Empathy is increasingly recognized as a key factor in human–AI communication, yet conventional approaches to “digital empathy” often focus on simulating internal, human like emotional states while overlooking the inherently subjective, contextual, and relational facets of empathy as perceived by users. In this work, we propose a human-centered taxonomy that emphasizes observable empathic behaviors and introduce a new dataset, SENSE-7, of real-world conversations between information workers and Large Language Models (LLMs), which includes per-turn empathy annotations directly from the users, along with user characteristics, and contextual details, offering a more user-grounded representation of empathy. Analysis of 695 conversations from 109 participants reveals that empathy judgments are highly individualized, context-sensitive, and vulnerable to disruption when conversational continuity fails or user expectations go unmet. To promote further research, we provide a subset of 672 anonymized conversation and provide exploratory classification analysis, showing that an LLM-based classifier can recognize 5 levels of empathy with an encouraging average Spearman ρ = 0.369 and Accuracy = 0.487 over this set. Overall, our findings underscore the need for AI designs that dynamically tailor empathic behaviors to user contexts and goals, offering a roadmap for future research and practical development of socially attuned, human-centered artificial agents.

Information workers increasingly struggle with productivity challenges in modern workplaces, facing difficulties in managing time and effectively utilizing workplace analytics data for behavioral improvement. Despite the availability of productivity metrics through enterprise tools, workers often fail to translate this data into actionable insights. We present a comprehensive, user-centric approach to address these challenges through AI-based productivity agents tailored to users' needs. Utilizing a two-phase method, we first conducted a survey with 363 participants, exploring various aspects of productivity, communication style, agent approach, personality traits, personalization, and privacy. Drawing on the survey insights, we developed a GPT-4 powered personalized productivity agent that utilizes telemetry data gathered via Viva Insights from information workers to provide tailored assistance. We compared its performance with alternative productivity-assistive tools, such as dashboard and narrative, in a study involving 40 participants. Our findings highlight the importance of user-centric design, adaptability, and the balance between personalization and privacy in AI-assisted productivity tools. By building on these insights, our work provides important guidance for developing more effective productivity solutions, ultimately leading to optimized efficiency and user experiences for information workers.

The future of work is rapidly changing, with remote and hybrid settings blurring the boundaries between professional and personal life. To understand how work rhythms vary across different work settings, we conducted a month-long study of 65 software developers, collecting anonymized computer activity data as well as daily ratings for perceived stress, productivity, and work setting. In addition to confirming the double-peak pattern of activity at 10:00 am and 2:00 pm observed in prior research, we observed a significant third peak around 9:00 pm. This third peak was associated with higher perceived productivity during remote days but increased stress during onsite and hybrid days, highlighting a nuanced interplay between work demands and work settings. Additionally, we found strong correlations between computer activity, productivity, and stress, including an inverted U-shaped relationship where productivity peaked at around six hours of computer activity before declining on more active days. These findings provide new insights into evolving work rhythms and highlight the impact of different work settings on productivity and stress.

Creative practitioners (like designers, software developers, and architects) have started to employ Generative AI models (GenAI) to produce text, images, and assets comparable to those made by people. While HCI research explores specific GenAI models and creativity support tools, little is known about practitioners’ evolving roles and workflows with GenAI models across a project’s stages. This knowledge is key to guide the development of the new generation of Creativity Support Tools. We contribute to this knowledge by employing a triangulated method to capture interviews, videos, and survey responses of creative practitioners reflecting on projects they completed with GenAI. Our observations let us derive a set of factors that capture practitioners’ perceived roles, challenges, benefits, and interaction patterns when creating with GenAI. From these factors, we offer insights and propose design opportunities and priorities that serve to encourage reflection from the wider community of Creativity Support Tools and GenAI stakeholders such as systems creators, researchers, and educators on how to develop systems that meet the needs of creatives in human-centered ways.

