Reviewerly/exHarmony huggingface dataset:
It Made Me Feel So Much More at Home Here: Patient Perspectives on Smart Home Technology Deployed at Scale in a Rehabilitation Hospital

Smart patient rooms are arriving; however, their value has yet to be explored. We interviewed 20 patients in a rehabilitation hospital, which has patient rooms equipped with off-the-shelf smart home technologies, so the entertainment and environment are digitally controllable. This novel implementation supports varying control abilities through touchscreen, voice command, and accessibility controllers. The smart rooms and controls are potentially transformative for patients with reduced motor function, helping them regain lost independence and control of their surroundings. Through semi-structured interviews, we explore how smart home technology deployed in patient rooms: interacts with patients' needs, presents new challenges, and fits into the hospital context. We identify a range of considerations that inform how hospitals can integrate smart technology into their environment, including technology design considerations and adjustments to how hospital staff supports its use. These results take an important step toward understanding and improving the value of smart patient rooms.
An Improved SAR Image Semantic Segmentation Deeplabv3+ Network Based on the Feature Post-Processing Module

Synthetic Aperture Radar (SAR) can provide rich feature information under all-weather and day-night conditions because it is not affected by climatic conditions. However, multiplicative speckle noise exists in SAR images, which makes it difficult to accurately identify some fuzzy targets in SAR images, such as roads and rivers, during semantic segmentation. This paper proposes an improved Deeplabv3+ network that can be effectively applied to the semantic segmentation task of SAR images. Firstly, this paper added the attention mechanism and, combined with the idea of an image pyramid, proposed the Feature Post-Processing Module (FPPM) to post-process the network output feature map, obtain better fine image features, and solve the problem of fuzzy texture and spectral features of SAR images. Compared to the original Deeplabv3+ network, the segmentation accuracy has been improved by 3.64% and mIoU improved by 1.09%. Secondly, to solve the problems of limited SAR image data and an unbalanced sample, this paper used the focal loss function to improve the backbone function of the network, which increased the mIoU by 1.01%. Finally, the Atrous Spatial Pyramid Pooling (ASPP) module was improved and the 3 × 3 void convolution in ASPP was decomposed into 2D, which can maintain the void ratio and effectively reduce the calculation amount of the module, shorten the training time by 19 ms and improve the semantic segmentation effect.
Exploring Chart Question Answering for Blind and Low Vision Users

Data visualizations can be complex or involve numerous data points, making them impractical to navigate using screen readers alone. Question answering (QA) systems have the potential to support visualization interpretation and exploration without overwhelming blind and low vision (BLV) users. To investigate if and how QA systems can help BLV users in working with visualizations, we conducted a Wizard of Oz study with 24 BLV people where participants freely posed queries about four visualizations. We collected 979 queries and mapped them to popular analytic task taxonomies. We found that retrieving value and finding extremum were the most common tasks, participants often made complex queries and used visual references, and the data topic notably influenced the queries. We compile a list of design considerations for accessible chart QA systems and make our question corpus publicly available to guide future research and development.
Combining deep reinforcement learning with technical analysis and trend monitoring on cryptocurrency markets

Abstract Cryptocurrency markets experienced a significant increase in the popularity, which motivated many financial traders to seek high profits in cryptocurrency trading. The predominant tool that traders use to identify profitable opportunities is technical analysis. Some investors and researchers also combined technical analysis with machine learning, in order to forecast upcoming trends in the market. However, even with the use of these methods, developing successful trading strategies is still regarded as an extremely challenging task. Recently, deep reinforcement learning (DRL) algorithms demonstrated satisfying performance in solving complicated problems, including the formulation of profitable trading strategies. While some DRL techniques have been successful in increasing profit and loss (PNL) measures, these techniques are not much risk-aware and present difficulty in maximizing PNL and lowering trading risks simultaneously. This research proposes the combination of DRL approaches with rule-based safety mechanisms to both maximize PNL returns and minimize trading risk. First, a DRL agent is trained to maximize PNL returns, using a novel reward function. Then, during the exploitation phase, a rule-based mechanism is deployed to prevent uncertain actions from being executed. Finally, another novel safety mechanism is proposed, which considers the actions of a more conservatively trained agent, in order to identify high-risk trading periods and avoid trading. Our experiments on 5 popular cryptocurrencies show that the integration of these three methods achieves very promising results.
The Attendant Perspective: Present Others in Public Technology Interactions

