsible Language Technologies: Foreseeing and Mitigating Harms

As increasingly powerful natural language generation, representation, and understanding models are developed, made available and deployed across numerous downstream applications, many researchers and practitioners have warned about possible adverse impacts. Harmful impacts include but are not limited to disparities in quality-of-service, unequal distribution of resources, erasure, stereotyping and misrepresentation of groups and individuals, they might limit people’s agency or affect their well-being. Given that language tasks are often complex, open-ended, and incorporated across a diversity of applications; effectively foreseeing and mitigating such harms has remained an elusive goal. Towards this goal, Natural Language Processing (NLP) literature has only recently started to engage with human-centered perspectives and methods—that are often central to HCI research. In this panel, we bring together researchers with expertise in both NLP and HCI, as well as in issues that pertain to the fairness, transparency, justice, and ethics of computational systems. Our main goals are to explore 1) how to leverage HCI perspectives and methodologies to help foresee potential harms of language technologies and inform their mitigation, 2) synergies between the HCI and the responsible NLP research that can help build common ground, and 3) complement existing efforts to facilitate conversations between the HCI and NLP communities.
Perspectives on Integrating Trusted Other Feedback in Therapy for Veterans with PTSD

Past research has demonstrated that accounts of trusted others can provide additional context into real world behavior relevant to clinical decision-making and patient engagement. Our research investigates the Social Sensing System, a concept which leverages trusted other feedback for veterans in therapy for PTSD. In our two phase study, we work with 10 clinicians to develop text-message queries and realistic scenarios to present to patients and trusted others. We then present the results in the form of a storyboard to 10 veterans with PTSD and 10 trusted others and gather feedback via semi-structured interview and survey. We find that while trusted other feedback may provide a unique and useful perspective, key design features and considerations of underlying relationships must be considered. We present our findings and utilize the mechanisms and conditions framework to assess the power dynamics of systems such as social sensing in the mental health realm.
“Sounds like a Cheesy Radio Ad”: Using User Perspectives for Enhancing Digital COVID Vaccine Communication Strategies for Public Health Agencies

The World Health Organization (WHO) and other public health agencies have identified vaccine hesitancy as a critical challenge in reducing future cases and deaths from COVID-19. The current study has investigated ways to improve a widely circulated vaccine infographic video by Centers for Disease Control and Prevention. After gathering qualitative feedback on properties of the message that could be improved (from online crowdworkers), we conducted a randomized experiment to investigate different combinations of these attributes. Our results suggest participants were more likely to share the video which was: (1) played more slowly; (2) had a female speaker; (3) did not have background music. The study demonstrates potential of user studies for improving existing communication strategies for encouraging vaccinations and alleviating vaccine hesitancy on social media platforms. Our contribution also includes a repository of messages to encourage vaccination, generated by online crowdworkers, which could be utilized by future studies.
What Pronouns for Pepper? A Critical Review of Gender/ing in Research

Gender/ing guides how we view ourselves, the world around us, and each other--including non-humans. Critical voices have raised the alarm about stereotyped gendering in the design of socially embodied artificial agents like voice assistants, conversational agents, and robots. Yet, little is known about how this plays out in research and to what extent. As a first step, we critically reviewed the case of Pepper, a gender-ambiguous humanoid robot. We conducted a systematic review (n=75) involving meta-synthesis and content analysis, examining how participants and researchers gendered Pepper through stated and unstated signifiers and pronoun usage. We found that ascriptions of Pepper's gender were inconsistent, limited, and at times discordant, with little evidence of conscious gendering and some indication of researcher influence on participant gendering. We offer six challenges driving the state of affairs and a practical framework coupled with a critical checklist for centering gender in research on artificial agents.
Exploiting Morpheme and Cross-lingual Knowledge to Enhance Mongolian Named Entity Recognition

