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    Transmedia Costume as 'Sustainable' Costume? Blending Physical and Virtual Bodily Materialities
    (Universidade Lusófona, 2024-06-21) Pantouvaki, Sofia; Department of Film
    Combining tangible and digital means in costume design by merging live digital content with traditional costume materials opens new possibilities to create evolving performance dramaturgies and ‘unusual’ bodies. This article focuses on recent and ongoing explorations from the field of costume design for live and mediated performance that employ a combination of physical and virtual tools to design multi-layered characters and costumes. The study analyses experimental works that address questions of virtuality and materiality through the costumed body. Such works explore in practice ways in which the physical meets the virtual, and how art, body-oriented design, and performance-making merge and juxtapose with digital means through the medium of costume. The combination of analogue materials, digital technology and moving bodies can provide characters and costumes that can change and reshape over time, while also blending physical and virtual bodies. On a theoretical level, the article addresses the many dimensions and multiple ‘physicalities’ and ‘materialities’ that such costumes offer to the representation of human and non-/super-/post-human bodies and characters.
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    Predicting the effect of headphones on the time to localize a target in an auditory-guided visual search task
    (Frontiers Media, 2024) Llado Gonzalez, Pedro; Barumerli, Roberto; Baumgartner, Robert; Majdak, Piotr; Department of Information and Communications Engineering; Communication Acoustics: Spatial Sound and Psychoacoustics; Austrian Academy of Sciences
    In augmented reality scenarios, headphones obstruct the direct path of the sound to the ears, affecting the users’ abilities to localize surrounding sound sources and compromising the immersive experience. Unfortunately, the assessment of the perceptual implications of wearing headphones on localization in ecologically valid scenarios is costly and time-consuming. Here, we propose a model-based tool for automatic assessment of the dynamic localization degradation (DLD) introduced by headphones describing the time required to find a target in an auditory-guided visual search task. First, we introduce the DLD score obtained for twelve headphones and the search times with actual listeners. Then, we describe the predictions of the headphone-induced DLD score obtained by an auditory model designed to simulate the listener’s search time. Our results indicate that our tool can predict the degradation score of unseen headphones. Thus, our tool can be applied to automatically assess the impact of headphones on listener experience in augmented reality applications.
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    Family Members’ Perspectives on the Unmet Care Needs of People Living With Dementia in Nursing Homes
    (Routledge, 2024-06-21) Paananen, Jenny; Moore, Vanessa; Blomqvist, Katarina; Kulmala, Jenni; Pirhonen, Jari; Department of Film; University of Turku; Trinity College Dublin; Tampere University
    While the number of older people living with dementia grows, savings in public resources leads to situations where services and supports for older people are cut, even in the traditionally generous Nordic welfare states, such as Finland. This development is expected to lead to care poverty, meaning older people are not receiving the services they need. Our aim was to uncover whether care poverty already exists within institutional care for older adults in Finland. Thematic analysis was utilized to study 19 interviews with family members of people with dementia living in a nursing home. Signs of care poverty were found in relation to timeliness and safety of access to long-term care and quality of professional care. In addition, the threshold of questioning the care system was high, and managing disagreements about care with professionals was challenging for the family members. The results raise concerns that reducing long-term care risks the whole concept of welfare states. Family members should be more systematically involved in needs assessment and decision-making concerning the care of older people with dementia.
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    Energy Harvesting Meets Data-Oriented Communication: Delay-Outage Ratio Analysis
    (IEEE, 2024-06-01) Ilter, Mehmet C.; Sofotasios, Paschalis C.; Valkama, Mikko; Hamalainen, Jyri; Department of Information and Communications Engineering; Wireless & Mobile Communications; Khalifa University of Science and Technology; Tampere University
    The data-oriented approach was initially proposed towards novel transmission strategies for individual data-transmission sessions by considering instantaneous operating conditions. Aligned with this, the present contribution focuses on the analysis of the delay outage ratio (DOR), a data-oriented performance metric, when far-field wireless power transfer is utilized with network elements capable of energy harvesting from ambient RF signals prior to the transmission periods. To that end, simple and accurate analytic expressions are derived considering the involved battery charging intervals as well as the length of the data segment, available bandwidth and delay constraints. These expressions are corroborated by numerical simulations and they are subsequently used in developing useful theoretical and practical insights of interest. Besides highlighting the importance of the new data-oriented DOR metric, the offered results are shown to be useful in the design of realistic energy harvesting-based data transmission systems in future networks.