Technology interactions found their way into public space and present others attend what users are doing. However, in HCI research, the attendant perspective has often been neglected or considered only vaguely in the sense of "social context". Aiming at a better understanding of different types of attendants and their experiences, we developed a typology of four types based on two differentiating criteria (conspicuousness and voluntariness of attending the user interaction). An experimental vignette study (N = 181) tested the typology and revealed typical experiential patterns (e.g., need fulfillment, emotions, desire to join the technology interaction) related to the four types based on quantitative and qualitative data. Our research provides various contributions to HCI theory and design. For example, the typology can be used analytically in UX research. Moreover, it can be used generatively to design positive technology experiences in public for all stakeholders, namely, users and attendants.
Remote Wavelength: Design and Evaluation of a System for Social Connectedness Through Distributed Tabletop Gameplay

Playing remote tabletop games is a fun way to connect with distant friends. Yet most systems for remote tabletop gaming lack support for tangible and social interaction, two important aspects of gameplay for most players. We are interested in how to better design systems for remote tangible gameplay that support social connection. We investigate this topic through the design and evaluation of a prototype system for playing the board game Wavelength across two locations. First, we describe the design goals that informed our prototype: "Remote Wavelength". Then, we discuss the results of a qualitative user study in which ten friend groups played Remote Wavelength. Our findings indicate that a synchronized, tangible gameboard benefits player engagement, communication, and awareness. Our results also illustrate the value of integration across communication and gameplay systems. We conclude by offering considerations for the design of both digital and remote tangible gameplay systems.
VRGit: A Version Control System for Collaborative Content Creation in Virtual Reality

Immersive authoring tools allow users to intuitively create and manipulate 3D scenes while immersed in Virtual Reality (VR). Collaboratively designing these scenes is a creative process that involves numerous edits, explorations of design alternatives, and frequent communication with collaborators. Version Control Systems (VCSs) help users achieve this by keeping track of the version history and creating a shared hub for communication. However, most VCSs are unsuitable for managing the version history of VR content because their underlying line differencing mechanism is designed for text and lacks the semantic information of 3D content; and the widely adopted commit model is designed for asynchronous collaboration rather than real-time awareness and communication in VR. We introduce VRGit, a new collaborative VCS that visualizes version history as a directed graph composed of 3D miniatures, and enables users to easily navigate versions, create branches, as well as preview and reuse versions directly in VR. Beyond individual uses, VRGit also facilitates synchronous collaboration in VR by providing awareness of users' activities and version history through portals and shared history visualizations. In a lab study with 14 participants (seven groups), we demonstrate that VRGit enables users to easily manage version history both individually and collaboratively in VR.
A Formal Logic for Formal Category Theory

Abstract We present a domain-specific type theory for constructions and proofs in category theory. The type theory axiomatizes notions of category, functor, profunctor and a generalized form of natural transformations. The type theory imposes an ordered linear restriction on standard predicate logic, which guarantees that all functions between categories are functorial, all relations are profunctorial, and all transformations are natural by construction, with no separate proofs necessary. Important category-theoretic proofs such as the Yoneda lemma and Co-yoneda lemma become simple type-theoretic proofs about the relationship between unit, tensor and (ordered) function types, and can be seen to be ordered refinements of theorems in predicate logic. The type theory is sound and complete for a categorical model in virtual equipments , which model both internal and enriched category theory. While the proofs in our type theory look like standard set-based arguments, the syntactic discipline ensure that all proofs and constructions carry over to enriched and internal settings as well.
AO-Finger: Hands-free Fine-grained Finger Gesture Recognition via Acoustic-Optic Sensor Fusing

Finger gesture recognition is gaining great research interest for wearable device interactions such as smartwatches and AR/VR headsets. In this paper, we propose a hands-free fine-grained finger gesture recognition system AO-Finger based on acoustic-optic sensor fusing. Specifically, we design a wristband with a modified stethoscope microphone and two high-speed optic motion sensors to capture signals generated from finger movements. We propose a set of natural, inconspicuous and effortless micro finger gestures that can be reliably detected from the complementary signals from both sensors. We design a multi-modal CNN-Transformer model for fast gesture recognition (flick/pinch/tap), and a finger swipe contact detection model to enable fine-grained swipe gesture tracking. We built a prototype which achieves an overall accuracy of 94.83% in detecting fast gestures and enables fine-grained continuous swipe gestures tracking. AO-Finger is practical for use as a wearable device and ready to be integrated into existing wrist-worn devices such as smartwatches.
Security Verification of the OpenTitan Hardware Root of Trust

