Dental clinics can be challenging sensory environments, creating discomfort and stress, especially for neurodivergent individuals with Sensory Processing Disorder. Interactive environmental systems offer potential to transform these spaces, providing adaptable, sensory-inclusive experiences. However, the design space for environment-based interventions in dental settings remains largely unexplored. To address this, we conducted in-depth 2-hour co-design sessions with 13 neurodivergent participants to explore environment-based strategies to meet diverse sensory needs. We identified five core design goals for inclusive dental environments: experience transformation, distraction, exposure management, restoration, and social facilitation. Our technology-agnostic design catalogue can inform multiple implementation approaches, including projection mapping, ambient displays, and responsive physical elements. We contribute design patterns for interactive environmental systems, methodological insights for participatory design with neurodivergent communities, and demonstrate how tangible materials serve as proxies for environmental interventions, with implications for Augmented Reality system design. This study advances inclusive design practices and highlights co-designing with neurodivergent individuals.

Despite efforts to increase the representation of disabled people in AI datasets, accessibility datasets are often annotated by crowdworkers without disability-specific expertise, leading to inconsistent or inaccurate labels. This paper examines these annotation challenges through a case study of annotating speech data from people who stutter (PWS). Given the variability of stuttering and differing views on how it manifests, annotating and transcribing stuttered speech remains difficult, even for trained professionals. Through interviews and co-design workshops with PWS and domain experts, we identify challenges in stuttered speech annotation and develop practices that integrate the lived experiences of PWS into the annotation process. Our findings highlight the value of embodied knowledge in improving dataset quality, while revealing tensions between the complexity of disability experiences and the rigidity of static labels. We conclude with implications for disability-first and multiplicity-aware approaches to data interpretation across the AI pipeline.

Routing congestion prediction expedites the closure of FPGA placement and routing (PnR). Current prediction methods employ convolutional models, taking advantage of their capacity of dealing with image-style inputs. However, these methods neglect the direct representation of circuit netlist and its information fusion with placement scheme. Moreover, the limited size of the convolutional kernel struggles to capture circuit connectivity in distant geometric regions. To address these issues, this article presents a graph-based routing congestion prediction framework that fuses the information contained in the circuit’s topological netlist and geometric placement scheme, and leverages a conditional generative adversarial network (cGAN) model to achieve optimized prediction performance compared to contemporary approaches. Our framework encompasses three key components: (1) the HeteroGraph, a heterogeneous graph that integrates a netlist subgraph and a layout subgraph by space mapping edges; (2) the HeteroGNN, a heterogeneous graph neural network that learns the latent features of both the circuit netlist and placement scheme through dual-space message-passing; and (3) the HeteroGNN-embedded cGAN, a model that combines the HeteroGNN with a cGAN for accurate FPGA routing congestion prediction. Compared to state-of-the-art approaches, our method reduces the routing congestion prediction’s root-mean-square error by 18.2% on the VTR7 benchmarks and by 15.0% on the large-scale Titan23 benchmarks. The code associated with this article can be found at https://github.com/AIPnR/FPGA_Hetero_Congestion_Prediction .

Executing neural networks on resource-constrained embedded devices faces challenges. Efforts have been made at the application and system levels to reduce the execution cost. Among them, the early-exit networks reduce computational cost through intermediate exits, while Dynamic Voltage and Frequency Scaling (DVFS) offers system energy reduction. Existing works strive to unify early-exit and DVFS for combined benefits on both timing and energy flexibility, yet limitations exist: 1) varying time constraints that make different exit points become more, or less, important in terms of inference accuracy, are not taken care of, and 2) the optimal decisions of unifying DVFS and early-exit as a multi-objective optimization problem are not achieved due to the large configuration space. To address these challenges, we propose Dr2r, a reinforcement learning-based framework that jointly optimizes early-exit points and DVFS settings for continuous inference. In particular, Dr2r includes a cross-training mechanism that fine-tunes the early-exit network to accommodate dynamic time constraints and system conditions. Experimental results demonstrate that Dr2r achieves up to 22.03% energy reduction and 3.23% accuracy gain compared to contemporary techniques.