Creating Computer Vision (CV) models remains a complex practice, despite their ubiquity. Access to data, the requirement for ML expertise, and model opacity are just a few points of complexity that limit the ability of end-users to build, inspect, and improve these models. Interactive ML perspectives have helped address some of these issues by considering a teacher in the loop where planning, teaching, and evaluating tasks take place. We present and evaluate two interactive visualizations in the context of Sprite, a system for creating CV classification and detection models for images originating from videos. We study how these visualizations help Sprite’s users identify (evaluate) and select (plan) images where a model is struggling and can lead to improved performance, compared to a baseline condition where users used a query language. We found that users who had used the visualizations found more images across a wider set of potential types of model errors.

Work-nonwork balance is an important aspect of workplace well-being with associations to improved physical and mental health, job performance, and quality of life. However, realizing work-nonwork balance goals is challenging due to competing demands and limited resources within organizational and interpersonal contexts. These challenges are compounded by technologies that blur the boundaries of work and nonwork in the always-on work cultures. At an individual level, such challenges can be subsided through the effective application of self-regulation techniques, such as implementation intentions and mental contrasting (IIMC). Further supporting these techniques through reflection on personal data, we implement the idea of data-driven IIMC into a self-tracking and behavior planning system and evaluate it in a three-week between-participant study with 43 information workers who used our system for improving work-nonwork balance. We find evidence that reflection on personal data improves awareness of behavior plan compliance and rescheduling, which are important in realizing work-nonwork balance goals. We also observe the value of micro-reflection, reflection on limited data of the very recent past, for IIMC. Our findings highlight opportunities for automation in data collection and sense-making and for further exploring the role of data-driven IIMC as boundary negotiating artifacts in support of work-nonwork balance goals.

Previous research suggests that biomusic, a type of biosignal sharing, is effective at promoting empathy and closeness among individuals. However, it is unclear whether these effects are due to the information it encodes or other emotional aspects of its resulting music. To explore this question, we developed a Generative Adversarial Network (GAN) to create synthetic biomusic that approximates real biomusic, and employed deception to evaluate its effects on 24 pairs of participants engaged in real-time emotional disclosure. Users reported that both real and synthetic biomusic provided the same amount of information about their conversational partner as observing body language, facial expressions, or vocal tone. Further, both conditions increased users’ ratings of closeness and empathy with each other compared to listening to no music. However, we found no statistically significant differences between the two biomusic conditions across any of our metrics. We discuss the implications of these results for the design of future biomusic systems.

Reflection on one’s personal data can be an effective tool for supporting wellbeing. However, current wellbeing reflection support tools tend to offer a one-size-fits-all approach, ignoring the diversity of people’s wellbeing goals and their agency in the self-reflection process. In this work, we identify an opportunity to help people work toward their wellbeing goals by empowering them to reflect on their data on their own terms. Through a formative study, we inform the design and implementation of Pearl, a workplace wellbeing reflection support tool that allows users to explore their personal data in relation to their wellbeing goal. Pearl is a calendar-based interactive machine teaching system that allows users to visualize data sources and tag regions of interest on their calendar. In return, the system provides insights about these tags that can be saved to a reflection journal. We used Pearl as a technology probe with 12 participants without data science expertise and found that all participants successfully gained insights into their workplace wellbeing. In our analysis, we discuss how Pearl’s capabilities facilitate insights, the role of machine assistance in the self-reflection process, and the data sources that participants found most insightful. We conclude with design dimensions for intelligent reflection support systems as inspiration for future work.

Workplace stress has been increasing in recent decades and has worsened by the unique demands imposed by COVID-19 and the new remote/hybrid work settings. High-stress working conditions can be detrimental to the health and wellness of workers and can lead to significant business costs in terms of productivity loss and medical expenses. An essential step toward managing stress involves finding comfortable ways to sense workers and recognizing stress as soon as it happens. This work explores the potential value of using pervasive sensors such as keyboards, webcams, and behavioral data such as calendar and e-mail activity to passively assess individual stress levels of work in real-life. In particular, we collected a large corpus of such data from 46 remote information workers over one month and asked them to self-report their stress levels and other relevant factors several times a day. Analysis of the data demonstrates that passive sensors can effectively detect both triggers and manifestations of workplace stress and that having access to prior data of the worker is critical for developing well-performing stress recognition models. Furthermore, we provide qualitative feedback capturing workers' preferences in workplace stress monitoring.

