Miracle-dz/UniFAR-FTUs huggingface dataset:
A Field Study of Developer Documentation Format

Documentation facilitates the transfer of knowledge among programmers and helps them become familiar with new technologies. However, the effectiveness with which a reader can find information in a document depends on its presentation format. In a prior publication, we presented Casdoc, a novel dynamic format for code examples. In this work, we synthesized five documentation presentation guidelines from prior research on programmer information needs and search behaviors. We then used Casdoc as an instrument to evaluate the impact of these guidelines in a field study with 326 students who used 126 documents over several months. Participants overwhelmingly chose to use Casdoc instead of a static baseline format. We observed that interactive documents can contain more information without distracting its readers. We also found some limitations that authors should consider when applying the guidelines, such as the large impact of small differences in visual cues to help readers navigate a document.
The Cane Game: An Educational Tool for Orientation and Mobility

Proficiency in Orientation and Mobility (O&M) is vital for people who are blind or visually impaired (BVI) to access physical environments and economic opportunities. While much engineering effort has focused on using technology to augment the practice of using a mobility cane, there is comparatively little work on using technology in service of teaching the use of the cane itself. We report a case study of a multi-year collaboration between assistive technology researchers and a certified O&M instructor at Perkins School for the Blind to utilize technology to create O&M teaching tools that use the concept of gamification to make such training more fun and effective. In this collaboration we adopted an action research approach, and through the application of action research HCI methods created several prototype systems for teaching O&M skills. These prototypes were refined to create the Cane Game: a system for teaching students cane sweeping technique using interactive music and sound. The Cane Game system can be constructed for less than $100 and is capable of being distributed at large-scale. A qualitative study of Perkins School for the Blind students’ O&M educational trajectories while using the system illuminates the conditions in which this tool is effective as a teaching aid.
VR Almost There: Simulating Co-located Multiplayer Experiences in Social Virtual Reality

Consumer social virtual reality (VR) applications have recently started to enable social interactions at a distance. Yet it is still relatively unknown if and to what extent such applications provide meaningful social experiences in cases where in-person leisure activities are not feasible. To explore this, we developed a custom social VR application and conducted an exploratory lab study with 25 dyads in which we compared an in-person and a virtual version of a co-located multiplayer scenario. Our mixed-methods analysis revealed that both scenarios created a socially rich atmosphere and strengthened the social closeness between players. However, the lack of facial animations, limited body language, and a low field of view led to VR’s main social experiential limitations: a reduced mutual awareness and emotional understanding compared to the in-person scenario. We derive implications for social VR design and research as well as game user research.
VisLab: Enabling Visualization Designers to Gather Empirically Informed Design Feedback

When creating a visualization, designers face various conflicting design choices. They typically rely on their hunches to deal with intricate trade-offs or resort to feedback from their colleagues. On the other hand, researchers have long used empirical methods to derive useful quantitative insights into visualization designs. Taking inspiration from this research tradition, we developed VisLab, an open-source online system to complement the existing qualitative feedback practice and help visualization practitioners run experiments to gather empirically informed design feedback. We surveyed practitioners’ perceptions of quantitative feedback and analyzed the research literature to inform VisLab’s motivation and design. VisLab operationalizes the experiment process using templates and dashboards to make empirical methods amenable for practitioners while supporting sharing and remixing experiments to aid knowledge exchange and validation. We demonstrated the validity of experiments in VisLab and evaluated the usability and potential usefulness of VisLab in visualization design practice.
Augmenting Human Cognition with an AI-Mediated Intelligent Visual Feedback

In this paper, we introduce an AI-mediated framework that can provide intelligent feedback to augment human cognition. Specifically, we leverage deep reinforcement learning (DRL) to provide adaptive time pressure feedback to improve user performance in a math arithmetic task. Time pressure feedback could either improve or deteriorate user performance by regulating user attention and anxiety. Adaptive time pressure feedback controlled by a DRL policy according to users’ real-time performance could potentially solve this trade-off problem. However, the DRL training and hyperparameter tuning may require large amounts of data and iterative user studies. Therefore, we propose a dual-DRL framework that trains a regulation DRL agent to regulate user performance by interacting with another simulation DRL agent that mimics user cognition behaviors from an existing dataset. Our user study demonstrates the feasibility and effectiveness of the dual-DRL framework in augmenting user performance, in comparison to the baseline group.
VizProg: Identifying Misunderstandings By Visualizing Students’ Coding Progress

