huashen218/convxai-cia-dataset huggingface dataset:
Title: FlexHaptics: A Design Method for Passive Haptic Inputs Using Planar Compliant Structures
Authors: family: Lin
given: Hongnan; family: He
given: Liang; family: Song
given: Fangli; family: Li
given: Yifan; family: Cheng
given: Tingyu; family: Zheng
given: Clement; family: Wang
given: Wei; family: Oh
given: HyunJoo
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Compliant structure, Digital fabrication, Haptics, Parametric design, Tangible interface.

Abstract: This paper presents FlexHaptics, a design method for creating custom haptic input interfaces. Our approach leverages planar compliant structures whose force-deformation relationship can be altered by adjusting the geometries. Embedded with such structures, a FlexHaptics module exerts a fine-tunable haptic effect (i.e., resistance, detent, or bounce) along a movement path (i.e., linear, rotary, or ortho-planar). These modules can work separately or combine into an interface with complex movement paths and haptic effects. To enable the parametric design of FlexHaptic modules, we provide a design editor that converts user-specified haptic properties into underlying mechanical structures of haptic modules. We validate our approach and demonstrate the potential of FlexHaptic modules through six application examples, including a slider control for a painting application and a piano keyboard interface on touchscreens, a tactile low vision timer, VR game controllers, and a compound input device of a joystick and a two-step button.
Title: ReCompFig: Designing Dynamically Reconfigurable Kinematic Devices Using Compliant Mechanisms and Tensioning Cables
Authors: family: Yang
given: Humphrey; family: Johnson
given: Tate; family: Zhong
given: Ke; family: Patel
given: Dinesh; family: Olson
given: Gina; family: Majidi
given: Carmel; family: Islam
given: Mohammad; family: Yao
given: Lining
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: kinematic devices, design tool, wearables, compliant mechanism, Haptic proxies

Abstract: From creating input devices to rendering tangible information, the field of HCI is interested in using kinematic mechanisms to create human-computer interfaces. Yet, due to fabrication and design challenges, it is often difficult to create kinematic devices that are compact and have multiple reconfigurable motional degrees of freedom (DOFs) depending on the interaction scenarios. In this work, we combine compliant mechanisms (CMs) with tensioning cables to create dynamically reconfigurable kinematic mechanisms. The devices’ kinematics (DOFs) is enabled and determined by the layout of bendable rods. The additional cables function as on-demand motion constraints that can dynamically lock or unlock the mechanism's DOFs as they are tightened or loosened. We provide algorithms and a design tool prototype to help users design such kinematic devices. We also demonstrate various HCI use cases including a kinematic haptic display, a haptic proxy, and a multimodal input device.
Title: Shape-Haptics: Planar & Passive Force Feedback Mechanisms for Physical Interfaces
Authors: family: Zheng
given: Clement; family: Yong
given: Zhen Zhou; family: Lin
given: Hongnan; family: Oh
given: HyunJoo; family: Yen
given: Ching Chiuan
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Digital Fabrication, Passive Haptics, Tangible Interactions

Abstract: We present Shape-Haptics, an approach for designers to rapidly design and fabricate passive force feedback mechanisms for physical interfaces. Such mechanisms are used in everyday interfaces and tools, and they are challenging to design. Shape-Haptics abstracts and broadens the haptic expression of this class of force feedback systems through 2D laser cut configurations that are simple to fabricate. They leverage the properties of polyoxymethylene plastic and comprise a compliant spring structure that engages with a sliding profile during tangible interaction. By shaping the sliding profile, designers can easily customize the haptic force feedback delivered by the mechanism. We provide a computational design sandbox to facilitate designers to explore and fabricate Shape-Haptics mechanisms. We also propose a series of applications that demonstrate the utility of Shape-Haptics in creating and customizing haptics for different physical interfaces.
Title: PITAS: Sensing and Actuating Embedded Robotic Sheet for Physical Information Communication
Authors: family: Cheng
given: Tingyu; family: Park
given: Jung Wook; family: Li
given: Jiachen; family: Ramey
given: Charles; family: Lin
given: Hongnan; family: Abowd
given: Gregory D.; family: Brum Medeiros
given: Carolina; family: Oh
given: HyunJoo; family: Giordano
given: Marcello
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: shape-changing interface, phase transition actuator, physical telecommunication

