huashen218/convxai-cia-dataset huggingface dataset:
Title: IdeaBot: Investigating Social Facilitation in Human-Machine Team Creativity
Authors: family: Hwang
given: Angel Hsing-Chi; family: Won
given: Andrea Stevenson
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: creativity, teamworking, idea generation, chatbot, social facilitation

Abstract: The present study investigates how human subjects collaborate with a computer-mediated chatbot in creative idea generation tasks. In three text-based between-group studies, we tested whether the perceived identity (i.e., whether a partner was believed to be a bot or as a human) or conversational style (human or robotic) of a teammate would moderate the outcomes of participants’ creative production. In Study 1, participants worked with either a chatbot or a human confederate. In Study 2, all participants worked with a human teammate but were informed that their partner was either a human or a chatbot. Conversely, all participants worked with a chatbot in Study 3, but their partner was described as either a chatbot or a human. We investigated differences in idea generation outcomes and found that participants consistently contributed more ideas and ideas of higher quality when they perceived their teamworking partner to be a bot. Furthermore, when the conversational style of the partner was robotic, participants with high anxiety in group communication reported greater creative self-efficacy in task performance. Finally, whether the perceived dominance of a partner and the pressure to come up with ideas during the task mediated positive outcomes of idea generation depended on whether the conversational style of the bot partner was robot- or human-like. Based on our findings, we discussed implications for future design of artificial agents as active team players in collaboration tasks.
Title: The Effects of System Interpretation Errors on Learning New Input Mechanisms
Authors: family: Lam
given: Kevin C.; family: Gutwin
given: Carl; family: Klarkowski
given: Madison; family: Cockburn
given: Andy
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: input techniques, expertise development, memory-based retrieval

Abstract: Input mechanisms can produce noisy signals that computers must interpret, and this interpretation can misconstrue the user’s intention. Researchers have studied how interpretation errors can affect users’ task performance, but little is known about how these errors affect learning, and whether they help or hinder the transition to expertise. Previous findings suggest that increasing the user’s attention can facilitate learning, so frequent interpretation errors may increase attention and learning; alternatively, however, interpretation errors may negatively interfere with skill development. To explore these potentially important effects, we conducted studies where participants learned commands with various rates of artificially injected interpretation errors. Our results showed that higher rates of interpretation error led to worse memory retention, higher completion times, higher occurrences of user error (beyond those injected by the system), and greater perceived effort. These findings indicate that when input mechanisms must interpret the user’s input, interpretation errors cause problems for user learning.
Title: Typing Efficiency and Suggestion Accuracy Influence the Benefits and Adoption of Word Suggestions
Authors: family: Roy
given: Quentin; family: Berlioux
given: Sébastien; family: Casiez
given: Géry; family: Vogel
given: Daniel
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: text-entry, word prediction

Abstract: Suggesting words to complete a given sequence of characters is a common feature of typing interfaces. Yet, previous studies have not found a clear benefit, some even finding it detrimental. We report on the first study to control for two important factors, word suggestion accuracy and typing efficiency. Our accuracy factor is enabled by a new methodology that builds on standard metrics of word suggestions. Typing efficiency is based on device type. Results show word suggestions are used less often in a desktop condition, with little difference between tablet and phone conditions. Very accurate suggestions do not improve entry speed on desktop, but do on tablet and phone. Based on our findings, we discuss implications for the design of automation features in typing systems.
Title: People May Punish, But Not Blame Robots
Authors: family: Lee
given: Minha; family: Ruijten
given: Peter; family: Frank
given: Lily; family: de Kort
given: Yvonne; family: IJsselsteijn
given: Wijnand
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: morality, human-robot interaction, Blame, robots, retributive justice, retribution gap, responsibility gap, punishment

Abstract: As robots may take a greater part in our moral decision-making processes, whether people hold them accountable for moral harm becomes critical to explore. Blame and punishment signify moral accountability, often involving emotions. We quantitatively looked into people’s willingness to blame or punish an emotional vs. non-emotional robot that admits to its wrongdoing. Studies 1 and 2 (online video interaction) showed that people may punish a robot due to its lack of perceived emotional capacity than its perceived agency. Study 3 (in the lab) demonstrated that people were neither willing to blame nor punish the robot. Punishing non-emotional robots seems more likely than blaming them, yet punishment towards robots is more likely to arise online than offline. We reflect on if and why victimized humans (and those who care for them) may seek out retributive justice against robot scapegoats when there are no humans to hold accountable for moral harm.
Title: Drone in Love: Emotional Perception of Facial Expressions on Flying Robots
Authors: family: Herdel
given: Viviane; family: Kuzminykh
given: Anastasia; family: Hildebrandt
given: Andrea; family: Cauchard
given: Jessica R.
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: Emotion Recognition, Anthropomorphism, Affective Computing, UAV., Robot, Human-Drone Interaction, Facial Expressions