Document Image Inpainting (DII) has been applied to degraded documents, including financial and historical documents, to enhance the legibility of images for: (1) human readers by providing high visual quality images; and (2) machine recognizers such as Optical Character Recognition (OCR), thereby reducing recognition errors. With the advent of Deep Learning (DL), DL-based DII methods have achieved remarkable enhancements in terms of either human or machine legibility. However, focusing on improving machine legibility causes visual image degradation, affecting human readability. To address this contradiction, we propose an adaptive learning-based DII method, namely ALDII, that applies domain adaptation strategy, our approach acts like a plug-in module that is capable of constraining a total feature space before optimizing legibility of human and machine, respectively. We evaluate our ALDII on a Chinese handwritten character dataset, which includes single-character and text-line images. Compared to other state-of-the-art approaches, experimental results demonstrated superior performance of our ALDII with metrics of both human and machine legibility.

Deep Reinforcement Learning (DRL)-based Dynamic Voltage Frequency Scaling (DVFS) has shown great promise for energy conservation in embedded systems. While many works were devoted to validating its efficacy or improving its performance, few discuss the feasibility of the DRL agent deployment for embedded computing. State-of-the-art approaches focus on the miniaturization of agents’ inferential networks, such as pruning and quantization, to minimize their energy and resource consumption. However, this spatial-based paradigm still proves inadequate for resource-stringent systems. In this paper, we address the feasibility from a temporal perspective, where FiDRL, a flexible invocation-based DRL model is proposed to judiciously invoke itself to minimize the overall system energy consumption, given that the DRL agent incurs non-negligible energy overhead during invocations. Our approach is three-fold: (1) FiDRL that extends DRL by incorporating the agent's invocation interval into the action space to achieve invocation flexibility; (2) a FiDRL-based DVFS approach for both inter- and intra-task scheduling that minimizes the overall execution energy consumption; and (3) a FiDRL-based DVFS platform design and an on/off-chip hybrid algorithm specialized for training the DRL agent for embedded systems. Experiment results show that FiDRL achieves 55.1% agent invocation cost reduction, under 23.3% overall energy reduction, compared to state-of-the-art approaches.

Videoconferencing (VC) has become a prominent and normalized mode of professional and personal communication, introducing universally experienced challenges such as reduced non-verbal cues and "Zoom Fatigue." But People who stutter (PWS) encounter these obstacles with extra hurdles as existing VC technologies often rely on assumptions about speech patterns that don’t accommodate stuttering. Leveraging and driven by the unique insights and experiences of PWS on VC, we conducted a two-phase co-design study with PWS to explore and reflect on the design space for inclusive and empowering VC technologies from their perspectives. Our findings present a broad design space for tools that support PWS before, during, and after VC, focusing on aspects such as supporting self-disclosure, educating non-stuttering audiences, and promoting personal reflection for long-term self-growth. While many design ideas by our participants embody universal value to all VC users, some carry an activism approach that proactively disrupts existing communication flows and norms to redistribute the power between stuttering and non-stuttering speakers in VC meetings. This work contributes to a thorough analysis of the design space and empowering PWS to be drivers and designers of inclusive VC experiences.

Existing videoconferencing (VC) technologies are often optimized for productivity and efficiency, with little support for the “soft side” of VC meetings such as empathy, authenticity, belonging, and emotional connections. This paper presents findings from a 15-month long autoethnographic study of VC experiences by the first author, a person who stutters (PWS). Our research shed light on the hidden costs of VC for PWS, uncovering the substantial emotional and cognitive efforts that other meeting attendants are often unaware of. Recognizing the disproportionate burden on PWS to be heard in VC, we propose a set of design implications for a more inclusive communication environment, advocating for shared responsibility among all, including communication technologies, to ensure the inclusion and respect of every voice.