Workplace stress-reduction interventions have produced mixed results due to engagement and adherence barriers. Leveraging technology to integrate such interventions into the workday may address these barriers and help mitigate the mental, physical, and monetary effects of workplace stress. To inform the design of a workplace stress-reduction intervention system, we conducted a four-week longitudinal study with 86 participants, examining the effects of intervention type and timing on usage, stress reduction impact, and user preferences. We compared three intervention types and two delivery timing conditions: Pre-scheduled (PS) by users and Just-in-time (JIT) prompted by the system-identified user stress-levels. We found JIT participants completed significantly more interventions than PS participants, but post-intervention and study-long stress reduction was not significantly different between conditions. Participants rated low-effort interventions highest, but high-effort interventions reduced the most stress. Participants felt JIT provided accountability but desired partial agency over timing. We present type and timing implications.

Online research is a frequent and important activity people perform on the Internet, yet current support for this task is basic, fragmented and not well integrated into web browser experiences. Guided by sensemaking theory, we present ForSense, a browser extension for accelerating people’s online research experience. The two primary sources of novelty of ForSense are the integration of multiple stages of online research and providing machine assistance to the user by leveraging recent advances in neural-driven machine reading. We use ForSense as a design probe to explore (1) the benefits of integrating multiple stages of online research, (2) the opportunities to accelerate online research using current advances in machine reading, (3) the opportunities to support online research tasks in the presence of imprecise machine suggestions, and (4) insights about the behaviors people exhibit when performing online research, the pages they visit, and the artifacts they create. Through our design probe, we observe people performing online research tasks, and see that they benefit from ForSense’s integration and machine support for online research. From the information and insights we collected, we derive and share key recommendations for designing and supporting imprecise machine assistance for research tasks.

Designing natural language interfaces for querying databases remains an important goal pursued by researchers in natural language processing, databases, and HCI. These systems receive natural language as input, translate it into a formal database query, and execute the query to compute a result. Because the responses from these systems are not always correct, it is important to provide people with mechanisms to assess the correctness of the generated query and computed result. However, this assessment can be challenging for people who lack expertise in query languages. We present Debug-It-Yourself (DIY), an interactive technique that enables users to assess the responses from a state-of-the-art natural language to SQL (NL2SQL) system for correctness and, if possible, fix errors. DIY provides users with a sandbox where they can interact with (1) the mappings between the question and the generated query, (2) a small-but-relevant subset of the underlying database, and (3) a multi-modal explanation of the generated query. End-users can then employ a back-of-the-envelope calculation debugging strategy to evaluate the system’s response. Through an exploratory study with 12 users, we investigate how DIY helps users assess the correctness of the system’s answers and detect & fix errors. Our observations reveal the benefits of DIY while providing insights about end-user debugging strategies and underscore opportunities for further improving the user experience.

Online research is a frequent and important activity people perform on the Internet, yet current support for this task is basic, fragmented and not well integrated into web browser experiences. Guided by sensemaking theory, we present ForSense, a browser extension for accelerating people’s online research experience. The two primary sources of novelty of ForSense are the integration of multiple stages of online research and providing machine assistance to the user by leveraging recent advances in neural-driven machine reading. We use ForSense as a design probe to explore (1) the benefits of integrating multiple stages of online research, (2) the opportunities to accelerate online research using current advances in machine reading, and (3) the opportunities to support online research tasks under the presence of imprecise machine suggestions. In our study, we observe people performing online research tasks, and see that they benefit from ForSense’s integration and machine support for online research. From our study, we derive and share key recommendations for designing and supporting imprecise machine assistance for research tasks.

Ahmed Elgohary, Christopher Meek, Matthew Richardson, Adam Fourney, Gonzalo Ramos, Ahmed Hassan Awadallah. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2021.