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    Impulse Response Interpolation Using Optimal Transport
    (2024-04-01) Sundström, David; Elvander, Filip; Jakobsson, Andreas; Department of Information and Communications Engineering; Matthews, Michael B.; Structured and Stochastic Modeling; Lund University
    The spatial impulse response (IR) interpolation problem is of general interest, e.g. for imaging of subsurface structures based on seismic waves, rendering of audio and radar IRs, as well as for numerous spatial audio applications. A commonly used model represents the occurring reflections as equivalent source positions, often being determined using a sparse re-construction framework employing spatial dictionaries. How-ever, in the presence of calibration errors, such spatial dictionaries tend to inaccurately represent the actual propagation, limiting these methods from being used in practice. In-stead of explicitly assuming an equivalent source model, we here introduce a trade-off between minimizing the distance to an equivalent source model and fitting the data by means of a multi-marginal optimal transport problem. The proposed method is evaluated on real acoustic IRs illustrating its prefer-able performance.
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    Evaluating privacy, security, and trust perceptions in conversational AI: A systematic review
    (Elsevier Ltd, 2024-10) Leschanowsky, Anna; Rech, Silas; Popp, Birgit; Bäckström, Tom; Department of Information and Communications Engineering; Speech Interaction Technology; Fraunhofer Institute for Integrated Circuits
    Conversational AI (CAI) systems which encompass voice- and text-based assistants are on the rise and have been largely integrated into people’s everyday lives. Despite their widespread adoption, users voice concerns regarding privacy, security and trust in these systems. However, the composition of these perceptions, their impact on technology adoption and usage and the relationship between privacy, security and trust perceptions in the CAI context remain open research challenges. This study contributes to the field by conducting a Systematic Literature Review and offers insights into the current state of research on privacy, security and trust perceptions in the context of CAI systems. The review covers application fields and user groups and sheds light on empirical methods and tools used for assessment. Moreover, it provides insights into the reliability and validity of privacy, security and trust scales, as well as extensively investigating the subconstructs of each item as well as additional concepts which are concurrently collected. We point out that the perceptions of trust, privacy and security overlap based on the subconstructs we identified. While the majority of studies investigate one of these concepts, only a few studies were found exploring privacy, security and trust perceptions jointly. Our research aims to inform on directions to develop and use reliable scales for users’ privacy, security and trust perceptions and contribute to the development of trustworthy CAI systems.
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    MAPII - Map-based Interfaces and Interactions
    (2024-06-03) Masoodian, Masood; Luz, Saturnino; Department of Art and Media; Conati, Cristina; Torre, Ilaria; Volpe, Gualtiero; University of Edinburgh
    Maps have been used for centuries as tools for exploring the real and the imagined, the physical and the metaphysical worlds. Today, in the world of technology, maps also play an important role as underlying representation tools, forming the basis of a wide range of digital devices, applications, and services. Despite this, there are hardly any venues for sharing of research and design expertise, learnings, practices, and experiences of the use of maps and map-like visualizations in the context of visual interfaces and interactions. This workshop aims to fill this gap by providing a much-needed interdisciplinary forum focusing on map-based interfaces and interactions.