Mongolian named entity recognition (NER) is not only one of the most crucial and fundamental tasks in Mongolian natural language processing, but also an important step to improve the performance of downstream tasks such as information retrieval, machine translation, and dialog system. However, traditional Mongolian NER models heavily rely on the feature engineering. Even worse, the complex morphological structure of Mongolian words makes the data sparser. To alleviate the feature engineering and data sparsity in Mongolian named entity recognition, we propose a novel NER framework with Multi-Knowledge Enhancement (MKE-NER). Specifically, we introduce both linguistic knowledge through Mongolian morpheme representation and cross-lingual knowledge from Mongolian-Chinese parallel corpus. Furthermore, we design two methods to exploit cross-lingual knowledge sufficiently, i.e., cross-lingual representation and cross-lingual annotation projection. Experimental results demonstrate the effectiveness of our MKE-NER model, which outperforms strong baselines and achieves the best performance (94.04% F1 score) on the traditional Mongolian benchmark. Particularly, extensive experiments with different data scales highlight the superiority of our method in low-resource scenarios.
Using Intersection of Unions to Minimize Multi-directional Linearization Error in Reachability Analysis

In piecewise linearization based reachable set computation, different linear approximations are computed around smaller pieces of the reachable set to reduce the linearization error in reachability analysis. However, this approach suffers from curse of dimensionality because the number of pieces required to restrict the linearization error below a threshold can blow up intractably for high-dimensional systems. Alternatively, we can fix the maximum number of divisions of the reachable set and optimize the division vector to minimize the linearization error. But the functions projecting the linearization error along different directions can be different, which have different optimal solutions for the division vector. Still, we may need to minimize the linearization error along multiple directions to achieve good accuracy along any one direction because the differential equations can be coupled. Therefore, we develop a new method of piecewise linearization based reachable set computation that incorporates different optimized divisions of reachable set for different projections of linearization error to improve accuracy. To do so, we use intersection of unions of sets (IoU) to approximate reachable sets such that different unions in the intersection are obtained from optimized division along different directions and forward propagation. We develop an algorithm to propagate the reachable set of the IoU in a coupled way, such that each intersecting union complements the approximation accuracy of other unions. We validate the advantage of using multiple optimal divisions instead of one optimized division. For this, we compare the performance on high dimensional examples, of the proposed algorithm with a variant of the algorithm which uses only one division vector at each time step. We also draw comparison with state-of-the-art methods and demonstrate that the accuracy of our algorithm is at par or better for the benchmarks.
PriCheck– An Online Privacy Assistant for Smart Device Purchases

In this paper, we present PriCheck, a browser extension that provides privacy-relevant information about smart devices (e.g., in an online shop). This information is oftentimes hidden, difficult to access, and, thus, often neglected when buying a new device. With PriCheck, we enable users to make informed purchase decisions. We conducted an exploratory study using the browser extension in a simplified (mock) online shop for smart devices. Participants chose devices with and without using the extension. We found that participants (N = 11) appreciated the usability and available information of PriCheck, helping them with informed decisions for privacy-preserving products. We hope our work will stimulate further discussion on how to make privacy information for novel products available, understandable, and easy to access for users.
Supporting Shy & Neurodivergent Children in Social Play

There is an opportunity to support shy and neurodivergent children in the development of critical executive function (EF) skills through social play. Through a within-subjects study at a preschool and a remote Zoom observation case study of neurodivergent children and their parents, I have identified the potential for StoryCarnival, a system that supports evidence-based sociodramatic play activities through e-book stories, a play-planning app, and a tangible, adult-controlled voice agent, to empower shy children to more confidently engage with their peers, to motivate neurodivergent children through various modalities, to encourage neurodivergent children to engage in symbolic play, and to afford children different types of agency in different settings. Through my future work, I hope to confirm the validity of these findings and examine the potential for StoryCarnival to support inclusive play in mixed-abilities groups through a large-scale deployment study and field studies.
Mouth Haptics in VR using a Headset Ultrasound Phased Array

Today’s consumer virtual reality (VR) systems offer limited haptic feedback via vibration motors in handheld controllers. Rendering haptics to other parts of the body is an open challenge, especially in a practical and consumer-friendly manner. The mouth is of particular interest, as it is a close second in tactile sensitivity to the fingertips, offering a unique opportunity to add fine-grained haptic effects. In this research, we developed a thin, compact, beamforming array of ultrasonic transducers, which can render haptic effects onto the mouth. Importantly, all components are integrated into the headset, meaning the user does not need to wear an additional accessory, or place any external infrastructure in their room. We explored several effects, including point impulses, swipes, and persistent vibrations. Our haptic sensations can be felt on the lips, teeth and tongue, which can be incorporated into new and interesting VR experiences.
SilentSpeller: Towards mobile, hands-free, silent speech text entry using electropalatography