Year-round ultraviolet exposure silently causes skin damage that goes unnoticed until sunburn. Current personal wearables for monitoring UV exposure have not seen significant uptake, which may be attributed to their one-size-fits-all aesthetic or inapplicability to people with different skin tones. We present EcoPatches, inkjet-printable chemical patches that mediate a person's relationship with their environment by allowing them to create designs and formulations that resonate with them. Supporting human- and machine-interpretability for EcoPatches' visual changes means that users can glance at their EcoPatch during the day to see large exposure changes or take a picture of their EcoPatch with a smartphone app for more accurate and precise readings. We conducted an online survey to elicit visual design recommendations that support these features. We also evaluated both interpretation methods, finding that they achieved strong Pearson correlation coefficients with the \projectnames' known exposure levels (human: 0.79, app: 0.90).

Machine teaching (MT) is an emerging field that studies non-machine learning (ML) experts incrementally building semantic ML models in efficient ways. While MT focuses on the types of knowledge a human teacher provides a machine learner, not much is known about how people perform or can be supported in this essential task of identifying and expressing useful knowledge. We refer to this process as knowledge decomposition. To address the challenges of this type of Human-AI collaboration, we seek to build foundational frameworks for understanding and supporting knowledge decomposition. We present results of a study investigating what types of knowledge people teach, what cognitive processes they use, and what challenges they encounter when teaching a learner to classify text documents. From our observations, we introduce design opportunities for new tools to support knowledge decomposition. Our findings carry implications for applying the benefits of knowledge decomposition to MT and ML.

Object detection is a key application of machine learning. Currently, these detector models rely on deep networks that offer model builders limited agency over model construction, refinement and maintenance. Human-centered approaches to address these issues explore the exchange of knowledge between a human-in-the-loop and a learning system. This exchange, mediated through a teaching language, is often restricted to the specification of labels and constrains user expressiveness communicating other forms of knowledge to the system. We propose and assess an expressive teaching language for specifying object detectors which includes constructs such as concepts and relationships. From a formative study, we identified language building blocks and articulated design goals for creating interactive experiences in teaching object detection. We applied these goals through a design probe that highlighted further research questions and a set of design takeaways.

Real-world recommender systems often allow users to adjust the presented content through a variety of preference elicitation techniques such as “liking” or interest profiles. These elicitation techniques trade-off time and effort to users with the richness of the signal they provide to learning component driving the recommendations. In this paper, we explore this trade-off, seeking new ways for people to express their preferences with the goal of improving communication channels between users and the recommender system. Through a need-finding study, we observe the patterns in how people express their preferences during curation task, propose a taxonomy for organizing them, and point out research opportunities. We present a case study that illustrates how using this taxonomy to design an onboarding experience can lead to more accurate machine-learned recommendations while maintaining user satisfaction under low effort.

Modern systems can augment people’s capabilities by using machine-learned models to surface intelligent behaviors. Unfortunately, building these models remains challenging and beyond the reach of non-machine learning experts. We describe interactive machine teaching (IMT) and its potential to simplify the creation of machine-learned models. One of the key characteristics of IMT is its iterative process in which the human-in-the-loop takes the role of a teacher teaching a machine how to perform a task. We explore alternative learning theories as potential theoretical foundations for IMT, the intrinsic human capabilities related to teaching, and how IMT systems might leverage them. We argue that IMT processes that enable people to leverage these capabilities have a variety of benefits, including making machine learning methods accessible to subject-matter experts and the creation of semantic and debuggable machine learning (ML) models. We present an integrated teaching environment (ITE) that embodies principles from IMT, and use it as a design probe to observe how non-ML experts do IMT and as the basis of a system that helps us study how to guide teachers. We explore and highlight the benefits and challenges of IMT systems. We conclude by outlining six research challenges to advance the field of IMT.