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    Optical Wireless Communications for Underwater Monitoring and Smart Indoor Farming Applications
    (2024-05-30) Dowhuszko, Alexis A.; Rodrigues, Luis; Alves, Luis Nero; Cespedes, Maximo Morales; Matus, Vicente; Perez-Jimenez, Rafael; Rufo, Julio; Romano, Alessandro; Vegni, Anna Maria; Ijeh, Ikenna Chinazaekpere; Department of Information and Communications Engineering; Wireless & Mobile Communications; University of Aveiro; Universidad Carlos III de Madrid; University of Las Palmas de Gran Canaria; University of La Laguna; Roma Tre University; Alex Ekwueme Federal University
    Climate change and the activities of humans are putting constant pressure on ecosystems around the globe. Without notable changes in the linear systems for production and consumption, the growing demand for natural resources will lead to serious impacts on the planet. In this context, Optical Wireless Communications (OWC), a technology that relies on electromagnetic signals in a much higher frequency band than radio communications, has been identified as a competitive solution to enable IoT connectivity in applications related to the Cluster 6 - Food, Bioeconomy, Natural Resources, Agriculture and Environment of the Horizon Europe program (2021-2027). In this paper, we present an overview of two IoT applications in which OWC can make a difference with respect to radio, which are underwater monitoring and smart indoor farming. The intrinsic advantages of using light signals for IoT connectivity are discussed, including the challenges to be faced for its massive adoption. This paper elaborates in further detail the discussion that has been summarized in the OWC roadmap that NEWFOCUS COST Action 19111 has been recently releasing.
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    RIS-Assisted Three-Dimensional Drone Localization and Tracking Under Hardware Impairments
    (IEEE, 2024) Meles, Mehari; Rajasekaran, Akash; Mela, Lauri; Jantti, Riku; Department of Information and Communications Engineering; Communication Engineering; Department of Information and Communications Engineering
    The concept of Reconfigurable Intelligent Surfaces (RIS) has emerged as a promising method for communications and the localization of aeronautical vehicles. In this paper, we explore the impact of hardware impairments on three-dimensional (3D) drone localization within a single-input single-output (SISO) system assisted by RIS. Our methodology begins by modeling the channel from the base station (BS), equipped with a single-antenna transmitter, to each RIS at known positions. This model accounts for hardware impairments at the BS, particularly beam downtilt, which influences the accuracy of drone location estimation. Moreover, we model the channel from the RIS to the drone, employing exhaustive beam sweeping in both azimuth and elevation angles to estimate the Angles of Departure (AODs) from the RIS to the drone. We adopt a unique phase noise (PN) model for each element within the RIS and assess the impact of these impairments on angle and location estimation accuracy through extensive simulations. Additionally, we examine the effects of RIS configuration and the Inter-Site Distance (ISD) between two RIS units on localization performance. An Unscented Kalman Filter (UKF) algorithm is integrated for tracking of the drone trajectory. Our simulation results demonstrate that the RIS-assisted 3D drone localization approach achieves significant accuracy despite various impairments. The findings of this paper underscore the potential of RIS-enabled 3D drone localization to maintain high accuracy under hardware impairments, paving the way for future research in RIS-enabled drone localization systems.
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    Classification of Phonation Types in Singing Voice Using Wavelet Scattering Network-based Features
    (Acoustical Society of America, 2024-06-01) Mittapalle, Kiran; Alku, Paavo; Department of Information and Communications Engineering; Speech Communication Technology
    The automatic classification of phonation types in singing voice is essential for tasks such as identification of singing style. In this study, it is proposed to use wavelet scattering network (WSN)-based features for classification of phonation types in singing voice. WSN, which has a close similarity with auditory physiological models, generates acoustic features that greatly characterize the information related to pitch, formants, and timbre. Hence, the WSN-based features can effectively capture the discriminative information across phonation types in singing voice. The experimental results show that the proposed WSN-based features improved phonation classification accuracy by at least 9% compared to state-of-the-art features.
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    Collaborative Watermarking for Adversarial Speech Synthesis
    (2024-03-18) Juvela, Lauri; Wang, Xin; Department of Information and Communications Engineering; Speech Synthesis; National Institute of Informatics
    Advances in neural speech synthesis have brought us technology that is not only close to human naturalness, but is also capable of instant voice cloning with little data, and is highly accessible with pre-trained models available. Naturally, the potential flood of generated content raises the need for synthetic speech detection and watermarking. Recently, considerable research effort in synthetic speech detection has been related to the Automatic Speaker Verification and Spoofing Countermeasure Challenge (ASVspoof), which focuses on passive countermeasures. This paper takes a complementary view to generated speech detection: a synthesis system should make an active effort to watermark the generated speech in a way that aids detection by another machine, but remains transparent to a human listener. We propose a collaborative training scheme for synthetic speech watermarking and show that a HiFi-GAN neural vocoder collaborating with the ASVspoof 2021 baseline countermeasure models consistently improves detection performance over conventional classifier training. Furthermore, we demonstrate how collaborative training can be paired with augmentation strategies for added robustness against noise and time-stretching. Finally, listening tests demonstrate that collaborative training has little adverse effect on perceptual quality of vocoded speech.