Speech is inappropriate in many situations, limiting when voice control can be used. Most unvoiced speech text entry systems can not be used while on-the-go due to movement artifacts. Using a dental retainer with capacitive touch sensors, SilentSpeller tracks tongue movement, enabling users to type by spelling words without voicing. SilentSpeller achieves an average 97% character accuracy in offline isolated word testing on a 1164-word dictionary. Walking has little effect on accuracy; average offline character accuracy was roughly equivalent on 107 phrases entered while walking (97.5%) or seated (96.5%). To demonstrate extensibility, the system was tested on 100 unseen words, leading to an average 94% accuracy. Live text entry speeds for seven participants averaged 37 words per minute at 87% accuracy. Comparing silent spelling to current practice suggests that SilentSpeller may be a viable alternative for silent mobile text entry.
Optimality and Asymptotic Stability in Two-Player Zero-Sum Hybrid Games

In this work, we formulate a two-player zero-sum game under dynamic constraints given in terms of hybrid dynamical systems. Find the full version in [8], including the main results and outlines of the corresponding proofs. We propose sufficient conditions to guarantee attaining a solution to the game. When the players select the optimal strategy, the value function can be evaluated without the need of computing solutions. Under additional conditions, the optimal feedback laws render a set of interest asymptotically stable. Using this framework, we address an optimal control problem under the presence of an adversarial action in which the decision-making agents have dynamics that might exhibit both continuous and discrete behavior.
Still Creepy After All These Years:The Normalization of Affective Discomfort in App Use

It is not well understood why people continue to use privacy-invasive apps they consider creepy. We conducted a scenario-based study (n = 751) to investigate how the intention to use an app is influenced by affective perceptions and privacy concerns. We show that creepiness is one facet of affective discomfort, which is becoming normalized in app use. We found that affective discomfort can be negatively associated with the intention to use a privacy-invasive app. However, the influence is mitigated by other factors, including data literacy, views regarding app data practices, and ambiguity of the privacy threat. Our findings motivate a focus on affective discomfort when designing user experiences related to privacy-invasive data practices. Treating affective discomfort as a fundamental aspect of user experience requires scaling beyond the point where the thumb meets the screen and accounting for entrenched data practices and the sociotechnical landscape within which the practices are embedded.
Modeling Political Activism around Gun Debate via Social Media

The United States have some of the highest rates of gun violence among developed countries. Yet, there is a disagreement about the extent to which firearms should be regulated. In this study, we employ social media signals to examine the predictors of offline political activism, at both population and individual level. We show that it is possible to classify the stance of users on the gun issue, especially accurately when network information is available. Alongside socioeconomic variables, network information such as the relative size of the two sides of the debate is also predictive of state-level gun policy. On individual level, we build a statistical model using network, content, and psycho-linguistic features that predicts real-life political action, and explore the most predictive linguistic features. Thus, we argue that, alongside demographics and socioeconomic indicators, social media provides useful signals in the holistic modeling of political engagement around the gun debate.
Instagram Data Donation: A Case Study on Collecting Ecologically Valid Social Media Data for the Purpose of Adolescent Online Risk Detection

In this work, we present a case study on an Instagram Data Donation (IGDD) project, which is a user study and web-based platform for youth (ages 13-21) to donate and annotate their Instagram data with the goal of improving adolescent online safety. We employed human-centered design principles to create an ecologically valid dataset that will be utilized to provide insights from teens’ private social media interactions and train machine learning models to detect online risks. Our work provides practical insights and implications for Human-Computer Interaction (HCI) researchers that collect and study social media data to address sensitive problems relating to societal good.
StoryBox: Independent Multi-modal Interactive Storytelling for Children with Visual Impairment

