This post has been republished via RSS; it originally appeared at: Microsoft Research.
The ways in which people are able to interact with technologies can have a profound effect on a technology’s utility and adoptability. Building computing tools and services around people’s natural styles of work, communication, and play can give technology the value it needs to have meaningful impact. For decades, human-computer interaction (HCI) has examined the relationship between people and computers to help maximize the capabilities of each across a range of experiences and situations.
The ACM CHI Conference on Human Factors in Computing Systems (CHI) is a renowned meeting ground for top talent in the HCI field and a showcase for some of its most compelling work. Hosted April 23 through April 28, this year’s conference drew more than 4,500 participants from 79 countries. Contributions from Microsoft researchers and their collaborators demonstrated the breadth of work inspired by the myriad and diverse ways people use computing today and will in the future.
Check out a few highlights from this year’s conference below, including researchers’ efforts to better understand the role of wellbeing in work, to augment memory through our sense of smell, and to bridge the gap between programmers and code-generating models, which received honorable mention at the conference.
“What It Wants Me To Say”: Bridging the Abstraction Gap Between End-User Programmers and Code-Generating Large Language Models
CHI 2023 Honorable Mention
Michael Xieyang Liu, Advait Sarkar, Carina Negreanu, Ben Zorn, Jack Williams, Neil Toronto, Andy Gordon
Programming languages are an extremely powerful form of user interface. They also happen to be extremely difficult to learn, especially for non-expert end-user programmers who lack training in computing. What if end-user programmers could instead use a natural language they already know? This prospect can be realized through large language models (LLM): deep neural networks using the transformer architecture, trained on large corpora, and fine-tuned to generate code from natural language. Despite impressive benchmark performance, LLMs are beset with issues in practical use. Lab and field studies have shown that the mapping between natural language and code is poorly understood, that generated code can contain subtle bugs, and that generated code can be difficult to verify.
In their paper, researchers consider the specific problem of abstraction matching: when the user has well-formed intent, how do they select an utterance from the near infinite space of naturalistic utterances that they believe the system will reliably map to a satisfactory solution? This involves “matching” the utterance to the right level of “abstraction” by specifying the utterance at a level of granularity and detail that matches the set of actions the system can take and selecting suitable words and grammar.
Workplace Rhythm Variability and Emotional Distress in Information Workers
Subigya Kumar Nepal, Javier Hernandez, Judith Amores, Mehrab Bin Morshed, Robert Lewis, Hemma Prafullchandra, Mary Czerwinski
Regularity in daily activities has been linked to positive wellbeing outcomes, but previous studies have mainly focused on clinical populations and traditional daily activities such as sleep and exercise. This research extends prior work by examining the regularity of both self-reported and digital activities of 49 information workers in a four-week naturalistic study. Findings suggest that greater variability in self-reported mood, job demands, lunch time, and sleep quality may be associated with increased stress, anxiety, and depression. However, when it comes to digital activity–based measures, greater variability in rhythm is associated with reduced emotional distress. This study expands our understanding of workers and the potential insights that can be gained from analyzing technology interactions and wellbeing.
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Olfactory Wearables for Targeted Memory Reactivation
Judith Amores, Nirmita Mehra, Bjoern Rasch, Pattie Maes
This paper investigates how a smartphone-controlled olfactory wearable might improve memory recall. Researchers conducted a within-subjects experiment with 32 participants using the device and not using the device (control). In the experimental condition, bursts of odor were released during visuo-spatial memory navigation tasks, which also had a language learning component, and rereleased during sleep the following night in the subjects’ home. The researchers found that compared with control, there was an improvement in memory performance when using the scent wearable in memory tasks that involved walking in a physical space. Furthermore, participants recalled more objects and translations when re-exposed to the same scent during the recall test in addition to during sleep. These effects were statistically significant, and in the object recall task, they also persisted for more than a week. This experiment demonstrates a potential practical application of olfactory interfaces that can interact with a user during wake, as well as sleep, to support memory.
