Large text corpora, such as Reddit posts, have become an increasingly prevalent site of qualitative inquiry. However, most large text corpora are intractable for qualitative researchers. Instead, teams rely on statistical subsampling to reduce corpora to a manageable size for qualitative analysis. While previous work for navigating large corpora involves visualizing the dataset at the corpus-level using high-level statistical summaries, few systems offer the ability to curate data using an interpretivist approach. To address this, we developed Teleoscope, a web-based interface designed to scaffold iterative, interactive, and reflexive refinement of a large corpus, in a process we call thematic curation. Across three deployments, we learned that Teleoscope supports serendipitous discovery of new keywords, results in greater feelings of confidence in search saturation, and aids collaborative discussion of alternative curation pathways. Teleoscope empowers researchers to stay "close to the data" in order to make qualitative workflows methodologically coherent with large text corpora.

Modern multi-threaded systems are highly complex. This makes their behavior difficult to understand. Developers frequently capture behavior in the form of program traces and then manually inspect these traces. Existing tools, however, fail to scale to traces larger than a million events. In this paper we present an approach to compress multi-threaded traces in order to allow developers to visually explore these traces at scale. Our approach is able to compress traces that contain millions of events down to a few hundred events. We use this approach to design and implement a tool called NonSequitur. We present three case studies which demonstrate how we used NonSequitur to analyze real-world performance issues with Meta’s storage engine RocksDB and MongoDB’s storage engine WiredTiger, two complex database backends. We also evaluate NonSequitur with 42 participants on traces from RocksDB and WiredTiger. We demonstrate that, in some cases, participants on average scored 11 times higher when performing performance analysis tasks on large execution traces. Additionally, for some performance analysis tasks, the participants spent on average three times longer with other tools than with NonSequitur.

Practical affect recognition needs to be efficient and unobtrusive in interactive contexts. One approach to a robust realtime system is to sense and automatically integrate multiple nonverbal sources. We investigated how users’touch, and secondarilygaze, perform as affect-encoding modalities during physical interaction with a robot pet, in comparison to more-studied biometric channels. To elicit authentically experienced emotions, participants recounted two intense memories of opposing polarity inStressed-RelaxedorDepressed-Excitedconditions. We collected data (N=30) from a touch sensor embedded under robot fur (force magnitude and location), a robot-adjacent gaze tracker (location), and biometric sensors (skin conductance, blood volume pulse, respiration rate). Cross-validation of Random Forest classifiers achieved best-case accuracy for combined touch-with-gaze approaching that of biometric results: where training and test sets include adjacent temporal windows, subject-dependent prediction was 94% accurate. In contrast, subject-independent Leave-One-participant-Out predictions resulted in 30% accuracy (chance 25%). Performance was best where participant information was available in both training and test sets. Addressing computational robustness for dynamic, adaptive realtime interactions, we analyzed subsets of our multimodal feature set, varying sample rates and window sizes. We summarize design directions based on these parameters for this touch-based, affective, and hard, realtime robot interaction application.

Emotion researchers have begun to converge on the theory that emotions are psychologically and socially constructed. A common assumption in affective robotics is that emotions are categorical brain-body states that can be confidently modeled. But if emotions are constructed, then they are interpretive, ambiguous, and specific to an individual’s unique experience. Constructivist views of emotion pose several challenges to affective robotics: first, it calls into question the validity of attempting to obtain objective measures of emotion through rating scales or biometrics. Second, ambiguous subjective data poses a challenge to computational systems that need structured and definite data to operate. How can a constructivist view of emotion be rectified with these challenges? In this article, we look to psychotherapy for ontological, epistemic, and methodological guidance. These fields (1) already understand emotions to be intrinsically embodied, relative, and metaphorical and (2) have built up substantial knowledge informed by everyday practice. It is our hope that by using interpretive methods inspired by therapeutic approaches, HRI researchers will be able to focus on the practicalities of designing effective embodied emotional interactions.

Many emotion classification and prediction approaches focus on emotion state, defined as static and single-valued. In contrast, our in-body experience is of sensations that can quickly evolve, consistent with scientific evidence of physiological regulation mechanisms. Can we reframe classification to estimate dynamic emotion parameters at interactive rates? For insight into dynamic emotion characteristics, we developed a multipass labelling protocol to capture controlled yet genuine emotion evolution elicited as 16 participants played a tense video game. We analyze and align multiple self-report outputs, inspect the signals for emotion dynamics, and consider label metaphors of position and angle — “where I am” vs. “where I'm going”. Finally, we reflect on the benefits and drawbacks of such a protocol for developing models of fast-evolving emotion.

