huashen218/convxai-cia-dataset huggingface dataset:
Title: Auth+Track: Enabling Authentication Free Interaction on Smartphone by Continuous User Tracking
Authors: family: Liang
given: Chen; family: Yu
given: Chun; family: Wei
given: Xiaoying; family: Xu
given: Xuhai; family: Hu
given: Yongquan; family: Wang
given: Yuntao; family: Shi
given: Yuanchun
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: continuous user tracking, authentication model

Abstract: We propose Auth+Track, a novel authentication model that aims to reduce redundant authentication in everyday smartphone usage. By sparse authentication and continuous tracking of the user’s status, Auth+Track eliminates the “gap” authentication between fragmented sessions and enables “Authentication Free when User is Around”. To instantiate the Auth+Track model, we present PanoTrack, a prototype that integrates body and near field hand information for user tracking. We install a fisheye camera on the top of the phone to achieve a panoramic vision that can capture both user’s body and on-screen hands. Based on the captured video stream, we develop an algorithm to extract 1) features for user tracking, including body keypoints and their temporal and spatial association, near field hand status, and 2) features for user identity assignment. The results of our user studies validate the feasibility of PanoTrack and demonstrate that Auth+Track not only improves the authentication efficiency but also enhances user experiences with better usability.
Title: ElectroRing: Subtle Pinch and Touch Detection with a Ring
Authors: family: Kienzle
given: Wolf; family: Whitmire
given: Eric; family: Rittaler
given: Chris; family: Benko
given: Hrvoje
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: mixed-reality, touch detection, smart ring

Abstract: We present ElectroRing, a wearable ring-based input device that reliably detects both onset and release of a subtle finger pinch, and more generally, contact of the fingertip with the user’s skin. ElectroRing addresses a common problem in ubiquitous touch interfaces, where subtle touch gestures with little movement or force are not detected by a wearable camera or IMU. ElectroRing’s active electrical sensing approach provides a step-function-like change in the raw signal, for both touch and release events, which can be easily detected using only basic signal processing techniques. Notably, ElectroRing requires no second point of instrumentation, but only the ring itself, which sets it apart from existing electrical touch detection methods. We built three demo applications to highlight the effectiveness of our approach when combined with a simple IMU-based 2D tracking system.
Title: Project Tasca: Enabling Touch and Contextual Interactions with a Pocket-based Textile Sensor
Authors: family: Wu
given: Te-Yen; family: Xu
given: Zheer; family: Yang
given: Xing-Dong; family: Hodges
given: Steve; family: Seyed
given: Teddy
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: Capacitive sensing, wearables, NFC, interactive pockets, gesture recognition, object recognition, inductive sensing, resistive sensing, interactive fabrics

Abstract: We present Project Tasca, a pocket-based textile sensor that detects user input and recognizes everyday objects that a user carries in the pockets of a pair of pants (e.g., keys, coins, electronic devices, or plastic items). By creating a new fabric-based sensor capable of detecting in-pocket touch and pressure, and recognizing metallic, non-metallic, and tagged objects inside the pocket, we enable a rich variety of subtle, eyes-free, and always-available input, as well as context-driven interactions in wearable scenarios. We developed our prototype by integrating four distinct types of sensing methods, namely: inductive sensing, capacitive sensing, resistive sensing, and NFC in a multi-layer fabric structure into the form factor of a jeans pocket. Through a ten-participant study, we evaluated the performance of our prototype across 11 common objects including hands, 8 force gestures, and 30 NFC tag placements. We yielded a 92.3% personal cross-validation accuracy for object recognition, 96.4% accuracy for gesture recognition, and a 100% accuracy for detecting NFC tags at close distance . We conclude by demonstrating the interactions enabled by our pocket-based sensor in several applications.
Title: User Authentication via Electrical Muscle Stimulation
Authors: family: Chen
given: Yuxin; family: Yang
given: Zhuolin; family: Abbou
given: Ruben; family: Lopes
given: Pedro; family: Zhao
given: Ben Y.; family: Zheng
given: Haitao
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: biometric authentication, wearable, electrical muscle stimulation