When building a classifier in interactive machine learning (iML), human knowledge about the target class can be a powerful reference to make the classifier robust to unseen items. The main challenge lies in finding unlabeled items that can either help discover or refine concepts for which the current classifier has no corresponding features (i.e., it has feature blindness ). Yet it is unrealistic to ask humans to come up with an exhaustive list of items, especially for rare concepts that are hard to recall. This article presents AnchorViz , an interactive visualization that facilitates the discovery of prediction errors and previously unseen concepts through human-driven semantic data exploration. By creating example-based or dictionary-based anchors representing concepts, users create a topology that (a) spreads data based on their similarity to the concepts and (b) surfaces the prediction and label inconsistencies between data points that are semantically related. Once such inconsistencies and errors are discovered, users can encode the new information as labels or features and interact with the retrained classifier to validate their actions in an iterative loop. We evaluated AnchorViz through two user studies. Our results show that AnchorViz helps users discover more prediction errors than stratified random and uncertainty sampling methods. Furthermore, during the beginning stages of a training task, an iML tool with AnchorViz can help users build classifiers comparable to the ones built with the same tool with uncertainty sampling and keyword search, but with fewer labels and more generalizable features. We discuss exploration strategies observed during the two studies and how AnchorViz supports discovering, labeling, and refining of concepts through a sensemaking loop.

Machine learning (ML) promises data-driven insights and solutions for people from all walks of life, but the skill of crafting these solutions is possessed by only a few. Emerging research addresses this issue by creating ML tools that are easy and accessible to people who are not formally trained in ML (non-experts). This work investigated how non-experts build ML solutions for themselves in real life. Our interviews and surveys revealed unique potentials of non-expert ML, as well several pitfalls that non-experts are susceptible to. For example, many perceived percentage accuracy as a sole measure of performance, thus problematic models proceeded to deployment. These observations suggested that, while challenging, making ML easy and robust should both be important goals of designing novice-facing ML tools. To advance on this insight, we discuss design implications and created a sensitizing concept to demonstrate how designers might guide non-experts to easily build robust solutions.

When building a classifier in interactive machine learning, human knowledge about the target class can be a powerful reference to make the classifier robust to unseen items. The main challenge lies in finding unlabeled items that can either help discover or refine concepts for which the current classifier has no corresponding features (i.e., it has feature blindness). Yet it is unrealistic to ask humans to come up with an exhaustive list of items, especially for rare concepts that are hard to recall. This paper presents AnchorViz, an interactive visualization that facilitates error discovery through semantic data exploration. By creating example-based anchors, users create a topology to spread data based on their similarity to the anchors and examine the inconsistencies between data points that are semantically related. The results from our user study show that AnchorViz helps users discover more prediction errors than stratified random and uncertainty sampling methods.

We present PivotPaths, an interactive visualization for exploring faceted information resources. During both work and leisure, we increasingly interact with information spaces that contain multiple facets and relations, such as authors, keywords, and citations of academic publications, or actors and genres of movies. To navigate these interlinked resources today, one typically selects items from facet lists resulting in abrupt changes from one subset of data to another. While filtering is useful to retrieve results matching specific criteria, it can be difficult to see how facets and items relate and to comprehend the effect of filter operations. In contrast, the PivotPaths interface exposes faceted relations as visual paths in arrangements that invite the viewer to `take a stroll' through an information space. PivotPaths supports pivot operations as lightweight interaction techniques that trigger gradual transitions between views. We designed the interface to allow for casual traversal of large collections in an aesthetically pleasing manner that encourages exploration and serendipitous discoveries. This paper shares the findings from our iterative design-and-evaluation process that included semi-structured interviews and a two-week deployment of PivotPaths applied to a large database of academic publications.

Computing and visualizing sets of elements and their relationships is one of the most common tasks one performs when analyzing and organizing large amounts of data. Common representations of sets such as convex or concave geometries can become cluttered and difficult to parse when these sets overlap in multiple or complex ways, e.g., when multiple elements belong to multiple sets. In this paper, we present a design study of a novel set visual representation, LineSets, consisting of a curve connecting all of the set's elements. Our approach to design the visualization differs from traditional methodology used by the InfoVis community. We first explored the potential of the visualization concept by running a controlled experiment comparing our design sketches to results from the state-of-the-art technique. Our results demonstrated that LineSets are advantageous for certain tasks when compared to concave shapes. We discuss an implementation of LineSets based on simple heuristics and present a study demonstrating that our generated curves do as well as human-drawn ones. Finally, we present two applications of our technique in the context of search tasks on a map and community analysis tasks in social networks.