Programming instructors often conduct in-class exercises to help them identify students that are falling behind and surface students’ misconceptions. However, as we found in interviews with programming instructors, monitoring students’ progress during exercises is difficult, particularly for large classes. We present VizProg, a system that allows instructors to monitor and inspect students’ coding progress in real-time during in-class exercises. VizProg represents students’ statuses as a 2D Euclidean spatial map that encodes the students’ problem-solving approaches and progress in real-time. VizProg allows instructors to navigate the temporal and structural evolution of students’ code, understand relationships between code, and determine when to provide feedback. A comparison experiment showed that VizProg helped to identify more students’ problems than a baseline system. VizProg also provides richer and more comprehensive information for identifying important student behavior. By managing students’ activities at scale, this work presents a new paradigm for improving the quality of live learning.
Healthcare AI Treatment Decision Support: Design Principles to Enhance Clinician Adoption and Trust

Artificial intelligence (AI) supported clinical decision support (CDS) technologies can parse vast quantities of patient data into meaningful insights for healthcare providers. Much work is underway to determine the technical feasibility and the accuracy of AI-driven insights. Much less is known about what insights are considered useful and actionable by healthcare providers, their trust in the insights, and clinical workflow integration challenges. Our research team used a conceptual prototype based on AI-generated treatment insights for type 2 diabetes medications to elicit feedback from 41 U.S.-based clinicians, including primary care and internal medicine physicians, endocrinologists, nurse practitioners, physician assistants, and pharmacists. We contribute to the human-computer interaction (HCI) community by describing decision optimization and design objective tensions between population-level and personalized insights, and patterns of use and trust of AI systems. We also contribute a set of 6 design principles for AI-supported CDS.
Investigating Tangible Privacy-Preserving Mechanisms for Future Smart Homes

Most smart home devices have multiple sensors, such as cameras and microphones; however, most cannot be controlled individually. Tangible privacy mechanisms provide control over individual sensors and instill high certainty of privacy. Yet, it remains unclear how they can be used in future smart homes. We conducted three studies to understand how tangible privacy mechanisms scale across multiple devices and respond to user needs. First, we conducted a focus group (N=8) on speculative tangible control artifacts to understand the user perspective. Second, we ran a workshop at a human-computer interaction conference (N=8) on tangible privacy. Third, we conducted a six-week in-the-wild study with a tangible, static privacy dashboard across six households. Our findings help to contrast the need for tangible privacy mechanisms on the sensor level with user needs on a smart home level. Finally, we discuss our design implications for future smart homes through the lens of inclusive privacy.
Continuous prediction of finger joint angles based on time series feature fusion CNN

Continuous estimation of joints based on surface electromyography (sEMG) signals has been widely used in smooth motion of robots. Currently, deep learning techniques have become the main means of continuous motion estimation. Although the deep learning technique can reduce the workload of manual features by feature learning, its learned features lack the temporal information of continuous joint activity and cannot provide an effective description of the continuous joint trajectory. Therefore, this paper proposes a method for continuous finger joint angle prediction based on time series feature fusion convolutional neural network (CNN), which first extracts feature information of sEMG signals by manual features, then constructs time series, then inputs to convolutional neural network to extract deep features, and finally estimates finger joint angle. The experimental results show that the CNN method based on time series feature fusion is significantly better than the methods of CNN and multilayer perceptron (MLP). It is proved that the method in this study can effectively extract the features of sEMG signals as well as better prediction performance, which provides a new idea for continuous motion estimation methods.
Flying Names: Manipulating Name Tags in XR Social Environments for Target Selection Tasks

Recent developments in Extended Reality (XR) have led to the creation of various Virtual Reality (VR) social applications that allow small groups of people to interact and participate in various activities. However, these applications are not suitable for larger-scale social scenarios. One major challenge is the lacking of effective solutions for target selection because existing methods for occluded-target selection do not apply well to humans. In this paper, we propose a method called “Flying Names” to improve decision-making accuracy during target selection for both VR and Augmented Reality (AR) social environments by manipulating the heights of name tags on the fly and connecting an auxiliary line from a name tag to its highlighted owner. Our user study results indicate that this proposal effectively improves the accuracy of identifying the actual target person and is better than using either one or none of manipulating heights and connecting lines.
Chart Reader: Accessible Visualization Experiences Designed with Screen Reader Users

Even though screen readers are a core accessibility tool for blind and low vision individuals (BLVIs), most visualizations are incompatible with screen readers. To improve accessible visualization experiences, we partnered with 10 BLV screen reader users (SRUs) in an iterative co-design study to design and develop accessible visualization experiences that afford SRUs the autonomy to interactively read and understand visualizations and their underlying data. During the five-month study, we explored accessible visualization prototypes with our design partners for three one-hour sessions. Our results provide feedback on the synthesized design concepts we explored, why (or why not) they aid comprehension and exploration for SRUs, and how differing design concepts can fit into cohesive accessible visualization experiences. We contribute both Chart Reader, a web-based accessibility engine resulting from our design iterations, and our distilled study findings—organized by design dimensions—in the creation of comprehensive accessible visualization experiences.
Finite layer method for numerical simulation of unsteady groundwater flow in cylindrical coordinate system