Abstract: This work presents PITAS, a thin-sheet robotic material composed of a reversible phase transition actuating layer and a heating/sensing layer. The synthetic sheet material enables non-expert makers to create shape-changing devices that can locally or remotely convey physical information such as shape, color, texture and temperature changes. PITAS sheets can be manipulated into various 2D shapes or 3D geometries using subtractive fabrication methods such as laser, vinyl, or manual cutting or an optional additive 3D printing method for creating 3D objects. After describing the design of PITAS, this paper also describes a study conducted with thirteen makers to gauge the accessibility, design space, and limitations encountered when PITAS is used as a soft robotic material while designing physical information communication devices. Lastly, this work reports on the results of a mechanical and electrical evaluation of PITAS and presents application examples to demonstrate its utility.
Title: FabricatINK: Personal Fabrication of Bespoke Displays Using Electronic Ink from Upcycled E Readers
Authors: family: Hanton
given: Ollie; family: Shen
given: Zichao; family: Fraser
given: Mike; family: Roudaut
given: Anne
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Electrophoretic, Prototyping, Fabrication, Free-Form Display, Display, E reader, E ink

Abstract: Abstract: FabricatINK explores the personal fabrication of irregularly-shaped low-power displays using electronic ink (E ink). E ink is a programmable bicolour material used in traditional form-factors such as E readers. It has potential for more versatile use within the scope of personal fabrication of custom-shaped displays, and it has the promise to be the pre-eminent material choice for this purpose. We appraise technical literature to identify properties of E ink, suited to fabrication. We identify a key roadblock, universal access to E ink as a material, and we deliver a method to circumvent this by upcycling broken electronics. We subsequently present a novel fabrication method for irregularly-shaped E ink displays. We demonstrate our fabrication process and E ink’s versatility through ten prototypes showing different applications and use cases. By addressing E ink as a material for display fabrication, we uncover the potential for users to create custom-shaped truly bistable displays.
Title: Interactive Robotic Plastering: Augmented Interactive Design and Fabrication for On-site Robotic Plastering
Authors: family: Mitterberger
given: Daniela; family: Ercan Jenny
given: Selen; family: Vasey
given: Lauren; family: Lloret-Fritschi
given: Ena; family: Aejmelaeus-Lindström
given: Petrus; family: Gramazio
given: Fabio; family: Kohler
given: Matthias
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: augmented reality, interactive fabrication, robot, digital fabrication

Abstract: This paper presents Interactive Robotic Plastering (IRoP), a system enabling designers and skilled workers to engage intuitively with an in-situ robotic plastering process. The research combines three elements: interactive design tools, an augmented reality interface, and a robotic spraying system. Plastering is a complex process relying on tacit knowledge and craftsmanship, making it difficult to simulate and automate. However, our system utilizes a controller-based interaction system to enable diverse users to interactively create articulated plasterwork in-situ. A customizable computational toolset converts human intentions into robotic motions while respecting robotic and material constraints. To accomplish this, we developed both an interactive computational model to translate the data from a motion-tracking system into robotic trajectories using design and editing tools as well as an audio-visual guidance system for in-situ projection. We then conducted two user-studies of designers and skilled workers who used IRoP to design and fabricate a full-scale demonstrator.
Title: Digital Fabrication of Pneumatic Actuators with Integrated Sensing by Machine Knitting
Authors: family: Luo
given: Yiyue; family: Wu
given: Kui; family: Spielberg
given: Andrew; family: Foshey
given: Michael; family: Rus
given: Daniela; family: Palacios
given: Tomás; family: Matusik
given: Wojciech
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: smart textiles, Pneumatic actuators, machine knitting

Abstract: Soft actuators with integrated sensing have shown utility in a variety of applications such as assistive wearables, robotics, and interactive input devices. Despite their promise, these actuators can be difficult to both design and fabricate. As a solution, we present a workflow for computationally designing and digitally fabricating soft pneumatic actuators via a machine knitting process. Machine knitting is attractive as a fabrication process because it is fast, digital (programmable), and provides access to a rich material library of functional yarns for specified mechanical behavior and integrated sensing. Our method uses elastic stitches to construct non-homogeneous knitting structures, which program the bending of actuators when inflated. Our method also integrates pressure and swept frequency capacitive sensing structures using conductive yarns. The entire knitted structure is fabricated automatically in a single machine run. We further provide a computational design interface for the user to interactively preview actuators’ quasi-static shape when authoring elastic stitches. Our sensing-integrated actuators are cost-effective, easy to design, robust to large actuation, and require minimal manual post-processing. We demonstrate five use-cases of our actuators in relevant application settings.
Title: Making New Worlds – Transformative Becomings with Soma Design
Authors: family: Ståhl
given: Anna; family: Balaam
given: Madeline; family: Comber
given: Rob; family: Sanches
given: Pedro; family: Höök
given: Kristina
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: somaesthetic experiences, soma mat, soma design, new materialism, longer term study, entanglement theories, breathing light