The impact of interruptions on workflow and productivity has been extensively studied in the PC domain, but while fragmented user attention is recognized as an inherent aspect of mobile phone usage, little formal evidence exists of its effect on mobile productivity. Using a survey and a screenshot-based diary study we investigated the types of barriers people face when performing tasks on their mobile phones, the ways they follow up with such suspended tasks, and how frustrating the experience of task disruption is for mobile users. From 386 situated samples provided by 12 iPhone and 12 Pocket PC users, we distill a classification of barriers to the completion of mobile tasks. Our data suggest that moving to a PC to complete a phone task is common, yet not inherently problematic, depending on the task. Finally, we relate our findings to prior design guidelines for desktop workflow, and discuss how the guidelines can be extended to mitigate disruptions to mobile taskflow.

When browsing a long video using a traditional timeline slider control, its effectiveness and precision degrade as a video's length grows. When browsing videos with more frames than pixels in the slider, aside from some frames being inaccessible, scrolling actions cause sudden jumps in a video's continuity as well as video frames to flash by too fast for one to assess the content. We propose a content-aware dynamic timeline control that is designed to overcome these limitations. Our timeline control decouples video speed and playback speed, and leverages video content analysis to allow salient shots to be presented at an intelligible speed. Our control also takes advantage of previous work on elastic sliders, which allows us to produce an accurate navigation control.

People regularly interact with different representations of Web pages. A person looking for new information may initially find a Web page represented as a short snippet rendered by a search engine. When he wants to return to the same page the next day, the page may instead be represented by a link in his browser history. Previous research has explored how to best represent Web pages in support of specific task types, but, as we find in this paper, consistency in representation across tasks is also important. We explore how different representations are used in a variety of contexts and present a compact representation that supports both the identification of new, relevant Web pages and the re-finding of previously viewed pages.

Synchronous gestures are patterns of sensed user or users' activity, spanning a distributed system that take on a new meaning when they occur together in time. Synchronous gestures draw inspiration from real-world social rituals such as toasting by tapping two drinking glasses together. In this article, we explore several interactions based on synchronous gestures, including bumping devices together, drawing corresponding pen gestures on touch-sensitive displays, simultaneously pressing a button on multiple smart-phones, or placing one or more devices on the sensing surface of a tabletop computer. These interactions focus on wireless composition of physically colocated devices, where users perceive one another and coordinate their actions through social protocol. We demonstrate how synchronous gestures may be phrased together with surrounding interactions. Such connection-action phrases afford a rich syntax of cross-device commands, operands, and one-to-one or one-to-many associations with a flexible physical arrangement of devices. Synchronous gestures enable colocated users to combine multiple devices into a heterogeneous display environment, where the users may establish a transient network connection with other select colocated users to facilitate the pooling of input capabilities, display resources, and the digital contents of each device. For example, participants at a meeting may bring mobile devices including tablet computers, PDAs, and smart-phones, and the meeting room infrastructure may include fixed interactive displays, such as a tabletop computer. Our techniques facilitate creation of an ad hoc display environment for tasks such as viewing a large document across multiple devices, presenting information to another user, or offering files to others. The interactions necessary to establish such ad hoc display environments must be rapid and minimally demanding of attention: during face-to-face communication, a pause of even 5 sec is socially awkward and disrupts collaboration. Current devices may associate using a direct transport such as Infrared Data Association ports, or the emerging Near Field Communication standard. However, such transports can only support one-to-one associations between devices and require close physical proximity as well as a specific relative orientation to connect the devices (e.g., the devices may be linked when touching head-to-head but not side-to-side). By contrast, sociology research in proxemics (the study of how people use the “personal space” surrounding their bodies) demonstrates that people carefully select physical distance as well as relative body orientation to suit the task, mood, and social relationship with other persons. Wireless networking can free device-to-device connections from the limitations of direct transports but results in a potentially large number of candidate devices. Synchronous gestures address these problems by allowing users to express naturally a spontaneous wireless connection between specific proximal (collocated) interactive displays.