A test function by using the Bessel function and basic function,which satisfies constant-head and no flow boundary conditions,is put forward to describe unsteady groundwater flow in the cylindrical coordinate system.Based on Galerkin's method,semi-analytical finite layer equations are derived in the non-homogeneous and anisotropic layered leaky confined aquifers.The finite layer method(FLM) for the unsteady groundwater flow is presented in the cylindrical coordinate system.A computer program is developed for solving the finite layer equations.Numerical examples are presented and compared with the analytical solutions to demonstrate the validity and efficiency of the present method.
The Whys and Wherefores of Cubes

The Intentional Analytics Model (IAM) has been devised to couple OLAP and analytics by (i) letting users express their analysis intentions on multidimensional data cubes and (ii) returning enhanced cubes, i.e., multidimensional data annotated with knowledge insights in the form of models (e.g., correlations). Five intention operators were proposed to this end; of these, describe and assess have been investigated in previous papers. In this work we enrich the IAM picture by focusing on the explain operator, whose goal is to provide an answer to the user asking “why does measure 𝑚 show these values? ”. Specifically, we pro-pose a syntax for the operator and discuss how enhanced cubes are built by (i) finding the polynomials that best approximate the relationship between 𝑚 and the other cube measures, and (ii) highlighting the most interesting one. Finally, we test the operator implementation in terms of efficiency.
Online Geometric Hitting Set and Set Cover Beyond Unit Balls in ℝ2

We investigate the geometric hitting set problem in the online setup for the range space $\Sigma=({\cal P},{\cal S})$, where the set $\P\subset\mathbb{R}^2$ is a collection of $n$ points and the set $\cal S$ is a family of geometric objects in $\mathbb{R}^2$. In the online setting, the geometric objects arrive one by one. Upon the arrival of an object, an online algorithm must maintain a valid hitting set by making an irreversible decision, i.e., once a point is added to the hitting set by the algorithm, it can not be deleted in the future. The objective of the geometric hitting set problem is to find a hitting set of the minimum cardinality. Even and Smorodinsky (Discret. Appl. Math., 2014) considered an online model (Model-I) in which the range space $\Sigma$ is known in advance, but the order of arrival of the input objects in $\cal S$ is unknown. They proposed online algorithms having optimal competitive ratios of $\Theta(\log n)$ for intervals, half-planes and unit disks in $\mathbb{R}^2$. Whether such an algorithm exists for unit squares remained open for a long time. This paper considers an online model (Model-II) in which the entire range space $\Sigma$ is not known in advance. We only know the set $\cal P$ but not the set $\cal S$ in advance. Note that any algorithm for Model-II will also work for Model-I, but not vice-versa. In Model-II, we obtain an optimal competitive ratio of $\Theta(\log(n))$ for unit disks and regular $k$-gon with $k\geq 4$ in $\mathbb{R}^2$. All the above-mentioned results also hold for the equivalent geometric set cover problem in Model-II.
Can Icons Outperform Text? Understanding the Role of Pictograms in OHMD Notifications

Optical see-through head-mounted displays (OHMDs) can provide just-in-time digital assistance to users while they are engaged in ongoing tasks. However, given users’ limited attentional resources when multitasking, there is a need to concisely and accurately present information in OHMDs. Existing approaches for digital information presentation involve using either text or pictograms. While pictograms have enabled rapid recognition and easier use in warning messages and traffic signs, most studies using pictograms for digital notifications have exhibited unfavorable results. We thus conducted a series of four iterative studies to understand how we can support effective notification presentation on OHMDs during multitasking scenarios. We find that while icon-augmented notifications can outperform text-only notifications, their effectiveness depends on icon familiarity, encoding density, and environmental brightness. We reveal design implications when using icon-augmented notifications in OHMDs and present plausible reasons for the observed disparity in literature.
Predicting Gaze-based Target Selection in Augmented Reality Headsets based on Eye and Head Endpoint Distributions

Target selection is a fundamental task in interactive Augmented Reality (AR) systems. Predicting the intended target of selection in such systems can provide users with a smooth, low-friction interaction experience. Our work aims to predict gaze-based target selection in AR headsets with eye and head endpoint distributions, which describe the probability distribution of eye and head 3D orientation when a user triggers a selection input. We first conducted a user study to collect users’ eye and head behavior in a gaze-based pointing selection task with two confirmation mechanisms (air tap and blinking). Based on the study results, we then built two models: a unimodal model using only eye endpoints and a multimodal model using both eye and head endpoints. Results from a second user study showed that the pointing accuracy is improved by approximately 32% after integrating our models into gaze-based selection techniques.
iFAD Gestures: Understanding Users’ Gesture Input Performance with Index-Finger Augmentation Devices