Abstract: Drones are rapidly populating human spaces, yet little is known about how these flying robots are perceived and understood by humans. Recent works suggested that their acceptance is predicated upon their sociability. This paper explores the use of facial expressions to represent emotions on social drones. We leveraged design practices from ground robotics and created a set of rendered robotic faces that convey basic emotions. We evaluated individuals’ response to these emotional facial expressions on drones in two empirical studies (N = 98, N = 98). Our results demonstrate that individuals accurately recognize five drone emotional expressions, as well as make sense of intensities within emotion categories. We describe how participants were emotionally affected by the drone, showed empathy towards it, and created narratives to interpret its emotions. As a consequence, we formulate design recommendations for social drones and discuss methodological insights on the use of static versus dynamic stimuli in affective robotics studies.
Title: Should Robots Blush?
Authors: family: Park
given: Soomi; family: Healey
given: Patrick G. T.; family: Kaniadakis
given: Antonios
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: Cultural Probes, Affective Robotics, Symbolic Interactionism, Speculative Design, Human-Robot Interactions, Embarrassment, Design Workshop

Abstract: Social interaction is the most complex challenge in daily life. Inevitably, social robots will encounter interactions that are outside their competence. This raises a basic design question: how can robots fail gracefully in social interaction? The characteristic human response to social failure is embarrassment. Usefully, embarrassment signals both recognition of a problem and typically enlists sympathy and assistance to resolve it. This could enhance robot acceptability and provides an opportunity for interactive learning. Using a speculative design approach we explore how, when and why robots might communicate embarrassment. A series of specially developed cultural probes, scenario development and low-fidelity prototyping exercises suggest that: embarrassment is relevant for managing a diverse range of social scenarios, impacts on both humanoid and non-humanoid robot design, and highlights the critical importance of understanding interactional context. We conclude that embarrassment is fundamental to competent social functioning and provides a potentially fertile area for interaction design.
Title: Programmable Smart Home Toolkits Should Better Address Households’ Social Needs
Authors: family: Salovaara
given: Antti; family: Bellucci
given: Andrea; family: Vianello
given: Andrea; family: Jacucci
given: Giulio
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: smart homes, internet of things, end-user programming, in-the-wild study, trigger–action programming, appropriation

Abstract: End-user-programmable smart-home toolkits have engendered excitement in recent years. However, modern homes already cater quite well to users’ needs, and genuinely new needs for smart-home automation seldom arise. Acknowledging this challenging starting point, we conducted a six-week in-the-wild study of smart-home toolkits with four carefully recruited technology-savvy families. Interleaved with free toolkit use in the home were several creativity workshops to facilitate ideation and programming. We evaluated use experiences at the end of the six weeks. Even with extensive facilitation, families faced difficulties in identifying needs for smart-home automation, except for social needs that emerged in all the families. We present analysis of those needs and discuss how end-user-programmable toolkits could better engage with both those household members who design new automated functions and those who merely ‘use’ them.
Title: Creepy Technology:What Is It and How Do You Measure It?
Authors: family: Woźniak
given: Paweł W.; family: Karolus
given: Jakob; family: Lang
given: Florian; family: Eckerth
given: Caroline; family: Schöning
given: Johannes; family: Rogers
given: Yvonne; family: Niess
given: Jasmin
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: evaluation, perceived creepiness of technology scale, creepy, first impression, questionnaire, scale, creepiness

Abstract: Interactive technologies are getting closer to our bodies and permeate the infrastructure of our homes. While such technologies offer many benefits, they can also cause an initial feeling of unease in users. It is important for Human-Computer Interaction to manage first impressions and avoid designing technologies that appear creepy. To that end, we developed the Perceived Creepiness of Technology Scale (PCTS), which measures how creepy a technology appears to a user in an initial encounter with a new artefact. The scale was developed based on past work on creepiness and a set of ten focus groups conducted with users from diverse backgrounds. We followed a structured process of analytically developing and validating the scale. The PCTS is designed to enable designers and researchers to quickly compare interactive technologies and ensure that they do not design technologies that produce initial feelings of creepiness in users.
Title: Touchscreen Typing As Optimal Supervisory Control
Authors: family: Jokinen
given: Jussi; family: Acharya
given: Aditya; family: Uzair
given: Mohammad; family: Jiang
given: Xinhui; family: Oulasvirta
given: Antti
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: rational adaptation, computational modelling, touchscreen typing