The initiation of interaction in face-to-face environments is a gradual process, and takes place in a rich information landscape of awareness, attention, and social signals. One of the main benefits of this process is that people can be more sensitive to issues of privacy and interruption while they are moving towards interaction. However, on-line communication tools do not provide this subtlety, and often lead to unwanted interruptions. We have developed a prototype message system called OpenMessenger (OM) that adds the idea of gradual initiation of interaction to on-line communication. OpenMessenger provides multiple levels of awareness about people, and provides notification to those about whom information is being gathered. OpenMessenger allows people to negotiate interaction in a richer fashion than is possible with any other current messaging system. Preliminary evaluation data suggest the utility of the approach, but also shows that there are a number of issues yet to be resolved in this area.

We present a method for browsing videos by directly dragging their content. This method brings the benefits of direct manipulation to an activity typically mediated by widgets. We support this new type of interactivity by: 1) automatically extracting motion data from videos; and 2) a new technique called relative flow dragging that lets users control video playback by moving objects of interest along their visual trajectory. We show that this method can outperform the traditional seeker bar in video browsing tasks that focus on visual content rather than time.

Current pen input mainly utilizes the position of the pen tip, and occasionally, a button press. Other possible device parameters, such as rolling the pen around its longitudinal axis, are rarely used. We explore pen rolling as a supporting input modality for pen-based interaction. Through two studies, we are able to determine 1) the parameters that separate intentional pen rolling for the purpose of interaction from incidental pen rolling caused by regular writing and drawing, and 2) the parameter range within which accurate and timely intentional pen rolling interactions can occur. Building on our experimental results, we present an exploration of the design space of rolling-based interaction techniques, which showcase three scenarios where pen rolling interactions can be useful: enhanced stimulus-response compatibility in rotation tasks [7], multi-parameter input, and simplified mode selection.

Selections and actions in GUI's are often separated -- i.e. an action or command typically follows a selection. This sequence imposes a lower bound on the interaction time that is equal to or greater than the sum of its parts. In this paper, we introduce pressure marks -- pen strokes where the variations in pressure make it possible to indicate both a selection and an action simultaneously. We propose a series of design guidelines from which we develop a set of four basictypes of pressure marks. We first assess the viability of this set through an exploratory study that looks at the way users draw straight and lasso pressure marks of different sizes and orientations. We then present the results of a quantitative experiment that shows that users perform faster selection-action interactions with pressure marks than with a combination of lassos and pigtails. Based on these results, we present and discuss a number of interaction designs that incorporate pressure marks.

Using a stylus on a tablet computer to acquire small targets can be challenging. In this paper we present pointing lenses -- interaction techniques that help users acquire and select targets by presenting them with an enlarged visual and interaction area. We present and study three pointing lenses for pen-based systems and find that our proposed Pressure-Activated Lens is the top overall performer in terms of speed, accuracy and user preference. In addition, our experimental results not only show that participants find all pointing lenses beneficial for targets smaller than 5 pixels, but they also suggest that this benefit may extend to larger targets as well.

Accessing and manipulating occluded content in layered 2D drawings can be difficult. This paper characterizes a design space of techniques that facilitate access to occluded content. In addition, we introduce two new tools, Tumbler and Splatter, which represent unexplored areas of the design space. Finally, we present results of a study that contrasts these two tools against the traditional scene index used in most drawing applications. Results show that Splatter is comparable to and can be better than the scene index. Our findings allow us to understand the inherent design tradeoffs, and to identify areas for further improvement.