We examine gestures performed with a class of input devices with distinctive quality properties in the wearables landscape, which we call “index-Finger Augmentation Devices” (iFADs). We introduce a four-level taxonomy to characterize the diversity of iFAD gestures, evaluate iFAD gesture articulation on a dataset of 6,369 gestures collected from 20 participants, and compute recognition accuracy rates. Our findings show that iFAD gestures are fast (1.84s on average), easy to articulate (1.52 average rating on a difficulty scale from 1 to 5), and socially acceptable (81% willingness to use them in public places). We compare iFAD gestures with gestures performed using other devices (styli, touchscreens, game controllers) from several public datasets (39,263 gestures, 277 participants), and report that iFAD gestures are two times faster than whole-body gestures and as fast as stylus and finger strokes performed on touchscreens.
Children’s Trust in Robots and the Information They Provide

Previous work has shown that children tend to trust embodied conversational agents such as social robots. Also, that children have difficulty assessing the credibility of information. The study reported in this paper addresses how children’s attitudes toward and trust in a robot affect their acceptance of information provided by the robot. We conducted a within-subjects study (N=30) where children engaged with a ‘trustworthy’ versus an ‘untrustworthy’ robot. Due to the pandemic period, this interaction was carried out via video call. The children played a quiz with the robot where we measured whether they accepted the information provided by the robot. Results show that the manipulation of trustworthiness was successful. We did not find evidence for a causal relationship between trust in the robot and acceptance of the information. Furthermore, semi-structured interviews offered a more in-depth understanding of how children perceived the two different robots and their preference for the trustworthy robot.
Expectation vs Reality in Users’ Willingness to Delegate to Digital Assistants

We report results of a survey with 2500 US-based users of popular digital assistants (DAs) to understand and prioritize the factors that drive consumer adoption. Using structural equation modelling, we investigated the relationship between respondents’ behavioural intentions to delegate tasks to their DAs and two predictor layers: DA (users’ attitudes and familiarity with them) and task factors (need for control/transparency, subjectivity, risk, self-efficacy, and frequency), mediated by a values layer (trust, perceived ease of use and usefulness). Perceived usefulness and trust were strong direct predictors of willingness to delegate. Both DA-related factors had indirect effects, with a surprising negative influence of familiarity on both trust and usefulness. Our novel findings of task-related effects on the value factors may explain this disparity. These results imply a mismatch between users’ expectations for DAs and their actual experiences. We interpret these findings in light of related work and derive implications for practitioners.
Eye-Perspective View Management for Optical See-Through Head-Mounted Displays

Optical see-through (OST) head-mounted displays (HMDs) enable users to experience Augmented Reality (AR) support in the form of helpful real-world annotations. Unfortunately, the blend of the environment with virtual augmentations due to semitransparent OST displays often deteriorates the contrast and legibility of annotations. View management algorithms adapt the annotations’ layout to improve legibility based on real-world information, typically captured by built-in HMD cameras. However, the camera views are different from the user’s view through the OST display which decreases the final layout quality. We present eye-perspective view management that synthesizes high-fidelity renderings of the user’s view to optimize annotation placement. Our method significantly improves over traditional camera-based view management in terms of annotation placement and legibility. Eye-perspective optimizations open up opportunities for further research on use cases relying on the user’s true view through OST HMDs.
Is cartoonized life-vlogging the key to increasing adoption of activity-oriented wearable camera systems?

Health science researchers studying human behavior rely on wearable cameras to visually confirm behaviors in real-world settings. However, privacy concerns significantly impede their adoption. Lens orientation and activity-oriented cameras have potential in balancing the need to visually validate the wearers’ activities while reducing privacy concerns. To increase adoption and further alleviate privacy concerns while maintaining utility, generative stylizing approaches, like cartooning using generative adversarial networks (GANs), have recently shown promise. We investigate different cartoon-based obfuscation of activity-oriented footage through two studies. The first deploys crowdsourcing methods (n=60), while the second is experiential, where participants (n=49) don the device for an entire day and report concerns on their footage. Our findings support that cartoonization of activity-oriented data significantly reduces privacy concerns, particularly among bystanders in high privacy-concerning scenarios, while maintaining context verification (90% of participants). Through thematic analysis, we provide further insight for the community on best practices for cartoonization of activity-oriented videos.