We describe the security verification of OpenTitan.We illustrate how information flow tracking turns human knowledge of assets and security requirements into formal security properties verified using Cycuity's Radix.The verification uncovered weaknesses and helped produce hardware fixes to eliminate vulnerabilities.
The Influence of Context on Response to Spear-Phishing Attacks: an In-Situ Deception Study

In today's digitized societies, phishing attacks are a security threat with damaging consequences. Organizations remain vulnerable to phishing attacks, and it is not clear how the work context influences people's perceptions and behaviors related to phishing attempts. I investigate (1) how contextual factors influence reactions to a spear-phishing attempt, (2) why people report or do not report phishing attempts, (3) which opportunities for security-enhancing interventions people identify. I use an in-situ deception methodology to observe participants (N=14) in their realistic work environment. I triangulate observational and self-reported data to obtain rich qualitative insights into participants' emotions, thoughts, and actions when receiving a targeted phishing email. I find that task, IT, internal and social context play an important role. The email's request being aligned with expectations and perceived time pressure when responding to emails were associated with insecure behavior. The social context positively influenced phishing detection, but "phished" participants did not tell anyone.
Data Abstraction Elephants: The Initial Diversity of Data Representations and Mental Models

Two people looking at the same dataset will create different mental models, prioritize different attributes, and connect with different visualizations. We seek to understand the space of data abstractions associated with mental models and how well people communicate their mental models when sketching. Data abstractions have a profound influence on the visualization design, yet it’s unclear how universal they may be when not initially influenced by a representation. We conducted a study about how people create their mental models from a dataset. Rather than presenting tabular data, we presented each participant with one of three datasets in paragraph form, to avoid biasing the data abstraction and mental model. We observed various mental models, data abstractions, and depictions from the same dataset, and how these concepts are influenced by communication and purpose-seeking. Our results have implications for visualization design, especially during the discovery and data collection phase.
Keep it simple? Evaluation of Transitions in Virtual Reality

The impact of different transitions between two virtual reality (VR) environments is still an open research question, and related work often serves only an isolated view on different techniques, i.e., with low ecological validity. The purpose of this study was to start closing this gap and evaluate the impact of six transitions while the user is solving a task that keeps them engaged. Therefore, we first propose a suitable and reproducible task design. Then we evaluate the six transitions in a user study. The results show that in contrast to prior work, the users preferred a short and efficient transition against a transition that was designed to achieve higher interactivity and continuity but was perceived as more cumbersome to use.
ESCAPE: Countering Systematic Errors from Machine’s Blind Spots via Interactive Visual Analysis

Classification models learn to generalize the associations between data samples and their target classes. However, researchers have increasingly observed that machine learning practice easily leads to systematic errors in AI applications, a phenomenon referred to as "AI blindspots." Such blindspots arise when a model is trained with training samples (e.g., cat/dog classification) where important patterns (e.g., black cats) are missing or periphery/undesirable patterns (e.g., dogs with grass background) are misleading towards a certain class. Even more sophisticated techniques cannot guarantee to capture, reason about, and prevent the spurious associations. In this work, we propose ESCAPE, a visual analytic system that promotes a human-in-the-loop workflow for countering systematic errors. By allowing human users to easily inspect spurious associations, the system facilitates users to spontaneously recognize concepts associated misclassifications and evaluate mitigation strategies that can reduce biased associations. We also propose two statistical approaches, relative concept association to better quantify the associations between a concept and instances, and debias method to mitigate spurious associations. We demonstrate the utility of our proposed ESCAPE system and statistical measures through extensive evaluation including quantitative experiments, usage scenarios, expert interviews, and controlled user experiments.
Eliciting Security &amp; Privacy-Informed Sharing Techniques for Multi-User Augmented Reality

The HCI community has explored new interaction designs for collaborative AR interfaces in terms of usability and feasibility; however, security & privacy (S&P) are often not considered in the design process and left to S&P professionals. To produce interaction proposals with S&P in mind, we extend the user-driven elicitation method with a scenario-based approach that incorporates a threat model involving access control in multi-user AR. We conducted an elicitation study in two conditions, pairing AR/AR experts in one condition and AR/S&P experts in the other, to investigate the impact of each pairing. We contribute a set of expert-elicited interactions for sharing AR content enhanced with access control provisions, analyze the benefits and tradeoffs of pairing AR and S&P experts, and present recommendations for designing future multi-user AR interactions that better balance competing design goals of usability, feasibility, and S&P in collaborative AR.
The Impact of Navigation Aids on Search Performance and Object Recall in Wide-Area Augmented Reality