Abstract: Traditionally, touchscreen typing has been studied in terms of motor performance. However, recent research has exposed a decisive role of visual attention being shared between the keyboard and the text area. Strategies for this are known to adapt to the task, design, and user. In this paper, we propose a unifying account of touchscreen typing, regarding it as optimal supervisory control. Under this theory, rules for controlling visuo-motor resources are learned via exploration in pursuit of maximal typing performance. The paper outlines the control problem and explains how visual and motor limitations affect it. We then present a model, implemented via reinforcement learning, that simulates co-ordination of eye and finger movements. Comparison with human data affirms that the model creates realistic finger- and eye-movement patterns and shows human-like adaptation. We demonstrate the model’s utility for interface development in evaluating touchscreen keyboard designs.
Title: Rethinking Eye-blink: Assessing Task Difficulty through Physiological Representation of Spontaneous Blinking
Authors: family: Cho
given: Youngjun
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: Mental Workload Assessment, Physiological Computing, Physiological Representation, Eye-blink, Task Difficulty, Eye-blink Spectrogram

Abstract: Continuous assessment of task difficulty and mental workload is essential in improving the usability and accessibility of interactive systems. Eye tracking data has often been investigated to achieve this ability, with reports on the limited role of standard blink metrics. Here, we propose a new approach to the analysis of eye-blink responses for automated estimation of task difficulty. The core module is a time-frequency representation of eye-blink, which aims to capture the richness of information reflected on blinking. In our first study, we show that this method significantly improves the sensitivity to task difficulty. We then demonstrate how to form a framework where the represented patterns are analyzed with multi-dimensional Long Short-Term Memory recurrent neural networks for their non-linear mapping onto difficulty-related parameters. This framework outperformed other methods that used hand-engineered features. This approach works with any built-in camera, without requiring specialized devices. We conclude by discussing how Rethinking Eye-blink can benefit real-world applications.
Title: A User-Oriented Approach to Space-Adaptive Augmentation: The Effects of Spatial Affordance on Narrative Experience in an Augmented Reality Detective Game
Authors: family: Shin
given: Jae-eun; family: Yoon
given: Boram; family: Kim
given: Dooyoung; family: Woo
given: Woontack
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: Augmented Reality, Head Mounted Displays, narrative experience, storytelling, space adaptivity, spatial affordance, spatial mapping

Abstract: Space-adaptive algorithms aim to effectively align the virtual with the real to provide immersive user experiences for Augmented Reality(AR) content across various physical spaces. While such measures are reliant on real spatial features, efforts to understand those features from the user’s perspective and reflect them in designing adaptive augmented spaces have been lacking. For this, we compared factors of narrative experience in six spatial conditions during the gameplay of Fragments, a space-adaptive AR detective game. Configured by size and furniture layout, each condition afforded disparate degrees of traversability and visibility. Results show that whereas centered furniture clusters are suitable for higher presence in sufficiently large rooms, the same layout leads to lower narrative engagement. Based on our findings, we suggest guidelines that can enhance the effects of space adaptivity by considering how users perceive and navigate augmented space generated from different physical environments.
Title: Augmented Reality and Older Adults: A Comparison of Prompting Types
Authors: family: Williams
given: Thomas J.; family: Jones
given: Simon L.; family: Lutteroth
given: Christof; family: Dekoninck
given: Elies; family: Boyd
given: Hazel C
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: prompting tools, augmented reality, older adults, task prompting

Abstract: Older adults can benefit from technologies that help them to complete everyday tasks. However, they are an often-under-represented population in augmented reality (AR) research. We present the results of a study in which people aged 50 years or older were asked to perform actions by interpreting visual AR prompts in a lab setting. Our results show that users were less successful at completing actions when using ARROW and HIGHLIGHT augmentations than when using ghosted OBJECT or GHOSTHAND augmentations. We found that user confidence in performing actions varied according to action and augmentation type. Users preferred combined AUDIO+TEXT prompts (our control condition) overall, but the GHOSTHAND was the most preferred visual prompt. We discuss reasons for these differences and provide insight for developers of AR content for older adults. Our work provides the first comparative study of AR with older adults in a non-industrial context.
Title: “I Can’t Reply with That”: Characterizing Problematic Email Reply Suggestions
Authors: family: Robertson
given: Ronald E; family: Olteanu
given: Alexandra; family: Diaz
given: Fernando; family: Shokouhi
given: Milad; family: Bailey
given: Peter
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: algorithm auditing, AI-MC, smart reply, email, AI-assisted writing, smart compose, CMC