Abstract: We propose a novel modality for active biometric authentication: electrical muscle stimulation (EMS). To explore this, we engineered an interactive system, which we call ElectricAuth, that stimulates the user’s forearm muscles with a sequence of electrical impulses (i.e., EMS challenge) and measures the user’s involuntary finger movements (i.e., response to the challenge). ElectricAuth leverages EMS’s intersubject variability, where the same electrical stimulation results in different movements in different users because everybody’s physiology is unique (e.g., differences in bone and muscular structure, skin resistance and composition, etc.). As such, ElectricAuth allows users to login without memorizing passwords or PINs. ElectricAuth’s challenge-response structure makes it secure against data breaches and replay attacks, a major vulnerability facing today’s biometrics such as facial recognition and fingerprints. Furthermore, ElectricAuth never reuses the same challenge twice in authentications – in just one second of stimulation it encodes one of 68M possible challenges. In our user studies, we found that ElectricAuth resists: (1) impersonation attacks (false acceptance rate: 0.17% at 5% false rejection rate); (2) replay attacks (false acceptance rate: 0.00% at 5% false rejection rate); and, (3) synthesis attacks (false acceptance rates: 0.2-2.5%). Our longitudinal study also shows that ElectricAuth produces consistent results over time and across different humidity and muscle conditions.
Title: BackTrack: 2D Back-of-device Interaction Through Front Touchscreen
Authors: family: Xiao
given: Chang; family: Bayer
given: Karl; family: Zheng
given: Changxi; family: Nayar
given: Shree K.
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems

Abstract: We present BackTrack, a trackpad placed on the back of a smartphone to track fine-grained finger motions. Our system has a small form factor, with all the circuits encapsulated in a thin layer attached to a phone case. It can be used with any off-the-shelf smartphone, requiring no power supply or modification of the operating systems. BackTrack simply extends the finger tracking area of the front screen, without interrupting the use of the front screen. It also provides a switch to prevent unintentional touch on the trackpad. All these features are enabled by a battery-free capacitive circuit, part of which is a transparent, thin-film conductor coated on a thin glass and attached to the front screen. To ensure accurate and robust tracking, the capacitive circuits are carefully designed. Our design is based on a circuit model of capacitive touchscreens, justified through both physics-based finite-element simulation and controlled laboratory experiments. We conduct user studies to evaluate the performance of using BackTrack. We also demonstrate its use in a number of smartphone applications.
Title: ProxiMic: Convenient Voice Activation via Close-to-Mic Speech Detected by a Single Microphone
Authors: family: Qin
given: Yue; family: Yu
given: Chun; family: Li
given: Zhaoheng; family: Zhong
given: Mingyuan; family: Yan
given: Yukang; family: Shi
given: Yuanchun
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: activity recognition, voice input, sensing technique

Abstract: Wake-up-free techniques (e.g., Raise-to-Speak) are important for improving the voice input experience. We present ProxiMic, a close-to-mic (within 5 cm) speech sensing technique using only one microphone. With ProxiMic, a user keeps a microphone-embedded device close to the mouth and speaks directly to the device without wake-up phrases or button presses. To detect close-to-mic speech, we use the feature from pop noise observed when a user speaks and blows air onto the microphone. Sound input is first passed through a low-pass adaptive threshold filter, then analyzed by a CNN which detects subtle close-to-mic features (mainly pop noise). Our two-stage algorithm can achieve 94.1% activation recall, 12.3 False Accepts per Week per User (FAWU) with 68 KB memory size, which can run at 352 fps on the smartphone. The user study shows that ProxiMic is efficient, user-friendly, and practical.
Title: Pose-on-the-Go: Approximating User Pose with Smartphone Sensor Fusion and Inverse Kinematics
Authors: family: Ahuja
given: Karan; family: Mayer
given: Sven; family: Goel
given: Mayank; family: Harrison
given: Chris
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: Mobile, Motion capture, Body pose, Smartphone, Motion tracking

Abstract: We present Pose-on-the-Go, a full-body pose estimation system that uses sensors already found in today’s smartphones. This stands in contrast to prior systems, which require worn or external sensors. We achieve this result via extensive sensor fusion, leveraging a phone’s front and rear cameras, the user-facing depth camera, touchscreen, and IMU. Even still, we are missing data about a user’s body (e.g., angle of the elbow joint), and so we use inverse kinematics to estimate and animate probable body poses. We provide a detailed evaluation of our system, benchmarking it against a professional-grade Vicon tracking system. We conclude with a series of demonstration applications that underscore the unique potential of our approach, which could be enabled on many modern smartphones with a simple software update.
Title: FaceSight: Enabling Hand-to-Face Gesture Interaction on AR Glasses with a Downward-Facing Camera Vision
Authors: family: Weng
given: Yueting; family: Yu
given: Chun; family: Shi
given: Yingtian; family: Zhao
given: Yuhang; family: Yan
given: Yukang; family: Shi
given: Yuanchun
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: Computer Vision, Hand-to-Face Gestures, AR Glasses