Head-worn augmented reality (AR) is a hotly pursued and increasingly feasible contender paradigm for replacing or complementing smartphones and watches for continual information consumption. Here, we compare three different AR navigation aids (on-screen compass, on-screen radar and in-world vertical arrows) in a wide-area outdoor user study (n=24) where participants search for hidden virtual target items amongst physical and virtual objects. We analyzed participants' search task performance, movements, eye-gaze, survey responses and object recall. There were two key findings. First, all navigational aids enhanced search performance relative to a control condition, with some benefit and strongest user preference for in-world arrows. Second, users recalled fewer physical objects than virtual objects in the environment, suggesting reduced awareness of the physical environment. Together, these findings suggest that while navigational aids presented in AR can enhance search task performance, users may pay less attention to the physical environment, which could have undesirable side-effects.
StyleMe: Towards Intelligent Fashion Generation with Designer Style

Hand-drawn sketches and sketch colourization are the most laborious but necessary steps for fashion designers to design exquisite clothes, especially when the fashion design requires distinctive and personal characteristics from designer style. This paper presents an artificial intelligent aided fashion design system, namely StyleMe, to support the automatic generation of clothing sketches with designer style. Given the clothing pictures specified by the designer, StyleMe can use deep learning based generative model to generate clothing sketches that are consistent with the designer style. The system also supports intelligent colourization on clothing sketch by style transfer, according to specified styles from the real fashion images. Through a series of performance evaluations and user studies, we found that our system can generate effective clothing sketches as good as fashion designers' human work, and significantly improve the efficiency of fashion design with its sketch colourization method.
Towards a Bedder Future: A Study of Using Virtual Reality while Lying Down

Most contemporary Virtual Reality (VR) experiences are made for standing users. However, when a user is lying down—either by choice or necessity—it is unclear how they can walk around, dodge obstacles, or grab distant objects. We rotate the virtual coordinate space to study the movement requirements and user experience of using VR while lying down. Fourteen experienced VR users engaged with various popular VR applications for 40 minutes in a study using a think-aloud protocol and semi-structured interviews. Thematic analysis of captured videos and interviews reveals that using VR while lying down is comfortable and usable and that the virtual perspective produces a potent illusion of standing up. However, commonplace movements in VR are surprisingly difficult when lying down, and using alternative interactions is fatiguing and hampers performance. To conclude, we discuss design opportunities to tackle the most significant challenges and to create new experiences.
Digital Flash Card Media for Early Reading Learning in Elementary Schools

Many students need to learn the letters correctly, often make mistakes in distinguishing letters that are almost similar in shape, have difficulty, and are not fluent in reading. For this reason, teachers need to develop learning media that can attract students' interest, are practical to use, digital-based, and are by student characteristics and technological developments so that learning to read becomes fun. This study aimed to develop digital Flash Card media in early reading learning in elementary schools. The subjects in this study were first-grade students, totaling 30 students. The research method used is the method of research and development or Research and Development (R&amp;D). Data collection was carried out through interviews, questionnaires, and observation. The collected data were analyzed using qualitative and quantitative descriptive analysis techniques. The results showed that the validation test for digital Flash Card media products by material experts was 87% in a very valid category. The assessment by media experts is 90% with a very valid category, and an evaluation by linguists is 89% with a very valid type. Furthermore, the results of observing trials using digital Flash Card media on a large scale for elementary school students obtained an average of 87.7% in the practical category. It was concluded that the development of digital Flash Card media met the valid, helpful, and effective criteria for learning to read in elementary schools.
Imprimer: Computational Notebooks for CNC Milling

Digital fabrication in industrial contexts involves standardized procedures that prioritize precision and repeatability. However, fabrication machines are now available for practitioners who focus instead on experimentation. In this paper, we reframe hobbyist CNC milling as writing literate programs which interleave documentation, interactive graphics, and source code for machine control. To test this approach, we present Imprimer, a machine infrastructure for a CNC mill and an associated library for a computational notebook. Imprimer lets makers learn experimentally, prototype new interactions for making, and understand physical processes by writing and debugging code. We demonstrate three experimental milling workflows as computational notebooks, conduct a user study with practitioners with a range of backgrounds, and discuss literate programming as a future vision for digital fabrication altogether.