Abstract: In email interfaces, providing users with reply suggestions may simplify or accelerate correspondence. While the “success” of such systems is typically quantified using the number of suggestions selected by users, this ignores the impact of social context, which can change how suggestions are perceived. To address this, we developed a mixed-methods framework involving qualitative interviews and crowdsourced experiments to characterize problematic email reply suggestions. Our interviews revealed issues with over-positive, dissonant, cultural, and gender-assuming replies, as well as contextual politeness. In our experiments, crowdworkers assessed email scenarios that we generated and systematically controlled, showing that contextual factors like social ties and the presence of salutations impacts users’ perceptions of email correspondence. These assessments created a novel dataset of human-authored corrections for problematic email replies. Our study highlights the social complexity of providing suggestions for email correspondence, raising issues that may apply to all social messaging systems.
Title: CoNotate: Suggesting Queries Based on Notes Promotes Knowledge Discovery
Authors: family: Palani
given: Srishti; family: Ding
given: Zijian; family: Nguyen
given: Austin; family: Chuang
given: Andrew; family: MacNeil
given: Stephen; family: Dow
given: Steven P.
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: Context Mining, Query Suggestions, Exploratory Search, Note-taking

Abstract: When exploring a new domain through web search, people often struggle to articulate queries because they lack domain-specific language and well-defined informational goals. Perhaps search tools rely too much on the query to understand what a searcher wants. Towards expanding this contextual understanding of a user during exploratory search, we introduce a novel system, CoNotate, which offers query suggestions based on analyzing the searcher’s notes and previous searches for patterns and gaps in information. To evaluate this approach, we conducted a within-subjects study where participants (n=38) conducted exploratory searches using a baseline system (standard web search) and the CoNotate system. The CoNotate approach helped searchers issue significantly more queries, and discover more terminology than standard web search. This work demonstrates how search can leverage user-generated content to help people get started when exploring complex, multi-faceted information spaces.
Title: ToonNote: Improving Communication in Computational Notebooks Using Interactive Data Comics
Authors: family: Kang
given: DaYe; family: Ho
given: Tony; family: Marquardt
given: Nicolai; family: Mutlu
given: Bilge; family: Bianchi
given: Andrea
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: data summarization, multi-level interface, data comics, computational notebooks

Abstract: Computational notebooks help data analysts analyze and visualize datasets, and share analysis procedures and outputs. However, notebooks typically combine code (e.g., Python scripts), notes, and outputs (e.g., tables, graphs). The combination of disparate materials is known to hinder the comprehension of notebooks, making it difficult for analysts to collaborate with other analysts unfamiliar with the dataset. To mitigate this problem, we introduce ToonNote, a JupyterLab extension that enables the conversion of notebooks into “data comics.” ToonNote provides a simplified view of a Jupyter notebook, highlighting the most important results while supporting interactive and free exploration of the dataset. This paper presents the results of a formative study that motivated the system, its implementation, and an evaluation with 12 users, demonstrating the effectiveness of the produced comics. We discuss how our findings inform the future design of interfaces for computational notebooks and features to support diverse collaborators.
Title: Assisting Manipulation and Grasping in Robot Teleoperation with Augmented Reality Visual Cues
Authors: family: Arevalo Arboleda
given: Stephanie; family: Rücker
given: Franziska; family: Dierks
given: Tim; family: Gerken
given: Jens
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: human-robot interaction, augmented reality, robot teleoperation, visual cues

Abstract: Teleoperating industrial manipulators in co-located spaces can be challenging. Facilitating robot teleoperation by providing additional visual information about the environment and the robot affordances using augmented reality (AR), can improve task performance in manipulation and grasping. In this paper, we present two designs of augmented visual cues, that aim to enhance the visual space of the robot operator through hints about the position of the robot gripper in the workspace and in relation to the target. These visual cues aim to improve the distance perception and thus, the task performance. We evaluate both designs against a baseline in an experiment where participants teleoperate a robotic arm to perform pick-and-place tasks. Our results show performance improvements in different levels, reflecting in objective and subjective measures with trade-offs in terms of time, accuracy, and participants’ views of teleoperation. These findings show the potential of AR not only in teleoperation, but in understanding the human-robot workspace.