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    Managing Personal Health and Well-being by Integrating Data Streams using Interactive Visualizations
    (2024-06-03) Luz, Saturnino; Masoodian, Masood; Department of Art and Media; Conati, Cristina; Torre, Ilaria; Volpe, Gualtiero; University of Edinburgh
    There has been in a rapid growth in the use of consumer health monitoring devices integrated with smartphones that help people to record and analyse data relating to different factors contributing to their health and well-being. However, these factors are often presented separately, making it hard for users to visualise potential relationships among different health data streams, and missing an opportunity to use other information sources available on their devices to contextualise these health indicators. Here we present a practical concept for integrating health and other contextual data with different analytic and visualization tools as the basis for an interactive health and well-being management dashboard.
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    Listening like a speech-training app: Expert and non-expert listeners’ goodness ratings of children’s speech
    (Taylor & Francis, 2024-06-09) Strömbergsson, Sofia; Fröjdh, Molly; Pettersson, Magdalena; Grósz, Tamás; Getman, Yaroslav; Kurimo, Mikko; Department of Information and Communications Engineering; Speech Recognition; Karolinska Institutet
    Speech training apps are being developed that provide automatic feedback concerning children’s production of known target words, as a score on a 1–5 scale. However, this ‘goodness’ scale is still poorly understood. We investigated listeners’ ratings of ‘how many stars the app should provide as feedback’ on children’s utterances, and whether listener agreement is affected by clinical experience and/or access to anchor stimuli. In addition, we explored the association between goodness ratings and clinical measures of speech accuracy; the Percentage of Consonants Correct (PCC) and the Percentage of Phonemes Correct (PPC). Twenty speech-language pathologists and 20 non-expert listeners participated; half of the listeners in each group had access to anchor stimuli. The listeners rated 120 words, collected from children with and without speech sound disorder. Concerning reliability, intra-rater agreement was generally high, whereas inter-rater agreement was moderate. Access to anchor stimuli was associated with higher agreement, but only for non-expert listeners. Concerning the association between goodness ratings and the PCC/PPC, correlations were moderate for both listener groups, under both conditions. The results indicate that the task of rating goodness is difficult, regardless of clinical experience, and that access to anchor stimuli is insufficient for achieving reliable ratings. This raises concerns regarding the 1–5 rating scale as the means of feedback in speech training apps. More specific listener instructions, particularly regarding the intended context for the app, are suggested in collection of human ratings underlying the development of speech training apps. Until then, alternative means of feedback should be preferred.
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    Whittle index approach to multiserver scheduling with impatient customers and DHR service times
    (Springer, 2024-06) Aalto, Samuli; Department of Information and Communications Engineering; Performance analysis
    We consider the optimal scheduling problem in a multiserver queue with impatient customers belonging to multiple classes. We assume that each customer has a random abandonment time, after which the customer leaves the system if its service has not been completed before that. In addition, we assume that the scheduler is not able to anticipate the expiration of the abandonment times but only knows their distributions and how long each customer has been in the system. Many papers consider this scheduling problem under Poisson arrivals and linear holding costs assuming further that both the service times and the abandonment times have exponential distributions. Even with these additional assumptions, the exact solution is known only in very few special cases. To tackle this tricky problem, we apply the Whittle index approach. Unlike the earlier papers, which were restricted to exponential service times, we allow the service time distributions for which the hazard rate is decreasing. The Whittle index approach is applied to the discrete-time multiserver queueing problem with discounted costs. As our main theoretical result, we prove that the related relaxed optimization problem is indexable and derive the corresponding Whittle index explicitly. Based on this discrete-time result, we develop a reasonable heuristic for the original continuous-time multiserver scheduling problem. The performance of the resulting policy is evaluated in the M/G/M setup by numerical simulations, which demonstrate that it, indeed, gives better performance than the other policies included in the comparison.