Abstract: We present FaceSight, a computer vision-based hand-to-face gesture sensing technique for AR glasses. FaceSight fixes an infrared camera onto the bridge of AR glasses to provide extra sensing capability of the lower face and hand behaviors. We obtained 21 hand-to-face gestures and demonstrated the potential interaction benefits through five AR applications. We designed and implemented an algorithm pipeline that segments facial regions, detects hand-face contact (f1 score: 98.36%), and trains convolutional neural network (CNN) models to classify the hand-to-face gestures. The input features include gesture recognition, nose deformation estimation, and continuous fingertip movement. Our algorithm achieves classification accuracy of all gestures at 83.06%, proved by the data of 10 users. Due to the compact form factor and rich gestures, we recognize FaceSight as a practical solution to augment input capability of AR glasses in the future.
Title: AtaTouch: Robust Finger Pinch Detection for a VR Controller Using RF Return Loss
Authors: family: Kim
given: Daehwa; family: Park
given: Keunwoo; family: Lee
given: Geehyuk
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: Touch segmentation, Hand gesture, RF Sensing, Virtual Reality (VR), Antenna

Abstract: Handheld controllers are an essential part of VR systems. Modern sensing techniques enable them to track users’ finger movements to support natural interaction using hands. The sensing techniques, however, often fail to precisely determine whether two fingertips touch each other, which is important for the robust detection of a pinch gesture. To address this problem, we propose AtaTouch, which is a novel, robust sensing technique for detecting the closure of a finger pinch. It utilizes a change in the coupled impedance of an antenna and human fingers when the thumb and finger form a loop. We implemented a prototype controller in which AtaTouch detects the finger pinch of the grabbing hand. A user test with the prototype showed a finger-touch detection accuracy of 96.4%. Another user test with the scenarios of moving virtual blocks demonstrated low object-drop rate (2.75%) and false-pinch rate (4.40%). The results and feedback from the participants support the robustness and sensitivity of AtaTouch.
Title: Super-Resolution Capacitive Touchscreens
Authors: family: Mayer
given: Sven; family: Xu
given: Xiangyu; family: Harrison
given: Chris
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: Tracking, Super-resolution, Tangibles, Touch input, Capacitive sensing

Abstract: Capacitive touchscreens are near-ubiquitous in today’s touch-driven devices, such as smartphones and tablets. By using rows and columns of electrodes, specialized touch controllers are able to capture a 2D image of capacitance at the surface of a screen. For over a decade, capacitive “pixels” have been around 4 millimeters in size – a surprisingly low resolution that precludes a wide range of interesting applications. In this paper, we show how super-resolution techniques, long used in fields such as biology and astronomy, can be applied to capacitive touchscreen data. By integrating data from many frames, our software-only process is able to resolve geometric details finer than the original sensor resolution. This opens the door to passive tangibles with higher-density fiducials and also recognition of every-day metal objects, such as keys and coins. We built several applications to illustrate the potential of our approach and report the findings of a multipart evaluation.
Title: RElectrode: A Reconfigurable Electrode For Multi-Purpose Sensing Based on Microfluidics
Authors: family: Sun
given: Wei; family: Chen
given: Yanjun; family: Zhan
given: Simon; family: Han
given: Teng; family: Tian
given: Feng; family: Wang
given: Hongan; family: Yang
given: Xing-Dong
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: capacitive and inductive sensing, microfluidics, object recognition, gesture

Abstract: In this paper, we propose a reconfigurable electrode, RElectrode, using a microfluidic technique that can change the geometry and material properties of the electrode to satisfy the needs for sensing a variety of different types of user input through touch/touchless gestures, pressure, temperature, and distinguish between different types of objects or liquids. Unlike the existing approaches, which depend on the specific-shaped electrode for particular sensing (e.g., coil for inductive sensing), RElectrode enables capacity, inductance, resistance/pressure, temperature, pH sensings all in a single package. We demonstrate the design and fabrication of the microfluidic structure of our RElectrode, evaluate its sensing performance through several studies, and provide some unique applications. RElectrode demonstrates technical feasibility and application values of integrating physical and biochemical properties of microfluidics into novel sensing interfaces.
Title: Learning to Automate Chart Layout Configurations Using Crowdsourced Paired Comparison
Authors: family: Wu
given: Aoyu; family: Xie
given: Liwenhan; family: Lee
given: Bongshin; family: Wang
given: Yun; family: Cui
given: Weiwei; family: Qu
given: Huamin
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: Visualization, Machine Learning, Crowdsourced, Visual Design, Image Quality Assessment

Abstract: We contribute a method to automate parameter configurations for chart layouts by learning from human preferences. Existing charting tools usually determine the layout parameters using predefined heuristics, producing sub-optimal layouts. People can repeatedly adjust multiple parameters (e.g., chart size, gap) to achieve visually appealing layouts. However, this trial-and-error process is unsystematic and time-consuming, without a guarantee of improvement. To address this issue, we develop Layout Quality Quantifier (LQ2), a machine learning model that learns to score chart layouts from paired crowdsourcing data. Combined with optimization techniques, LQ2 recommends layout parameters that improve the charts’ layout quality. We apply LQ2 on bar charts and conduct user studies to evaluate its effectiveness by examining the quality of layouts it produces. Results show that LQ2 can generate more visually appealing layouts than both laypeople and baselines. This work demonstrates the feasibility and usages of quantifying human preferences and aesthetics for chart layouts.
Title: Data Animator: Authoring Expressive Animated Data Graphics
Authors: family: Thompson
given: John R; family: Liu
given: Zhicheng; family: Stasko
given: John
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: authoring, staggering, animated data graphics, object matching, staging, keyframing