Storytelling promotes the development of imagination and creativity and facilitates the impregnation of linguistic, emotional and moral skills in children. Limited storytelling media for children with blindness and visual impairment (BVI) confines their access to this unique experience. StoryBox is a multi-modal storytelling agent, facilitating an independent and interactive storytelling experience for children (5-12 years) with BVI. The platform supports audio-based story narration and enables tangible interaction with story elements via sensor-enabled tactile clay figurines. StoryBox takes inspiration from ‘kaavad baanchana’ - an oral storytelling tradition from Rajasthan, India. This paper presents the design of the proposed accessible multi-modal storytelling platform and findings from initial user evaluation with blindfolded participants.
Identifying Game Mechanics for Integrating Fabrication Activities within Existing Digital Games

Integrating fabrication activities into existing video games provides opportunities for players to construct objects from their gameplay and bring the digital content into the physical world. In our prior work, we outlined a framework and developed a toolkit for integrating fabrication activities within existing digital games. Insights from our prior study highlighted the challenge of aligning fabrication mechanics with the existing game mechanics in order to strengthen the player aesthetics. In this paper, we address this challenge and build on our prior work by adding fabrication components to the Mechanics-Dynamics-Aesthetics (MDA) framework. We use this f-MDA framework to analyze the 47 fabrication events from the prior study. We list the new player-object aesthetics that emerge from integrating the existing game mechanics with fabrication mechanics. We identify connections between these emergent player-object aesthetics and the existing game mechanics. We discuss how designers can use this mapping to identify potential game mechanics for integrating with fabrication activities.
Actuated Materials and Soft Robotics Strategies for Human-Computer Interaction Design

The fields of programmable matter, actuated materials, and Soft Robotics are becoming increasingly more relevant for the design of novel applications, interfaces, and user experiences in the domain of Human-Computer Interaction (HCI). These research fields often use soft, flexible materials with elastic actuation mechanisms to build systems that are more adaptable, compliant, and suitable for a very broad range of environments. However, at the intersection between HCI and the aforementioned domains, there are numerous challenges related to fabrication methods, development tools, resource availability, nomenclature, design for inclusion, etc. This workshop aims to explore how to make Soft Robotics more accessible to both researchers and nonresearchers alike. We will (1) investigate and identify the various difficulties people face when developing HCI applications that require the transfer of knowledge from those other domains, and (2) discuss possible solutions and visions on how to overcome those difficulties.
Semantic Gap in Predicting Mental Wellbeing through Passive Sensing

When modeling passive data to infer individual mental wellbeing, a common source of ground truth is self-reports. But these tend to represent the psychological facet of mental states, which might not align with the physiological facet of that state. Our paper demonstrates that when what people “feel” differs from what people “say they feel”, we witness a semantic gap that limits predictions. We show that predicting mental wellbeing with passive data (offline sensors or online social media) is related to how the ground-truth is measured (objective arousal or self-report). Features with psycho-social signals (e.g., language) were better at predicting self-reported anxiety and stress. Conversely, features with behavioral signals (e.g., sleep), were better at predicting stressful arousal. Regardless of the source of ground truth, integrating both signals boosted prediction. To reduce the semantic gap, we provide recommendations to evaluate ground truth measures and adopt parsimonious sensing.
There Is No First- or Third-Person View in Virtual Reality: Understanding the Perspective Continuum

Modern games make creative use of First- and Third-person perspectives (FPP and TPP) to allow the player to explore virtual worlds. Traditionally, FPP and TPP perspectives are seen as distinct concepts. Yet, Virtual Reality (VR) allows for flexibility in choosing perspectives. We introduce the notion of a perspective continuum in VR, which is technically related to the camera position and conceptually to how users perceive their environment in VR. A perspective continuum enables adapting and manipulating the sense of agency and involvement in the virtual world. This flexibility of perspectives broadens the design space of VR experiences through deliberately manipulating perception. In a study, we explore users’ attitudes, experiences and perceptions while controlling a virtual character from the two known perspectives. Statistical analysis of the empirical results shows the existence of a perspective continuum in VR. Our findings can be used to design experiences based on shifts of perception.