Rapid iteration is crucial to effective prototyping; yet making certain objects - large, smoothly curved and/or of specific material - requires specialized equipment or considerable time. To improve access to casting such objects, we developed FoldMold: a low-cost, simply-resourced and eco-friendly technique for creating scalable, curved mold shapes (any developable surface) with wax-stiffened paper. Starting with a 3D digital shape, we define seams, add bending, joinery and mold-strengthening features, and "unfold" the shape into a 2D pattern, which is then cut, assembled, wax-dipped and cast with materials like silicone, plaster, or ice. To access the concept's full power, we facilitated digital pattern creation with a custom Blender add-on. We assessed FoldMold's viability, first with several molding challenges in which it produced smooth, curved shapes far faster than 3D printing would; then with a small user study that confirmed automation usability. Finally, we describe a range of opportunities for further development.

To create emotionally expressive robots, designers of human-robot interaction routinely translate emotion theories into instruments through which we estimate, quantify and analyze human emotional responses to robot behaviour. Pragmatically, we often use straightforward models such as Russell's circumplex, treating emotion as a single point in a two-dimensional space. However, this simple metaphor and its consequent representations omit many aspects of real emotional experience, can lead to erroneous data and may undermine computational models that rely on them. Problems with emotion representations currently prevalent in human-robot interaction fall into three categories: (1)Representations are static and singular, whereas real emotions can be dynamic, multi-valued, uncertain or conflicting. (2)The framing of an interaction is unspecified (i.e., in an affective rating task: which part of an interaction involving multiple parties and perspectives the participant is meant to consider). (3) Participant responses captured with instruments and methods that are not well-understood by experimenters nor participants produce data that is hard to interpret. We propose alternative emotion representations to account for dynamic emotions inherent in interactive contexts; scrutinize framing ambiguities in study tasks and argue for mixed-methods approaches to achieve shared understanding of emotion representations between participants and researchers.

Critical to social human-robot interaction is a robot's emotional richness, expressed within the parameters of its physical display. While emotion arousal is straightforward to convey, human valence (positivity) evaluations are famously ambiguous, whether we are assessing other humans or a robot. Imagine someone breathing raggedly: are they nervous, or excited? To assess the premise that irregular breathing connotes low valence (emotion negativity), we implemented different levels of breathing variability and complexity in simple furry robots. We asked 10 participants to watch and feel the behaviors, rate their valence, and explain their impressions. While a quantitative exploration of new and previous data showed correlation between multi-scale entropy and valence, the rich narratives revealed by thematic analysis of participant explanations call into question whether a single motion can, alone, be unambiguously valenced. Based on this evidence that people perceive robots as having inner lives, we recommend ways to build up narrative contexts over multiple interactions.

Social robots must be believable to be effective; but creating believable, affectively expressive robot behaviours requires time and skill. Inspired by the directness with which performers use their voices to craft characters, we introduce Voodle (vocal doodling), which uses the form of utterances -- e.g., tone and rhythm -- to puppet and eventually control robot motion. Voodle offers an improvisational platform capable of conveying hard-to-express ideas like emotion. We created a working Voodle system by collecting a set of vocal features and associated robot motions, then incorporating them into a prototype for sketching robot behaviour. We explored and refined Voodle's expressive capacity by engaging expert performers in an iterative design process. We found that users develop a personal language with Voodle; that a vocalization's meaning changed with narrative context; and that voodling imparts a sense of life to the robot, inviting designers to suspend disbelief and engage in a playful, conversational style of design.

Social robots that physically display emotion invite natural communication with their human interlocutors, enabling applications like robot-assisted therapy where a complex robot's breathing influences human emotional and physiological state. Using DIY fabrication and assembly, we explore how simple 1-DOF robots can express affect with economy and user customizability, leveraging open-source designs.

We present a real-time touch gesture recognition system using a low-cost fabric pressure sensor mounted on a small zoomorphic robot, affectionately called the `CuddleBit'. We explore the relationship between gesture recognition and affect through the lens of human-robot interaction. We demonstrate our real-time gesture recognition system, including both software and hardware, and a haptic display that brings the CuddleBit to life.

Social touch is an essential non-verbal channel whose great interactive potential can be realized by the ability to recognize gestures performed on inviting surfaces. To assess impact on recognition performance of sensor motion, substrate and coverings, we collected gesture data from a low-cost multitouch fabric pressure-location sensor while varying these factors. For six gestures most relevant in a haptic social robot context plus a no-touch control, we conducted two studies, with the sensor (1) stationary, varying substrate and cover (n=10); and (2) attached to a robot under a fur covering, flexing or stationary (n=16). For a stationary sensor, a random forest model achieved 90.0% recognition accuracy (chance 14.2%) when trained on all data, but as high as 94.6% (mean 89.1%) when trained on the same individual. A curved, flexing surface achieved 79.4% overall but averaged 85.7% when trained and tested on the same individual. These results suggest that under realistic conditions, recognition with this type of flexible sensor is sufficient for many applications of interactive social touch. We further found evidence that users exhibit an idiosyncratic `touch signature', with potential to identify the toucher. Both findings enable varied contexts of affective or functional touch communication, from physically interactive robots to any touch-sensitive object.