Abstract: Animation helps viewers follow transitions in data graphics. When authoring animations that incorporate data, designers must carefully coordinate the behaviors of visual objects such as entering, exiting, merging and splitting, and specify the temporal rhythms of transition through staging and staggering. We present Data Animator, a system for authoring animated data graphics without programming. Data Animator leverages the Data Illustrator framework to analyze and match objects between two static visualizations, and generates automated transitions by default. Designers have the flexibility to interpret and adjust the matching results through a visual interface. Data Animator also supports the division of a complex animation into stages through hierarchical keyframes, and uses data attributes to stagger the start time and vary the speed of animating objects through a novel timeline interface. We validate Data Animator’s expressiveness via a gallery of examples, and evaluate its usability in a re-creation study with designers.
Title: Leveraging Text-Chart Links to Support Authoring of Data-Driven Articles with VizFlow
Authors: family: Sultanum
given: Nicole; family: Chevalier
given: Fanny; family: Bylinskii
given: Zoya; family: Liu
given: Zhicheng
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: Data-Driven Articles, Text-Chart Links, Data Stories, Scrollytelling

Abstract: Data-driven articles—i.e., articles featuring text and supporting charts—play a key role in communicating information to the public. New storytelling formats like scrollytelling apply compelling dynamics to these articles to help walk readers through complex insights, but are challenging to craft. In this work, we investigate ways to support authors of data-driven articles using such storytelling forms via a text-chart linking strategy. From formative interviews with 6 authors and an assessment of 43 scrollytelling stories, we built VizFlow, a prototype system that uses text-chart links to support a range of dynamic layouts. We validate our text-chart linking approach via an authoring study with 12 participants using VizFlow, and a reading study with 24 participants comparing versions of the same article with different VizFlow intervention levels. Assessments showed our approach enabled a rapid and expressive authoring experience, and informed key design recommendations for future efforts in the space.
Title: Integrated Visualization Editing via Parameterized Declarative Templates
Authors: family: McNutt
given: Andrew M; family: Chugh
given: Ravi
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: Systems, Information Visualization, Templates, User Interfaces, Ivy, Declarative Grammars

Abstract: Interfaces for creating visualizations typically embrace one of several common forms. Textual specification enables fine-grained control, shelf building facilitates rapid exploration, while chart choosing promotes immediacy and simplicity. Ideally these approaches could be unified to integrate the user- and usage-dependent benefits found in each modality, yet these forms remain distinct. We propose parameterized declarative templates, a simple abstraction mechanism over JSON-based visualization grammars, as a foundation for multimodal visualization editors. We demonstrate how templates can facilitate organization and reuse by factoring the more than 160 charts that constitute Vega-Lite’s example gallery into approximately 40 templates. We exemplify the pliability of abstracting over charting grammars by implementing—as a template—the functionality of the shelf builder Polestar (a simulacra of Tableau) and a set of templates that emulate the Google Sheets chart chooser. We show how templates support multimodal visualization editing by implementing a prototype and evaluating it through an approachability study.
Title: Data Prophecy: Exploring the Effects of Belief Elicitation in Visual Analytics
Authors: family: Koonchanok
given: Ratanond; family: Baser
given: Parul; family: Sikharam
given: Abhinav; family: Raveendranath
given: Nirmal Kumar; family: Reda
given: Khairi
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: sensemaking, confirmatory analysis, Belief elicitation

Abstract: Interactive visualizations are widely used in exploratory data analysis, but existing systems provide limited support for confirmatory analysis. We introduce PredictMe, a tool for belief-driven visual analysis, enabling users to draw and test their beliefs against data, as an alternative to data-driven exploration. PredictMe combines belief elicitation with traditional visualization interactions to support mixed analysis styles. In a comparative study, we investigated how these affordances impact participants’ cognition. Results show that PredictMe prompts participants to incorporate their working knowledge more frequently in queries. Participants were more likely to attend to discrepancies between their mental models and the data. However, those same participants were also less likely to engage in interactions associated with exploration, and ultimately inspected fewer visualizations and made fewer discoveries. The results suggest that belief elicitation may moderate exploratory behaviors, instead nudging users to be more deliberate in their analysis. We discuss the implications for visualization design.