Abstract: Soma design is intended to increase our ability to appreciate through all our senses and lead to more meaningful interactions with the world. We contribute a longer-term study of soma design that shows evidence of this promise. Using storytelling approaches we draw on qualitative data from a three-month study of the soma mat and breathing light in four households. We tell stories of people’s becomings in the world as they learn of new possibilities for their somas; and as their somas transform. We show how people drew on their somaesthetic experiences with the prototypes to find their way through troubled times; and how through continued engagement some felt compelled to make transformations in how they live their lives. We discuss the implications for the overarching soma design program, focusing on what is required to design for ways of leading a better life.
Title: Focusing for Interaction Design: An Introspective Somatic Method
Authors: family: Núñez-Pacheco
given: Claudia; family: Loke
given: Lian
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Focusing, Interaction Design, Design Methods, HCI, Soma Design

Abstract: Attending to the challenges of describing first-person experience, this article illustrates different uses of the Focusing method in interaction design and HCI, offering a systematic way of accessing the subtle qualities of lived experiences for design use. In this approach, the implicit bodily knowledge -or felt sense- becomes the material capture of aesthetic experiences used to inform data collection, ideation and prototyping. We offer a high-level, yet systematic coverage of Focusing applied to two case studies, informing both a set of instructions to use the method and a series of design considerations to adopt this understudied tool of introspection in interaction design research and practice.
Title: Vulnerability as an ethical stance in soma design processes
Authors: family: Popova
given: Kristina; family: Garrett
given: Rachael; family: Núñez-Pacheco
given: Claudia; family: Lampinen
given: Airi; family: Höök
given: Kristina
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: soma design, drones, vulnerability, ethics

Abstract: We articulate vulnerability as an ethical stance in soma design processes and discuss the conditions of its emergence. We argue that purposeful vulnerability – an act of taking risk, exposing oneself, and resigning part of one’s autonomy – is a necessary although often neglected part of design, and specifically soma design, which builds on felt experience and stimulates designers to engage with the non-habitual by challenging norms, habitual movements, and social interactions. With the help of ethnography, video analysis, and micro-phenomenological interviews, we document an early design exploration around drones, describing how vulnerability is accomplished in collaboration between members of the design team and the design materials. We (1) define vulnerability as an active ethical stance; (2) make vulnerability visible as a necessary but often neglected part of an exploratory design process; and (3) discuss the conditions of its emergence, demonstrating the importance of deliberating ethics within the design process.
Title: How Accurate Does It Feel? – Human Perception of Different Types of Classification Mistakes
Authors: family: Papenmeier
given: Andrea; family: Kern
given: Dagmar; family: Hienert
given: Daniel; family: Kammerer
given: Yvonne; family: Seifert
given: Christin
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Ground Truth, Perception, Accuracy, Annotations

Abstract: Supervised machine learning utilizes large datasets, often with ground truth labels annotated by humans. While some data points are easy to classify, others are hard to classify, which reduces the inter-annotator agreement. This causes noise for the classifier and might affect the user’s perception of the classifier’s performance. In our research, we investigated whether the classification difficulty of a data point influences how strongly a prediction mistake reduces the “perceived accuracy”. In an experimental online study, 225 participants interacted with three fictive classifiers with equal accuracy (73%). The classifiers made prediction mistakes on three different types of data points (easy, difficult, impossible). After the interaction, participants judged the classifier’s accuracy. We found that not all prediction mistakes reduced the perceived accuracy equally. Furthermore, the perceived accuracy differed significantly from the calculated accuracy. To conclude, accuracy and related measures seem unsuitable to represent how users perceive the performance of classifiers.
Title: Towards Relatable Explainable AI with the Perceptual Process
Authors: family: Zhang
given: Wencan; family: Lim
given: Brian Y
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: contrastive explanations, Explainable AI, vocal emotion, audio

Abstract: Machine learning models need to provide contrastive explanations, since people often seek to understand why a puzzling prediction occurred instead of some expected outcome. Current contrastive explanations are rudimentary comparisons between examples or raw features, which remain difficult to interpret, since they lack semantic meaning. We argue that explanations must be more relatable to other concepts, hypotheticals, and associations. Inspired by the perceptual process from cognitive psychology, we propose the XAI Perceptual Processing Framework and RexNet model for relatable explainable AI with Contrastive Saliency, Counterfactual Synthetic, and Contrastive Cues explanations. We investigated the application of vocal emotion recognition, and implemented a modular multi-task deep neural network to predict and explain emotions from speech. From think-aloud and controlled studies, we found that counterfactual explanations were useful and further enhanced with semantic cues, but not saliency explanations. This work provides insights into providing and evaluating relatable contrastive explainable AI for perception applications.
Title: Debiased-CAM to mitigate image perturbations with faithful visual explanations of machine learning
Authors: family: Zhang
given: Wencan; family: Dimiccoli
given: Mariella; family: Lim
given: Brian Y
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Image perturbations, Misleading explanations, Robust machine learning, Class activation map, Explainable AI, User studies