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    A Survey on AI-Enabled Attacks and AI-Empowered Countermeasures in Physical Layer
    (2024-05-30) Chang, Jingdong; Li, Zhao; Kaveh, Masoud; Zhang, Yifan; Li, Jiajun; Yan, Zheng; Department of Information and Communications Engineering; Network Security and Trust; Xidian University
    As artificial intelligence (AI) continues to integrated into our daily lives, it becomes increasingly crucial to prioritize the security of these systems, particularly at the physical layer. The physical layer is the foundational level of communication systems, responsible for transmitting data over a communication medium. The advent of AI has brought about significant advancements in the field of cybersecurity, introducing new attack methodologies and empowering countermeasures at the physical layer of communication systems. This dual aspect of AI has made it a critical component in maintaining the security and integrity of these systems. However, despite many existing efforts to apply AI into physical layer security, there is still a lack of comprehensive overview of attacks and countermeasures regarding AI-based attacks and countermeasures in this domain. In this article, we provide a general survey on AI -enabled attacks and AI-empowered countermeasures in the physical layer, and analyze its performance using two sets of proposed standards: evaluation criteria for AI -enabled attacks and AI -empowered countermeasures. Through this review, we aim to identify the key challenges that require extensive investigation and propose suggestions for future research directions.
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    Museums as intersectional spaces for artivist solidarity
    (2024-06) Haapalainen, Riikka; Suominen, Anniina; Pusa, Tiina; Järvinen, Jasmin; Orenius, Melanie; Department of Art and Media; Sinner, Anita; Osler, Patricia; White, Boyd; University of the Arts Helsinki; Amos Rex
    This chapter responds to the themes of pedagogical sensibilities and anticolonial museum work by exploring two separate, but intertwined art education projects (cases) carried out in collaboration between the Art Education Programme at Aalto University, the Finnish National Gallery's Ateneum Art Museum, the Amos Rex art museum, and individuals associated with local NGOs. The chapter concludes with an emphasis on solidarity; to sensibly and ethically combat systemic discrimination supported and maintained by museums and countless educators despite of their personal and collective aims and ideals. The chapter challenges the prevailing binary divisions within museum knowledges and their inscribed hegemonic, hierarchical structures between people, objects and knowledges. As a critical stance, the authors present the need for anticolonial and co-constructed knowledges where authority is shared, and where critical knowledge as a discourse strives to move past normative ways to perceive, experience, articulate and contextualise museum objects. The notions of sensible, anticolonial museum also brings forth non-verbal, embodied knowledge and sensuous epistemic orientation: artivist solidarity.
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    Sampling the user controls in neural modeling of audio devices
    (Springer, 2024-12) Mikkonen, Otto; Wright, Alec; Välimäki, Vesa; Department of Information and Communications Engineering; Audio Signal Processing
    This work studies neural modeling of nonlinear parametric audio circuits, focusing on how the diversity of settings of the target device user controls seen during training affects network generalization. To study the problem, a large corpus of training datasets is synthetically generated using SPICE simulations of two distinct devices, an analog equalizer and an analog distortion pedal. A proven recurrent neural network architecture is trained using each dataset. The difference in the datasets is in the sampling resolution of the device user controls and in their overall size. Based on objective and subjective evaluation of the trained models, a sampling resolution of five for the device parameters is found to be sufficient to capture the behavior of the target systems for the types of devices considered during the study. This result is desirable, since a dense sampling grid can be impractical to realize in the general case when no automated way of setting the device parameters is available, while collecting large amounts of data using a sparse grid only incurs small additional costs. Thus, the result provides guidance for efficient collection of training data for neural modeling of other similar audio devices.
