RageSense introduces a novel system for detecting and regulating player frustration during mobile gaming. Instead of relying on coarse emotion labels, RageSense estimates users’ valence and arousal levels in real time using near-ultrasonic acoustic sensing. By analyzing facial muscle movements via built-in smartphone speakers and microphones, our approach enables emotion sensing without requiring cameras or wearables, constituting a more unobtrusive, environment-resilient, and privacy-friendly approach than traditional emotion recognition. To transform detection into action, we integrate a large language model (LLM) that generates empathetic, context-aware interventions based on gameplay screenshots, behavioral signals, and emotional trajectories. These interventions are delivered in real time, tailored to the user’s emotional state, and designed to mitigate rage while enhancing player well-being. In a 53-participant field study, our system improved emotional state immediately after triggers and was preferred over random or template-based messages. To our knowledge, this is the first demonstration of near-ultrasonic, on-phone valence-arousal regression during mobile gameplay that directly drives real-time, context-aware interventions.

Quantum weight enumerators are fundamental tools for analyzing quantum error-correcting codes and multipartite entanglement, offering insights into the existence of quantum error-correcting codes andk-uniform states. In this work, we establish a connection between quantum weight enumerators and then-qubit parallelized SWAP test. We demonstrate that each shadow enumerator corresponds to a probability derived from this test, providing a physical interpretation for the shadow enumerators. Leveraging the non-negativity of these probabilities, we present an elegant proof for the shadow inequalities. Additionally, we show that the Shor-Laflamme weight enumerators and the Rains unitary enumerators can be calculated using then-qubit parallelized SWAP test. For applications, we utilize this test to compute the distances of quantum error-correcting codes, determine thek-uniformity of pure states, and evaluate multipartite entanglement measures. Our results indicate that quantum weight enumerators can be efficiently estimated on quantum computers, opening a path to calculate and verify the distances of quantum error-correcting codes.

Silent speech interface (SSI) enables users to interact with their devices without making audible sounds, thus preventing potential eavesdropping or disruptions to others. Recent advancements in acoustic sensing technology have made SSI on mobile devices highly promising, requiring no hardware modifications and operating in a non-contact manner. However, one major challenge faced by existing acoustic sensing-based SSI is its limited sensing range, typically less than 7cm. Users often need to speak in close proximity to the speakers/microphones, severely constraining its applicability on mobile devices. In this paper, we introduce EchoLip, which can significantly increase the sensing range and enhance long-term usability in real-world settings. EchoLip utilizes the smartphone’s two built-in speakers and microphones to transmit and receive mutually orthogonal wave signals to capture multi-view information. Then, a specially designed signal processing pipeline and neural network are used to extract fine-grained features that adapt to different angles and distances. We also design a lip movement monitoring algorithm to handle various interference. We evaluate EchoLip on 20 individuals using a set of 500 sentences. EchoLip achieves an average Word Error Rate of 13.9% and 19.7% at 15cm and 40cm. Evaluations in various scenarios further validate the robustness of EchoLip.

Accurately recognizing facial expressions and emotions at any time and in any place can significantly improve people's quality of life and mental well-being. However, existing methods lack the convenient capability for long-term monitoring in the wild environment. In this paper, we introduce EchoExpress, an in-the-wild emotion-related facial expression recognition system that works in an unobtrusive, low-power, and privacy-friendly way. EchoExpress uses two speakers and two microphones mounted on a glass-frame for transmitting and receiving mutually orthogonal wave signals. Concurrently, a unique attention mechanism dynamically extracts crucial features, enabling the capture of nuanced facial expressions and emotions. Furthermore, we introduce an open-set filtering mechanism with a specially designed loss function, which effectively filters out irrelevant actions, thereby reducing the risk of misidentification. Finally, a semi-supervised training method is employed to address the significant variability in wild expressions across different individuals. In extensive testing, EchoExpress achieves an accuracy of 84% in a laboratory environment and over 75% in real-world conditions. We believe that EchoExpress can serve as an unobtrusive and reliable way to monitor facial expressions.