Mindfulness, a practice of bringing attention to the present non-judgmentally, has many mental and physical well-being benefits, especially when practiced consistently. Many technologies, such as mobile apps, live streams, virtual reality environments, and wearables, have been invented to support solo or group mindfulness practice. In this paper, we present findings from an interview study with 20 experienced mindfulness practitioners about their everyday mindfulness practices and technology use. Participants identify the benefits and challenges of developing long-term commitment to mindfulness practice. They employ various strategies, such as brief mindfulness exercises, social accountability, and guidance from teachers, to sustain their practice. While conflicted about technology, they adopt and appropriate a range of technologies in their practice for reminders, emotion tracking, connecting with others, and attending online sessions. They also carefully consider when to use technology, when and how to limit its use, and ways to incorporate technology as an object for mindfulness. Based on our findings, we discuss expanding the definition of mindfulness and the tension between supporting short- and long-term mindfulness practice. We also propose a set of design recommendations to support everyday mindfulness, including through the lens of metaphor, reappropriating non-mindfulness technology, and bringing community support into personal practice.

Mindfulness practice has many mental and physical well-being benefits. With the increased popularity of live stream technologies and the impact of COVID-19, many people have turned to live stream tools to participate in online meditation sessions. To better understand the practices, challenges, and opportunities in live-stream meditation, we conducted a three-month autoethnographic study, during which two researchers participated in live-stream meditation sessions as the audience. Then we conducted a follow-up semi-structured interview study with 10 experienced live meditation teachers who use different live-stream tools. We found that live meditation, although having a weaker social presence than in-person meditation, facilitates attendees in establishing a practice routine and connecting with other meditators. Teachers use live streams to deliver the meditation practice to the world which also enhances their practice and brand building. We identified the challenges of using live-stream tools for meditation from the perspectives of both audiences and teachers, and provided design recommendations to better utilize live meditation as a resource for mental wellbeing.

Many digital technologies have been invented to support mindfulness, the practice of bringing attention to the present moment without judgment. While most technologies focus on mindfulness meditation training for novices, in this paper, we explore designing technology to support everyday mindfulness activities for people with varying levels of experience. Through 9 magic machine workshops, 30 mindfulness practitioners explored and reflected on their personal experiences of everyday mindfulness, and generated designs that support their daily practice. Our findings identified six categories of designs conceptualized by our participants: everyday objects, physical spaces, wearables, metaphorical art, companions, and toys. We further analyze the practitioners’ thought processes and considerations for designs that support everyday mindfulness, such as eliciting and regulating emotion and associating mindfulness with routine daily activities. Finally, we discuss the implications of designing individualized mindfulness products and the potential of using co-design magic machine workshops to explore a practical design space.

Voice assistant applications have become omniscient nowadays. Two models that provide the two most important functions for real-life applications (i.e., Google Home, Amazon Alexa, Siri, etc.) are Automatic Speech Recognition (ASR) models and Speaker Identification (SI) models. According to recent studies, security and privacy threats have also emerged with the rapid development of the Internet of Things (IoT). The security issues researched include attack techniques toward machine learning models and other hardware components widely used in voice assistant applications. The privacy issues include technical-wise information stealing and policy-wise privacy breaches. The voice assistant application takes a steadily growing market share every year, but their privacy and security issues never stopped causing huge economic losses and endangering users' personal sensitive information. Thus, it is important to have a comprehensive survey to outline the categorization of the current research regarding the security and privacy problems of voice assistant applications. This paper concludes and assesses five kinds of security attacks and three types of privacy threats in the papers published in the top-tier conferences of cyber security and voice domain.

To share a virtual reality (VR) experience remotely together, users usually record videos from an individual's point of view and then co-watch these videos. However, co-watching recorded videos limits users to reliving their memories from the perspective from which the video was captured. In this paper, we describe ReliveInVR, a new time-machine-like VR experience sharing method. ReliveInVR allows multiple users to immerse themselves in the relived experience together and independently view the experience from any perspective. We conducted a 1x3 within-subject study with 26 dyads to compare ReliveInVR with (1) co-watching 360-degree videos on desktop, and (2) co-watching 360-degree videos in VR. Our results suggest that participants reported higher levels of immersion and social presence in ReliveInVR. Participants in ReliveInVR also understood the shared experience better, discovered unnoticed things together and found the sharing experience more fulfilling. We discuss the design implications for sharing VR experiences over time and space.

We present a new type of sharing VR experience over distance which allows people to relive their recorded experience in VR together. We describe a pilot study examining the user experience when people share their VR experience together remotely. Finally, we discuss the implications for sharing VR experiences over time and space.