AdHocProx: Sensing Mobile, Ad-Hoc Collaborative Device Formations using Dual Ultra-Wideband Radios
Richard Li, Teddy Seyed, Nicolai Marquardt, Eyal Ofek, Steve Hodges, Mike Sinclair, Hugo Romat, Michel Pahud, Jatin Sharma, William A. S. Buxton, Ken Hinckley, Nathalie Henry Riche
In their paper, researchers present AdHocProx, a system that uses device-relative, inside-out sensing to augment co-located collaboration across multiple devices without recourse to externally anchored beacons or even reliance on Wi-Fi connectivity.
AdHocProx achieves this via sensors, including dual ultra-wideband (UWB) radios for sensing distance and angle to other devices in dynamic, ad-hoc arrangements and capacitive grip to determine where the user’s hands hold the device and to partially correct for the resulting UWB signal attenuation. All spatial sensing and communication take place via the side-channel capability of the UWB radios, suitable for small-group collaboration across up to four devices (eight UWB radios).
Together, these sensors detect proximity and natural, socially meaningful device movements to enable contextual interaction techniques. Researchers find that AdHocProx can obtain 95 percent accuracy recognizing various ad-hoc device arrangements in an offline evaluation, with participants particularly appreciative of interaction techniques that automatically leverage proximity-awareness and relative orientation among multiple devices.
Escapement: A Tool for Interactive Prototyping with Video via Sensor-Mediated Abstraction of Time
Molly Jane Nicholas, Nicolai Marquardt, Michel Pahud, Nathalie Henry Riche, Hugo Romat, Christopher Collins, David Ledo, Rohan Kadekodi, Badrish Chandramouli, Ken Hinckley
This paper introduces Escapement, a video prototyping tool that introduces a powerful new concept for prototyping screen-based interfaces by flexibly mapping sensor values to dynamic playback control of videos. This recasts the time dimension of video mockups as sensor-mediated interaction.
This abstraction of time as interaction, which the researchers dub video-escapement prototyping, empowers designers to rapidly explore and viscerally experience direct touch or sensor-mediated interactions across one or more device displays. The system affords cross-device and bidirectional remote (telepresent) experiences via cloud-based state sharing across multiple devices. This makes Escapement especially potent for exploring multi-device, dual-screen, or remote-work interactions for screen-based applications. Researchers share the results of observations of long-term usage of video-escapement techniques with experienced interaction designers and articulate design choices for supporting a reflective, iterative, and open-ended creative design process.
Andriana Boudouraki, Joel E. Fischer, Stuart Reeves, Sean Rintel
Organizations wishing to maintain employee satisfaction for hybrid collaboration need to explore flexible solutions that provide value for both remote and on-site employees. This case study reports on the roll-out of a telepresence robot pilot at Microsoft Research Cambridge to test whether robots would provide enjoyable planned and unplanned encounters between remote and on-site employees. Researchers describe the work that was undertaken to prepare for the roll-out, including the occupational health and safety assessment, systems for safety and security, and the information for employees on safe and effective use practices. The pilot ended after three months, and robot use has been discontinued after weighing the opportunities against low adoption and other challenges. The researchers discuss the pros and cons within this organizational setting and make suggestions for future work and roll-outs.
Focus Time for Wellbeing and Work Engagement of Information Workers
Koustuv Saha, Shamsi Iqbal
Having little time for focused work is a major challenge in information work. While research has explored computing-assisted user-facing solutions for protecting time for focused work, there’s limited empirical evidence about the effectiveness of these features on wellbeing and work engagement. Toward this problem, researchers study the effects of automatically scheduling time for focused work on people’s work calendars using the “focus time” feature on Outlook calendars. The researchers conducted an experimental study over six weeks with 15 treatment and 10 control participants, who responded to survey questions on wellbeing and work engagement throughout the study. The researchers found that the treatment participants showed higher wellbeing, including increased excitement, relaxation, and satisfaction, and decreased anger, frustration, tiredness, and stress. The researchers study the needs, benefits, and challenges of scheduling focus time and discuss the importance of and design recommendations for enabling mechanisms and tools supporting focused work.
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