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    Supporting Task Switching with Reinforcement Learning
    (2024-05-11) Lingler, Alexander; Talypova, Dinara; Jokinen, Jussi P. P.; Oulasvirta, Antti; Wintersberger, Philipp; Department of Information and Communications Engineering; Helsinki Institute for Information Technology (HIIT); User Interfaces; Upper Austria University of Applied Sciences; University of Jyväskylä
    Attention management systems aim to mitigate the negative effects of multitasking. However, sophisticated real-time attention management is yet to be developed. We present a novel concept for attention management with reinforcement learning that automatically switches tasks. The system was trained with a user model based on principles of computational rationality. Due to this user model, the system derives a policy that schedules task switches by considering human constraints such as visual limitations and reaction times. We evaluated its capabilities in a challenging dual-task balancing game. Our results confirm our main hypothesis that an attention management system based on reinforcement learning can significantly improve human performance, compared to humans' self-determined interruption strategy. The system raised the frequency and difficulty of task switches compared to the users while still yielding a lower subjective workload. We conclude by arguing that the concept can be applied to a great variety of multitasking settings.
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    A Paradigm Shift from an Experimental-Based to a Simulation-Based Framework Using Motion-Capture Driven MIMO Radar Data Synthesis
    (IEEE, 2024-05-15) Waqar, Sahil; Muaaz, Muhammad; Sigg, Stephan; Patzold, Matthias; Department of Information and Communications Engineering; Ambient Intelligence; University of Agder
    The development of radar-based classifiers driven by empirical data can be highly demanding and expensive due to the unavailability of radar data. In this article, we introduce an innovative simulation-based approach that addresses the data scarcity problem, particularly for our multiple-input multiple-output (MIMO) radar-based direction-independent human activity recognition (HAR) system. To simulate realistic MIMO radar signatures, we first synthesize human motion and generate corresponding spatial trajectories. From these trajectories, a received radio frequency (RF) signal is synthesized using our MIMO channel model, which considers the non-stationary behavior of human motion and the multipath components originating from the scatterers on human body segments. Subsequently, the synthesized RF signals are processed to simulate MIMO radar signatures for various human activities. The proposed simulation-based direction-independent HAR system achieves a classification accuracy of 97.83% when tested with real MIMO radar data. A significant advantage of our simulation-based framework lies in its ability to facilitate multistage data augmentation techniques at the motion-layer, physical-layer, and signal-layer syntheses. This capability significantly reduces the training workload for radar-based classifiers. Importantly, our simulation-based proof-of-concept is applicable to single-input single-output (SISO) and MIMO radars in monostatic, bistatic, and multistatic configurations, making it a versatile solution for realizing other radar-based classifiers, such as gesture classifiers.
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    AXNav: Replaying Accessibility Tests from Natural Language
    (2024-05-11) Taeb, Maryam; Swearngin, Amanda; Schoop, Eldon; Cheng, Ruijia; Jiang, Yue; Nichols, Jeffrey; Department of Information and Communications Engineering; User Interfaces; Florida State University; Apple
    Developers and quality assurance testers often rely on manual testing to test accessibility features throughout the product lifecycle. Unfortunately, manual testing can be tedious, often has an overwhelming scope, and can be difficult to schedule amongst other development milestones. Recently, Large Language Models (LLMs) have been used for a variety of tasks including automation of UIs. However, to our knowledge, no one has yet explored the use of LLMs in controlling assistive technologies for the purposes of supporting accessibility testing. In this paper, we explore the requirements of a natural language based accessibility testing workflow, starting with a formative study. From this we build a system that takes a manual accessibility test instruction in natural language (e.g., “Search for a show in VoiceOver”) as input and uses an LLM combined with pixel-based UI Understanding models to execute the test and produce a chaptered, navigable video. In each video, to help QA testers, we apply heuristics to detect and flag accessibility issues (e.g., Text size not increasing with Large Text enabled, VoiceOver navigation loops). We evaluate this system through a 10-participant user study with accessibility QA professionals who indicated that the tool would be very useful in their current work and performed tests similarly to how they would manually test the features. The study also reveals insights for future work on using LLMs for accessibility testing.