Sometimes users fail to notice a change that just took place on their display. For example, the user may have accidentally deleted an icon or a remote collaborator may have changed settings in a control panel. Animated transitions can help, but they force users to wait for the animation to complete. This can be cumbersome, especially in situations where users did not need an explanation. We propose a different approach. Phosphor objects show the outcome of their transition instantly; at the same time they explain their change in retrospect. Manipulating a phosphor slider, for example, leaves an afterglow that illustrates how the knob moved. The parallelism of instant outcome and explanation supports both types of users. Users who already understood the transition can continue interacting without delay, while those who are inexperienced or may have been distracted can take time to view the effects at their own pace. We present a framework of transition designs for widgets, icons, and objects in drawing programs. We evaluate phosphor objects in two user studies and report significant performance benefits for phosphor objects.

We present a quantitative analysis of delimiters for pen gestures. A delimiter is "something different" in the input stream that a computer can use to determine the structure of input phrases. We study four techniques for delimiting a selection-action gesture phrase consisting of lasso selection plus marking-menu-based command activation. Pigtail is a new technique that uses a small loop to delimit lasso selection from marking (Fig. 1). Handle adds a box to the end of the lasso, from which the user makes a second stroke for marking. Timeout uses dwelling with the pen to delimit the lasso from the mark. Button uses a button press to signal when to delimit the gesture. We describe the role of delimiters in our Scriboli pen interaction testbed, and show how Pigtail supports scope selection, command activation, and direct manipulation all in a single fluid pen gesture.

High precision parameter manipulation tasks typically require adjustment of the scale of manipulation in addition to the parameter itself. This paper introduces the notion of Zoom Sliding, or Zliding, for fluid integrated manipulation of scale (zooming) via pressure input while parameter manipulation within that scale is achieved via x-y cursor movement (sliding). We also present the Zlider (Figure 1), a widget that instantiates the Zliding concept. We experimentally evaluate three different input techniques for use with the Zlider in conjunction with a stylus for x-y cursor positioning, in a high accuracy zoom and select task. Our results marginally favor the stylus with integrated isometric pressure sensing tip over bimanual techniques which separate zooming and sliding controls over the two hands. We discuss the implications of our results and present further designs that make use of Zliding.

Stitching is a new interaction technique that allows users to combine pen-operated mobile devices with wireless networking by using pen gestures that span multiple displays. To stitch, a user starts moving the pen on one screen, crosses over the bezel, and finishes the stroke on the screen of a nearby device. Properties of each portion of the pen stroke are observed by the participating devices, synchronized via wireless network communication, and recognized as a unitary act performed by one user, thus binding together the devices. We identify the general requirements of stitching and describe a prototype photo sharing application that uses stitching to allow users to copy images from one tablet to another that is nearby, expand an image across multiple screens, establish a persistent shared workspace, or use one tablet to present images that a user selects from another tablet. We also discuss design issues that arise from proxemics, that is, the sociological implications of users collaborating in close quarters.

Current user interface widgets typically assume that the input device can only provide x-y position and binary button press information. Other inputs such as the continuous pressure data provided by styluses on tablets are rarely used. We explore the design space of using the continuous pressure sensing capabilities of styluses to operate multi-state widgets. We present the results of a controlled experiment that investigates human ability to perform discrete target selection tasks by varying a stylus' pressure, with full or partial visual feedback. The experiment also considers different techniques for confirming selection once the target is acquired. Based on the experimental results, we discuss implications for the design of pressure sensitive widgets. A taxonomy of pressure widgets is presented, along with a set of initial concept sketches of various pressure widget designs.

We explore a variety of interaction and visualization techniques for fluid navigation, segmentation, linking, and annotation of digital videos. These techniques are developed within a concept prototype called LEAN that is designed for use with pressure-sensitive digitizer tablets. These techniques include a transient position+velocity widget that allows users not only to move around a point of interest on a video, but also to rewind or fast forward at a controlled variable speed. We also present a new variation of fish-eye views called twist-lens, and incorporate this into a position control slider designed for the effective navigation and viewing of large sequences of video frames. We also explore a new style of widgets that exploit the use of the pen's pressure-sensing capability, increasing the input vocabulary available to the user. Finally, we elaborate on how annotations referring to objects that are temporal in nature, such as video, may be thought of as links, and fluidly constructed, visualized and navigated.