Abstract: Model explanations such as saliency maps can improve user trust in AI by highlighting important features for a prediction. However, these become distorted and misleading when explaining predictions of images that are subject to systematic error (bias) by perturbations and corruptions. Furthermore, the distortions persist despite model fine-tuning on images biased by different factors (blur, color temperature, day/night). We present Debiased-CAM to recover explanation faithfulness across various bias types and levels by training a multi-input, multi-task model with auxiliary tasks for explanation and bias level predictions. In simulation studies, the approach not only enhanced prediction accuracy, but also generated highly faithful explanations about these predictions as if the images were unbiased. In user studies, debiased explanations improved user task performance, perceived truthfulness and perceived helpfulness. Debiased training can provide a versatile platform for robust performance and explanation faithfulness for a wide range of applications with data biases.
Title: Interpretable Directed Diversity: Leveraging Model Explanations for Iterative Crowd Ideation
Authors: family: Wang
given: Yunlong; family: Venkatesh
given: Priyadarshini; family: Lim
given: Brian Y
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Collective Creativity, Explainable AI, Diversity, Crowdsourcing

Abstract: Feedback in creativity support tools can help crowdworkers to improve their ideations. However, current feedback methods require human assessment from facilitators or peers. This is not scalable to large crowds. We propose Interpretable Directed Diversity to automatically predict ideation quality and diversity scores, and provide AI explanations — Attribution, Contrastive Attribution, and Counterfactual Suggestions — to feedback on why ideations were scored (low), and how to get higher scores. These explanations provide multi-faceted feedback as users iteratively improve their ideations. We conducted formative and controlled user studies to understand the usage and usefulness of explanations to improve ideation diversity and quality. Users appreciated that explanation feedback helped focus their efforts and provided directions for improvement. This resulted in explanations improving diversity compared to no feedback or feedback with scores only. Hence, our approach opens opportunities for explainable AI towards scalable and rich feedback for iterative crowd ideation and creativity support tools.
Title: How can Explainability Methods be Used to Support Bug Identification in Computer Vision Models?
Authors: family: Balayn
given: Agathe; family: Rikalo
given: Natasa; family: Lofi
given: Christoph; family: Yang
given: Jie; family: Bozzon
given: Alessandro
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: computer vision, machine learning explainability, machine learning model debugging, user interface

Abstract: Deep learning models for image classification suffer from dangerous issues often discovered after deployment. The process of identifying bugs that cause these issues remains limited and understudied. Especially, explainability methods are often presented as obvious tools for bug identification. Yet, the current practice lacks an understanding of what kind of explanations can best support the different steps of the bug identification process, and how practitioners could interact with those explanations. Through a formative study and an iterative co-creation process, we build an interactive design probe providing various potentially relevant explainability functionalities, integrated into interfaces that allow for flexible workflows. Using the probe, we perform 18 user-studies with a diverse set of machine learning practitioners. Two-thirds of the practitioners engage in successful bug identification. They use multiple types of explanations, e.g. visual and textual ones, through non-standardized sequences of interactions including queries and exploration. Our results highlight the need for interactive, guiding, interfaces with diverse explanations, shedding light on future research directions.
Title: AirRacket: Perceptual Design of Ungrounded, Directional Force Feedback to Improve Virtual Racket Sports Experiences
Authors: family: Tsai
given: Ching-Yi; family: Tsai
given: I-Lun; family: Lai
given: Chao-Jung; family: Chow
given: Derrek; family: Wei
given: Lauren; family: Cheng
given: Lung-Pan; family: Chen
given: Mike Y.
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: perceptual design, Haptics, force perception, virtual reality., ungrounded force feedback, air propulsion

Abstract: We present AirRacket, perceptual modeling and design of ungrounded, directional force feedback for virtual racket sports. Using compressed air propulsion jets to provide directional impact forces, we iteratively designed for three popular sports that span a wide range of force magnitudes: ping-pong, badminton, and tennis. To address the limited force magnitude of ungrounded force feedback technologies, we conducted a perception study which discovered the novel illusion that users perceive larger impact force magnitudes with longer impact duration, by an average factor of 2.57x. Through a series of formative, perceptual, and user experience studies with a combined total of 72 unique participants, we explored several perceptual designs using force magnitude scaling and duration scaling methods to expand the dynamic range of perceived force magnitude. Our user experience evaluation showed that perceptual designs can significantly improve realism and preference vs. physics-based designs for ungrounded force feedback systems.